From 37c73d6f54be65fbbdb4c8c79c7329af3e9c1a38 Mon Sep 17 00:00:00 2001
From: Philip ABBET <philip.abbet@idiap.ch>
Date: Wed, 21 Feb 2018 09:41:16 +0100
Subject: [PATCH] Update to beat.backend.python 1.5.x
---
advanced/databases/atnt/5.json | 124 ++
advanced/databases/atnt/5.py | 572 ++++++
advanced/databases/atnt/5.rst | 74 +
advanced/databases/atvskeystroke/4.json | 41 +
advanced/databases/atvskeystroke/4.py | 266 +++
advanced/databases/atvskeystroke/4.rst | 83 +
advanced/databases/avspoof/4.json | 950 ++++++++++
advanced/databases/avspoof/4.py | 534 ++++++
advanced/databases/avspoof/4.rst | 193 ++
advanced/databases/banca/4.json | 608 ++++++
advanced/databases/banca/4.py | 382 ++++
advanced/databases/banca/4.rst | 70 +
advanced/databases/biosecurid_face/3.json | 87 +
advanced/databases/biosecurid_face/3.py | 324 ++++
advanced/databases/biosecurid_face/3.rst | 41 +
advanced/databases/biowave/3.json | 1579 ++++++++++++++++
advanced/databases/biowave/3.py | 499 +++++
advanced/databases/biowave/3.rst | 212 +++
advanced/databases/casme2/4.json | 1010 ++++++++++
advanced/databases/casme2/4.py | 148 ++
advanced/databases/casme2/4.rst | 65 +
advanced/databases/cbsr_nir_vis_2/4.json | 866 +++++++++
advanced/databases/cbsr_nir_vis_2/4.py | 413 +++++
advanced/databases/cbsr_nir_vis_2/4.rst | 64 +
advanced/databases/cpqd/4.json | 694 +++++++
advanced/databases/cpqd/4.py | 424 +++++
advanced/databases/cpqd/4.rst | 144 ++
advanced/databases/frgc/4.json | 330 ++++
advanced/databases/frgc/4.py | 412 +++++
advanced/databases/frgc/4.rst | 67 +
advanced/databases/gbu/4.json | 159 ++
advanced/databases/gbu/4.py | 381 ++++
advanced/databases/gbu/4.rst | 61 +
advanced/databases/kboc16/4.json | 76 +
advanced/databases/kboc16/4.py | 259 +++
advanced/databases/kboc16/4.rst | 84 +
advanced/databases/lfw/4.json | 54 +
advanced/databases/lfw/4.py | 285 +++
advanced/databases/lfw/4.rst | 72 +
advanced/databases/livdet2013/4.json | 164 ++
advanced/databases/livdet2013/4.py | 109 ++
advanced/databases/livdet2013/4.rst | 75 +
advanced/databases/mnist/4.json | 38 +
advanced/databases/mnist/4.py | 111 ++
advanced/databases/mnist/4.rst | 93 +
advanced/databases/mobio/4.json | 188 ++
advanced/databases/mobio/4.py | 402 ++++
advanced/databases/mobio/4.rst | 74 +
advanced/databases/putvein/4.json | 1646 +++++++++++++++++
advanced/databases/putvein/4.py | 222 +++
advanced/databases/putvein/4.rst | 138 ++
advanced/databases/replay/4.json | 1122 +++++++++++
advanced/databases/replay/4.py | 571 ++++++
advanced/databases/replay/4.rst | 225 +++
advanced/databases/utfvp/4.json | 623 +++++++
advanced/databases/utfvp/4.py | 308 +++
advanced/databases/utfvp/4.rst | 108 ++
advanced/databases/voxforge/4.json | 80 +
advanced/databases/voxforge/4.py | 323 ++++
advanced/databases/voxforge/4.rst | 56 +
advanced/databases/xm2vts/4.json | 350 ++++
advanced/databases/xm2vts/4.py | 382 ++++
advanced/databases/xm2vts/4.rst | 103 ++
test/algorithms/username/integers_sum/1.json | 26 +
test/algorithms/username/integers_sum/1.py | 37 +
test/databases/simple/1.json | 29 +
test/databases/simple/1.py | 200 +-
.../username/username/duo/1/split_2.json | 37 +
.../username/username/single/1/single.json | 4 +-
.../username/single/1/single_add.json | 6 +-
.../username/single/1/single_error.json | 2 +-
.../single/1/single_error_split_2.json | 2 +-
.../username/single/1/single_split_10.json | 2 +-
.../username/single/1/single_split_2.json | 4 +-
test/toolchains/username/duo/1.json | 60 +
75 files changed, 20533 insertions(+), 94 deletions(-)
create mode 100644 advanced/databases/atnt/5.json
create mode 100644 advanced/databases/atnt/5.py
create mode 100644 advanced/databases/atnt/5.rst
create mode 100644 advanced/databases/atvskeystroke/4.json
create mode 100644 advanced/databases/atvskeystroke/4.py
create mode 100644 advanced/databases/atvskeystroke/4.rst
create mode 100644 advanced/databases/avspoof/4.json
create mode 100644 advanced/databases/avspoof/4.py
create mode 100644 advanced/databases/avspoof/4.rst
create mode 100644 advanced/databases/banca/4.json
create mode 100644 advanced/databases/banca/4.py
create mode 100644 advanced/databases/banca/4.rst
create mode 100644 advanced/databases/biosecurid_face/3.json
create mode 100644 advanced/databases/biosecurid_face/3.py
create mode 100644 advanced/databases/biosecurid_face/3.rst
create mode 100644 advanced/databases/biowave/3.json
create mode 100644 advanced/databases/biowave/3.py
create mode 100644 advanced/databases/biowave/3.rst
create mode 100644 advanced/databases/casme2/4.json
create mode 100644 advanced/databases/casme2/4.py
create mode 100644 advanced/databases/casme2/4.rst
create mode 100644 advanced/databases/cbsr_nir_vis_2/4.json
create mode 100644 advanced/databases/cbsr_nir_vis_2/4.py
create mode 100644 advanced/databases/cbsr_nir_vis_2/4.rst
create mode 100644 advanced/databases/cpqd/4.json
create mode 100644 advanced/databases/cpqd/4.py
create mode 100644 advanced/databases/cpqd/4.rst
create mode 100644 advanced/databases/frgc/4.json
create mode 100644 advanced/databases/frgc/4.py
create mode 100644 advanced/databases/frgc/4.rst
create mode 100644 advanced/databases/gbu/4.json
create mode 100644 advanced/databases/gbu/4.py
create mode 100644 advanced/databases/gbu/4.rst
create mode 100644 advanced/databases/kboc16/4.json
create mode 100644 advanced/databases/kboc16/4.py
create mode 100644 advanced/databases/kboc16/4.rst
create mode 100644 advanced/databases/lfw/4.json
create mode 100644 advanced/databases/lfw/4.py
create mode 100644 advanced/databases/lfw/4.rst
create mode 100644 advanced/databases/livdet2013/4.json
create mode 100644 advanced/databases/livdet2013/4.py
create mode 100644 advanced/databases/livdet2013/4.rst
create mode 100644 advanced/databases/mnist/4.json
create mode 100644 advanced/databases/mnist/4.py
create mode 100644 advanced/databases/mnist/4.rst
create mode 100644 advanced/databases/mobio/4.json
create mode 100644 advanced/databases/mobio/4.py
create mode 100644 advanced/databases/mobio/4.rst
create mode 100644 advanced/databases/putvein/4.json
create mode 100644 advanced/databases/putvein/4.py
create mode 100644 advanced/databases/putvein/4.rst
create mode 100644 advanced/databases/replay/4.json
create mode 100644 advanced/databases/replay/4.py
create mode 100644 advanced/databases/replay/4.rst
create mode 100644 advanced/databases/utfvp/4.json
create mode 100644 advanced/databases/utfvp/4.py
create mode 100644 advanced/databases/utfvp/4.rst
create mode 100644 advanced/databases/voxforge/4.json
create mode 100644 advanced/databases/voxforge/4.py
create mode 100644 advanced/databases/voxforge/4.rst
create mode 100644 advanced/databases/xm2vts/4.json
create mode 100644 advanced/databases/xm2vts/4.py
create mode 100644 advanced/databases/xm2vts/4.rst
create mode 100644 test/algorithms/username/integers_sum/1.json
create mode 100644 test/algorithms/username/integers_sum/1.py
create mode 100644 test/experiments/username/username/duo/1/split_2.json
create mode 100644 test/toolchains/username/duo/1.json
diff --git a/advanced/databases/atnt/5.json b/advanced/databases/atnt/5.json
new file mode 100644
index 0000000..25459fc
--- /dev/null
+++ b/advanced/databases/atnt/5.json
@@ -0,0 +1,124 @@
+{
+ "description": "The AT&T Database of Faces",
+ "root_folder": "/idiap/group/biometric/databases/orl",
+ "protocols": [
+ {
+ "name": "idiap",
+ "template": "simple_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1"
+ }
+ },
+ {
+ "name": "templates",
+ "template": "templates",
+ "view": "Templates",
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1"
+ }
+ },
+ {
+ "name": "probes",
+ "template": "probes",
+ "view": "Probes",
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "idiap_test_eyepos",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "TrainEyePositions",
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "TemplatesEyePositions",
+ "parameters": {
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "ProbesEyePositions",
+ "parameters": {
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "TemplatesEyePositions",
+ "parameters": {
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "ProbesEyePositions",
+ "parameters": {
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ }
+ ]
+}
diff --git a/advanced/databases/atnt/5.py b/advanced/databases/atnt/5.py
new file mode 100644
index 0000000..9056066
--- /dev/null
+++ b/advanced/databases/atnt/5.py
@@ -0,0 +1,572 @@
+###############################################################################
+# #
+# Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import numpy as np
+from collections import namedtuple
+
+from beat.backend.python.database import View
+
+import bob.io.base
+import bob.io.image
+import bob.ip.color
+import bob.db.atnt
+
+
+#----------------------------------------------------------
+
+
+class Train(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_2d_uint8/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ One "file_id" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'file_id', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.atnt.Database()
+ objs = sorted(db.objects(groups='world', purposes=None),
+ key=lambda x: (x.client_id, x.id))
+
+ return [ Entry(x.client_id, x.id, x.make_path(root_folder, '.pgm')) for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Templates(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_2d_uint8/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - template_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ One "file_id" is associated with a given "image".
+ Several "image" are associated with a given "template_id".
+ Several "template_id" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | template_id | | template_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | client_id |
+ -----------------------------------------------------------------------------------------------
+
+ Note: for this particular database, there is only one "template_id"
+ per "client_id".
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'template_id', 'file_id', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.atnt.Database()
+
+ template_ids = db.model_ids(groups='dev')
+
+ entries = []
+
+ for template_id in template_ids:
+ objs = db.objects(groups='dev', purposes='enroll',
+ model_ids=[template_id])
+
+ entries.extend([ Entry(x.client_id, template_id, x.id, x.make_path(root_folder, '.pgm'))
+ for x in objs ])
+
+ return sorted(entries, key=lambda x: (x.client_id, x.template_id, x.file_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'template_id':
+ return {
+ 'value': np.uint64(obj.template_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Probes(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_2d_uint8/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+ - probe_id: "{{ system_user.username }}/uint64/1"
+ - template_ids: "{{ system_user.username }}/array_1d_uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "probe_id" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+ Several "client_id" are associated with a given "template_ids".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | probe_id | | probe_id | | probe_id | | probe_id | | probe_id | | probe_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | template_ids |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['template_ids', 'client_id', 'probe_id', 'file_id', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.atnt.Database()
+
+ template_ids = np.array(sorted(db.model_ids(groups='dev'),
+ key=lambda x: int(x)),
+ dtype='uint64')
+
+ objs = sorted(db.objects(groups='dev', purposes='probe'),
+ key=lambda x: (x.client_id, x.id))
+
+ return [ Entry(template_ids, x.client_id, x.id, x.id, x.make_path(root_folder, '.pgm'))
+ for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'template_ids':
+ return {
+ 'value': obj.template_ids
+ }
+
+ elif output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'probe_id':
+ return {
+ 'value': np.uint64(obj.probe_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class TrainEyePositions(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'eye_centers', 'file_id', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.atnt.Database()
+ objs = sorted(db.objects(groups='world', purposes=None),
+ key=lambda x: (x.client_id, x.id))
+
+ eye_centers = {
+ 'left': {
+ 'y': np.int32(48),
+ 'x': np.int32(63),
+ },
+ 'right': {
+ 'y': np.int32(48),
+ 'x': np.int32(27),
+ }
+ }
+
+ return [ Entry(x.client_id, eye_centers, x.id, x.make_path(root_folder, '.pgm'))
+ for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'eye_centers':
+ return obj.eye_centers
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class TemplatesEyePositions(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - template_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ Several "image" are associated with a given "template_id".
+ Several "template_id" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | template_id | | template_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | client_id |
+ -----------------------------------------------------------------------------------------------
+
+ Note: for this particular database, there is only one "template_id"
+ per "client_id".
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'template_id', 'file_id', 'eye_centers', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.atnt.Database()
+
+ eye_centers = {
+ 'left': {
+ 'y': np.int32(48),
+ 'x': np.int32(63),
+ },
+ 'right': {
+ 'y': np.int32(48),
+ 'x': np.int32(27),
+ }
+ }
+
+ template_ids = db.model_ids(groups='dev')
+
+ entries = []
+
+ for template_id in template_ids:
+ objs = db.objects(groups='dev', purposes='enroll',
+ model_ids=[template_id])
+
+ entries.extend([ Entry(x.client_id, template_id, x.id, eye_centers, x.make_path(root_folder, '.pgm'))
+ for x in objs ])
+
+ return sorted(entries, key=lambda x: (x.client_id, x.template_id, x.file_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'template_id':
+ return {
+ 'value': np.uint64(obj.template_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'eye_centers':
+ return obj.eye_centers
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class ProbesEyePositions(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+ - probe_id: "{{ system_user.username }}/uint64/1"
+ - template_ids: "{{ system_user.username }}/array_1d_uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ One "probe_id" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+ Several "client_id" are associated with a given "template_ids".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | probe_id | | probe_id | | probe_id | | probe_id | | probe_id | | probe_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | template_ids |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['template_ids', 'client_id', 'probe_id', 'file_id',
+ 'eye_centers', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.atnt.Database()
+
+ eye_centers = {
+ 'left': {
+ 'y': np.int32(48),
+ 'x': np.int32(63),
+ },
+ 'right': {
+ 'y': np.int32(48),
+ 'x': np.int32(27),
+ }
+ }
+
+ template_ids = np.array(sorted(db.model_ids(groups='dev'),
+ key=lambda x: int(x)),
+ dtype='uint64')
+
+ objs = sorted(db.objects(groups='dev', purposes='probe'),
+ key=lambda x: (x.client_id, x.id))
+
+ return [ Entry(template_ids, x.client_id, x.id, x.id, eye_centers,
+ x.make_path(root_folder, '.pgm'))
+ for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'template_ids':
+ return {
+ 'value': obj.template_ids
+ }
+
+ elif output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'probe_id':
+ return {
+ 'value': np.uint64(obj.probe_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'eye_centers':
+ return obj.eye_centers
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+def setup_tests():
+ # Install a mock load function for the images
+ def mock_load(root_folder):
+ return np.ndarray((92, 112), dtype=np.uint8)
+
+ bob.io.base.load = mock_load
+
+
+#----------------------------------------------------------
+
+
+# Test the behavior of the views (on fake data)
+if __name__ == '__main__':
+
+ setup_tests()
+
+ view = Train()
+ view.objs = view.index(root_folder='', parameters=dict())
+ view.get('client_id', 0)
+ view.get('file_id', 0)
+ view.get('image', 0)
+
+ view = Templates()
+ view.objs = view.index(root_folder='', parameters=dict())
+ view.get('client_id', 0)
+ view.get('template_id', 0)
+ view.get('file_id', 0)
+ view.get('image', 0)
+
+ view = Probes()
+ view.objs = view.index(root_folder='', parameters=dict())
+ view.get('template_ids', 0)
+ view.get('client_id', 0)
+ view.get('probe_id', 0)
+ view.get('file_id', 0)
+ view.get('image', 0)
+
+ view = TrainEyePositions()
+ view.objs = view.index(root_folder='', parameters=dict())
+ view.get('client_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
+
+ view = TemplatesEyePositions()
+ view.objs = view.index(root_folder='', parameters=dict())
+ view.get('client_id', 0)
+ view.get('template_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
+
+ view = ProbesEyePositions()
+ view.objs = view.index(root_folder='', parameters=dict())
+ view.get('template_ids', 0)
+ view.get('client_id', 0)
+ view.get('probe_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
diff --git a/advanced/databases/atnt/5.rst b/advanced/databases/atnt/5.rst
new file mode 100644
index 0000000..49cd0e1
--- /dev/null
+++ b/advanced/databases/atnt/5.rst
@@ -0,0 +1,74 @@
+.. Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ ..
+.. Contact: beat.support@idiap.ch ..
+.. ..
+.. This file is part of the beat.examples module of the BEAT platform. ..
+.. ..
+.. Commercial License Usage ..
+.. Licensees holding valid commercial BEAT licenses may use this file in ..
+.. accordance with the terms contained in a written agreement between you ..
+.. and Idiap. For further information contact tto@idiap.ch ..
+.. ..
+.. Alternatively, this file may be used under the terms of the GNU Affero ..
+.. Public License version 3 as published by the Free Software and appearing ..
+.. in the file LICENSE.AGPL included in the packaging of this file. ..
+.. The BEAT platform is distributed in the hope that it will be useful, but ..
+.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
+.. or FITNESS FOR A PARTICULAR PURPOSE. ..
+.. ..
+.. You should have received a copy of the GNU Affero Public License along ..
+.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
+
+
+The AT&T Database of Faces
+--------------------------
+
+Changelog
+=========
+
+* **Version 5**, 30/Jan/2018:
+
+ - Port to beat.backend.python v1.5.0
+
+* **Version 4**, 30/Oct/2017:
+
+ - Port to beat.backend.python v1.4.2
+
+* **Version 3**, 20/Jan/2016:
+
+ - Port to Bob v2
+
+* **Version 2**, 03/Sep/2015:
+
+ - Fix ``idiap_eye_pos`` protocol that was returning the **wrong** eye
+ positions and was generating rotated crops instead.
+
+* **Version 1**, 01/Apr/2014:
+
+ - Initial release
+
+
+Description
+===========
+
+The `AT&T Database of Faces
+<http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html>`_,
+(formerly 'The ORL Database of Faces'), contains a set of face images taken
+between April 1992 and April 1994. The database was used in the context of a
+face recognition project carried out at `Cambridge University
+<http://www.cam.ac.uk/>`.
+
+There are ten different images of each of 40 distinct subjects. For some
+subjects, the images were taken at different times, varying the lighting,
+facial expressions (open / closed eyes, smiling / not smiling) and
+facial details (glasses / no glasses). All the images were taken against a
+dark homogeneous background with the subjects in an upright, frontal position
+(with tolerance for some side movement).
+
+A preview image of the Database of Faces is shown below:
+
+.. image:: http://www.cl.cam.ac.uk/research/dtg/attarchive/images/pictures/faces.gif
+ :width: 700 px
+
+The files are in PGM format. The size of each image is 92x112 pixels, with
+256 grey levels per pixel. There are 40 subjects, each subject having 10
+different images.
diff --git a/advanced/databases/atvskeystroke/4.json b/advanced/databases/atvskeystroke/4.json
new file mode 100644
index 0000000..7cf1002
--- /dev/null
+++ b/advanced/databases/atvskeystroke/4.json
@@ -0,0 +1,41 @@
+{
+ "description": "The ATVS Keystroke database",
+ "root_folder": "/idiap/group/biometric/databases/atvs_keystroke",
+ "protocols": [
+ {
+ "name": "A",
+ "template": "simple_keystroke_recognition",
+ "sets": [
+ {
+ "name": "templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "A"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "keystroke": "{{ user.username }}/atvs_keystroke/1"
+ }
+ },
+ {
+ "name": "probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "A"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "keystroke": "{{ user.username }}/atvs_keystroke/1"
+ }
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/advanced/databases/atvskeystroke/4.py b/advanced/databases/atvskeystroke/4.py
new file mode 100644
index 0000000..c9a4339
--- /dev/null
+++ b/advanced/databases/atvskeystroke/4.py
@@ -0,0 +1,266 @@
+###############################################################################
+# #
+# Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import numpy as np
+from collections import namedtuple
+
+from beat.backend.python.database import View
+
+import bob.db.atvskeystroke
+
+
+keystroke_feature = ['', 'holdtime', 'rplatency', 'pplatency', 'rrlatency', 'prlatency']
+keystroke_type = ['', 'given_name', 'family_name', 'email', 'nationality', 'id_number']
+
+
+#----------------------------------------------------------
+
+
+def keystroke_reader(filename):
+ counter = 0
+ feat = 0
+
+ data = {}
+ for line in open(filename, 'r').readlines():
+ if not line.strip(): continue
+ if counter % 6 == 0:
+ feat += 1
+ label = line.strip()
+ data[keystroke_feature[feat]] = {}
+ else:
+ values = [np.int32(v) for v in line.strip().split(' ')]
+ data[keystroke_feature[feat]][keystroke_type[counter % 6]] = np.array(values)
+ counter += 1
+
+ return data
+
+
+#----------------------------------------------------------
+
+
+class Templates(View):
+ """Outputs:
+ - keystroke: "{{ user.username }}/atvs_keystroke/1
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - template_id: "{{ system_user.username }}/text/1"
+ - client_id: "{{ system_user.username }}/text/1"
+
+ One "file_id" is associated with a given "keystroke".
+ Several "keystroke" are associated with a given "template_id".
+ Several "template_id" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | keystroke | | keystroke | | keystroke | | keystroke | | keystroke | | keystroke |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | template_id | | template_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | client_id |
+ -----------------------------------------------------------------------------------------------
+
+ Note: for this particular database, there is only one "template_id"
+ per "client_id".
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'template_id', 'file_id', 'keystroke'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.atvskeystroke.Database()
+
+ template_ids = db.model_ids(groups='eval',
+ protocol=parameters['protocol'])
+
+ entries = []
+
+ for template_id in template_ids:
+ objs = db.objects(groups='eval',
+ protocol=parameters['protocol'],
+ purposes='enrol',
+ model_ids=[template_id])
+
+ entries.extend([ Entry(x.client_id, template_id, x.id, x.make_path(root_folder, '.txt'))
+ for x in objs ])
+
+ return sorted(entries, key=lambda x: (x.client_id, x.template_id, x.file_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'text': str(obj.client_id)
+ }
+
+ elif output == 'template_id':
+ return {
+ 'text': str(obj.template_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'keystroke':
+ return keystroke_reader(obj.keystroke)
+
+
+#----------------------------------------------------------
+
+
+class Probes(View):
+ """Outputs:
+ - keystroke: "{{ user.username }}/atvs_keystroke/1
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/text/1"
+ - probe_id: "{{ system_user.username }}/uint64/1",
+ - template_ids: "{{ system_user.username }}/array_1d_text/1",
+
+ One "file_id" is associated with a given "keystroke".
+ One "probe_id" is associated with a given "keystroke".
+ Several "keystroke" are associated with a given "client_id".
+ Several "client_id" are associated with a given "template_ids".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | keystroke | | keystroke | | keystroke | | keystroke | | keystroke | | keystroke |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | probe_id | | probe_id | | probe_id | | probe_id | | probe_id | | probe_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | template_ids |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['template_ids', 'client_id', 'probe_id', 'file_id',
+ 'keystroke'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.atvskeystroke.Database()
+
+ template_ids = sorted(db.model_ids(protocol=parameters['protocol'],
+ groups='eval'),
+ key=lambda x: int(x))
+
+ template_probes = {}
+ for template_id in template_ids:
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups='eval',
+ purposes='probe',
+ model_ids=[template_id]),
+ key=lambda x: (x.client_id, x.id))
+
+ template_probes[template_id] = [ p.id for p in objs ]
+
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups='eval',
+ purposes='probe'),
+ key=lambda x: (x.client_id, x.id))
+
+ entries = []
+ for obj in objs:
+ templates = [ template_id for template_id in template_ids
+ if obj.id in template_probes[template_id] ]
+ entries.append( Entry(templates, obj.client_id, obj.id, obj.id,
+ obj.make_path(root_folder, '.txt')) )
+
+ return sorted(entries, key=lambda x: (len(x.template_ids), x.template_ids,
+ x.client_id, x.probe_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'template_ids':
+ return {
+ 'text': [ str(x) for x in obj.template_ids ]
+ }
+
+ elif output == 'client_id':
+ return {
+ 'text': str(obj.client_id)
+ }
+
+ elif output == 'probe_id':
+ return {
+ 'value': np.uint64(obj.probe_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'keystroke':
+ return keystroke_reader(obj.keystroke)
+
+
+#----------------------------------------------------------
+
+
+def setup_tests():
+ # Install a mock load function for the keystrokes
+ def mock_keystroke_reader(filename):
+ return {}
+
+ global keystroke_reader
+ keystroke_reader = mock_keystroke_reader
+
+
+#----------------------------------------------------------
+
+
+# Test the behavior of the views (on fake data)
+if __name__ == '__main__':
+
+ setup_tests()
+
+ view = Templates()
+ view.objs = view.index(root_folder='', parameters=dict(protocol = 'A'))
+ view.get('client_id', 0)
+ view.get('template_id', 0)
+ view.get('file_id', 0)
+ view.get('keystroke', 0)
+
+ view = Probes()
+ view.objs = view.index(root_folder='', parameters=dict(protocol = 'A'))
+ view.get('template_ids', 0)
+ view.get('client_id', 0)
+ view.get('probe_id', 0)
+ view.get('file_id', 0)
+ view.get('keystroke', 0)
diff --git a/advanced/databases/atvskeystroke/4.rst b/advanced/databases/atvskeystroke/4.rst
new file mode 100644
index 0000000..8e43bc3
--- /dev/null
+++ b/advanced/databases/atvskeystroke/4.rst
@@ -0,0 +1,83 @@
+.. Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ ..
+.. Contact: beat.support@idiap.ch ..
+.. ..
+.. This file is part of the beat.examples module of the BEAT platform. ..
+.. ..
+.. Commercial License Usage ..
+.. Licensees holding valid commercial BEAT licenses may use this file in ..
+.. accordance with the terms contained in a written agreement between you ..
+.. and Idiap. For further information contact tto@idiap.ch ..
+.. ..
+.. Alternatively, this file may be used under the terms of the GNU Affero ..
+.. Public License version 3 as published by the Free Software and appearing ..
+.. in the file LICENSE.AGPL included in the packaging of this file. ..
+.. The BEAT platform is distributed in the hope that it will be useful, but ..
+.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
+.. or FITNESS FOR A PARTICULAR PURPOSE. ..
+.. ..
+.. You should have received a copy of the GNU Affero Public License along ..
+.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
+
+
+The ATVS-Keystroke Database
+---------------------------
+
+Changelog
+=========
+
+* **Version 4**, 30/Jan/2018:
+
+ - Port to beat.backend.python v1.5.0
+
+* **Version 3**, 30/Oct/2017:
+
+ - Port to beat.backend.python v1.4.2
+
+* **Version 2**, 26/Jan/2016:
+
+ - Port to Bob v2
+
+* **Version 1**, 09/Jul/2015:
+
+ - Initial release
+
+
+Description
+===========
+
+The `ATVS-Keystroke database <http://atvs.ii.uam.es/keystroke_db.html>`_
+is a dataset captured for performance evaluation of Keystroke Dynamics
+recognition systems (see [BTAS15]_ for all the details). The database
+comprises 63 users with 12 genuine access and 12 impostor access for
+each user for a total number of samples equal to 7680 (63 users x 24 access
+x 5 data). There are people from two different nationalities with 60% of males
+and 40% females. The acquisition was made in two sessions according a
+semi-supervised protocol:
+
+1. **First session**: the users were asked to introduce their personal data
+in the platform. This process was repeated six times.
+
+2. **Second session**: after at least 24 hours, the users were asked to
+introduce once again their personal data in the platform. The process was
+repeated six times. In addition, in this second session, each user acted as an
+impostor trying to spoof the system with the personal data of another user.
+The personal data of three other users was showed to each of the impostor and
+they introduced them four times for a total number of impostor access of
+twelve per user.
+
+The information provided by the users includes sensitive data and therefore,
+it has been post-processed to remove all the personal information (the
+characters pressed) and to maintain the privacy of the users enrolled in the
+database. The keystroke dynamic patterns were recorded using a key-logger
+(programmed in Java). The key-logger detects two different types of events:
+press and release. The timestamps for each of the detected events were
+recorded in milliseconds.
+
+For further information on the database we refer the reader to (the following
+article is publicly available in the publications section of the
+`ATVS group webpage <http://atvs.ii.uam.es/listpublications.do>`_ .)
+
+.. [BTAS15] A. Morales, M. Falanga, J. Fierrez, C. Sansone and J. Ortega-Garcia, ''Keystroke Dynamics Recognition based on Personal Data: A Comparative Experimental Evaluation Implementing Reproducible Research'', in Proc. of the IEEE Seventh International Conference on Biometrics: Theory, Applications and Systems, Arlington, Virginia, USA, September 2015.
+
+Please remember to reference [BTAS15]_ on any work made public, whatever the
+form, based directly or indirectly on any part of the ATVS-Keystroke DB.
diff --git a/advanced/databases/avspoof/4.json b/advanced/databases/avspoof/4.json
new file mode 100644
index 0000000..8164081
--- /dev/null
+++ b/advanced/databases/avspoof/4.json
@@ -0,0 +1,950 @@
+{
+ "description": "The AVspoof Database",
+ "root_folder": "/idiap/resource/database/AVSpoof",
+ "protocols": [
+ {
+ "name": "smalltest_verify_train",
+ "template": "verify_trainset_speech",
+ "sets": [
+ {
+ "outputs": {
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "protocol": "smalltest",
+ "purpose": "enroll"
+ },
+ "name": "train_templates",
+ "template": "templates",
+ "view": "RecognitionTemplates"
+ },
+ {
+ "outputs": {
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "protocol": "smalltest"
+ },
+ "name": "train_probes",
+ "template": "probes",
+ "view": "Probes"
+ }
+ ]
+ },
+ {
+ "name": "smalltest_verify_train_spoof",
+ "template": "verify_trainset_speech_spoof",
+ "sets": [
+ {
+ "outputs": {
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "attack_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "protocol": "smalltest"
+ },
+ "name": "train_attacks",
+ "template": "attacks",
+ "view": "Attacks"
+ }
+ ]
+ },
+ {
+ "name": "smalltest_verification",
+ "template": "advanced_speaker_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "protocol": "smalltest"
+ },
+ "name": "train",
+ "template": "train",
+ "view": "RecognitionTraining"
+ },
+ {
+ "outputs": {
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "smalltest",
+ "purpose": "enroll"
+ },
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "RecognitionTemplates"
+ },
+ {
+ "outputs": {
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "smalltest"
+ },
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes"
+ },
+ {
+ "outputs": {
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "smalltest",
+ "purpose": "enroll"
+ },
+ "name": "test_templates",
+ "template": "templates",
+ "view": "RecognitionTemplates"
+ },
+ {
+ "outputs": {
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "smalltest"
+ },
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes"
+ }
+ ]
+ },
+ {
+ "name": "smalltest_verification_spoof",
+ "template": "speaker_recognition_spoof",
+ "sets": [
+ {
+ "outputs": {
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "attack_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "smalltest"
+ },
+ "name": "dev_attacks",
+ "template": "attacks",
+ "view": "Attacks"
+ },
+ {
+ "outputs": {
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "attack_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "smalltest"
+ },
+ "name": "test_attacks",
+ "template": "attacks",
+ "view": "Attacks"
+ }
+ ]
+ },
+ {
+ "name": "grandtest_verify_train",
+ "template": "verify_trainset_speech",
+ "sets": [
+ {
+ "outputs": {
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "protocol": "grandtest",
+ "purpose": "enroll"
+ },
+ "name": "train_templates",
+ "template": "templates",
+ "view": "RecognitionTemplates"
+ },
+ {
+ "outputs": {
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "protocol": "grandtest"
+ },
+ "name": "train_probes",
+ "template": "probes",
+ "view": "Probes"
+ }
+ ]
+ },
+ {
+ "name": "grandtest_verify_train_spoof",
+ "template": "verify_trainset_speech_spoof",
+ "sets": [
+ {
+ "outputs": {
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "attack_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "protocol": "grandtest"
+ },
+ "name": "train_attacks",
+ "template": "attacks",
+ "view": "Attacks"
+ }
+ ]
+ },
+ {
+ "name": "grandtest_verification",
+ "template": "advanced_speaker_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "RecognitionTraining",
+ "parameters": {
+ "protocol": "grandtest",
+ "group": "train"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "RecognitionTemplates",
+ "parameters": {
+ "protocol": "grandtest",
+ "purpose": "enroll",
+ "group": "devel"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "grandtest",
+ "group": "devel"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "RecognitionTemplates",
+ "parameters": {
+ "protocol": "grandtest",
+ "purpose": "enroll",
+ "group": "test"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "grandtest",
+ "group": "test"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "grandtest_verification_spoof",
+ "template": "speaker_recognition_spoof",
+ "sets": [
+ {
+ "name": "dev_attacks",
+ "template": "attacks",
+ "view": "Attacks",
+ "parameters": {
+ "protocol": "grandtest",
+ "group": "devel"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "attack_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ },
+ {
+ "name": "test_attacks",
+ "template": "attacks",
+ "view": "Attacks",
+ "parameters": {
+ "protocol": "grandtest",
+ "group": "test"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "attack_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "physicalaccess_verify_train",
+ "template": "verify_trainset_speech",
+ "sets": [
+ {
+ "outputs": {
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "protocol": "physical_access",
+ "purpose": "enroll"
+ },
+ "name": "train_templates",
+ "template": "templates",
+ "view": "RecognitionTemplates"
+ },
+ {
+ "outputs": {
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "protocol": "physical_access"
+ },
+ "name": "train_probes",
+ "template": "probes",
+ "view": "Probes"
+ }
+ ]
+ },
+ {
+ "name": "physicalaccess_verify_train_spoof",
+ "template": "verify_trainset_speech_spoof",
+ "sets": [
+ {
+ "outputs": {
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "attack_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "protocol": "physical_access"
+ },
+ "name": "train_attacks",
+ "template": "attacks",
+ "view": "Attacks"
+ }
+ ]
+ },
+ {
+ "name": "physicalaccess_verification",
+ "template": "advanced_speaker_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "RecognitionTraining",
+ "parameters": {
+ "protocol": "physical_access",
+ "group": "train"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "RecognitionTemplates",
+ "parameters": {
+ "protocol": "physical_access",
+ "purpose": "enroll",
+ "group": "devel"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "physical_access",
+ "group": "devel"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "RecognitionTemplates",
+ "parameters": {
+ "protocol": "physical_access",
+ "purpose": "enroll",
+ "group": "test"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "physical_access",
+ "group": "test"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "physicalaccess_verification_spoof",
+ "template": "speaker_recognition_spoof",
+ "sets": [
+ {
+ "name": "dev_attacks",
+ "template": "attacks",
+ "view": "Attacks",
+ "parameters": {
+ "protocol": "physical_access",
+ "group": "devel"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "attack_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ },
+ {
+ "name": "test_attacks",
+ "template": "attacks",
+ "view": "Attacks",
+ "parameters": {
+ "protocol": "physical_access",
+ "group": "test"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "attack_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "logicalaccess_verify_train",
+ "template": "verify_trainset_speech",
+ "sets": [
+ {
+ "outputs": {
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "protocol": "logical_access",
+ "purpose": "enroll"
+ },
+ "name": "train_templates",
+ "template": "templates",
+ "view": "RecognitionTemplates"
+ },
+ {
+ "outputs": {
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "protocol": "logical_access"
+ },
+ "name": "train_probes",
+ "template": "probes",
+ "view": "Probes"
+ }
+ ]
+ },
+ {
+ "name": "logicalaccess_verify_train_spoof",
+ "template": "verify_trainset_speech_spoof",
+ "sets": [
+ {
+ "outputs": {
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "attack_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "protocol": "logical_access"
+ },
+ "name": "train_attacks",
+ "template": "attacks",
+ "view": "Attacks"
+ }
+ ]
+ },
+ {
+ "name": "logicalaccess_verification",
+ "template": "advanced_speaker_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "RecognitionTraining",
+ "parameters": {
+ "protocol": "logical_access",
+ "group": "train"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "RecognitionTemplates",
+ "parameters": {
+ "protocol": "logical_access",
+ "purpose": "enroll",
+ "group": "devel"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "logical_access",
+ "group": "devel"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "RecognitionTemplates",
+ "parameters": {
+ "protocol": "logical_access",
+ "purpose": "enroll",
+ "group": "test"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "logical_access",
+ "group": "test"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "logicalaccess_verification_spoof",
+ "template": "speaker_recognition_spoof",
+ "sets": [
+ {
+ "name": "dev_attacks",
+ "template": "attacks",
+ "view": "Attacks",
+ "parameters": {
+ "protocol": "logical_access",
+ "group": "devel"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "attack_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ },
+ {
+ "name": "test_attacks",
+ "template": "attacks",
+ "view": "Attacks",
+ "parameters": {
+ "protocol": "logical_access",
+ "group": "test"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "attack_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "smalltest_antispoofing",
+ "template": "simple_speech_antispoofing",
+ "sets": [
+ {
+ "outputs": {
+ "attack_type": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "protocol": "smalltest"
+ },
+ "name": "train",
+ "template": "train",
+ "view": "SimpleAntispoofing"
+ },
+ {
+ "outputs": {
+ "attack_type": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "smalltest"
+ },
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "SimpleAntispoofing"
+ },
+ {
+ "outputs": {
+ "attack_type": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "smalltest"
+ },
+ "name": "eval_probes",
+ "template": "probes",
+ "view": "SimpleAntispoofing"
+ }
+ ]
+ },
+ {
+ "name": "grandtest_antispoofing",
+ "template": "simple_speech_antispoofing",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "SimpleAntispoofing",
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "attack_type": "{{ system_user.username }}/text/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "protocol": "grandtest",
+ "group": "train"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "SimpleAntispoofing",
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "attack_type": "{{ system_user.username }}/text/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "protocol": "grandtest",
+ "group": "devel"
+ }
+ },
+ {
+ "name": "eval_probes",
+ "template": "probes",
+ "view": "SimpleAntispoofing",
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "attack_type": "{{ system_user.username }}/text/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "protocol": "grandtest",
+ "group": "test"
+ }
+ }
+ ]
+ },
+ {
+ "name": "physicalaccess_antispoofing",
+ "template": "simple_speech_antispoofing",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "SimpleAntispoofing",
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "attack_type": "{{ system_user.username }}/text/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "protocol": "physical_access",
+ "group": "train"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "SimpleAntispoofing",
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "attack_type": "{{ system_user.username }}/text/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "protocol": "physical_access",
+ "group": "devel"
+ }
+ },
+ {
+ "name": "eval_probes",
+ "template": "probes",
+ "view": "SimpleAntispoofing",
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "attack_type": "{{ system_user.username }}/text/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "protocol": "physical_access",
+ "group": "test"
+ }
+ }
+ ]
+ },
+ {
+ "name": "logicalaccess_antispoofing",
+ "template": "simple_speech_antispoofing",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "SimpleAntispoofing",
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "attack_type": "{{ system_user.username }}/text/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "protocol": "logical_access",
+ "group": "train"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "SimpleAntispoofing",
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "attack_type": "{{ system_user.username }}/text/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "protocol": "logical_access",
+ "group": "devel"
+ }
+ },
+ {
+ "name": "eval_probes",
+ "template": "probes",
+ "view": "SimpleAntispoofing",
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1",
+ "attack_type": "{{ system_user.username }}/text/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "protocol": "logical_access",
+ "group": "test"
+ }
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/advanced/databases/avspoof/4.py b/advanced/databases/avspoof/4.py
new file mode 100644
index 0000000..1b5a45f
--- /dev/null
+++ b/advanced/databases/avspoof/4.py
@@ -0,0 +1,534 @@
+###############################################################################
+# #
+# Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import numpy as np
+from collections import namedtuple
+
+from beat.backend.python.database import View
+
+import bob.io.base
+import bob.db.avspoof
+import scipy.io.wavfile
+
+from bob.db.avspoof.driver import Interface
+
+INFO = Interface()
+SQLITE_FILE = INFO.files()[0]
+
+
+#----------------------------------------------------------
+
+
+class RecognitionTraining(View):
+ """Outputs:
+ - speech: "{{ system_user.username }}/array_1d_floats/1"
+ - file_id: "{{ system_user.username }}/text/1"
+ - client_id: "{{ system_user.username }}/text/1"
+
+ One "file_id" is associated with a given "speech".
+ Several "speech" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | speech | | speech | | speech | | speech | | speech | | speech |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'file_id', 'speech'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.avspoof.Database()
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ cls=('enroll', 'probe')),
+ key=lambda x: (x.client_id, x.id))
+
+ return [ Entry(x.client_id, x.id, x.make_path(root_folder, '.wav')) for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'text': str(obj.client_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'text': str(obj.file_id)
+ }
+
+ elif output == 'speech':
+ rate, audio = scipy.io.wavfile.read(obj.speech)
+
+ return {
+ 'value': np.cast['float'](audio)
+ }
+
+
+#----------------------------------------------------------
+
+
+class RecognitionTemplates(View):
+ """Outputs:
+ - speech: "{{ system_user.username }}/array_1d_floats/1"
+ - file_id: "{{ system_user.username }}/text/1"
+ - template_id: "{{ system_user.username }}/text/1"
+ - client_id: "{{ system_user.username }}/text/1"
+
+ One "file_id" is associated with a given "speech".
+ Several "speech" are associated with a given "template_id".
+ Several "template_id" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | speech | | speech | | speech | | speech | | speech | | speech |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | template_id | | template_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | client_id |
+ -----------------------------------------------------------------------------------------------
+
+ Note: for this particular database, there is only one "template_id"
+ per "client_id".
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'template_id', 'file_id', 'speech'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.avspoof.Database()
+
+ if parameters['protocol'] == "smalltest":
+ if parameters['group'] == "train":
+ template_ids = sorted([1, 3])
+ if parameters['group'] == "devel":
+ template_ids = sorted([15, 20])
+ if parameters['group'] == "test":
+ template_ids = sorted([18, 33])
+ else:
+ template_ids = [ client.id for client in db.clients(groups=parameters['group']) ]
+
+ entries = []
+
+ for template_id in template_ids:
+ objs = db.objects(groups=parameters['group'],
+ protocol=parameters['protocol'],
+ cls=parameters['purpose'],
+ clients=(template_id,))
+
+ entries.extend([ Entry(x.client_id, template_id, x.id, x.make_path(root_folder, '.wav'))
+ for x in objs ])
+
+ return sorted(entries, key=lambda x: (x.client_id, x.template_id, x.file_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'text': str(obj.client_id)
+ }
+
+ elif output == 'template_id':
+ return {
+ 'text': str(obj.template_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'text': str(obj.file_id)
+ }
+
+ elif output == 'speech':
+ rate, audio = scipy.io.wavfile.read(obj.speech)
+
+ return {
+ 'value': np.cast['float'](audio)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Probes(View):
+ """Outputs:
+ - speech: "{{ system_user.username }}/array_1d_floats/1"
+ - file_id: "{{ system_user.username }}/text/1"
+ - probe_id: "{{ system_user.username }}/text/1",
+ - client_id: "{{ system_user.username }}/text/1"
+ - template_ids: "{{ system_user.username }}/array_1d_text/1",
+
+ One "file_id" is associated with a given "speech".
+ One "probe_id" is associated with a given "speech".
+ Several "speech" are associated with a given "client_id".
+ Several "client_id" are associated with a given "template_ids".
+
+ Each probe must be matched against a number of templates defined by a list of
+ client identifiers.
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | speech | | speech | | speech | | speech | | speech | | speech |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | probe_id | | probe_id | | probe_id | | probe_id | | probe_id | | probe_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | template_ids |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['template_ids', 'client_id', 'probe_id', 'file_id', 'speech'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.avspoof.Database()
+
+ if parameters['protocol'] == "smalltest":
+ if parameters['group'] == "train":
+ template_ids = sorted([1, 3])
+ if parameters['group'] == "devel":
+ template_ids = sorted([15, 20])
+ if parameters['group'] == "test":
+ template_ids = sorted([18, 33])
+ else:
+ template_ids = sorted([ client.id for client in db.clients(groups=parameters['group']) ])
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ cls='probe'),
+ key=lambda x: (x.client_id, x.id))
+
+ return [ Entry(template_ids, x.client_id, x.id, x.id, x.make_path(root_folder, '.wav')) for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'template_ids':
+ return {
+ 'text': [ str(x) for x in obj.template_ids ]
+ }
+
+ elif output == 'client_id':
+ return {
+ 'text': str(obj.client_id)
+ }
+
+ elif output == 'probe_id':
+ return {
+ 'text': str(obj.probe_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'text': str(obj.file_id)
+ }
+
+ elif output == 'speech':
+ rate, audio = scipy.io.wavfile.read(obj.speech)
+
+ return {
+ 'value': np.cast['float'](audio)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Attacks(View):
+ """Outputs:
+ - speech: "{{ system_user.username }}/array_1d_floats/1"
+ - file_id: "{{ system_user.username }}/text/1"
+ - attack_id: "{{ system_user.username }}/text/1",
+ - client_id: "{{ system_user.username }}/text/1"
+ - template_ids: "{{ system_user.username }}/array_1d_text/1",
+
+ One "file_id" is associated with a given "speech".
+ One "probe_id" is associated with a given "speech".
+ Several "speech" are associated with a given "client_id".
+ Several "client_id" are associated with a given "template_ids".
+
+ Each probe must be matched against a number of templates defined by a list of
+ client identifiers.
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | speech | | speech | | speech | | speech | | speech | | speech |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | attack_id | | attack_id | | attack_id | | attack_id | | attack_id | | attack_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | template_ids |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['template_ids', 'client_id', 'attack_id', 'file_id', 'speech'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.avspoof.Database()
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ cls='attack'),
+ key=lambda x: (x.client_id, x.id))
+
+ return [ Entry([ str(x.client_id) ], x.client_id, x.id, x.id,
+ x.make_path(root_folder, '.wav')) for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'template_ids':
+ return {
+ 'text': obj.template_ids
+ }
+
+ elif output == 'client_id':
+ return {
+ 'text': str(obj.client_id)
+ }
+
+ elif output == 'attack_id':
+ return {
+ 'text': str(obj.attack_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'text': str(obj.file_id)
+ }
+
+ elif output == 'speech':
+ rate, audio = scipy.io.wavfile.read(obj.speech)
+
+ return {
+ 'value': np.cast['float'](audio)
+ }
+
+
+#----------------------------------------------------------
+
+
+class SimpleAntispoofing(View):
+ """Outputs:
+ - speech: "{{ system_user.username }}/array_1d_floats/1"
+ - file_id: "{{ system_user.username }}/text/1"
+ - client_id: "{{ system_user.username }}/text/1"
+ - attack_type: "{{ system_user.username }}/text/1"
+ - class: "{{ system_user.username }}/text/1"
+
+
+ One "file_id" is associated with a given "speech".
+ Several "speech" are associated with a given "client_id".
+ Several "client_id" are associated with a given "template_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | speech | | speech | | speech | | speech | | speech | | speech |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ------------------------------- ------------------------------- -------------------------------
+ | attack_type | | attack_type | | attack_type |
+ ------------------------------- ------------------------------- -------------------------------
+ --------------------------------------------------------------- -------------------------------
+ | client_id | | client_id
+ --------------------------------------------------------------- -------------------------------
+ -----------------------------------------------------------------------------------------------
+ | class |
+ -----------------------------------------------------------------------------------------------
+
+ Note: for this particular database, there is only one "client_id"
+ per "template_id".
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['cls', 'client_id', 'attack_type', 'file_id', 'speech'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.avspoof.Database()
+
+ objs_real = sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ cls='real'),
+ key=lambda x: (x.client_id, x.id))
+
+ objs_attack = sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ cls='attack'),
+ key=lambda x: (x.client_id, x.get_attack(), x.id))
+
+ return [ Entry('real', x.client_id, 'human', x.id, x.make_path(root_folder, '.wav')) for x in objs_real ] + \
+ [ Entry('attack', x.client_id, x.get_attack(), x.id, x.make_path(root_folder, '.wav')) for x in objs_attack ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'class':
+ return {
+ 'text': obj.cls
+ }
+
+ elif output == 'client_id':
+ return {
+ 'text': str(obj.client_id)
+ }
+
+ elif output == 'attack_type':
+ return {
+ 'text': obj.attack_type
+ }
+
+ elif output == 'file_id':
+ return {
+ 'text': str(obj.file_id)
+ }
+
+ elif output == 'speech':
+ rate, audio = scipy.io.wavfile.read(obj.speech)
+
+ return {
+ 'value': np.cast['float'](audio)
+ }
+
+
+#----------------------------------------------------------
+
+
+def setup_tests():
+ # Install a mock read function for the audio files
+ def mock_read(filename):
+ return 44100, np.ndarray((128,))
+
+ scipy.io.wavfile.read = mock_read
+
+
+#----------------------------------------------------------
+
+
+# Test the behavior of the views (on fake data)
+if __name__ == '__main__':
+
+ setup_tests()
+
+ view = RecognitionTraining()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol = 'smalltest',
+ group = 'train',
+ )
+ )
+ view.get('client_id', 0)
+ view.get('file_id', 0)
+ view.get('speech', 0)
+
+
+ view = RecognitionTemplates()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol = 'smalltest',
+ group = 'train',
+ purpose = 'enroll',
+ )
+ )
+ view.get('client_id', 0)
+ view.get('template_id', 0)
+ view.get('file_id', 0)
+ view.get('speech', 0)
+
+
+ view = Probes()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol = 'smalltest',
+ group = 'train',
+ )
+ )
+ view.get('template_ids', 0)
+ view.get('client_id', 0)
+ view.get('probe_id', 0)
+ view.get('file_id', 0)
+ view.get('speech', 0)
+
+
+ view = Attacks()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol = 'smalltest',
+ group = 'train',
+ )
+ )
+ view.get('template_ids', 0)
+ view.get('client_id', 0)
+ view.get('attack_id', 0)
+ view.get('file_id', 0)
+ view.get('speech', 0)
+
+
+ view = SimpleAntispoofing()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol = 'smalltest',
+ group = 'train',
+ )
+ )
+ view.get('class', 0)
+ view.get('client_id', 0)
+ view.get('attack_type', 0)
+ view.get('file_id', 0)
+ view.get('speech', 0)
diff --git a/advanced/databases/avspoof/4.rst b/advanced/databases/avspoof/4.rst
new file mode 100644
index 0000000..5ef2edf
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+++ b/advanced/databases/avspoof/4.rst
@@ -0,0 +1,193 @@
+.. Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ ..
+.. Contact: beat.support@idiap.ch ..
+.. ..
+.. This file is part of the beat.examples module of the BEAT platform. ..
+.. ..
+.. Commercial License Usage ..
+.. Licensees holding valid commercial BEAT licenses may use this file in ..
+.. accordance with the terms contained in a written agreement between you ..
+.. and Idiap. For further information contact tto@idiap.ch ..
+.. ..
+.. Alternatively, this file may be used under the terms of the GNU Affero ..
+.. Public License version 3 as published by the Free Software and appearing ..
+.. in the file LICENSE.AGPL included in the packaging of this file. ..
+.. The BEAT platform is distributed in the hope that it will be useful, but ..
+.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
+.. or FITNESS FOR A PARTICULAR PURPOSE. ..
+.. ..
+.. You should have received a copy of the GNU Affero Public License along ..
+.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
+
+
+The AVspoof Database
+--------------------
+
+Changelog
+=========
+
+* **Version 4**, 30/Jan/2018:
+
+ - Port to beat.backend.python v1.5.0
+
+* **Version 3**, 30/Oct/2017:
+
+ - Port to beat.backend.python v1.4.2
+
+* **Version 2**, 24/Mar/2016:
+
+ - Added ``_verify_train`` protocols that allow using train set in the
+ verification evaluations.
+
+* **Version 1**, 09/Feb/2016:
+
+ - Initial release
+
+
+Description
+===========
+
+The `AVspoof Database <https://www.idiap.ch/dataset/avspoof>`_ provides
+non-biased spoofing attacks in order for researchers to test both their
+ASV systems and anti-spoofing algorithms. The attacks are created based on
+newly acquired audio recordings. The data acquisition process lasted approximately
+two months with 44 persons, each participating in several sessions configured in
+different environmental conditions and setups. After the collection of the data,
+the attacks, more precisely, replay, voice conversion and speech synthesis attacks
+were generated. This Database was produced at the Idiap Research Institute, in Switzerland.
+
+
+Acknowledgements
+================
+
+If you use this database, please cite the following publication on your paper::
+
+ @INPROCEEDINGS{KucurErgunay_IEEEBTAS_2015,
+ author = {Kucur Ergunay, Serife and Khoury, Elie and Lazaridis, Alexandros and Marcel, S{\'{e}}bastien},
+ projects = {Idiap, SNSF-LOBI, BEAT},
+ month = sep,
+ title = {On the Vulnerability of Speaker Verification to Realistic Voice Spoofing},
+ booktitle = {IEEE International Conference on Biometrics: Theory, Applications and Systems},
+ year = {2015},
+ pdf = {http://publications.idiap.ch/downloads/papers/2015/KucurErgunay_IEEEBTAS_2015.pdf}
+ }
+
+
+Database Description
+====================
+
+The data acquisition process is divided into four different sessions, each scheduled several
+days apart in different setups and environmental conditions (e.g. different in terms of
+background noise, reverberation, etc.) for each of 31 male and 13 female participants.
+The first session which is supposed to be used as training set while creating the attacks,
+was performed in the most controlled conditions. Besides, the conditions for the last
+three sessions dedicated to test trials were more relaxed in order to grasp the challenging
+scenarios. The audio data were recorded by three different devices including (a) one
+good-quality microphone, AT2020USB+, and two mobiles, (b) Samsung Galaxy S4 (phone1)
+and (c) iPhone 3GS (phone2). The positioning of the devices was stabilized for each
+session and each participant in order to standardize the recording settings.
+
+For each session, the participant was subjected to three different data acquisition protocols as in the following:
+* **Reading part (read)**: 10/40 pre-defined sentences are read by the participant.
+* **Pass-phrases part (pass)**: 5 short prompts are read by the participant.
+* **Free speech part (free)**: The participant speaks freely about any topic for 3 to 10 minutes.
+
+The number, the length, as well as the content of the sentences for the reading and
+pass-phrases part are carefully selected in order to satisfy the constraints in terms
+of readability, data acquisition and attack quality. Similarly, the minimum duration of
+the free speech part is also determined according to our preliminary investigations
+mostly on the voice conversion attacks for which the free speech data would be included
+in the training set.
+
+Spoofing Attacks
+================
+
+In the spoofing attack creation phase, we considered creating spoofing trials for the
+text-dependent utterances of the testing data, i.e. reading parts of sessions 2-4
+and the pass-phrases of all four sessions. As a preliminary step before the creation
+of the attacks, the speech data originally recorded at 44.1 KHz sampling rate is
+down-sampled to 16 KHz.
+
+There are four main spoofing attacks for ASV systems: Impersonation, replay,
+speech synthesis and voice conversion. As the impersonation is known not to be a
+serious threat for ASV systems, we did not include it in our database. For the
+remaining three spoofing types, we designed ten different scenarios (see table below).
+We gave special attention to physical access attacks. These attacks are more realistic
+than logic access attacks considering the fact that the attacker often has no direct
+access to the system. The acquisition devices (sensors) are open to anyone, therefore
+more subjected to such attacks.
+
+
++-----------------------+--------------+-------------+--------------+-------------+
+| | Num. of trials per speaker | Total num. of trials |
++ Attacks +--------------+-------------+--------------+-------------+
+| | Male | Female | Male | Female |
++-----------------------+--------------+-------------+--------------+-------------+
+|Replay-phone1 | 50 | 50 | 1550 | 650 |
++-----------------------+--------------+-------------+--------------+-------------+
+|Replay-phone2 | 50 | 50 | 1550 | 650 |
++-----------------------+--------------+-------------+--------------+-------------+
+|Replay-laptop | 50 | 50 | 1550 | 650 |
++-----------------------+--------------+-------------+--------------+-------------+
+|Replay-laptop-HQ | 50 | 50 | 1550 | 650 |
++-----------------------+--------------+-------------+--------------+-------------+
+|Speech-Synthesis-LA | 35 | 35 | 1085 | 455 |
++-----------------------+--------------+-------------+--------------+-------------+
+|Speech-Synthesis-PA | 35 | 35 | 1085 | 455 |
++-----------------------+--------------+-------------+--------------+-------------+
+|Speech-Synthesis-PA-HQ | 35 | 35 | 1085 | 455 |
++-----------------------+--------------+-------------+--------------+-------------+
+|Voice-Conversion-LA | 1500 | 600 | 46500 | 7800 |
++-----------------------+--------------+-------------+--------------+-------------+
+|Voice-Conversion-PA | 1500 | 600 | 46500 | 7800 |
++-----------------------+--------------+-------------+--------------+-------------+
+|Voice-Conversion-PA-HQ | 1500 | 600 | 46500 | 7800 |
++-----------------------+--------------+-------------+--------------+-------------+
+
+
+Replay Attacks
+--------------
+
+A replay attack consists of replaying a pre-recorded speech to an ASV system.
+We assume that the ASV system has a good quality microphone and the replay attack targets this sensor:
+
+* **Replay-phone1**: Replay attack using the data captured by the Samsung mobile. The speech recorded by this mobile is replayed using its own speakers and re-recorded by the microphone of the ASV system.
+* **Replay-phone2**: Replay attack using the data captured by the iPhone mobile. The speech recorded by this mobile is replayed using its own speakers and re-recorded by the microphone of the ASV system.
+* **Replay-laptop**: Replay attack using the data captured by the microphone of the ASV system. The speech recorded by this microphone is replayed using the laptop speakers and re-recorded again by the microphone of the system.
+* **Replay-laptop-HQ**: Replay attack using the data captured by the microphone of the ASV system. The speech recorded by this microphone is replayed using external high-quality loudspeakers and re-recorded using the microphone of the ASV system.
+
+Speech Synthesis Attacks
+------------------------
+
+The speech synthesis attacks were based on statistical parametric speech synthesis (SPSS).
+More specific, hidden Markov model (HMM)-based speech synthesis technique was used.
+
+* **Speech-Synthesis-LA**: Speech synthesis via logical access. The synthesized speech is directly presented to the ASV system without being re-recorded.
+* **Speech-Synthesis-PA**: Speech synthesis via physical access. The synthesized speech is replayed using the laptop speakers and re-recorded by the microphone of the ASV system.
+* **Speech-Synthesis-PA-HQ**: Speech synthesis via high-quality physical access. The synthesized speech is replayed using external high-quality loudspeakers and re-recorded by the microphone of the ASV system.
+
+Voice Conversion Attacks
+------------------------
+
+The voice conversion attacks were created using Festvox. A conversion function
+for each pair of source-target speaker is found based on the learned GMM model/parameters
+by using the source and target speakers training data. We did not consider cross-gender
+voice conversion attacks, that is only male-to-male and female-to-female conversions
+were taken into account. As in the case of speech synthesis, three possible scenarios are involved:
+
+* **Voice-Conversion-LA**: Voice conversion via logical access. The converted speech is directly presented to the system without being re-recorded.
+* **Voice-Conversion-PA**: Voice conversion via physical access. The converted speech is replayed using the speakers of the laptop and re-recorded by the microphone of the ASV system.
+* **Voice-Conversion-PA-HQ**: Voice conversion via high-quality physical access. The converted speech is replayed using external high-quality loudspeakers and re-recorded by the microphone of the ASV system.
+
+Specificities to the BEAT View
+==============================
+
+Spoofing and genuine samples are each labelled with a text field that defines
+the class of the sample: ``"attack"`` or ``"real"`` for simple anti-spoofing
+binary classification systems. Code using this
+database views may use the ``class`` field to differentiate samples.
+
+The view supports the following protocols, which are also available in the database:
+``smalltest`` (for prove of concept experiments only, as a subset of only three clients is
+provided for each set), ``grandtest`` (data of the whole database is provided), ``physical_access``
+(only replay/presentation attack are provided), and ``logical_access`` (only logical access
+attacks are provided with no replay attacks).
diff --git a/advanced/databases/banca/4.json b/advanced/databases/banca/4.json
new file mode 100644
index 0000000..a39a276
--- /dev/null
+++ b/advanced/databases/banca/4.json
@@ -0,0 +1,608 @@
+{
+ "description": "The BANCA Database of Faces",
+ "root_folder": "/idiap/group/biometric/databases/banca/english/images/images",
+ "protocols": [
+ {
+ "name": "P",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "P"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "P",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "P",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "P",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "P",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "G",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "G"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "G",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "G",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "G",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "G",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "Mc",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "Mc"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "Mc",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "Mc",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "Mc",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "Mc",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "Md",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "Md"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "Md",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "Md",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "Md",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "Md",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "Ma",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "Ma"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "Ma",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "Ma",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "Ma",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "Ma",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "Ud",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "Ud"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "Ud",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "Ud",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "Ud",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "Ud",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "Ua",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "Ua"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "Ua",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "Ua",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "Ua",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "Ua",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ }
+ ]
+}
diff --git a/advanced/databases/banca/4.py b/advanced/databases/banca/4.py
new file mode 100644
index 0000000..c90de39
--- /dev/null
+++ b/advanced/databases/banca/4.py
@@ -0,0 +1,382 @@
+###############################################################################
+# #
+# Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import numpy as np
+from collections import namedtuple
+
+from beat.backend.python.database import View
+
+import bob.io.base
+import bob.io.image
+import bob.db.banca
+
+
+#----------------------------------------------------------
+
+
+class Train(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'file_id', 'eye_centers', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.banca.Database()
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups='world'),
+ key=lambda x: (x.client_id, x.id))
+
+ return [ Entry(x.client_id, x.id, db.annotations(x), x.make_path(root_folder, '.ppm'))
+ for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'eye_centers':
+ return {
+ 'left': {
+ 'y': np.int32(obj.eye_centers['leye'][0]),
+ 'x': np.int32(obj.eye_centers['leye'][1]),
+ },
+ 'right': {
+ 'y': np.int32(obj.eye_centers['reye'][0]),
+ 'x': np.int32(obj.eye_centers['reye'][1]),
+ }
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Templates(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - template_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ Several "image" are associated with a given "template_id".
+ Several "template_id" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | template_id | | template_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | client_id |
+ -----------------------------------------------------------------------------------------------
+
+ Note: for this particular database, there is only one "template_id"
+ per "client_id".
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'template_id', 'file_id', 'eye_centers', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.banca.Database()
+
+ template_ids = db.model_ids(protocol=parameters['protocol'],
+ groups=parameters['group'])
+
+ entries = []
+
+ for template_id in template_ids:
+ objs = db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ purposes='enroll',
+ model_ids=[template_id])
+
+ entries.extend([ Entry(x.client_id, template_id, x.id, db.annotations(x),
+ x.make_path(root_folder, '.ppm'))
+ for x in objs ])
+
+ return sorted(entries, key=lambda x: (x.client_id, x.template_id, x.file_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'template_id':
+ return {
+ 'value': np.uint64(obj.template_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'eye_centers':
+ return {
+ 'left': {
+ 'y': np.int32(obj.eye_centers['leye'][0]),
+ 'x': np.int32(obj.eye_centers['leye'][1]),
+ },
+ 'right': {
+ 'y': np.int32(obj.eye_centers['reye'][0]),
+ 'x': np.int32(obj.eye_centers['reye'][1]),
+ }
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Probes(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - probe_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+ - template_ids: "{{ system_user.username }}/array_1d_uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ One "probe_id" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+ Several "client_id" are associated with a given "template_ids".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | probe_id | | probe_id | | probe_id | | probe_id | | probe_id | | probe_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | template_ids |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['template_ids', 'client_id', 'probe_id', 'file_id',
+ 'eye_centers', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.banca.Database()
+
+ template_ids = sorted(db.model_ids(protocol=parameters['protocol'],
+ groups=parameters['group']))
+
+
+ template_probes = {}
+ for template_id in template_ids:
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ purposes='probe',
+ model_ids=[template_id]),
+ key=lambda x: (x.client_id, x.id))
+
+ template_probes[template_id] = [ p.id for p in objs ]
+
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ purposes='probe'),
+ key=lambda x: (x.client_id, x.id))
+
+ entries = []
+ for obj in objs:
+ templates = [ template_id for template_id in template_ids
+ if obj.id in template_probes[template_id] ]
+
+ entries.append( Entry(templates, obj.client_id, obj.id, obj.id,
+ db.annotations(obj), obj.make_path(root_folder, '.ppm')) )
+
+ return sorted(entries, key=lambda x: (len(x.template_ids), x.template_ids,
+ x.client_id, x.probe_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'template_ids':
+ return {
+ 'value': np.uint64(obj.template_ids)
+ }
+
+ elif output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'probe_id':
+ return {
+ 'value': np.uint64(obj.probe_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'eye_centers':
+ return {
+ 'left': {
+ 'y': np.int32(obj.eye_centers['leye'][0]),
+ 'x': np.int32(obj.eye_centers['leye'][1]),
+ },
+ 'right': {
+ 'y': np.int32(obj.eye_centers['reye'][0]),
+ 'x': np.int32(obj.eye_centers['reye'][1]),
+ }
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+def setup_tests():
+ # Install a mock load function for the images
+ def mock_load(root_folder):
+ return np.ndarray((3, 10, 20), dtype=np.uint8)
+
+ bob.io.base.load = mock_load
+
+
+#----------------------------------------------------------
+
+
+# Test the behavior of the views (on fake data)
+if __name__ == '__main__':
+
+ setup_tests()
+
+ view = Train()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='P'
+ )
+ )
+ view.get('client_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
+
+
+ view = Templates()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='P',
+ group='dev',
+ )
+ )
+ view.get('client_id', 0)
+ view.get('template_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
+
+
+ view = Probes()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='P',
+ group='dev',
+ )
+ )
+ view.get('template_ids', 0)
+ view.get('client_id', 0)
+ view.get('probe_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
diff --git a/advanced/databases/banca/4.rst b/advanced/databases/banca/4.rst
new file mode 100644
index 0000000..fe4593a
--- /dev/null
+++ b/advanced/databases/banca/4.rst
@@ -0,0 +1,70 @@
+.. Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ ..
+.. Contact: beat.support@idiap.ch ..
+.. ..
+.. This file is part of the beat.examples module of the BEAT platform. ..
+.. ..
+.. Commercial License Usage ..
+.. Licensees holding valid commercial BEAT licenses may use this file in ..
+.. accordance with the terms contained in a written agreement between you ..
+.. and Idiap. For further information contact tto@idiap.ch ..
+.. ..
+.. Alternatively, this file may be used under the terms of the GNU Affero ..
+.. Public License version 3 as published by the Free Software and appearing ..
+.. in the file LICENSE.AGPL included in the packaging of this file. ..
+.. The BEAT platform is distributed in the hope that it will be useful, but ..
+.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
+.. or FITNESS FOR A PARTICULAR PURPOSE. ..
+.. ..
+.. You should have received a copy of the GNU Affero Public License along ..
+.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
+
+
+The BANCA Database of Faces
+---------------------------
+
+Changelog
+=========
+
+* **Version 4**, 30/Jan/2018:
+
+ - Port to beat.backend.python v1.5.0
+
+* **Version 3**, 30/Oct/2017:
+
+ - Port to beat.backend.python v1.4.2
+
+* **Version 2**, 20/Jan/2016:
+
+ - Port to Bob v2
+
+* **Version 1**, 01/Apr/2014:
+
+ - Initial release
+
+
+Description
+===========
+
+The `BANCA database <http://www.ee.surrey.ac.uk/CVSSP/banca/>`_ is a large,
+realistic and challenging multi-modal database intended for training and
+testing multi-modal verification systems. The BANCA database was captured
+in four European languages in two modalities (face and voice). For recording,
+both high and low quality microphones and cameras were used. The subjects
+were recorded in three different scenarios, controlled, degraded and adverse
+over 12 different sessions spanning three months. In total 208 people were
+captured, half men and half women.
+
+.. image:: http://www.ee.surrey.ac.uk/CVSSP/banca/samples/1021_1.jpg
+ :width: 200 px
+.. image:: http://www.ee.surrey.ac.uk/CVSSP/banca/samples/1029_1.jpg
+ :width: 200 px
+.. image:: http://www.ee.surrey.ac.uk/CVSSP/banca/samples/1015_1.jpg
+ :width: 200 px
+
+This deployment of the BANCA database only consists of the images of the
+English recordings.
+
+Associated with the database is the `BANCA protocol <http://www.ee.surrey.ac.uk/CVSSP/banca/documentation/messer-avbpa03.pdf>`_.
+The protocol defines which sets of data to use for training, evaluation and
+testing. Performing experiments according to the protocol allows institutions
+to easily compare their results to others.
diff --git a/advanced/databases/biosecurid_face/3.json b/advanced/databases/biosecurid_face/3.json
new file mode 100644
index 0000000..f6e8c8c
--- /dev/null
+++ b/advanced/databases/biosecurid_face/3.json
@@ -0,0 +1,87 @@
+{
+ "description": "The biosecurid Database of Faces",
+ "root_folder": "/idiap/resource/database/BiosecurID_Definitiva",
+ "protocols": [
+ {
+ "name": "A",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "A"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "A",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "A",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "A",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "A",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1"
+ }
+ }
+ ]
+ }
+ ]
+}
diff --git a/advanced/databases/biosecurid_face/3.py b/advanced/databases/biosecurid_face/3.py
new file mode 100644
index 0000000..9cba7ea
--- /dev/null
+++ b/advanced/databases/biosecurid_face/3.py
@@ -0,0 +1,324 @@
+###############################################################################
+# #
+# Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import numpy as np
+from collections import namedtuple
+
+from beat.backend.python.database import View
+
+import bob
+import bob.io.base
+import bob.io.image
+import bob.db.biosecurid.face
+
+
+#----------------------------------------------------------
+
+
+class Train(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ One "file_id" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'file_id', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.biosecurid.face.Database()
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups='world'),
+ key=lambda x: (x.client_id, x.id))
+
+ return [ Entry(x.client_id, x.id, x.make_path(root_folder, '.bmp')) for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Templates(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - template_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ One "file_id" is associated with a given "image".
+ Several "image" are associated with a given "template_id".
+ Several "template_id" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | template_id | | template_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | client_id |
+ -----------------------------------------------------------------------------------------------
+
+ Note: for this particular database, there is only one "template_id"
+ per "client_id".
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'template_id', 'file_id', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.biosecurid.face.Database()
+
+ template_ids = db.model_ids(protocol=parameters['protocol'],
+ groups=parameters['group'])
+
+ entries = []
+
+ for template_id in template_ids:
+ objs = db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ purposes='enrol',
+ model_ids=[template_id])
+
+ entries.extend([ Entry(x.client_id, template_id, x.id, x.make_path(root_folder, '.bmp'))
+ for x in objs ])
+
+ return sorted(entries, key=lambda x: (x.client_id, x.template_id, x.file_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'template_id':
+ return {
+ 'value': np.uint64(obj.template_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Probes(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - probe_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+ - template_ids: "{{ system_user.username }}/array_1d_uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "probe_id" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+ Several "client_id" are associated with a given "template_ids".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | probe_id | | probe_id | | probe_id | | probe_id | | probe_id | | probe_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | template_ids |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['template_ids', 'client_id', 'probe_id', 'file_id', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.biosecurid.face.Database()
+
+ template_ids = sorted(db.model_ids(protocol=parameters['protocol'],
+ groups=parameters['group']))
+
+ template_probes = {}
+ for template_id in template_ids:
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ purposes='probe',
+ model_ids=[template_id]),
+ key=lambda x: (x.client_id, x.id))
+
+ template_probes[template_id] = [ p.id for p in objs ]
+
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ purposes='probe'),
+ key=lambda x: (x.client_id, x.id))
+
+ entries = []
+ for obj in objs:
+ templates = [ template_id for template_id in template_ids
+ if obj.id in template_probes[template_id] ]
+ entries.append( Entry(templates, obj.client_id, obj.id, obj.id,
+ obj.make_path(root_folder, '.bmp')) )
+
+ return sorted(entries, key=lambda x: (len(x.template_ids), x.template_ids,
+ x.client_id, x.file_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'template_ids':
+ return {
+ 'value': obj.template_ids
+ }
+
+ elif output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'probe_id':
+ return {
+ 'value': np.uint64(obj.probe_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+def setup_tests():
+ # Install a mock load function for the images
+ def mock_load(root_folder):
+ return np.ndarray((3, 10, 20), dtype=np.uint8)
+
+ bob.io.base.load = mock_load
+
+
+#----------------------------------------------------------
+
+
+# Test the behavior of the views (on fake data)
+if __name__ == '__main__':
+
+ setup_tests()
+
+ view = Train()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='A'
+ )
+ )
+ view.get('client_id', 0)
+ view.get('file_id', 0)
+ view.get('image', 0)
+
+
+ view = Templates()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='A',
+ group='dev',
+ )
+ )
+ view.get('client_id', 0)
+ view.get('template_id', 0)
+ view.get('file_id', 0)
+ view.get('image', 0)
+
+
+ view = Probes()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='A',
+ group='dev',
+ )
+ )
+ view.get('template_ids', 0)
+ view.get('client_id', 0)
+ view.get('probe_id', 0)
+ view.get('file_id', 0)
+ view.get('image', 0)
diff --git a/advanced/databases/biosecurid_face/3.rst b/advanced/databases/biosecurid_face/3.rst
new file mode 100644
index 0000000..bcfa016
--- /dev/null
+++ b/advanced/databases/biosecurid_face/3.rst
@@ -0,0 +1,41 @@
+.. Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ ..
+.. Contact: beat.support@idiap.ch ..
+.. ..
+.. This file is part of the beat.examples module of the BEAT platform. ..
+.. ..
+.. Commercial License Usage ..
+.. Licensees holding valid commercial BEAT licenses may use this file in ..
+.. accordance with the terms contained in a written agreement between you ..
+.. and Idiap. For further information contact tto@idiap.ch ..
+.. ..
+.. Alternatively, this file may be used under the terms of the GNU Affero ..
+.. Public License version 3 as published by the Free Software and appearing ..
+.. in the file LICENSE.AGPL included in the packaging of this file. ..
+.. The BEAT platform is distributed in the hope that it will be useful, but ..
+.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
+.. or FITNESS FOR A PARTICULAR PURPOSE. ..
+.. ..
+.. You should have received a copy of the GNU Affero Public License along ..
+.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
+
+
+The BiosecurID Database of Faces
+--------------------------------
+
+No documentation provided by author.
+
+
+Changelog
+=========
+
+* **Version 3**, 30/Jan/2018:
+
+ - Port to beat.backend.python v1.5.0
+
+* **Version 2**, 30/Oct/2017:
+
+ - Port to beat.backend.python v1.4.2
+
+* **Version 1**, 26/Jan/2016:
+
+ - initial release
diff --git a/advanced/databases/biowave/3.json b/advanced/databases/biowave/3.json
new file mode 100644
index 0000000..63a4586
--- /dev/null
+++ b/advanced/databases/biowave/3.json
@@ -0,0 +1,1579 @@
+{
+ "root_folder": "/idiap/project/biowave/biowave_v1/data",
+ "description": "BIOWAVE Vein Database",
+ "protocols": [
+ {
+ "name": "Idiap_1_1_R_BEAT_test",
+ "template": "advanced_vein_recognition_2",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "Idiap_1_1_R_BEAT_test",
+ "group": "world"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "Idiap_1_1_R_BEAT_test",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "Idiap_1_1_R_BEAT_test",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "Idiap_1_1_R_BEAT_test",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "Idiap_1_1_R_BEAT_test",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "Idiap_5_5_R_BEAT_test",
+ "template": "advanced_vein_recognition_2",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "Idiap_5_5_R_BEAT_test",
+ "group": "world"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "Idiap_5_5_R_BEAT_test",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "Idiap_5_5_R_BEAT_test",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "Idiap_5_5_R_BEAT_test",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "Idiap_5_5_R_BEAT_test",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "Idiap_1_5_R_BEAT_test",
+ "template": "advanced_vein_recognition_2",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "Idiap_1_5_R_BEAT_test",
+ "group": "world"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "Idiap_1_5_R_BEAT_test",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "Idiap_1_5_R_BEAT_test",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "Idiap_1_5_R_BEAT_test",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "Idiap_1_5_R_BEAT_test",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "Idiap_1_1_R",
+ "template": "advanced_vein_recognition_2",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "Idiap_1_1_R",
+ "group": "world"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "Idiap_1_1_R",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "Idiap_1_1_R",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "Idiap_1_1_R",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "Idiap_1_1_R",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "Idiap_1_5_R",
+ "template": "advanced_vein_recognition_2",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "Idiap_1_5_R",
+ "group": "world"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "Idiap_1_5_R",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "Idiap_1_5_R",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "Idiap_1_5_R",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "Idiap_1_5_R",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "Idiap_3_5_R",
+ "template": "advanced_vein_recognition_2",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "Idiap_3_5_R",
+ "group": "world"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "Idiap_3_5_R",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "Idiap_3_5_R",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "Idiap_3_5_R",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "Idiap_3_5_R",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "Idiap_5_5_R",
+ "template": "advanced_vein_recognition_2",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "Idiap_5_5_R",
+ "group": "world"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "Idiap_5_5_R",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "Idiap_5_5_R",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "Idiap_5_5_R",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "Idiap_5_5_R",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "Idiap_1_1_L",
+ "template": "advanced_vein_recognition_2",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "Idiap_1_1_L",
+ "group": "world"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "Idiap_1_1_L",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "Idiap_1_1_L",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "Idiap_1_1_L",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "Idiap_1_1_L",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "Idiap_1_5_L",
+ "template": "advanced_vein_recognition_2",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "Idiap_1_5_L",
+ "group": "world"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "Idiap_1_5_L",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "Idiap_1_5_L",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "Idiap_1_5_L",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "Idiap_1_5_L",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "Idiap_5_5_L",
+ "template": "advanced_vein_recognition_2",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "Idiap_5_5_L",
+ "group": "world"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "Idiap_5_5_L",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "Idiap_5_5_L",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "Idiap_5_5_L",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "Idiap_5_5_L",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "Idiap_1_1_R_less",
+ "template": "advanced_vein_recognition_2",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "Idiap_1_1_R_less",
+ "group": "world"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "Idiap_1_1_R_less",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "Idiap_1_1_R_less",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "Idiap_1_1_R_less",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "Idiap_1_1_R_less",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "Idiap_1_5_R_less",
+ "template": "advanced_vein_recognition_2",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "Idiap_1_5_R_less",
+ "group": "world"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "Idiap_1_5_R_less",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "Idiap_1_5_R_less",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "Idiap_1_5_R_less",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "Idiap_1_5_R_less",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "Idiap_5_5_R_less",
+ "template": "advanced_vein_recognition_2",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "Idiap_5_5_R_less",
+ "group": "world"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "Idiap_5_5_R_less",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "Idiap_5_5_R_less",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "Idiap_5_5_R_less",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "Idiap_5_5_R_less",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "Idiap_1_1_R_annotations_BEAT_test",
+ "template": "advanced_vein_annotations",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "ViewAnnotations",
+ "template": "train",
+ "parameters": {
+ "protocol": "Idiap_1_1_R_BEAT_test",
+ "group": "world"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateViewAnnotations",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "Idiap_1_1_R_BEAT_test",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "ViewAnnotations",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "Idiap_1_1_R_BEAT_test",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateViewAnnotations",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "Idiap_1_1_R_BEAT_test",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateViewAnnotations",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "Idiap_1_1_R_BEAT_test",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "Idiap_2_1_R_annotations_BEAT_test",
+ "template": "advanced_vein_annotations",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "ViewAnnotations",
+ "template": "train",
+ "parameters": {
+ "protocol": "Idiap_5_5_R_BEAT_test",
+ "group": "world"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateViewAnnotations",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "Idiap_5_5_R_BEAT_test",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "ViewAnnotations",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "Idiap_5_5_R_BEAT_test",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateViewAnnotations",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "Idiap_5_5_R_BEAT_test",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateViewAnnotations",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "Idiap_5_5_R_BEAT_test",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "Idiap_1_1_R_annotations",
+ "template": "advanced_vein_annotations",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "ViewAnnotations",
+ "template": "train",
+ "parameters": {
+ "protocol": "Idiap_1_1_R",
+ "group": "world"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateViewAnnotations",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "Idiap_1_1_R",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "ViewAnnotations",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "Idiap_1_1_R",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateViewAnnotations",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "Idiap_1_1_R",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateViewAnnotations",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "Idiap_1_1_R",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "Idiap_2_1_R_annotations",
+ "template": "advanced_vein_annotations",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "ViewAnnotations",
+ "template": "train",
+ "parameters": {
+ "protocol": "Idiap_5_5_R",
+ "group": "world"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateViewAnnotations",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "Idiap_5_5_R",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "ViewAnnotations",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "Idiap_5_5_R",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateViewAnnotations",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "Idiap_5_5_R",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateViewAnnotations",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "Idiap_5_5_R",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "Idiap_1_1_L_annotations",
+ "template": "advanced_vein_annotations",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "ViewAnnotations",
+ "template": "train",
+ "parameters": {
+ "protocol": "Idiap_1_1_L",
+ "group": "world"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateViewAnnotations",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "Idiap_1_1_L",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "ViewAnnotations",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "Idiap_1_1_L",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateViewAnnotations",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "Idiap_1_1_L",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateViewAnnotations",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "Idiap_1_1_L",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "Idiap_2_1_L_annotations",
+ "template": "advanced_vein_annotations",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "ViewAnnotations",
+ "template": "train",
+ "parameters": {
+ "protocol": "Idiap_5_5_L",
+ "group": "world"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateViewAnnotations",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "Idiap_5_5_L",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "ViewAnnotations",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "Idiap_5_5_L",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateViewAnnotations",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "Idiap_5_5_L",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateViewAnnotations",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "Idiap_5_5_L",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "annotation_benchmark",
+ "template": "advanced_annotation_benchmark",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "vein_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "ROI_annotations": "{{ system_user.username }}/array_2d_uint8/1",
+ "alignment_annotations": "{{ system_user.username }}/array_1d_coordinates/1"
+ },
+ "view": "AnnotationBenchmark",
+ "template": "annotation_benchmark",
+ "parameters": {
+ "protocol": "Idiap_1_1_R"
+ },
+ "name": "annotations"
+ }
+ ]
+ }
+ ]
+}
diff --git a/advanced/databases/biowave/3.py b/advanced/databases/biowave/3.py
new file mode 100644
index 0000000..87a638d
--- /dev/null
+++ b/advanced/databases/biowave/3.py
@@ -0,0 +1,499 @@
+###############################################################################
+# #
+# Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import os
+import numpy as np
+from collections import namedtuple
+
+from beat.backend.python.database import View as BaseView
+
+import bob.io.base
+import bob.io.image
+import bob.db.biowave_v1
+
+from PIL import Image, ImageDraw, ImageFilter
+
+
+#----------------------------------------------------------
+
+
+def construct_ROI_image(annotations):
+ """Adapted from bob.db.biowave_v1, because we want to separate it in two steps:
+ indexing and image construction"""
+
+ if len(annotations) > 0:
+ return bob.db.biowave_v1.utils.ManualRoiCut(annotations).roi_mask()
+ else:
+ return np.array([], np.uint8)
+
+
+#----------------------------------------------------------
+
+
+def construct_vein_image(annotations, center=False):
+ """Adapted from bob.db.biowave_v1, because we want to separate it in two steps:
+ indexing and image construction"""
+
+ if len(annotations) > 0:
+ im = Image.new('L', (480, 480), (0))
+ draw = ImageDraw.Draw(im)
+ if center:
+ xes_all = [point[1] for line in annotations for point in line]
+ yes_all = [point[0] for line in annotations for point in line]
+ for line in annotations:
+ xes = [point[1] - np.round(np.mean(xes_all)) + 239 for point in line]
+ yes = [point[0] - np.round(np.mean(yes_all)) + 239 for point in line]
+ for point in range(len(line) - 1):
+ draw.line((xes[point],yes[point], xes[point+1], yes[point+1]), fill=255, width = 5)
+ else:
+ for line in annotations:
+ xes = [point[1] for point in line]
+ yes = [point[0] for point in line]
+ for point in range(len(line) - 1):
+ draw.line((xes[point],yes[point], xes[point+1], yes[point+1]), fill=255, width = 5)
+ im = im.filter(ImageFilter.MedianFilter(5))
+ return np.array(np.array(im, dtype = bool), dtype = np.uint8)
+ else:
+ return np.array([], np.uint8)
+
+
+#----------------------------------------------------------
+
+
+def construct_alignment_annotations(annotations):
+ return [ dict(
+ x = np.int32(annotation[1]),
+ y = np.int32(annotation[0])
+ ) for annotation in annotations ]
+
+
+#----------------------------------------------------------
+
+
+class View(BaseView):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_2d_uint8/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ Several "image" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.biowave_v1.Database()
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups=[parameters['group']],
+ purposes=parameters.get('purpose', None),
+ annotated_images=False,
+ imagedir=root_folder),
+ key=lambda x: (x.client_id, x.id))
+
+ return [ Entry(x.client_id, x.make_path(root_folder, '.png')) for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class TemplateView(BaseView):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_2d_uint8/1"
+ - model_id: "{{ system_user.username }}/text/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ Several "image" are associated with a given "model_id".
+ Several "model_id" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | model_id | | model_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | client_id |
+ -----------------------------------------------------------------------------------------------
+
+ Note: for this particular database, there is only one "image"
+ per "model_id".
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'model_id', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.biowave_v1.Database()
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ purposes=parameters['purpose'],
+ groups=[parameters['group']],
+ annotated_images=False,
+ imagedir=root_folder),
+ key=lambda x: (x.client_id, x.id))
+
+ return [ Entry(x.client_id, x.model_id.encode('utf-8'),
+ x.make_path(root_folder, '.png'))
+ for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'model_id':
+ return {
+ 'text': str(obj.model_id)
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class ViewAnnotations(BaseView):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_2d_uint8/1"
+ - vein_annotations: "{{ system_user.username }}/array_2d_uint8/1"
+ - ROI_annotations: "{{ system_user.username }}/array_2d_uint8/1"
+ - alignment_annotations: "{{ system_user.username }}/array_1d_coordinates/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ One "vein_annotations" are associated with a given "image".
+ One "ROI_annotations" are associated with a given "image".
+ One "alignment_annotations" are associated with a given "image".
+ Several "image" are associated with a given "client_id".
+
+ ------------------------- ------------------------- ------------------------- -------------------------
+ | image | | image | | image | | image |
+ ------------------------- ------------------------- ------------------------- -------------------------
+ ------------------------- ------------------------- ------------------------- -------------------------
+ | vein_annotations | | vein_annotations | | vein_annotations | | vein_annotations |
+ ------------------------- ------------------------- ------------------------- -------------------------
+ ------------------------- ------------------------- ------------------------- -------------------------
+ | ROI_annotations | | ROI_annotations | | ROI_annotations | | ROI_annotations |
+ ------------------------- ------------------------- ------------------------- -------------------------
+ ------------------------- ------------------------- ------------------------- -------------------------
+ | alignment_annotations | | alignment_annotations | | alignment_annotations | | alignment_annotations |
+ ------------------------- ------------------------- ------------------------- -------------------------
+ --------------------------------------------------- ---------------------------------------------------
+ | client_id | | client_id |
+ --------------------------------------------------- ---------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'alignment_annotations', 'ROI_annotations',
+ 'vein_annotations', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.biowave_v1.Database()
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups=[parameters['group']],
+ purposes=parameters.get('purpose', None),
+ annotated_images=True,
+ imagedir=root_folder),
+ key=lambda x: (x.client_id, x.id))
+
+ print objs
+
+ return [ Entry(x.client_id,
+ x.alignment_annotations(directory=root_folder),
+ x.roi_annotations(directory=root_folder),
+ x.vein_annotations(directory=root_folder),
+ x.make_path(root_folder, '.png'))
+ for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'alignment_annotations':
+ return {
+ 'value': construct_alignment_annotations(obj.alignment_annotations)
+ }
+
+ elif output == 'ROI_annotations':
+ return {
+ 'value': construct_ROI_image(obj.ROI_annotations)
+ }
+
+ elif output == 'vein_annotations':
+ return {
+ 'value': construct_vein_image(obj.vein_annotations)
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class TemplateViewAnnotations(BaseView):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_2d_uint8/1"
+ - vein_annotations: "{{ system_user.username }}/array_2d_uint8/1"
+ - ROI_annotations: "{{ system_user.username }}/array_2d_uint8/1"
+ - alignment_annotations: "{{ system_user.username }}/array_1d_coordinates/1"
+ - model_id: "{{ system_user.username }}/model_id/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ One "vein_annotations" are associated with a given "image".
+ One "ROI_annotations" are associated with a given "image".
+ One "alignment_annotations" are associated with a given "image".
+ Several "image" are associated with a given "model_id".
+ Several "model_id" are associated with a given "client_id".
+
+ ------------------------- ------------------------- ------------------------- -------------------------
+ | image | | image | | image | | image |
+ ------------------------- ------------------------- ------------------------- -------------------------
+ ------------------------- ------------------------- ------------------------- -------------------------
+ | vein_annotations | | vein_annotations | | vein_annotations | | vein_annotations |
+ ------------------------- ------------------------- ------------------------- -------------------------
+ ------------------------- ------------------------- ------------------------- -------------------------
+ | ROI_annotations | | ROI_annotations | | ROI_annotations | | ROI_annotations |
+ ------------------------- ------------------------- ------------------------- -------------------------
+ ------------------------- ------------------------- ------------------------- -------------------------
+ | alignment_annotations | | alignment_annotations | | alignment_annotations | | alignment_annotations |
+ ------------------------- ------------------------- ------------------------- -------------------------
+ --------------------------------------------------- ---------------------------------------------------
+ | model_id | | model_id |
+ --------------------------------------------------- ---------------------------------------------------
+ -------------------------------------------------------------------------------------------------------
+ | client_id |
+ -------------------------------------------------------------------------------------------------------
+
+ Note: for this particular database, there is only one "image"
+ per "model_id".
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'model_id', 'alignment_annotations',
+ 'ROI_annotations', 'vein_annotations', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.biowave_v1.Database()
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups=[parameters['group']],
+ purposes=parameters['purpose'],
+ annotated_images=True,
+ imagedir=root_folder),
+ key=lambda x: (x.client_id, x.model_id, x.id))
+
+ return [ Entry(x.client_id,
+ x.model_id.encode('utf-8'),
+ x.alignment_annotations(directory=root_folder),
+ x.roi_annotations(directory=root_folder),
+ x.vein_annotations(directory=root_folder),
+ x.make_path(root_folder, '.png'))
+ for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'model_id':
+ return {
+ 'text': obj.model_id
+ }
+
+ elif output == 'alignment_annotations':
+ return {
+ 'value': construct_alignment_annotations(obj.alignment_annotations)
+ }
+
+ elif output == 'ROI_annotations':
+ return {
+ 'value': construct_ROI_image(obj.ROI_annotations)
+ }
+
+ elif output == 'vein_annotations':
+ return {
+ 'value': construct_vein_image(obj.vein_annotations)
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class AnnotationBenchmark(BaseView):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_2d_uint8/1"
+ - vein_annotations: "{{ system_user.username }}/array_2d_uint8/1"
+ - ROI_annotations: "{{ system_user.username }}/array_2d_uint8/1"
+ - alignment_annotations: "{{ system_user.username }}/array_1d_coordinates/1"
+
+ One "vein_annotations" are associated with a given "image".
+ One "ROI_annotations" are associated with a given "image".
+ One "alignment_annotations" are associated with a given "image".
+
+ ------------------------- ------------------------- ------------------------- -------------------------
+ | image | | image | | image | | image |
+ ------------------------- ------------------------- ------------------------- -------------------------
+ ------------------------- ------------------------- ------------------------- -------------------------
+ | vein_annotations | | vein_annotations | | vein_annotations | | vein_annotations |
+ ------------------------- ------------------------- ------------------------- -------------------------
+ ------------------------- ------------------------- ------------------------- -------------------------
+ | ROI_annotations | | ROI_annotations | | ROI_annotations | | ROI_annotations |
+ ------------------------- ------------------------- ------------------------- -------------------------
+ ------------------------- ------------------------- ------------------------- -------------------------
+ | alignment_annotations | | alignment_annotations | | alignment_annotations | | alignment_annotations |
+ ------------------------- ------------------------- ------------------------- -------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['alignment_annotations', 'ROI_annotations',
+ 'vein_annotations', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.biowave_v1.Database()
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ annotated_images=True,
+ imagedir=root_folder),
+ key=lambda x: x.id)
+
+ return [ Entry(x.alignment_annotations(directory=root_folder),
+ x.roi_annotations(directory=root_folder),
+ x.vein_annotations(directory=root_folder),
+ x.make_path(root_folder, '.png'))
+ for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'alignment_annotations':
+ return {
+ 'value': construct_alignment_annotations(obj.alignment_annotations)
+ }
+
+ elif output == 'ROI_annotations':
+ return {
+ 'value': construct_ROI_image(obj.ROI_annotations)
+ }
+
+ elif output == 'vein_annotations':
+ return {
+ 'value': construct_vein_image(obj.vein_annotations)
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+def setup_tests():
+ # Install a mock methods
+ def mock_load(root_folder):
+ return np.ndarray((10, 20), dtype=np.uint8)
+
+ bob.io.base.load = mock_load
+
+
+#----------------------------------------------------------
+
+
+# Test the behavior of the views (on fake data)
+if __name__ == '__main__':
+
+ setup_tests()
+
+ view = View()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol="Idiap_1_1_R_BEAT_test",
+ group="world",
+ )
+ )
+ view.get('client_id', 0)
+ view.get('image', 0)
+
+
+ view = TemplateView()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol="Idiap_1_1_R_BEAT_test",
+ group="dev",
+ purpose="enroll",
+ )
+ )
+ view.get('client_id', 0)
+ view.get('model_id', 0)
+ view.get('image', 0)
diff --git a/advanced/databases/biowave/3.rst b/advanced/databases/biowave/3.rst
new file mode 100644
index 0000000..f6a9154
--- /dev/null
+++ b/advanced/databases/biowave/3.rst
@@ -0,0 +1,212 @@
+.. Copyright (c) 2017 Idiap Research Institute, http://www.idiap.ch/ ..
+.. Contact: beat.support@idiap.ch ..
+.. ..
+.. This file is part of the beat.examples module of the BEAT platform. ..
+.. ..
+.. Commercial License Usage ..
+.. Licensees holding valid commercial BEAT licenses may use this file in ..
+.. accordance with the terms contained in a written agreement between you ..
+.. and Idiap. For further information contact tto@idiap.ch ..
+.. ..
+.. Alternatively, this file may be used under the terms of the GNU Affero ..
+.. Public License version 3 as published by the Free Software and appearing ..
+.. in the file LICENSE.AGPL included in the packaging of this file. ..
+.. The BEAT platform is distributed in the hope that it will be useful, but ..
+.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
+.. or FITNESS FOR A PARTICULAR PURPOSE. ..
+.. ..
+.. You should have received a copy of the GNU Affero Public License along ..
+.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
+
+BIOWAVE Vein Database
+---------------------
+
+Changelog
+=========
+
+* **Version 3**, 30/Jan/2018:
+
+ - Port to beat.backend.python v1.5.0
+
+* **Version 2**, 17/Nov/2017:
+
+ - Add protocol 'Idiap_3_5_R'
+
+* **Version 1**, 30/Oct/2017:
+
+ - Initial release
+
+
+Description
+===========
+
+
+The ``BIOWAVE`` database contains wrist vein images that are acquired using
+BIOWATCH biometric sensor. For each subject of the database there are 3 session
+images (sessions were held at least 24 hours apart). Each session consists of 5
+attempts, in each attempt 5 images were acquired, meaning, that there are
+``3 sessions x 5 attempts x 5 images = 75`` images per each person's hand,
+``75 x 2 images`` per person.
+
+Images were previously manually evaluated, and if any of the 75 one hand's
+images were unusable (too blurred, veins couldn't be seen, etc), than all hand
+data were discarded. That is way some persons has only 1 hand's images in the
+database.
+
+Statistics of the data - in total 111 hands:
+
+1) Users with both hands images - 53
+2) Users with only R hand images - 4
+3) Users with only L hand images - 1
+
+
+
+Data division in ``groups`` and ``purposes``
+############################################
+
+
+In the BIOWAVE database there are following groups and purposes:
+
+
++-------------+-----------+-----------------------+------------------------+
+| **groups** | ``world`` | ``dev`` | ``eval`` |
++-------------+-----------+-----------------------+------------------------+
+|**purposes** | ``train`` |``enroll`` / ``probe`` | ``enroll`` / ``probe`` |
++-------------+-----------+-----------------------+------------------------+
+
+
+Data are divided into training, development and evolution data sets:
+
+1) ``dev`` data set -- ``20`` persons with both hand images;
+2) ``eval`` data set -- ``20`` persons with both hand images;
+3) ``world`` data set -- the rest of ``13`` persons with both hand images + ``4`` persons with only ``right`` hand and the ``1`` person with only ``left`` hand images.
+
+Data are divided as follows (the number means original persons ``ID`` (as in
+data collection)):
+
+1) ``dev`` data - [1, 3, 5, 6, 12, 15, 22, 23, 25, 45, 69, 71, 74, 76, 77, 78, 84, 129, 132, 134]
+2) ``eval`` data - [4, 7, 21, 42, 43, 48, 54, 57, 58, 72, 73, 75, 83, 90, 99, 130, 131, 138, 141, 143]
+3) ``world`` data - [13, 14, 17, 20, 24, 28, 31, 39, 51, 60, 68, 128, 133] + [37] + [2, 11, 46, 70]
+
+In the ``enroll`` data sets are images only from session ``1`` whereas in
+``probe`` data sets -- session's ``2`` and ``3`` data.
+
+
+Protocols
+#########
+
+There currently are 6 *standard* protocols:
+
+- ``Idiap_1_1_R``
+- ``Idiap_1_5_R``
+- ``Idiap_5_5_R``
+- ``Idiap_1_1_L``
+- ``Idiap_1_5_L``
+- ``Idiap_5_5_L``
+
+The general form::
+
+ Idiap_<a>_<b>_<c>
+
+* a -- number of attempts used to construct model;
+* b -- number of attempt images used to construct model;
+* c -- hand (``L`` or ``R``). Always all images are used, the opposite hand's images are mirrored.
+
+E.g. in protocol ``Idiap_5_5_R`` per one hand only one enroll model is
+constructed using all session ``1`` images (``5 attempt x 5 images``). In this
+protocol ``right`` hands are included unchanged, but ``left`` hands are
+mirrored.
+
+After data alignment were annotated, that included common point finding between
+each person images, we noted that some of the person data were almost impossible
+to annotate. Hands were:
+
+- Person_002/Right
+- Person_005/Left
+- Person_015/Right
+- Person_046/Right
+- Person_068/Right
+- Person_068/Left
+- Person_129/Right
+- Person_138/Left
+
+For protocols:
+
+- ``Idiap_1_1_R_less``
+- ``Idiap_1_5_R_less``
+- ``Idiap_5_5_R_less``
+
+also listed hand data were removed.
+
+
+Protocol ``Idiap_3_5_R`` is not *standard* - in this protocol enroll model is
+constructed using all 3 session 1st attempt images (thus each model consists of
+``3x5=15`` images). This protocol is made to understand the enroll image
+variabilities impact on the recognition rate.
+
+Finally, the protocols:
+
+- ``Idiap_1_1_R_BEAT_test``
+- ``Idiap_1_5_R_BEAT_test``
+- ``Idiap_5_5_R_BEAT_test``
+
+are made as test protocols mainly for the BEAT platform. Again, the numbers in
+the name indicates number of images per attempt and attempt count per each
+enroll model. Each of the protocol groups consists of only 2 person data:
+
+- train -- [128, 133],
+- dev -- [132, 134],
+- eval -- [141, 143].
+
+All these protocols uses the ``advanced_vein_recognition_2`` template.
+
+Annotations
+###########
+
+
+8% of database files have annotations (2 images per session, 6 images per hand,
+altogether 666 images). There are:
+
+- vein pattern binary annotations;
+- ROI binary annotations;
+- alignment point annotations (for each hand 3-5 points marked).
+
+
+Annotations to replace extractors
+#################################
+
+
+Special protocols that consists only the files that have annotations are made.
+Meaning, these protocols can be used to run !!!!! TBD
+
+
+
+
+
+The organisation of the database is the same as before, only now, because there
+are only 2 annotated images per session, protocols are renamed accordingly (also
+note, that now protocols ``Idiap_1_5_R`` and ``Idiap_1_5_L`` doesn't make any
+sense and are removed, because there are only 1 image per attempt that is
+annotated):
+
+- ``Idiap_1_1_R`` -> renamed as ``Idiap_1_1_R_annotations``;
+- ``Idiap_5_5_R`` -> renamed as ``Idiap_2_1_R_annotations``;
+- ``Idiap_1_1_L`` -> renamed as ``Idiap_1_1_L_annotations``;
+- ``Idiap_5_5_L`` -> renamed as ``Idiap_2_1_L_annotations``;
+
+
+Also, the same as ``Idiap_<a>_<b>_<c>_BEAT_test`` protocols were created to
+speedy test BEAT implementation and compare results with ``bob.bio.vein``, also
+test protocols for annotations are implemented:
+
+- ``Idiap_1_1_R_annotations_BEAT_test``;
+- ``Idiap_2_1_R_annotations_BEAT_test``;
+- ``Idiap_1_1_L_annotations_BEAT_test``;
+- ``Idiap_2_1_L_annotations_BEAT_test``;
+
+These protocols uses the ``advanced_vein_annotations`` template.
+
+Also, to benchmark different algorithms against **all** image annotations, there
+are implemented protocol ``annotations_benchmark`` that is implemented using
+``advanced_annotation_benchmark`` template.
+
diff --git a/advanced/databases/casme2/4.json b/advanced/databases/casme2/4.json
new file mode 100644
index 0000000..c165d1a
--- /dev/null
+++ b/advanced/databases/casme2/4.json
@@ -0,0 +1,1010 @@
+{
+ "description": "CASME 2 Spotaneous Subtle Expression Database",
+ "root_folder": "/idiap/resource/database/CASME2",
+ "protocols": [
+ {
+ "name": "fold_1",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_1"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_1"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+ {
+ "name": "fold_2",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_2"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_2"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+ {
+ "name": "fold_3",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_3"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_3"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+ {
+ "name": "fold_4",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_4"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_4"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+
+ {
+ "name": "fold_5",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_5"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_5"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+
+ {
+ "name": "fold_6",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_6"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_6"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+
+ {
+ "name": "fold_7",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_7"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_7"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+
+ {
+ "name": "fold_8",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_8"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_8"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+ {
+ "name": "fold_9",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_9"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_9"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+
+
+ {
+ "name": "fold_10",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_10"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_10"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+
+ {
+ "name": "fold_11",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_11"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_11"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+
+ {
+ "name": "fold_12",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_12"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_12"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+
+ {
+ "name": "fold_13",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_13"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_13"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+
+ {
+ "name": "fold_14",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_14"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_14"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+
+ {
+ "name": "fold_15",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_15"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_15"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+ {
+ "name": "fold_16",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_16"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_16"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+ {
+ "name": "fold_17",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_17"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_17"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+
+ {
+ "name": "fold_18",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_18"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_18"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+
+ {
+ "name": "fold_19",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_19"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_19"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+ {
+ "name": "fold_20",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_20"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_20"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+
+ {
+ "name": "fold_21",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_21"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_21"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+
+ {
+ "name": "fold_22",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_22"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_22"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+
+ {
+ "name": "fold_23",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_23"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_23"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+
+ {
+ "name": "fold_24",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_24"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_24"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+ {
+ "name": "fold_25",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_25"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_25"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ },
+
+
+
+ {
+ "name": "fold_26",
+ "template": "simple_expression_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train",
+ "protocol": "fold_26"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test",
+ "protocol": "fold_26"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_4d_uint8/1",
+ "emotion": "{{ system_user.username }}/text/1"
+ }
+ }
+ ]
+ }
+
+
+ ]
+}
diff --git a/advanced/databases/casme2/4.py b/advanced/databases/casme2/4.py
new file mode 100644
index 0000000..8f2ef31
--- /dev/null
+++ b/advanced/databases/casme2/4.py
@@ -0,0 +1,148 @@
+###############################################################################
+# #
+# Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import os
+import numpy as np
+from collections import namedtuple
+
+from beat.backend.python.database import View as BaseView
+
+import bob.io.base
+import bob.io.image
+import bob.db.casme2
+
+
+#----------------------------------------------------------
+
+
+class View(BaseView):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_4d_uint8/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+ - emotion: "{{ system_user.username }}/text/1"
+
+ One "file_id" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+ Several "client_id" are associated with a given "emotion".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | emotion |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['emotion', 'client_id', 'file_id', 'frames'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.casme2.Database()
+
+ objs = sorted(db.objects(protocol=str(parameters['protocol']),
+ groups=parameters['group']),
+ key=lambda x: (x.emotion, x.client_id, x.id))
+
+ entries = []
+
+ for obj in objs:
+ frames = [ str(os.path.join(obj.make_path(), x.filename)).
+ replace('/idiap/resource/database/CASME2/Cropped', root_folder)
+ for x in obj.frames ]
+
+ entries.append(Entry(obj.emotion, obj.client_id, obj.id, frames))
+
+ return entries
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'emotion':
+ return {
+ 'value': obj.emotion
+ }
+
+ elif output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'image':
+ frame = bob.io.base.load(obj.frames[0])
+
+ data = np.zeros(shape=(len(obj.frames), frame.shape[0], frame.shape[1], frame.shape[2]), dtype="uint8")
+ data[0] = frame
+
+ for i in range(1, len(obj.frames)):
+ data[i] = bob.io.base.load(obj.frames[i])
+
+ return {
+ 'value': data
+ }
+
+
+#----------------------------------------------------------
+
+
+def setup_tests():
+ # Install a mock load function for the images
+ def mock_load(root_folder):
+ return np.ndarray((3, 10, 20), dtype=np.uint8)
+
+ bob.io.base.load = mock_load
+
+
+#----------------------------------------------------------
+
+
+# Test the behavior of the views (on fake data)
+if __name__ == '__main__':
+
+ setup_tests()
+
+ view = View()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='fold_1',
+ group='train',
+ )
+ )
+ view.get('emotion', 0)
+ view.get('client_id', 0)
+ view.get('file_id', 0)
+ view.get('image', 0)
diff --git a/advanced/databases/casme2/4.rst b/advanced/databases/casme2/4.rst
new file mode 100644
index 0000000..0fa5dc8
--- /dev/null
+++ b/advanced/databases/casme2/4.rst
@@ -0,0 +1,65 @@
+.. Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ ..
+.. Contact: beat.support@idiap.ch ..
+.. ..
+.. This file is part of the beat.examples module of the BEAT platform. ..
+.. ..
+.. Commercial License Usage ..
+.. Licensees holding valid commercial BEAT licenses may use this file in ..
+.. accordance with the terms contained in a written agreement between you ..
+.. and Idiap. For further information contact tto@idiap.ch ..
+.. ..
+.. Alternatively, this file may be used under the terms of the GNU Affero ..
+.. Public License version 3 as published by the Free Software and appearing ..
+.. in the file LICENSE.AGPL included in the packaging of this file. ..
+.. The BEAT platform is distributed in the hope that it will be useful, but ..
+.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
+.. or FITNESS FOR A PARTICULAR PURPOSE. ..
+.. ..
+.. You should have received a copy of the GNU Affero Public License along ..
+.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
+
+
+-----------------------------------------------
+ CASME 2 Spotaneous Subtle Expression Database
+-----------------------------------------------
+
+Changelog
+=========
+
+* **Version 4**, 30/Jan/2018:
+
+ - Port to beat.backend.python v1.5.0
+
+* **Version 3**, 30/Oct/2017:
+
+ - Port to beat.backend.python v1.4.2
+
+* **Version 2**, 26/Jan/2016:
+
+ - Port to Bob v2
+
+* **Version 1**, 13/May/2015:
+
+ - Initial release
+
+
+Description
+===========
+
+The CASME II database (http://fu.psych.ac.cn/CASME/casme2-en.php) has the
+following characteristics:
+
+- The samples are spontaneous and dynamic micro-expressions. Baseline (usually
+ neutral) frames are kept before and after each micro-expression, making it
+ possible to evaluate different detection algorithms.
+- The recordings have high temporal resolution (200 fps) and relatively higher
+ face resolution at 280x340 pixels.
+- Micro-expression labeling is based on FACS investigator's guide and Yan et
+ al. findings (Yan et al., 2013) that is different from the traditional 6
+ categories on ordinary facial expression.
+- The recordings have proper illumination without lighting flickers and with
+ reduced highlight regions of the face.
+- Some types of facial expressions are difficult to elicit in laboratory
+ situations, thus the samples in different categories distributed unequally,
+ e.g., there are 60 disgust samples but only 7 sadness samples. In CASME II,
+ we provide 5 classes of micro-expressions.
diff --git a/advanced/databases/cbsr_nir_vis_2/4.json b/advanced/databases/cbsr_nir_vis_2/4.json
new file mode 100644
index 0000000..4910f19
--- /dev/null
+++ b/advanced/databases/cbsr_nir_vis_2/4.json
@@ -0,0 +1,866 @@
+{
+ "description": "CASIA NIR-VIS 2.0 Face Database",
+ "root_folder": "/idiap/resource/database/cbsr_nir_vis_2",
+ "protocols": [
+ {
+ "name": "view2_1",
+ "template": "advanced_face_recognition_textid",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "view2_1"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "view2_1",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "view2_1",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "view2_1",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "view2_1",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "view2_2",
+ "template": "advanced_face_recognition_textid",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "view2_2"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "view2_2",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "view2_2",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "view2_2",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "view2_2",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "view2_3",
+ "template": "advanced_face_recognition_textid",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "view2_3"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "view2_3",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "view2_3",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "view2_3",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "view2_3",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "view2_4",
+ "template": "advanced_face_recognition_textid",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "view2_4"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "view2_4",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "view2_4",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "view2_4",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "view2_4",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "view2_5",
+ "template": "advanced_face_recognition_textid",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "view2_5"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "view2_5",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "view2_5",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "view2_5",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "view2_5",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "view2_6",
+ "template": "advanced_face_recognition_textid",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "view2_6"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "view2_6",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "view2_6",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "view2_6",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "view2_6",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "view2_7",
+ "template": "advanced_face_recognition_textid",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "view2_7"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "view2_7",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "view2_7",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "view2_7",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "view2_7",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "view2_8",
+ "template": "advanced_face_recognition_textid",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "view2_8"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "view2_8",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "view2_8",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "view2_8",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "view2_8",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "view2_9",
+ "template": "advanced_face_recognition_textid",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "view2_9"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "view2_9",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "view2_9",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "view2_9",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "view2_9",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "view2_10",
+ "template": "advanced_face_recognition_textid",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "view2_10"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "view2_10",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "view2_10",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "view2_10",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "view2_10",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ }
+ ]
+}
diff --git a/advanced/databases/cbsr_nir_vis_2/4.py b/advanced/databases/cbsr_nir_vis_2/4.py
new file mode 100644
index 0000000..ef3c42a
--- /dev/null
+++ b/advanced/databases/cbsr_nir_vis_2/4.py
@@ -0,0 +1,413 @@
+###############################################################################
+# #
+# Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import os
+import numpy as np
+from collections import namedtuple
+
+from beat.backend.python.database import View
+
+import bob.io.base
+import bob.io.image
+import bob.db.cbsr_nir_vis_2
+
+
+#----------------------------------------------------------
+
+
+class Train(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - file_id: "{{ system_user.username }}/text/1"
+ - client_id: "{{ system_user.username }}/text/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'file_id', 'eye_centers', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.cbsr_nir_vis_2.Database(
+ annotation_directory=os.path.join(root_folder, 'annotations')
+ )
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups='world'),
+ key=lambda x: (x.client_id, x.id))
+
+ entries = []
+
+ for obj in objs:
+ if os.path.exists(obj.make_path(root_folder, '.jpg')):
+ filename = obj.make_path(root_folder, '.jpg')
+ else:
+ filename = obj.make_path(root_folder, '.bmp')
+
+ entries.append(Entry(obj.client_id, obj.id, db.annotations(obj), filename))
+
+ return entries
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'text': obj.client_id
+ }
+
+ elif output == 'file_id':
+ return {
+ 'text': obj.file_id
+ }
+
+ elif output == 'eye_centers':
+ return {
+ 'left': {
+ 'y': np.int32(obj.eye_centers['leye'][0]),
+ 'x': np.int32(obj.eye_centers['leye'][1]),
+ },
+ 'right': {
+ 'y': np.int32(obj.eye_centers['reye'][0]),
+ 'x': np.int32(obj.eye_centers['reye'][1]),
+ }
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Templates(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - file_id: "{{ system_user.username }}/text/1"
+ - template_id: "{{ system_user.username }}/text/1"
+ - client_id: "{{ system_user.username }}/text/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ Several "image" are associated with a given "template_id".
+ Several "template_id" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | template_id | | template_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | client_id |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'template_id', 'file_id', 'eye_centers', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.cbsr_nir_vis_2.Database(
+ annotation_directory=os.path.join(root_folder, 'annotations')
+ )
+
+ template_ids = db.model_ids(protocol=parameters['protocol'],
+ groups=parameters['group'])
+
+ entries = []
+
+ for template_id in template_ids:
+ objs = db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ purposes='enroll',
+ model_ids=[template_id])
+
+ for obj in objs:
+ if os.path.exists(obj.make_path(root_folder, '.jpg')):
+ filename = obj.make_path(root_folder, '.jpg')
+ else:
+ filename = obj.make_path(root_folder, '.bmp')
+
+ entries.append(Entry(obj.client_id, template_id, obj.id,
+ db.annotations(obj), filename))
+
+ return sorted(entries, key=lambda x: (x.client_id, x.template_id, x.file_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'text': obj.client_id
+ }
+
+ elif output == 'template_id':
+ return {
+ 'text': obj.template_id
+ }
+
+ elif output == 'file_id':
+ return {
+ 'text': obj.file_id
+ }
+
+ elif output == 'eye_centers':
+ return {
+ 'left': {
+ 'y': np.int32(obj.eye_centers['leye'][0]),
+ 'x': np.int32(obj.eye_centers['leye'][1]),
+ },
+ 'right': {
+ 'y': np.int32(obj.eye_centers['reye'][0]),
+ 'x': np.int32(obj.eye_centers['reye'][1]),
+ }
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Probes(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - file_id: "{{ system_user.username }}/text/1"
+ - probe_id: "{{ system_user.username }}/text/1"
+ - client_id: "{{ system_user.username }}/text/1"
+ - template_ids: "{{ system_user.username }}/array_1d_text/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ One "probe_id" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+ Several "client_id" are associated with a given "template_ids".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | probe_id | | probe_id | | probe_id | | probe_id | | probe_id | | probe_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | template_ids |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['template_ids', 'client_id', 'probe_id', 'file_id',
+ 'eye_centers', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.cbsr_nir_vis_2.Database(
+ annotation_directory=os.path.join(root_folder, 'annotations')
+ )
+
+ template_ids = sorted(db.model_ids(protocol=parameters['protocol'],
+ groups=parameters['group']))
+
+
+ template_probes = {}
+ for template_id in template_ids:
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ purposes='probe',
+ model_ids=[template_id]),
+ key=lambda x: (x.client_id, x.id))
+
+ template_probes[template_id] = [ p.id for p in objs ]
+
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ purposes='probe'),
+ key=lambda x: (x.client_id, x.id))
+
+ entries = []
+ for obj in objs:
+ templates = [ template_id for template_id in template_ids
+ if obj.id in template_probes[template_id] ]
+
+ if os.path.exists(obj.make_path(root_folder, '.jpg')):
+ filename = obj.make_path(root_folder, '.jpg')
+ else:
+ filename = obj.make_path(root_folder, '.bmp')
+
+ entries.append( Entry(templates, obj.client_id, obj.id, obj.id,
+ db.annotations(obj), filename) )
+
+ return sorted(entries, key=lambda x: (len(x.template_ids), x.template_ids,
+ x.client_id, x.probe_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'template_ids':
+ return {
+ 'text': obj.template_ids
+ }
+
+ elif output == 'client_id':
+ return {
+ 'text': obj.client_id
+ }
+
+ elif output == 'probe_id':
+ return {
+ 'text': obj.probe_id
+ }
+
+ elif output == 'file_id':
+ return {
+ 'text': obj.file_id
+ }
+
+ elif output == 'eye_centers':
+ return {
+ 'left': {
+ 'y': np.int32(obj.eye_centers['leye'][0]),
+ 'x': np.int32(obj.eye_centers['leye'][1]),
+ },
+ 'right': {
+ 'y': np.int32(obj.eye_centers['reye'][0]),
+ 'x': np.int32(obj.eye_centers['reye'][1]),
+ }
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+def setup_tests():
+ # Install a mock load function for the images
+ def mock_load(root_folder):
+ return np.ndarray((3, 10, 20), dtype=np.uint8)
+
+ # Install a mock os.path.exists function
+ def mock_exists(path):
+ return True
+
+ bob.io.base.load = mock_load
+ os.path.exists = mock_exists
+
+
+#----------------------------------------------------------
+
+
+# Test the behavior of the views (on fake data)
+if __name__ == '__main__':
+
+ setup_tests()
+
+ # Note: This database can't be tested without the actual data, since
+ # some files are needed by this implementation
+
+ view = Train()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='view2_1',
+ )
+ )
+ view.get('client_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
+
+
+ view = Templates()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='view2_1',
+ group='dev',
+ )
+ )
+ view.get('client_id', 0)
+ view.get('template_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
+
+
+ view = Probes()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='view2_1',
+ group='dev',
+ )
+ )
+ view.get('template_ids', 0)
+ view.get('client_id', 0)
+ view.get('probe_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
diff --git a/advanced/databases/cbsr_nir_vis_2/4.rst b/advanced/databases/cbsr_nir_vis_2/4.rst
new file mode 100644
index 0000000..339ff94
--- /dev/null
+++ b/advanced/databases/cbsr_nir_vis_2/4.rst
@@ -0,0 +1,64 @@
+.. Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ ..
+.. Contact: beat.support@idiap.ch ..
+.. ..
+.. This file is part of the beat.examples module of the BEAT platform. ..
+.. ..
+.. Commercial License Usage ..
+.. Licensees holding valid commercial BEAT licenses may use this file in ..
+.. accordance with the terms contained in a written agreement between you ..
+.. and Idiap. For further information contact tto@idiap.ch ..
+.. ..
+.. Alternatively, this file may be used under the terms of the GNU Affero ..
+.. Public License version 3 as published by the Free Software and appearing ..
+.. in the file LICENSE.AGPL included in the packaging of this file. ..
+.. The BEAT platform is distributed in the hope that it will be useful, but ..
+.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
+.. or FITNESS FOR A PARTICULAR PURPOSE. ..
+.. ..
+.. You should have received a copy of the GNU Affero Public License along ..
+.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
+
+
+CASIA NIR-VIS 2.0 Face Database
+-------------------------------
+
+Changelog
+=========
+
+* **Version 4**, 30/Jan/2017:
+
+ - Port to beat.backend.python v1.5.0
+
+* **Version 3**, 30/Oct/2017:
+
+ - Port to beat.backend.python v1.4.2
+
+* **Version 2**, 20/Jan/2016:
+
+ - Port to Bob v2 (should not affect results)
+
+* **Version 1**, 06/Nov/2014:
+
+ - Initial release
+
+
+Description
+===========
+
+`CASIA NIR-VIS 2.0
+<http://www.cbsr.ia.ac.cn/english/NIR-VIS-2.0-Database.html>`_ database offers
+pairs of mugshot images (VIS) and their correspondent Near Infrared (NIR)
+photos. Capured by CASIA (Chinese Academy of Sciences), the images of this
+database were collected in four recording sessions: 2007 spring, 2009 summer,
+2009 fall and 2010 summer, in which the first session is identical to the `HFB
+database <http://www.cbsr.ia.ac.cn/english/HFB%20Databases.asp>`_. The CASIA
+NIR-VIS 2.0 database consists of 725 subjects in total. There are 1-22 VIS and
+5-50 NIR face images per subject.
+
+A preview image of the Database of Faces is shown below:
+
+.. image:: http://www.cbsr.ia.ac.cn/english/NIR-VIS-2.0/NIR-VIS-2.0-Sample.jpg
+ :width: 400 px
+
+
+The VIS images are in JPG format and the NIR images are in BMP format. The size of each image is 640x480 pixels (RGB).
diff --git a/advanced/databases/cpqd/4.json b/advanced/databases/cpqd/4.json
new file mode 100644
index 0000000..b1ae9dc
--- /dev/null
+++ b/advanced/databases/cpqd/4.json
@@ -0,0 +1,694 @@
+{
+ "description": "The CPqD database",
+ "root_folder": "/this/database/is/not/installed",
+ "protocols": [
+ {
+ "name": "laptop_male",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "laptop_male"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "group": "dev",
+ "protocol": "laptop_male"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "group": "dev",
+ "protocol": "laptop_male"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "group": "eval",
+ "protocol": "laptop_male"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "group": "eval",
+ "protocol": "laptop_male"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "laptop_female",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "laptop_female"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "group": "dev",
+ "protocol": "laptop_female"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "group": "dev",
+ "protocol": "laptop_female"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "group": "eval",
+ "protocol": "laptop_female"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "group": "eval",
+ "protocol": "laptop_female"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "smartphone_male",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "smartphone_male"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "group": "dev",
+ "protocol": "smartphone_male"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "group": "dev",
+ "protocol": "smartphone_male"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "group": "eval",
+ "protocol": "smartphone_male"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "group": "eval",
+ "protocol": "smartphone_male"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "smartphone_female",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "smartphone_female"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "group": "dev",
+ "protocol": "smartphone_female"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "group": "dev",
+ "protocol": "smartphone_female"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "group": "eval",
+ "protocol": "smartphone_female"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "group": "eval",
+ "protocol": "smartphone_female"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "l2s_male",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "l2s_male"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "group": "dev",
+ "protocol": "l2s_male"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "group": "dev",
+ "protocol": "l2s_male"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "group": "eval",
+ "protocol": "l2s_male"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "group": "eval",
+ "protocol": "l2s_male"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "l2s_female",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "l2s_female"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "group": "dev",
+ "protocol": "l2s_female"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "group": "dev",
+ "protocol": "l2s_female"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "group": "eval",
+ "protocol": "l2s_female"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "group": "eval",
+ "protocol": "l2s_female"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "s2l_male",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "s2l_male"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "group": "dev",
+ "protocol": "s2l_male"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "group": "dev",
+ "protocol": "s2l_male"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "group": "eval",
+ "protocol": "s2l_male"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "group": "eval",
+ "protocol": "s2l_male"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "s2l_female",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "s2l_female"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "group": "dev",
+ "protocol": "s2l_female"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "group": "dev",
+ "protocol": "s2l_female"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "group": "eval",
+ "protocol": "s2l_female"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "group": "eval",
+ "protocol": "s2l_female"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ }
+ ]
+}
diff --git a/advanced/databases/cpqd/4.py b/advanced/databases/cpqd/4.py
new file mode 100644
index 0000000..0a09e7c
--- /dev/null
+++ b/advanced/databases/cpqd/4.py
@@ -0,0 +1,424 @@
+###############################################################################
+# #
+# Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import os
+import re
+import numpy as np
+from collections import namedtuple
+
+from beat.backend.python.database import View
+
+import bob.io.base
+import bob.io.image
+import bob.db.cpqd
+
+
+#----------------------------------------------------------
+
+
+class Train(View):
+
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'file_id', 'eye_centers', 'image'])
+
+ image_folder = os.path.join(root_folder, "images")
+ annotation_folder = os.path.join(root_folder, "eye_positions")
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.cpqd.Database()
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups='world'),
+ key=lambda x: (x.client_id, x.id))
+
+ entries = []
+
+ for obj in objs:
+ obj_id = obj.id.split('/')[-1]
+ digits = re.findall(r'\d+', obj_id)
+
+ entries.append(Entry(np.uint64(obj.client_id[1:]), np.uint64(''.join(digits)),
+ db.annotations(obj.make_path(annotation_folder, '.pos')),
+ obj.make_path(image_folder, '.jpg')))
+
+ return entries
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'eye_centers':
+ return {
+ 'left': {
+ 'y': np.int32(obj.eye_centers['leye'][0]),
+ 'x': np.int32(obj.eye_centers['leye'][1]),
+ },
+ 'right': {
+ 'y': np.int32(obj.eye_centers['reye'][0]),
+ 'x': np.int32(obj.eye_centers['reye'][1]),
+ }
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Templates(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - template_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ Several "image" are associated with a given "template_id".
+ Several "template_id" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | template_id | | template_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | client_id |
+ -----------------------------------------------------------------------------------------------
+
+ Note: for this particular database, there is only one "template_id"
+ per "client_id".
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'template_id', 'file_id', 'eye_centers', 'image'])
+
+ image_folder = os.path.join(root_folder, "images")
+ annotation_folder = os.path.join(root_folder, "eye_positions")
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.cpqd.Database()
+
+ template_ids = db.model_ids(protocol=parameters['protocol'],
+ groups=parameters['group'])
+
+ entries = []
+
+ for template_id in template_ids:
+ objs = db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ purposes='enroll',
+ model_ids=[template_id])
+
+ for obj in objs:
+ obj_id = obj.id.split('/')[-1]
+ digits = re.findall(r'\d+', obj_id)
+
+ entries.append(Entry(np.uint64(obj.client_id[1:]), np.uint64(template_id[1:]),
+ np.uint64(''.join(digits)),
+ db.annotations(obj.make_path(annotation_folder, '.pos')),
+ obj.make_path(image_folder, '.jpg')))
+
+ return sorted(entries, key=lambda x: (x.client_id, x.template_id, x.file_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'template_id':
+ return {
+ 'value': np.uint64(obj.template_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'eye_centers':
+ return {
+ 'left': {
+ 'y': np.int32(obj.eye_centers['leye'][0]),
+ 'x': np.int32(obj.eye_centers['leye'][1]),
+ },
+ 'right': {
+ 'y': np.int32(obj.eye_centers['reye'][0]),
+ 'x': np.int32(obj.eye_centers['reye'][1]),
+ }
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Probes(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - probe_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+ - template_ids: "{{ system_user.username }}/array_1d_uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ One "probe_id" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+ Several "client_id" are associated with a given "template_ids".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | probe_id | | probe_id | | probe_id | | probe_id | | probe_id | | probe_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | template_ids |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['template_ids', 'client_id', 'probe_id', 'file_id',
+ 'eye_centers', 'image'])
+
+ image_folder = os.path.join(root_folder, "images")
+ annotation_folder = os.path.join(root_folder, "eye_positions")
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.cpqd.Database()
+
+ template_ids = sorted(db.model_ids(protocol=parameters['protocol'],
+ groups=parameters['group']))
+
+
+ template_probes = {}
+ for template_id in template_ids:
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ purposes='probe',
+ model_ids=[template_id]),
+ key=lambda x: (x.client_id, x.id))
+
+ template_probes[template_id] = [ p.id for p in objs ]
+
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ purposes='probe'),
+ key=lambda x: (x.client_id, x.id))
+
+ entries = []
+ for obj in objs:
+ templates = [ template_id for template_id in template_ids
+ if obj.id in template_probes[template_id] ]
+
+ templates = [ np.uint64(x[1:]) for x in templates ]
+
+ obj_id = obj.id.split('/')[-1]
+ digits = re.findall(r'\d+', obj_id)
+
+ entries.append(Entry(templates,
+ np.uint64(obj.client_id[1:]),
+ np.uint64(''.join(digits)),
+ np.uint64(''.join(digits)),
+ db.annotations(obj.make_path(annotation_folder, '.pos')),
+ obj.make_path(image_folder, '.jpg')))
+
+ return sorted(entries, key=lambda x: (len(x.template_ids), x.template_ids,
+ x.client_id, x.probe_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'template_ids':
+ return {
+ 'value': obj.template_ids
+ }
+
+ elif output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'probe_id':
+ return {
+ 'value': np.uint64(obj.probe_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'eye_centers':
+ return {
+ 'left': {
+ 'y': np.int32(obj.eye_centers['leye'][0]),
+ 'x': np.int32(obj.eye_centers['leye'][1]),
+ },
+ 'right': {
+ 'y': np.int32(obj.eye_centers['reye'][0]),
+ 'x': np.int32(obj.eye_centers['reye'][1]),
+ }
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+def setup_tests():
+ # Install a mock load function for the images
+ def mock_load(root_folder):
+ return np.ndarray((3, 10, 20), dtype=np.uint8)
+
+ def mock_annotations(obj, path):
+ return dict(
+ leye=(5, 4),
+ reye=(7, 4),
+ )
+
+ bob.io.base.load = mock_load
+ bob.db.cpqd.Database.annotations = mock_annotations
+
+
+#----------------------------------------------------------
+
+
+# Test the behavior of the views (on fake data)
+if __name__ == '__main__':
+
+ setup_tests()
+
+ view = Train()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='laptop_male'
+ )
+ )
+ view.get('client_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
+
+
+ view = Templates()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='laptop_male',
+ group='dev',
+ )
+ )
+ view.get('client_id', 0)
+ view.get('template_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
+
+
+ view = Probes()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='s2l_female',
+ group='dev',
+ )
+ )
+ view.get('template_ids', 0)
+ view.get('client_id', 0)
+ view.get('probe_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
diff --git a/advanced/databases/cpqd/4.rst b/advanced/databases/cpqd/4.rst
new file mode 100644
index 0000000..7b50761
--- /dev/null
+++ b/advanced/databases/cpqd/4.rst
@@ -0,0 +1,144 @@
+.. Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ ..
+.. Contact: beat.support@idiap.ch ..
+.. ..
+.. This file is part of the beat.examples module of the BEAT platform. ..
+.. ..
+.. Commercial License Usage ..
+.. Licensees holding valid commercial BEAT licenses may use this file in ..
+.. accordance with the terms contained in a written agreement between you ..
+.. and Idiap. For further information contact tto@idiap.ch ..
+.. ..
+.. Alternatively, this file may be used under the terms of the GNU Affero ..
+.. Public License version 3 as published by the Free Software and appearing ..
+.. in the file LICENSE.AGPL included in the packaging of this file. ..
+.. The BEAT platform is distributed in the hope that it will be useful, but ..
+.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
+.. or FITNESS FOR A PARTICULAR PURPOSE. ..
+.. ..
+.. You should have received a copy of the GNU Affero Public License along ..
+.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
+
+
+CPqD Biometric Database (BioCPqD Phase 1)
+-----------------------------------------
+
+Changelog
+=========
+
+* **Version 4**, 30/Jan/2018:
+
+ - Port to beat.backend.python v1.5.0
+
+* **Version 3**, 30/Oct/2017:
+
+ - Port to beat.backend.python v1.4.2
+
+* **Version 2**, 20/Jan/2016:
+
+ - Port to Bob v2
+
+* **Version 1**, 01/Apr/2014:
+
+ - Initial release
+
+
+Description
+===========
+
+This database was designed to provide data that was recorded in a natural way,
+using various devices in different environments. Hence, algorithms that
+perform well on this database are expected to be suitable for other real-world
+applications that do not require a predefined audio/video recording setup.
+
+Database participants were selected among employees of CPqD Foundation who
+volunteered to make recordings. A unique ID was assigned for each participant,
+composed by a prefix (M for male and F for female) followed by a 4-digit number
+(odd for males and even for females). Each participant recorded up to five
+sessions, with a time lapse of at least 10 days between sessions.
+
+Sessions consisted of 27 recorded sentences, whose content was specified in a
+script. Each sentence was recorded on three different devices types:
+
+- Laptops (audio and video content);
+- Smartphones (audio and video content);
+- Phone calls (only audio).
+
+For each device type, a set of devices was used, as specified below:
+
+- Laptops:
+ - Compaq 510 with embedded mic and camera;
+ - Toshiba with USB Logitech QuickCam Pro 9000 webcam;
+ - DELL Latitude embedded mic and camera.
+
+- Smartphones:
+ - Samsung Galaxy S II;
+ - Apple iPhone 4;
+ - Apple iPhone 4.
+
+- Phone calls:
+ - landline phone call;
+ - personal mobile phone call.
+
+Recordings were made in three environments with different characteristics:
+garden, restaurant (public indoor) and office. The idea behind this strategy
+was to exploit the influence of environmental noise in audio recordings and the
+effect of illumination and background conditions in the video recordings.
+Since the database includes recordings captured on different devices of
+different types and in different environments, it allows a large number of
+experimental setups.
+
+
+Content
+=======
+
+The data collection followed a simple recording protocol that was replicated
+for all sessions. For each session there was a corresponding script describing
+the whole content to be recorded, as follows:
+
+Text reading:
+
+- a pre-defined text (extracted from the database's consent form);
+- four phonetically rich sentences (randomly selected among 562 options);
+- passphrase: three repetitions a single sentence (the same sentence for all
+ participants in all sessions).
+
+Spontaneous speech:
+
+- answers for generic questions (all participants answered all 15 questions
+ selected form a fixed set, distributed along the 5 sessions in random order);
+- a fake name;
+- a fake address;
+- a fake birthday date;
+- a fake ID number;
+- a fake phone number;
+- two command words (all participants spoke 10 words along the 5 sessions in
+ random order).
+
+Numbers, digits, time values and alphanumeric strings:
+
+- a monetary amount between 10 and 10 000, randomly generated;
+- a number between 10 and 1000, randomly generated;
+- a number between 1000 and 10 million, randomly generated;
+- three repetitions of a random digit sequence (first one read in a slow pace
+ and others naturally read);
+- a fake credit card number;
+- an alphanumeric string composed of 6 characters, randomly generated;
+- a time value, selected among a predefined set with 181 samples, equally
+ distributed among participants.
+
+It is important to note that all content was recorded in Brazilian Portuguese
+language.
+
+BioCPqD Phase I database provides unbiased biometric verification protocols,
+one for male and one for female participants, based on the MOBIO database
+protocols. These protocols partition the database in three different groups:
+
+- a Training set: used to train the parameters of algorithm to be tested, e.g.,
+ to create the projection matrix, Universal Background Models, etc.;
+- a Development set: used to evaluate hyper-parameters of the tested algorithms;
+- a Test set: used to evaluate the generalization performance of the tested
+ algorithms with previously unseen data.
+
+Both development and test sets are further split into an enrollment subset
+(used to enroll participants' models), and a probe set (whose files will be
+tested against all participants' models).
diff --git a/advanced/databases/frgc/4.json b/advanced/databases/frgc/4.json
new file mode 100644
index 0000000..0c061b4
--- /dev/null
+++ b/advanced/databases/frgc/4.json
@@ -0,0 +1,330 @@
+{
+ "description": "The Face Recognition Grand Challenge",
+ "root_folder": "/idiap/resource/database/frgc/FRGC-2.0-dist",
+ "protocols": [
+ {
+ "name": "2.0.1_maskI",
+ "template": "simple_face_recognition_frgc",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "mask": "maskI",
+ "protocol": "2.0.1"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "mask": "maskI",
+ "protocol": "2.0.1"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "mask": "maskI",
+ "protocol": "2.0.1"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "2.0.4_maskI",
+ "template": "simple_face_recognition_frgc",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "mask": "maskI",
+ "protocol": "2.0.4"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "mask": "maskI",
+ "protocol": "2.0.4"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "mask": "maskI",
+ "protocol": "2.0.4"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "2.0.1_maskII",
+ "template": "simple_face_recognition_frgc",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "mask": "maskII",
+ "protocol": "2.0.1"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "mask": "maskII",
+ "protocol": "2.0.1"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "mask": "maskII",
+ "protocol": "2.0.1"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "2.0.4_maskII",
+ "template": "simple_face_recognition_frgc",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "mask": "maskII",
+ "protocol": "2.0.4"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "mask": "maskII",
+ "protocol": "2.0.4"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "mask": "maskII",
+ "protocol": "2.0.4"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "2.0.1_maskIII",
+ "template": "simple_face_recognition_frgc",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "mask": "maskIII",
+ "protocol": "2.0.1"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "mask": "maskIII",
+ "protocol": "2.0.1"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "mask": "maskIII",
+ "protocol": "2.0.1"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "2.0.4_maskIII",
+ "template": "simple_face_recognition_frgc",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "mask": "maskIII",
+ "protocol": "2.0.4"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "mask": "maskIII",
+ "protocol": "2.0.4"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "mask": "maskIII",
+ "protocol": "2.0.4"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ }
+ ]
+}
diff --git a/advanced/databases/frgc/4.py b/advanced/databases/frgc/4.py
new file mode 100644
index 0000000..70e5301
--- /dev/null
+++ b/advanced/databases/frgc/4.py
@@ -0,0 +1,412 @@
+###############################################################################
+# #
+# Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import os
+import numpy as np
+from collections import namedtuple
+
+from beat.backend.python.database import View
+
+import bob.io.base
+import bob.io.image
+import bob.db.frgc
+
+
+#----------------------------------------------------------
+
+
+class Train(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - file_id: "{{ system_user.username }}/text/1"
+ - client_id: "{{ system_user.username }}/text/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'file_id', 'eye_centers', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.frgc.Database(original_directory=root_folder)
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups='world',
+ mask_type=parameters['mask']),
+ key=lambda x: (x.client_id, x.id))
+
+ entries = []
+
+ for obj in objs:
+ filename = obj.make_path(root_folder, '.jpg')
+ if not os.path.exists(filename):
+ filename = obj.make_path(root_folder, '.JPG')
+
+ entries.append(Entry(obj.client_id, obj.id, db.annotations(obj), filename))
+
+ return entries
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'text': obj.client_id
+ }
+
+ elif output == 'file_id':
+ return {
+ 'text': obj.file_id
+ }
+
+ elif output == 'eye_centers':
+ return {
+ 'left': {
+ 'y': np.int32(obj.eye_centers['leye'][0]),
+ 'x': np.int32(obj.eye_centers['leye'][1]),
+ },
+ 'right': {
+ 'y': np.int32(obj.eye_centers['reye'][0]),
+ 'x': np.int32(obj.eye_centers['reye'][1]),
+ }
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Templates(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - file_id: "{{ system_user.username }}/text/1"
+ - template_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/text/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ Several "image" are associated with a given "template_id".
+ Several "template_id" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | template_id | | template_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | client_id |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'template_id', 'file_id', 'eye_centers', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.frgc.Database(original_directory=root_folder)
+
+ model_files = bob.db.frgc.models.get_list(root_folder,
+ 'dev',
+ parameters['protocol'],
+ 'enroll')
+
+ mask = bob.db.frgc.models.get_mask(root_folder,
+ parameters['protocol'],
+ parameters['mask'])
+
+ entries = []
+ for model_index, model in enumerate(model_files):
+ if (mask[:, model_index] > 0).any():
+ for presentation in model.m_files:
+ obj = bob.db.frgc.models.File(model.m_signature,
+ presentation,
+ model.m_files[presentation])
+
+ filename = obj.make_path(root_folder, '.jpg')
+ if not os.path.exists(filename):
+ filename = obj.make_path(root_folder, '.JPG')
+
+ entries.append(Entry(obj.client_id, model.m_model, obj.id,
+ db.annotations(obj), filename))
+
+ return sorted(entries, key=lambda x: (x.client_id, x.template_id, x.file_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'text': obj.client_id
+ }
+
+ elif output == 'template_id':
+ return {
+ 'value': np.uint64(obj.template_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'text': obj.file_id
+ }
+
+ elif output == 'eye_centers':
+ return {
+ 'left': {
+ 'y': np.int32(obj.eye_centers['leye'][0]),
+ 'x': np.int32(obj.eye_centers['leye'][1]),
+ },
+ 'right': {
+ 'y': np.int32(obj.eye_centers['reye'][0]),
+ 'x': np.int32(obj.eye_centers['reye'][1]),
+ }
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Probes(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - file_id: "{{ system_user.username }}/text/1"
+ - probe_id: "{{ system_user.username }}/text/1"
+ - client_id: "{{ system_user.username }}/text/1"
+ - template_ids: "{{ system_user.username }}/array_1d_uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ One "probe_id" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+ Several "client_id" are associated with a given "template_ids".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | probe_id | | probe_id | | probe_id | | probe_id | | probe_id | | probe_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | template_ids |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['template_ids', 'client_id', 'probe_id', 'file_id',
+ 'eye_centers', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.frgc.Database(original_directory=root_folder)
+
+ probe_files = bob.db.frgc.models.get_list(root_folder,
+ 'dev',
+ parameters['protocol'],
+ 'probe')
+
+ model_files = bob.db.frgc.models.get_list(root_folder,
+ 'dev',
+ parameters['protocol'],
+ 'enroll')
+
+ mask = bob.db.frgc.models.get_mask(root_folder,
+ parameters['protocol'],
+ parameters['mask'])
+
+ template_ids = np.array([ x.m_model for x in model_files ])
+
+ entries = []
+ for probe_index, probe in enumerate(probe_files):
+ template_indices = mask[probe_index, :].nonzero()[0]
+ templates = sorted(template_ids.take(template_indices))
+
+ for presentation in probe.m_files:
+ obj = bob.db.frgc.models.File(probe.m_signature,
+ presentation,
+ probe.m_files[presentation])
+
+ filename = obj.make_path(root_folder, '.jpg')
+ if not os.path.exists(filename):
+ filename = obj.make_path(root_folder, '.JPG')
+
+ entries.append(Entry(templates, obj.client_id, obj.id, obj.id,
+ db.annotations(obj), filename))
+
+ return sorted(entries, key=lambda x: (len(x.template_ids), x.template_ids,
+ x.client_id, x.probe_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'template_ids':
+ return {
+ 'value': np.uint64(obj.template_ids)
+ }
+
+ elif output == 'client_id':
+ return {
+ 'text': obj.client_id
+ }
+
+ elif output == 'probe_id':
+ return {
+ 'text': obj.file_id
+ }
+
+ elif output == 'file_id':
+ return {
+ 'text': obj.file_id
+ }
+
+ elif output == 'eye_centers':
+ return {
+ 'left': {
+ 'y': np.int32(obj.eye_centers['leye'][0]),
+ 'x': np.int32(obj.eye_centers['leye'][1]),
+ },
+ 'right': {
+ 'y': np.int32(obj.eye_centers['reye'][0]),
+ 'x': np.int32(obj.eye_centers['reye'][1]),
+ }
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+def setup_tests():
+ # Install a mock load function for the images
+ def mock_load(root_folder):
+ return np.ndarray((3, 10, 20), dtype=np.uint8)
+
+ # Install a mock os.path.exists function
+ def mock_exists(path):
+ return True
+
+ bob.io.base.load = mock_load
+ os.path.exists = mock_exists
+
+
+#----------------------------------------------------------
+
+
+# Test the behavior of the views (on fake data)
+if __name__ == '__main__':
+
+ setup_tests()
+
+ # Note: This database can't be tested without the actual data, since
+ # some files are needed by this implementation
+
+ view = Train()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='2.0.1',
+ mask='maskI',
+ )
+ )
+ view.get('client_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
+
+
+ view = Templates()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='2.0.1',
+ mask='maskI',
+ )
+ )
+ view.get('client_id', 0)
+ view.get('template_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
+
+
+ view = Probes()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='2.0.1',
+ mask='maskI',
+ )
+ )
+ view.get('template_ids', 0)
+ view.get('client_id', 0)
+ view.get('probe_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
diff --git a/advanced/databases/frgc/4.rst b/advanced/databases/frgc/4.rst
new file mode 100644
index 0000000..ae5a754
--- /dev/null
+++ b/advanced/databases/frgc/4.rst
@@ -0,0 +1,67 @@
+.. Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ ..
+.. Contact: beat.support@idiap.ch ..
+.. ..
+.. This file is part of the beat.examples module of the BEAT platform. ..
+.. ..
+.. Commercial License Usage ..
+.. Licensees holding valid commercial BEAT licenses may use this file in ..
+.. accordance with the terms contained in a written agreement between you ..
+.. and Idiap. For further information contact tto@idiap.ch ..
+.. ..
+.. Alternatively, this file may be used under the terms of the GNU Affero ..
+.. Public License version 3 as published by the Free Software and appearing ..
+.. in the file LICENSE.AGPL included in the packaging of this file. ..
+.. The BEAT platform is distributed in the hope that it will be useful, but ..
+.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
+.. or FITNESS FOR A PARTICULAR PURPOSE. ..
+.. ..
+.. You should have received a copy of the GNU Affero Public License along ..
+.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
+
+
+The Face Recognition Grand Challenge
+------------------------------------
+
+Changelog
+=========
+
+* **Version 4**, 30/Jan/2018:
+
+ - Port to beat.backend.python v1.5.0
+
+* **Version 3**, 31/Oct/2017:
+
+ - Port to beat.backend.python v1.4.2
+
+* **Version 2**, 20/Jan/2016:
+
+ - Port to Bob v2
+
+* **Version 1**, 08/Apr/2015:
+
+ - Initial release
+
+
+Description
+===========
+
+The `FRGC <http://www.nist.gov/itl/iad/ig/frgc.cfm>`_ data distribution
+consists of three parts. The first is the FRGC data set. The second part is
+the FRGC BEE. The BEE distribution includes all the data sets for performing
+and scoring the six experiments. The third part is a set of baseline
+algorithms for experiments 1 through 4. With all three components, it is
+possible to run experiments 1 through 4, from processing the raw images
+to producing Receiver Operating Characteristics (ROCs).
+
+The data for FRGC consists of 50,000 recordings divided into training and
+validation partitions. The training partition is designed for training
+algorithms and the validation partition is for assessing performance of an
+approach in a laboratory setting. The validation partition consists of data
+from 4,003 subject sessions. A subject session is the set of all images of a
+person taken each time a person's biometric data is collected and consists of
+four controlled still images, two uncontrolled still images, and one
+three-dimensional image. The controlled images were taken in a studio setting,
+are full frontal facial images taken under two lighting conditions and with
+two facial expressions (smiling and neutral). The uncontrolled images were
+taken in varying illumination conditions; e.g., hallways, atriums, or outside.
+Each set of uncontrolled images contains two expressions, smiling and neutral.
diff --git a/advanced/databases/gbu/4.json b/advanced/databases/gbu/4.json
new file mode 100644
index 0000000..2ee7041
--- /dev/null
+++ b/advanced/databases/gbu/4.json
@@ -0,0 +1,159 @@
+{
+ "description": "The Good, the Bad and the Ugly Face Challenge",
+ "root_folder": "/idiap/resource/database/MBGC-V1",
+ "protocols": [
+ {
+ "name": "good",
+ "template": "simple_face_recognition_gbu",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "Good"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "Good"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "Good"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "bad",
+ "template": "simple_face_recognition_gbu",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "Bad"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "Bad"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "Bad"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "ugly",
+ "template": "simple_face_recognition_gbu",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "Ugly"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "Ugly"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "Ugly"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ }
+ ]
+}
diff --git a/advanced/databases/gbu/4.py b/advanced/databases/gbu/4.py
new file mode 100644
index 0000000..ca6dbd5
--- /dev/null
+++ b/advanced/databases/gbu/4.py
@@ -0,0 +1,381 @@
+###############################################################################
+# #
+# Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import numpy as np
+from collections import namedtuple
+
+from beat.backend.python.database import View
+
+import bob.io.base
+import bob.io.image
+import bob.db.gbu
+
+
+#----------------------------------------------------------
+
+
+class Train(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'file_id', 'eye_centers', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.gbu.Database()
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups='world',
+ subworld='x8'),
+ key=lambda x: (x.client_id, x.id))
+
+ return [ Entry(x.client_id, x.id, db.annotations(x), x.make_path(root_folder, '.jpg'))
+ for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'eye_centers':
+ return {
+ 'left': {
+ 'y': np.int32(obj.eye_centers['leye'][0]),
+ 'x': np.int32(obj.eye_centers['leye'][1]),
+ },
+ 'right': {
+ 'y': np.int32(obj.eye_centers['reye'][0]),
+ 'x': np.int32(obj.eye_centers['reye'][1]),
+ }
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Templates(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - template_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ Several "image" are associated with a given "template_id".
+ Several "template_id" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | template_id | | template_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | client_id |
+ -----------------------------------------------------------------------------------------------
+
+ Note: for this particular database, there is only one "template_id"
+ per "client_id".
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'template_id', 'file_id', 'eye_centers', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.gbu.Database()
+
+ template_ids = db.model_ids(protocol=parameters['protocol'],
+ groups='dev')
+
+ entries = []
+
+ for template_id in template_ids:
+ objs = db.objects(protocol=parameters['protocol'],
+ groups='dev',
+ purposes='enroll',
+ model_ids=[template_id])
+
+ entries.extend([ Entry(x.client_id, template_id, x.id, db.annotations(x),
+ x.make_path(root_folder, '.jpg'))
+ for x in objs ])
+
+ return sorted(entries, key=lambda x: (x.client_id, x.template_id, x.file_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'template_id':
+ return {
+ 'value': np.uint64(obj.template_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'eye_centers':
+ return {
+ 'left': {
+ 'y': np.int32(obj.eye_centers['leye'][0]),
+ 'x': np.int32(obj.eye_centers['leye'][1]),
+ },
+ 'right': {
+ 'y': np.int32(obj.eye_centers['reye'][0]),
+ 'x': np.int32(obj.eye_centers['reye'][1]),
+ }
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Probes:
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - probe_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+ - template_ids: "{{ system_user.username }}/array_1d_uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ One "probe_id" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+ Several "client_id" are associated with a given "template_ids".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | probe_id | | probe_id | | probe_id | | probe_id | | probe_id | | probe_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | template_ids |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['template_ids', 'client_id', 'probe_id', 'file_id',
+ 'eye_centers', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.gbu.Database()
+
+ template_ids = sorted(db.model_ids(protocol=parameters['protocol'],
+ groups='dev'))
+
+
+ template_probes = {}
+ for template_id in template_ids:
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups='dev',
+ purposes='probe',
+ model_ids=[template_id]),
+ key=lambda x: (x.client_id, x.id))
+
+ template_probes[template_id] = [ p.id for p in objs ]
+
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups='dev',
+ purposes='probe'),
+ key=lambda x: (x.client_id, x.id))
+
+ entries = []
+ for obj in objs:
+ templates = [ template_id for template_id in template_ids
+ if obj.id in template_probes[template_id] ]
+
+ entries.append( Entry(templates, obj.client_id, obj.id, obj.id,
+ db.annotations(obj), obj.make_path(root_folder, '.jpg')) )
+
+ return sorted(entries, key=lambda x: (len(x.template_ids), x.template_ids,
+ x.client_id, x.probe_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'template_ids':
+ return {
+ 'value': np.uint64(obj.template_ids)
+ }
+
+ elif output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'probe_id':
+ return {
+ 'value': np.uint64(obj.probe_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'eye_centers':
+ return {
+ 'left': {
+ 'y': np.int32(obj.eye_centers['leye'][0]),
+ 'x': np.int32(obj.eye_centers['leye'][1]),
+ },
+ 'right': {
+ 'y': np.int32(obj.eye_centers['reye'][0]),
+ 'x': np.int32(obj.eye_centers['reye'][1]),
+ }
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+def setup_tests():
+ # Install a mock load function for the images
+ def mock_load(root_folder):
+ return np.ndarray((3, 10, 20), dtype=np.uint8)
+
+ bob.io.base.load = mock_load
+
+
+#----------------------------------------------------------
+
+
+# Test the behavior of the views (on fake data)
+if __name__ == '__main__':
+
+ setup_tests()
+
+ view = Train()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='Good'
+ )
+ )
+ view.get('client_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
+
+
+ view = Templates()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='Good'
+ )
+ )
+ view.get('client_id', 0)
+ view.get('template_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
+
+
+ view = Probes()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='Good'
+ )
+ )
+ view.get('template_ids', 0)
+ view.get('client_id', 0)
+ view.get('probe_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
diff --git a/advanced/databases/gbu/4.rst b/advanced/databases/gbu/4.rst
new file mode 100644
index 0000000..edeb8d0
--- /dev/null
+++ b/advanced/databases/gbu/4.rst
@@ -0,0 +1,61 @@
+.. Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ ..
+.. Contact: beat.support@idiap.ch ..
+.. ..
+.. This file is part of the beat.examples module of the BEAT platform. ..
+.. ..
+.. Commercial License Usage ..
+.. Licensees holding valid commercial BEAT licenses may use this file in ..
+.. accordance with the terms contained in a written agreement between you ..
+.. and Idiap. For further information contact tto@idiap.ch ..
+.. ..
+.. Alternatively, this file may be used under the terms of the GNU Affero ..
+.. Public License version 3 as published by the Free Software and appearing ..
+.. in the file LICENSE.AGPL included in the packaging of this file. ..
+.. The BEAT platform is distributed in the hope that it will be useful, but ..
+.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
+.. or FITNESS FOR A PARTICULAR PURPOSE. ..
+.. ..
+.. You should have received a copy of the GNU Affero Public License along ..
+.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
+
+
+The Good, Bad and Ugly Database
+-------------------------------
+
+Changelog
+=========
+
+* **Version 4**, 30/Jan/2018:
+
+ - Port to beat.backend.python v1.5.0
+
+* **Version 3**, 31/Oct/2017:
+
+ - Port to beat.backend.python v1.4.2
+
+* **Version 2**, 20/Jan/2016:
+
+ - Port to Bob v2
+
+* **Version 1**, 08/Apr/2015:
+
+ - Initial release
+
+
+Description
+===========
+
+`The Good, the Bad, and the Ugly challenge
+<http://www.nist.gov/itl/iad/ig/focs.cfm>`_ consists of three frontal still
+face partitions. The paritions were designed to encourage the development of
+face recognition algorithms that excel at matching `hard` face pairs, but not
+at the expense of performance on `easy` face pairs.
+
+The images in this challenge problem are frontal face stills taken under
+uncontrolled illumination, both indoors and outdoors. The three partitions
+were constructed by analyzing results from the FRVT 2006. The Good set
+consisted of face pairs that had above average performance, the Bad set
+consisted of face pairs that had average performance, and the Ugly set
+consisted of face pairs that had below average performance. There are 437
+subjects in the data set. All three partitions have the same 437 subjects.
+All three paritions have 1085 images in both the target and query sets.
diff --git a/advanced/databases/kboc16/4.json b/advanced/databases/kboc16/4.json
new file mode 100644
index 0000000..3c4fb3c
--- /dev/null
+++ b/advanced/databases/kboc16/4.json
@@ -0,0 +1,76 @@
+{
+ "description": "The KBOC16 database",
+ "root_folder": "/idiap/group/biometric/databases/kboc16",
+ "protocols": [
+ {
+ "name": "A",
+ "template": "simple_keystroke_recognition_kboc16",
+ "sets": [
+ {
+ "name": "templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "A"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "keystroke": "{{ system_user.username }}/kboc16_keystroke/1"
+ }
+ },
+ {
+ "name": "probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "A"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "keystroke": "{{ system_user.username }}/kboc16_keystroke/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "D",
+ "template": "simple_keystroke_recognition_kboc16",
+ "sets": [
+ {
+ "name": "templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "D"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "keystroke": "{{ user.username }}/kboc16_keystroke/1"
+ }
+ },
+ {
+ "name": "probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "D"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "keystroke": "{{ user.username }}/kboc16_keystroke/1"
+ }
+ }
+ ]
+ }
+ ]
+}
diff --git a/advanced/databases/kboc16/4.py b/advanced/databases/kboc16/4.py
new file mode 100644
index 0000000..f2c544b
--- /dev/null
+++ b/advanced/databases/kboc16/4.py
@@ -0,0 +1,259 @@
+###############################################################################
+# #
+# Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import string
+import numpy as np
+from collections import namedtuple
+
+from beat.backend.python.database import View
+
+import bob.db.kboc16
+
+
+#----------------------------------------------------------
+
+
+def keystroke_reader(filename):
+ times = []
+ keys = []
+
+ for line in open(filename, 'r').readlines():
+ parts = string.split(line)
+ times.append(np.int32(parts[1]))
+ keys.append(parts[0])
+
+ return dict(
+ timestamps = times,
+ key_events = keys,
+ )
+
+
+#----------------------------------------------------------
+
+
+class Templates(View):
+ """Outputs:
+ - keystroke: "{{ system_user.username }}/kboc16_keystroke/1
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - template_id: "{{ system_user.username }}/text/1"
+ - client_id: "{{ system_user.username }}/text/1"
+
+ One "file_id" is associated with a given "keystroke".
+ Several "keystroke" are associated with a given "template_id".
+ Several "template_id" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | keystroke | | keystroke | | keystroke | | keystroke | | keystroke | | keystroke |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | template_id | | template_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | client_id |
+ -----------------------------------------------------------------------------------------------
+
+ Note: for this particular database, there is only one "template_id"
+ per "client_id".
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'template_id', 'file_id', 'keystroke'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.kboc16.Database()
+
+ template_ids = db.model_ids(groups='eval',
+ protocol=parameters['protocol'])
+
+ entries = []
+
+ for template_id in template_ids:
+ objs = db.objects(groups='eval',
+ protocol=parameters['protocol'],
+ purposes='enrol',
+ model_ids=[template_id])
+
+ entries.extend([ Entry(x.client_id, template_id, x.id, x.make_path(root_folder, '.txt'))
+ for x in objs ])
+
+ return sorted(entries, key=lambda x: (x.client_id, x.template_id, x.file_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'text': str(obj.client_id)
+ }
+
+ elif output == 'template_id':
+ return {
+ 'text': str(obj.template_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'keystroke':
+ return keystroke_reader(obj.keystroke)
+
+
+#----------------------------------------------------------
+
+
+class Probes(View):
+ """Outputs:
+ - keystroke: "{{ system_user.username }}/kboc16_keystroke/1
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/text/1"
+ - probe_id: "{{ system_user.username }}/uint64/1",
+ - template_ids: "{{ system_user.username }}/array_1d_text/1",
+
+ One "file_id" is associated with a given "keystroke".
+ One "probe_id" is associated with a given "keystroke".
+ Several "keystroke" are associated with a given "client_id".
+ Several "client_id" are associated with a given "template_ids".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | keystroke | | keystroke | | keystroke | | keystroke | | keystroke | | keystroke |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | probe_id | | probe_id | | probe_id | | probe_id | | probe_id | | probe_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | template_ids |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['template_ids', 'client_id', 'probe_id', 'file_id',
+ 'keystroke'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.kboc16.Database()
+
+ template_ids = sorted(db.model_ids(protocol=parameters['protocol'],
+ groups='eval'),
+ key=lambda x: int(x))
+
+ template_probes = {}
+ for template_id in template_ids:
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups='eval',
+ purposes='probe',
+ model_ids=[template_id]),
+ key=lambda x: (x.client_id, x.id))
+
+ template_probes[template_id] = [ p.id for p in objs ]
+
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups='eval',
+ purposes='probe'),
+ key=lambda x: (x.client_id, x.id))
+
+ entries = []
+ for obj in objs:
+ templates = [ template_id for template_id in template_ids
+ if obj.id in template_probes[template_id] ]
+ entries.append( Entry(templates, obj.client_id, obj.id, obj.id,
+ obj.make_path(root_folder, '.txt')) )
+
+ return sorted(entries, key=lambda x: (len(x.template_ids), x.template_ids,
+ x.client_id, x.probe_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'template_ids':
+ return {
+ 'text': [ str(x) for x in obj.template_ids ]
+ }
+
+ elif output == 'client_id':
+ return {
+ 'text': str(obj.client_id)
+ }
+
+ elif output == 'probe_id':
+ return {
+ 'value': np.uint64(obj.probe_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'keystroke':
+ return keystroke_reader(obj.keystroke)
+
+
+#----------------------------------------------------------
+
+
+def setup_tests():
+ # Install a mock load function for the keystrokes
+ def mock_keystroke_reader(filename):
+ return {}
+
+ global keystroke_reader
+ keystroke_reader = mock_keystroke_reader
+
+
+#----------------------------------------------------------
+
+
+# Test the behavior of the views (on fake data)
+if __name__ == '__main__':
+
+ setup_tests()
+
+ view = Templates()
+ view.objs = view.index(root_folder='', parameters=dict(protocol = 'A'))
+ view.get('client_id', 0)
+ view.get('template_id', 0)
+ view.get('file_id', 0)
+ view.get('keystroke', 0)
+
+ view = Probes()
+ view.objs = view.index(root_folder='', parameters=dict(protocol = 'A'))
+ view.get('template_ids', 0)
+ view.get('client_id', 0)
+ view.get('probe_id', 0)
+ view.get('file_id', 0)
+ view.get('keystroke', 0)
diff --git a/advanced/databases/kboc16/4.rst b/advanced/databases/kboc16/4.rst
new file mode 100644
index 0000000..11e44ea
--- /dev/null
+++ b/advanced/databases/kboc16/4.rst
@@ -0,0 +1,84 @@
+.. Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ ..
+.. Contact: beat.support@idiap.ch ..
+.. ..
+.. This file is part of the beat.examples module of the BEAT platform. ..
+.. ..
+.. Commercial License Usage ..
+.. Licensees holding valid commercial BEAT licenses may use this file in ..
+.. accordance with the terms contained in a written agreement between you ..
+.. and Idiap. For further information contact tto@idiap.ch ..
+.. ..
+.. Alternatively, this file may be used under the terms of the GNU Affero ..
+.. Public License version 3 as published by the Free Software and appearing ..
+.. in the file LICENSE.AGPL included in the packaging of this file. ..
+.. The BEAT platform is distributed in the hope that it will be useful, but ..
+.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
+.. or FITNESS FOR A PARTICULAR PURPOSE. ..
+.. ..
+.. You should have received a copy of the GNU Affero Public License along ..
+.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
+
+
+The KBOC16 Database
+-------------------
+
+Changelog
+=========
+
+* **Version 4**, 30/Jan/2018:
+
+ - Port to beat.backend.python v1.5.0
+
+* **Version 3**, 30/Oct/2017:
+
+ - Port to beat.backend.python v1.4.2
+
+* **Version 2**, 4/Apr/2016:
+
+ - Added protocol ``A``
+
+* **Version 1**:
+
+ - Initial release
+
+
+Description
+===========
+
+The KBOC16 database is part of the `KBOC competition
+<https://sites.google.com/site/btas16kboc/database>`_, an official competition
+of The IEEE Eighth International Conference on Biometrics: Theory,
+Applications, and Systems (BTAS 2016) organized by `ATVS Biometric Research
+Group <http://atvs.ii.uam.es>`_.
+
+
+The database consists of keystroke sequences from 300 subjects acquired in four
+different sessions (5 samples per session including genuine and impostors)
+distributed in a four month time span. Thus, three different levels of
+temporal variability are taken into account: (1) within the same session (the
+samples are not acquired consecutively), (2) within weeks (between two
+consecutive sessions), and (3) within months (between non-consecutive
+sessions).
+
+Each session comprises 5 case-insensitive repetitions of the subject’s name and
+surname typed in a natural and continuous manner (case insensitive). Mistakes
+(and backspace) are not permitted. The time (in milliseconds) elapsed between
+consecutive key events (press and release) is provided as keystroke dynamics
+sequence.
+
+There are two types of sequences: 1) Genuine sequences: typed by the owner of
+the password and 2) Impostor sequences: typed by other users (different to the
+owner) who try to spoof the system. The number of genuine and impostor samples
+per user varies (this information remains sequestered).
+
+For further information on the reproducible keystroke experiments and
+competition results we refer the reader to (the following articles are publicly
+available in the publications section of the `ATVS group webpage
+<http://atvs.ii.uam.es/listpublications.do>`_)
+
+.. [BTAS15] A. Morales, M. Falanga, J. Fierrez, C. Sansone and J. Ortega-Garcia, ''Keystroke Dynamics Recognition based on Personal Data: A Comparative Experimental Evaluation Implementing Reproducible Research'', in Proc. of the IEEE Seventh International Conference on Biometrics: Theory, Applications and Systems, Arlington, Virginia, USA, September 2015.
+
+.. [BTAS16] To appear.
+
+Please remember to reference [BTAS15]_ and [BTAS16]_ on any work made public,
+whatever the form, based directly or indirectly on any part of the KBOC16 DB.
diff --git a/advanced/databases/lfw/4.json b/advanced/databases/lfw/4.json
new file mode 100644
index 0000000..f9bf549
--- /dev/null
+++ b/advanced/databases/lfw/4.json
@@ -0,0 +1,54 @@
+{
+ "description": "The Labeled Faces in the Wild Database",
+ "root_folder": "/idiap/resource/database/lfw/all_images",
+ "protocols": [
+ {
+ "name": "view1",
+ "template": "simple_face_recognition_textid",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "view1"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1"
+ }
+ },
+ {
+ "name": "templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "view1"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1"
+ }
+ },
+ {
+ "name": "probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "view1"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1"
+ }
+ }
+ ]
+ }
+ ]
+}
diff --git a/advanced/databases/lfw/4.py b/advanced/databases/lfw/4.py
new file mode 100644
index 0000000..d7c01dd
--- /dev/null
+++ b/advanced/databases/lfw/4.py
@@ -0,0 +1,285 @@
+###############################################################################
+# #
+# Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import numpy as np
+from collections import namedtuple
+
+from beat.backend.python.database import View
+
+import bob.io.base
+import bob.io.image
+import bob.db.lfw
+
+
+#----------------------------------------------------------
+
+
+class Train(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/text/1"
+
+ One "file_id" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'file_id', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.lfw.Database()
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups='world',
+ world_type='unrestricted'),
+ key=lambda x: (x.client_id, x.id))
+
+ return [ Entry(x.client_id, x.id, x.make_path(root_folder, '.jpg')) for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'text': str(obj.client_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Templates(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - template_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/text/1"
+
+ One "file_id" is associated with a given "image".
+ Several "image" are associated with a given "template_id".
+ Several "template_id" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | template_id | | template_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | client_id |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'template_id', 'file_id', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.lfw.Database()
+
+ template_ids = db.model_ids(protocol=parameters['protocol'],
+ groups='dev')
+
+ entries = []
+
+ for template_id in template_ids:
+ objs = db.objects(protocol=parameters['protocol'],
+ groups='dev',
+ purposes='enroll',
+ model_ids=[template_id])
+
+ entries.extend([ Entry(x.client_id, template_id, x.id, x.make_path(root_folder, '.jpg'))
+ for x in objs ])
+
+ return sorted(entries, key=lambda x: (x.client_id, x.template_id, x.file_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'text': str(obj.client_id)
+ }
+
+ elif output == 'template_id':
+ return {
+ 'value': np.uint64(obj.template_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Probes(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - probe_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/text/1"
+ - template_ids: "{{ system_user.username }}/array_1d_uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "probe_id" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+ Several "client_id" are associated with a given "template_ids".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | probe_id | | probe_id | | probe_id | | probe_id | | probe_id | | probe_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | template_ids |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['template_ids', 'client_id', 'probe_id', 'file_id', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.lfw.Database()
+
+ template_ids = np.array(sorted(db.model_ids(protocol=parameters['protocol'],
+ groups='dev'),
+ key=lambda x: int(x)),
+ dtype='uint64')
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups='dev',
+ purposes='probe'),
+ key=lambda x: (x.client_id, x.id))
+
+ return [ Entry(template_ids, x.client_id, x.id, x.id, x.make_path(root_folder, '.jpg'))
+ for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'template_ids':
+ return {
+ 'value': obj.template_ids
+ }
+
+ elif output == 'client_id':
+ return {
+ 'text': str(obj.client_id)
+ }
+
+ elif output == 'probe_id':
+ return {
+ 'value': np.uint64(obj.probe_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+def setup_tests():
+ # Install a mock load function for the images
+ def mock_load(root_folder):
+ return np.ndarray((3, 10, 20), dtype=np.uint8)
+
+ bob.io.base.load = mock_load
+
+
+#----------------------------------------------------------
+
+
+# Test the behavior of the views (on fake data)
+if __name__ == '__main__':
+
+ setup_tests()
+
+ view = Train()
+ view.objs = view.index(root_folder='', parameters=dict(protocol='view1'))
+ view.get('client_id', 0)
+ view.get('file_id', 0)
+ view.get('image', 0)
+
+ view = Templates()
+ view.objs = view.index(root_folder='', parameters=dict(protocol='view1'))
+ view.get('client_id', 0)
+ view.get('template_id', 0)
+ view.get('file_id', 0)
+ view.get('image', 0)
+
+ view = Probes()
+ view.objs = view.index(root_folder='', parameters=dict(protocol='view1'))
+ view.get('template_ids', 0)
+ view.get('client_id', 0)
+ view.get('probe_id', 0)
+ view.get('file_id', 0)
+ view.get('image', 0)
diff --git a/advanced/databases/lfw/4.rst b/advanced/databases/lfw/4.rst
new file mode 100644
index 0000000..c6bc3e0
--- /dev/null
+++ b/advanced/databases/lfw/4.rst
@@ -0,0 +1,72 @@
+.. Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ ..
+.. Contact: beat.support@idiap.ch ..
+.. ..
+.. This file is part of the beat.examples module of the BEAT platform. ..
+.. ..
+.. Commercial License Usage ..
+.. Licensees holding valid commercial BEAT licenses may use this file in ..
+.. accordance with the terms contained in a written agreement between you ..
+.. and Idiap. For further information contact tto@idiap.ch ..
+.. ..
+.. Alternatively, this file may be used under the terms of the GNU Affero ..
+.. Public License version 3 as published by the Free Software and appearing ..
+.. in the file LICENSE.AGPL included in the packaging of this file. ..
+.. The BEAT platform is distributed in the hope that it will be useful, but ..
+.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
+.. or FITNESS FOR A PARTICULAR PURPOSE. ..
+.. ..
+.. You should have received a copy of the GNU Affero Public License along ..
+.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
+
+
+The Labeled Faces in the Wild Database
+--------------------------------------
+
+Changelog
+=========
+
+* **Version 4**, 30/Jan/2018:
+
+ - Port to beat.backend.python v1.5.0
+
+* **Version 3**, 31/Oct/2017:
+
+ - Port to beat.backend.python v1.4.2
+
+* **Version 2**, 20/Jan/2016:
+
+ - Port to Bob v2
+
+* **Version 1**, 08/Apr/2015:
+
+ - Initial release
+
+
+Description
+===========
+
+`Labeled Faces in the Wild <http://vis-www.cs.umass.edu/lfw/>`_ is a database
+of face photographs designed for studying the problem of unconstrained face
+recognition. The data set contains more than 13,000 images of faces collected
+from the web. Each face has been labeled with the name of the person pictured.
+1680 of the people pictured have two or more distinct photos in the data set.
+The only constraint on these faces is that they were detected by the
+Viola-Jones face detector. More details can be found in this
+`technical report <http://vis-www.cs.umass.edu/lfw/lfw_update.pdf>`_.
+
+There are four different sets of LFW images including the original and three
+different types of "aligned" images. The platform currently relies on the
+original images and provides the `view1` protocol.
+
+Preview of original images of this database are shown below:
+
+.. image:: http://vis-www.cs.umass.edu/lfw/images/AJ_Cook/AJ_Cook_0001.jpg
+ :width: 150px
+.. image:: http://vis-www.cs.umass.edu/lfw/images/AJ_Lamas/AJ_Lamas_0001.jpg
+ :width: 150px
+.. image:: http://vis-www.cs.umass.edu/lfw/images/Aaron_Eckhart/Aaron_Eckhart_0001.jpg
+ :width: 150px
+.. image:: http://vis-www.cs.umass.edu/lfw/images/Aaron_Guiel/Aaron_Guiel_0001.jpg
+ :width: 150px
+.. image:: http://vis-www.cs.umass.edu/lfw/images/Aaron_Patterson/Aaron_Patterson_0001.jpg
+ :width: 150px
diff --git a/advanced/databases/livdet2013/4.json b/advanced/databases/livdet2013/4.json
new file mode 100644
index 0000000..a3a1fa8
--- /dev/null
+++ b/advanced/databases/livdet2013/4.json
@@ -0,0 +1,164 @@
+{
+ "description": "The LivDet 2013 Fingerprint Liveness Database",
+ "root_folder": "/idiap/resource/database/LivDet/LivDet2013",
+ "protocols": [
+ {
+ "name": "Biometrika",
+ "template": "simple_fingerprint_antispoofing",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "All",
+ "parameters": {
+ "protocol": "Biometrika",
+ "group": "train"
+ },
+ "outputs": {
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "spoof": "{{ system_user.username }}/boolean/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "All",
+ "parameters": {
+ "protocol": "Biometrika",
+ "group": "test"
+ },
+ "outputs": {
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "spoof": "{{ system_user.username }}/boolean/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "Italdata",
+ "template": "simple_fingerprint_antispoofing",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "All",
+ "parameters": {
+ "protocol": "Italdata",
+ "group": "train"
+ },
+ "outputs": {
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "spoof": "{{ system_user.username }}/boolean/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "All",
+ "parameters": {
+ "protocol": "Italdata",
+ "group": "test"
+ },
+ "outputs": {
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "spoof": "{{ system_user.username }}/boolean/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "CrossMatch",
+ "template": "simple_fingerprint_antispoofing",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "All",
+ "parameters": {
+ "protocol": "CrossMatch",
+ "group": "train"
+ },
+ "outputs": {
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "spoof": "{{ system_user.username }}/boolean/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "All",
+ "parameters": {
+ "protocol": "CrossMatch",
+ "group": "test"
+ },
+ "outputs": {
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "spoof": "{{ system_user.username }}/boolean/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "Swipe",
+ "template": "simple_fingerprint_antispoofing",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "All",
+ "parameters": {
+ "protocol": "Swipe",
+ "group": "train"
+ },
+ "outputs": {
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "spoof": "{{ system_user.username }}/boolean/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "All",
+ "parameters": {
+ "protocol": "Swipe",
+ "group": "test"
+ },
+ "outputs": {
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "spoof": "{{ system_user.username }}/boolean/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "Full",
+ "template": "simple_fingerprint_antispoofing",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "All",
+ "parameters": {
+ "group": "train"
+ },
+ "outputs": {
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "spoof": "{{ system_user.username }}/boolean/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "All",
+ "parameters": {
+ "group": "test"
+ },
+ "outputs": {
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "spoof": "{{ system_user.username }}/boolean/1"
+ }
+ }
+ ]
+ }
+ ]
+}
diff --git a/advanced/databases/livdet2013/4.py b/advanced/databases/livdet2013/4.py
new file mode 100644
index 0000000..a0bc58c
--- /dev/null
+++ b/advanced/databases/livdet2013/4.py
@@ -0,0 +1,109 @@
+###############################################################################
+# #
+# Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import os
+import numpy as np
+from collections import namedtuple
+
+from beat.backend.python.database import View
+
+import bob.io.base
+import bob.io.image
+from bob.db.livdet2013 import Database
+
+
+#----------------------------------------------------------
+
+
+class All(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - spoof: "{{ system_user.username }}/boolean/1"
+
+ Several "image" are associated with a given "spoof".
+
+ --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image |
+ --------------- --------------- --------------- ---------------
+ ------------------------------- ------------------------------
+ | spoof | | spoof |
+ ------------------------------- ------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['spoof', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = Database()
+ objs = sorted(db.objects(protocols=parameters.get('protocol'),
+ groups=parameters['group'],
+ classes=parameters.get('class')),
+ key=lambda x: x.is_live())
+
+ return [ Entry(x.is_live(), x.make_path(root_folder)) for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'spoof':
+ return {
+ 'value': obj.spoof
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+def setup_tests():
+ # Install a mock load function for the images
+ def mock_load(root_folder):
+ return np.ndarray((3, 10, 20), dtype=np.uint8)
+
+ bob.io.base.load = mock_load
+
+
+#----------------------------------------------------------
+
+
+# Test the behavior of the views (on fake data)
+if __name__ == '__main__':
+
+ setup_tests()
+
+ view = All()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='Biometrika',
+ group='train'
+ )
+ )
+ view.get('spoof', 0)
+ view.get('image', 0)
diff --git a/advanced/databases/livdet2013/4.rst b/advanced/databases/livdet2013/4.rst
new file mode 100644
index 0000000..9f28aec
--- /dev/null
+++ b/advanced/databases/livdet2013/4.rst
@@ -0,0 +1,75 @@
+.. Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ ..
+.. Contact: beat.support@idiap.ch ..
+.. ..
+.. This file is part of the beat.examples module of the BEAT platform. ..
+.. ..
+.. Commercial License Usage ..
+.. Licensees holding valid commercial BEAT licenses may use this file in ..
+.. accordance with the terms contained in a written agreement between you ..
+.. and Idiap. For further information contact tto@idiap.ch ..
+.. ..
+.. Alternatively, this file may be used under the terms of the GNU Affero ..
+.. Public License version 3 as published by the Free Software and appearing ..
+.. in the file LICENSE.AGPL included in the packaging of this file. ..
+.. The BEAT platform is distributed in the hope that it will be useful, but ..
+.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
+.. or FITNESS FOR A PARTICULAR PURPOSE. ..
+.. ..
+.. You should have received a copy of the GNU Affero Public License along ..
+.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
+
+
+===========================================
+ LivDet 2013 Fingerprint Liveness Database
+===========================================
+
+
+Changelog
+=========
+
+* **Version 4**, 30/Jan/2018:
+
+ - Port to beat.backend.python v1.5.0
+
+* **Version 3**, 31/Oct/2017:
+
+ - Port to beat.backend.python v1.4.2
+
+* **Version 2**, 26/Jan/2016:
+
+ - Port to Bob v2
+
+* **Version 1**, 17/Sep/2015:
+
+ - Initial release
+
+
+Description
+===========
+
+
+The LivDet 2013 `Fingerprint Liveness Database <http://livdet.org>`_
+[Ghiani2013]_ is a fingerprint liveness database which consists of four
+sub-sets, which contain live and fake fingerprint images from four capture
+devices. Images have been collected by a consensual approach and using
+different materials for the artificial reproduction of the fingerprint
+(gelatine, silicone, play-doh, ecoflex, body double, wood glue).
+
+
+Data Set
+========
+
+========== =============== ========= ========== ============ ============
+ Scanner Model Res (dpi) Image size Live samples Fake samples
+========== =============== ========= ========== ============ ============
+Biometrika FX2000 569 312x372 2000 2000
+Italdata ET10 500 640x480 2000 2000
+Crossmatch L SCAN GUARDIAN 500 640x480 2500 2000
+Swipe 96 2374 1979
+========== =============== ========= ========== ============ ============
+
+
+References
+==========
+
+.. [Ghiani2013] L.Ghiani, D.Yambay, V.Mura, S.Tocco, G.L.Marcialis, F.Roli, and S.Schuckers, LivDet 2013 - Fingerprint Liveness Detection Competition 2013, 6th IAPR/IEEE Int. Conf. on Biometrics, June, 4-7, 2013, Madrid (Spain).
diff --git a/advanced/databases/mnist/4.json b/advanced/databases/mnist/4.json
new file mode 100644
index 0000000..46d01c2
--- /dev/null
+++ b/advanced/databases/mnist/4.json
@@ -0,0 +1,38 @@
+{
+ "description": "The MNIST Database of Handwritten Digits",
+ "root_folder": "/idiap/group/biometric/databases/mnist",
+ "protocols": [
+ {
+ "name": "idiap",
+ "template": "simple_digit_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "View",
+ "parameters": {
+ "group": "train"
+ },
+ "outputs": {
+ "id": "{{ system_user.username }}/uint64/1",
+ "class_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1"
+ }
+ },
+ {
+ "name": "test",
+ "template": "test",
+ "view": "View",
+ "parameters": {
+ "group": "test"
+ },
+ "outputs": {
+ "id": "{{ system_user.username }}/uint64/1",
+ "class_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1"
+ }
+ }
+ ]
+ }
+ ]
+}
diff --git a/advanced/databases/mnist/4.py b/advanced/databases/mnist/4.py
new file mode 100644
index 0000000..ba73de2
--- /dev/null
+++ b/advanced/databases/mnist/4.py
@@ -0,0 +1,111 @@
+###############################################################################
+# #
+# Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import numpy as np
+from collections import namedtuple
+
+from beat.backend.python.database import View as BaseView
+
+import bob.db.mnist
+
+
+#----------------------------------------------------------
+
+
+class View(BaseView):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_2d_uint8/1"
+ - id: "{{ system_user.username }}/uint64/1"
+ - class_id: "{{ system_user.username }}/uint64/1"
+
+ One "id" is associated with a given "image".
+ Several "image" are associated with a given "class_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | id | | id | | id | | id | | id | | id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | class_id | | class_id |
+ ----------------------------------------------- -----------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['class_id', 'id', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.mnist.Database(data_dir=root_folder)
+
+ features, labels = db.data(groups=parameters['group'])
+
+ objs = sorted([ (labels[i], i, features[i]) for i in range(len(features)) ],
+ key=lambda x: (x[0], x[1]))
+
+ return [ Entry(x[0], x[1], x[2]) for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'class_id':
+ return {
+ 'value': np.uint64(obj.class_id)
+ }
+
+ elif output == 'id':
+ return {
+ 'value': np.uint64(obj.id)
+ }
+
+ elif output == 'image':
+ return {
+ 'value': obj.features.reshape((28, 28))
+ }
+
+
+#----------------------------------------------------------
+
+
+def setup_tests():
+ pass
+
+
+#----------------------------------------------------------
+
+
+# Test the behavior of the views (on fake data)
+if __name__ == '__main__':
+
+ setup_tests()
+
+ # Note: This database can't be tested without the actual data, since
+ # the actual files are needed by this implementation
+
+ view = View()
+ view.objs = view.index(root_folder='', parameters=dict(group='train'))
+ view.get('class_id', 0)
+ view.get('id', 0)
+ view.get('image', 0)
diff --git a/advanced/databases/mnist/4.rst b/advanced/databases/mnist/4.rst
new file mode 100644
index 0000000..12d31d1
--- /dev/null
+++ b/advanced/databases/mnist/4.rst
@@ -0,0 +1,93 @@
+.. Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ ..
+.. Contact: beat.support@idiap.ch ..
+.. ..
+.. This file is part of the beat.examples module of the BEAT platform. ..
+.. ..
+.. Commercial License Usage ..
+.. Licensees holding valid commercial BEAT licenses may use this file in ..
+.. accordance with the terms contained in a written agreement between you ..
+.. and Idiap. For further information contact tto@idiap.ch ..
+.. ..
+.. Alternatively, this file may be used under the terms of the GNU Affero ..
+.. Public License version 3 as published by the Free Software and appearing ..
+.. in the file LICENSE.AGPL included in the packaging of this file. ..
+.. The BEAT platform is distributed in the hope that it will be useful, but ..
+.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
+.. or FITNESS FOR A PARTICULAR PURPOSE. ..
+.. ..
+.. You should have received a copy of the GNU Affero Public License along ..
+.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
+
+
+The MNIST Database of Handwritten Digits
+----------------------------------------
+
+Changelog
+=========
+
+* **Version 4**, 30/Jan/2018:
+
+ - Port to beat.backend.python v1.5.0
+
+* **Version 3**, 31/Oct/2017:
+
+ - Port to beat.backend.python v1.4.2
+
+* **Version 2**, 20/Jan/2016:
+
+ - Port to Bob v2
+
+* **Version 1**, 21/Oct/2014:
+
+ - Initial release
+
+
+Description
+===========
+
+The `MNIST database of handwritten digits <http://yann.lecun.com/exdb/mnist/>`_,
+available from this page, has a training set of 60,000 examples, and a test
+set of 10,000 examples. It is a subset of a larger set available from NIST.
+The digits have been size-normalized and centered in a fixed-size image.
+
+It is a good database for people who want to try learning techniques and
+pattern recognition methods on real-world data while spending minimal efforts
+on preprocessing and formatting.
+
+The original black and white (bilevel) images from NIST were size normalized
+to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting
+images contain grey levels as a result of the anti-aliasing technique used by
+the normalization algorithm. the images were centered in a 28x28 image by
+computing the center of mass of the pixels, and translating the image so as to
+position this point at the center of the 28x28 field.
+
+The MNIST database was constructed from NIST's Special Database 3 and Special
+Database 1 which contain binary images of handwritten digits. NIST originally
+designated SD-3 as their training set and SD-1 as their test set. However,
+SD-3 is much cleaner and easier to recognize than SD-1. The reason for this
+can be found on the fact that SD-3 was collected among Census Bureau
+employees, while SD-1 was collected among high-school students. Drawing
+sensible conclusions from learning experiments requires that the result be
+independent of the choice of training set and test among the complete set of
+samples. Therefore it was necessary to build a new database by mixing NIST's
+datasets.
+
+The MNIST training set is composed of 30,000 patterns from SD-3 and 30,000
+patterns from SD-1. Our test set was composed of 5,000 patterns from SD-3 and
+5,000 patterns from SD-1. The 60,000 pattern training set contained examples
+from approximately 250 writers. We made sure that the sets of writers of the
+training set and test set were disjoint.
+
+SD-1 contains 58,527 digit images written by 500 different writers. In
+contrast to SD-3, where blocks of data from each writer appeared in sequence,
+the data in SD-1 is scrambled. Writer identities for SD-1 is available and we
+used this information to unscramble the writers. We then split SD-1 in two:
+characters written by the first 250 writers went into our new training set.
+The remaining 250 writers were placed in our test set. Thus we had two sets
+with nearly 30,000 examples each. The new training set was completed with
+enough examples from SD-3, starting at pattern # 0, to make a full set of
+60,000 training patterns. Similarly, the new test set was completed with
+SD-3 examples starting at pattern # 35,000 to make a full set with 60,000
+test patterns. Only a subset of 10,000 test images (5,000 from SD-1 and
+5,000 from SD-3) is available on this site. The full 60,000 sample training
+set is available.
diff --git a/advanced/databases/mobio/4.json b/advanced/databases/mobio/4.json
new file mode 100644
index 0000000..ce946bc
--- /dev/null
+++ b/advanced/databases/mobio/4.json
@@ -0,0 +1,188 @@
+{
+ "description": "The MOBIO Database of Faces",
+ "root_folder": "/idiap/resource/database/mobio/IMAGES_PNG",
+ "protocols": [
+ {
+ "name": "male",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "male",
+ "annotations": "../IMAGE_ANNOTATIONS"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "male",
+ "group": "dev",
+ "annotations": "../IMAGE_ANNOTATIONS"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "male",
+ "group": "dev",
+ "annotations": "../IMAGE_ANNOTATIONS"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "male",
+ "group": "eval",
+ "annotations": "../IMAGE_ANNOTATIONS"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "male",
+ "group": "eval",
+ "annotations": "../IMAGE_ANNOTATIONS"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "female",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "female",
+ "annotations": "../IMAGE_ANNOTATIONS"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "female",
+ "group": "dev",
+ "annotations": "../IMAGE_ANNOTATIONS"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "female",
+ "group": "dev",
+ "annotations": "../IMAGE_ANNOTATIONS"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "female",
+ "group": "eval",
+ "annotations": "../IMAGE_ANNOTATIONS"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "female",
+ "group": "eval",
+ "annotations": "../IMAGE_ANNOTATIONS"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ }
+ ]
+}
diff --git a/advanced/databases/mobio/4.py b/advanced/databases/mobio/4.py
new file mode 100644
index 0000000..1dbcd61
--- /dev/null
+++ b/advanced/databases/mobio/4.py
@@ -0,0 +1,402 @@
+###############################################################################
+# #
+# Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import os
+import numpy as np
+from collections import namedtuple
+
+from beat.backend.python.database import View
+
+import bob.io.base
+import bob.io.image
+import bob.db.mobio
+
+
+#----------------------------------------------------------
+
+
+class Train(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'file_id', 'eye_centers', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ annotations = parameters['annotations']
+ if not os.path.isabs(annotations):
+ annotations = os.path.join(root_folder, annotations)
+
+ db = bob.db.mobio.Database(annotation_directory=annotations)
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups='world',
+ purposes='train'),
+ key=lambda x: (x.client_id, x.id))
+
+ return [ Entry(x.client_id, x.id, db.annotations(x), x.make_path(root_folder, '.png'))
+ for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'eye_centers':
+ return {
+ 'left': {
+ 'y': np.int32(obj.eye_centers['leye'][0]),
+ 'x': np.int32(obj.eye_centers['leye'][1]),
+ },
+ 'right': {
+ 'y': np.int32(obj.eye_centers['reye'][0]),
+ 'x': np.int32(obj.eye_centers['reye'][1]),
+ }
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Templates(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - template_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ Several "image" are associated with a given "template_id".
+ Several "template_id" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | template_id | | template_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | client_id |
+ -----------------------------------------------------------------------------------------------
+
+ Note: for this particular database, there is only one "template_id"
+ per "client_id".
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'template_id', 'file_id', 'eye_centers', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ annotations = parameters['annotations']
+ if not os.path.isabs(annotations):
+ annotations = os.path.join(root_folder, annotations)
+
+ db = bob.db.mobio.Database(annotation_directory=annotations)
+
+ template_ids = db.model_ids(protocol=parameters['protocol'],
+ groups=parameters['group'])
+
+ entries = []
+
+ for template_id in template_ids:
+ objs = db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ purposes='enroll',
+ model_ids=[template_id])
+
+ entries.extend([ Entry(x.client_id, template_id, x.id, db.annotations(x),
+ x.make_path(root_folder, '.png'))
+ for x in objs ])
+
+ return sorted(entries, key=lambda x: (x.client_id, x.template_id, x.file_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'template_id':
+ return {
+ 'value': np.uint64(obj.template_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'eye_centers':
+ return {
+ 'left': {
+ 'y': np.int32(obj.eye_centers['leye'][0]),
+ 'x': np.int32(obj.eye_centers['leye'][1]),
+ },
+ 'right': {
+ 'y': np.int32(obj.eye_centers['reye'][0]),
+ 'x': np.int32(obj.eye_centers['reye'][1]),
+ }
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Probes(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - probe_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+ - template_ids: "{{ system_user.username }}/array_1d_uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ One "probe_id" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+ Several "client_id" are associated with a given "template_ids".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | probe_id | | probe_id | | probe_id | | probe_id | | probe_id | | probe_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | template_ids |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['template_ids', 'client_id', 'probe_id', 'file_id',
+ 'eye_centers', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ annotations = parameters['annotations']
+ if not os.path.isabs(annotations):
+ annotations = os.path.join(root_folder, annotations)
+
+ db = bob.db.mobio.Database(annotation_directory=annotations)
+
+ template_ids = sorted(db.model_ids(protocol=parameters['protocol'],
+ groups=parameters['group']))
+
+
+ template_probes = {}
+ for template_id in template_ids:
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ purposes='probe',
+ model_ids=[template_id]),
+ key=lambda x: (x.client_id, x.id))
+
+ template_probes[template_id] = [ p.id for p in objs ]
+
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ purposes='probe'),
+ key=lambda x: (x.client_id, x.id))
+
+ entries = []
+ for obj in objs:
+ templates = [ template_id for template_id in template_ids
+ if obj.id in template_probes[template_id] ]
+
+ entries.append( Entry(templates, obj.client_id, obj.id, obj.id,
+ db.annotations(obj), obj.make_path(root_folder, '.png')) )
+
+ return sorted(entries, key=lambda x: (len(x.template_ids), x.template_ids,
+ x.client_id, x.probe_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'template_ids':
+ return {
+ 'value': np.uint64(obj.template_ids)
+ }
+
+ elif output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'probe_id':
+ return {
+ 'value': np.uint64(obj.probe_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'eye_centers':
+ return {
+ 'left': {
+ 'y': np.int32(obj.eye_centers['leye'][0]),
+ 'x': np.int32(obj.eye_centers['leye'][1]),
+ },
+ 'right': {
+ 'y': np.int32(obj.eye_centers['reye'][0]),
+ 'x': np.int32(obj.eye_centers['reye'][1]),
+ }
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+def setup_tests():
+ # Install a mock load function for the images
+ def mock_load(root_folder):
+ return np.ndarray((3, 10, 20), dtype=np.uint8)
+
+ bob.io.base.load = mock_load
+
+
+#----------------------------------------------------------
+
+
+# Test the behavior of the views (on fake data)
+if __name__ == '__main__':
+
+ setup_tests()
+
+ # Note: This database can't be tested without the actual data, since
+ # the actual files are needed by this implementation
+
+ view = Train()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='male',
+ annotations='../IMAGE_ANNOTATIONS',
+ )
+ )
+ view.get('client_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
+
+
+ view = Templates()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='male',
+ group='dev',
+ annotations='../IMAGE_ANNOTATIONS',
+ )
+ )
+ view.get('client_id', 0)
+ view.get('template_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
+
+
+ view = Probes()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='male',
+ group='dev',
+ annotations='../IMAGE_ANNOTATIONS',
+ )
+ )
+ view.get('template_ids', 0)
+ view.get('client_id', 0)
+ view.get('probe_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
diff --git a/advanced/databases/mobio/4.rst b/advanced/databases/mobio/4.rst
new file mode 100644
index 0000000..7e50160
--- /dev/null
+++ b/advanced/databases/mobio/4.rst
@@ -0,0 +1,74 @@
+.. Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ ..
+.. Contact: beat.support@idiap.ch ..
+.. ..
+.. This file is part of the beat.examples module of the BEAT platform. ..
+.. ..
+.. Commercial License Usage ..
+.. Licensees holding valid commercial BEAT licenses may use this file in ..
+.. accordance with the terms contained in a written agreement between you ..
+.. and Idiap. For further information contact tto@idiap.ch ..
+.. ..
+.. Alternatively, this file may be used under the terms of the GNU Affero ..
+.. Public License version 3 as published by the Free Software and appearing ..
+.. in the file LICENSE.AGPL included in the packaging of this file. ..
+.. The BEAT platform is distributed in the hope that it will be useful, but ..
+.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
+.. or FITNESS FOR A PARTICULAR PURPOSE. ..
+.. ..
+.. You should have received a copy of the GNU Affero Public License along ..
+.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
+
+
+The MOBIO Database of Faces
+---------------------------
+
+Changelog
+=========
+
+* **Version 4**, 30/Jan/2018:
+
+ - Port to beat.backend.python v1.5.0
+
+* **Version 3**, 31/Oct/2017:
+
+ - Port to beat.backend.python v1.4.2
+
+* **Version 2**, 20/Jan/2016:
+
+ - Port to Bob v2
+
+* **Version 1**, 21/Oct/2014:
+
+ - Initial release
+
+
+Description
+===========
+
+The `MOBIO database <https://www.idiap.ch/dataset/mobio>`_ consists of
+bi-modal (audio and video) data taken from 152 people. The database has a
+female-male ratio or nearly 1:2 (100 males and 52 females) and was collected
+from August 2008 until July 2010 in six different sites from five different
+countries. This led to a diverse bi-modal database with both native and
+non-native English speakers.
+
+.. image:: https://www.idiap.ch/dataset/mobio/video1.jpg
+ :width: 300px
+.. image:: https://www.idiap.ch/dataset/mobio/video2.jpg
+ :width: 300px
+
+In total 12 sessions were captured for each client: 6 sessions for Phase I and 6
+sessions for Phase II. The Phase I data consists of 21 questions with the
+question types ranging from: Short Response Questions, Short Response Free
+Speech, Set Speech, and Free Speech. The Phase II data consists of 11 questions
+with the question types ranging from: Short Response Questions, Set Speech, and
+Free Speech. A more detailed description of the questions asked of the clients
+is provided below.
+
+The database was recorded using two mobile devices: a mobile phone and a laptop
+computer. The mobile phone used to capture the database was a NOKIA N93i mobile
+while the laptop computer was a standard 2008 MacBook. The laptop was only used
+to capture part of the first session, this first session consists of data
+captured on both the laptop and the mobile phone.
+
+This deployment of the MOBIO database only consists of the images.
diff --git a/advanced/databases/putvein/4.json b/advanced/databases/putvein/4.json
new file mode 100644
index 0000000..c81cc3a
--- /dev/null
+++ b/advanced/databases/putvein/4.json
@@ -0,0 +1,1646 @@
+{
+ "description": "The PUT Vein Database",
+ "root_folder": "/idiap/resource/database/PUT_Vein_Dataset",
+ "protocols": [
+ {
+ "name": "palm-L_1",
+ "template": "advanced_vein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "L_1",
+ "kind": "palm",
+ "group": "train"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "L_1",
+ "kind": "palm",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "L_1",
+ "kind": "palm",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "L_1",
+ "kind": "palm",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "L_1",
+ "kind": "palm",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "palm-L_4",
+ "template": "advanced_vein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "L_4",
+ "kind": "palm",
+ "group": "train"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "L_4",
+ "kind": "palm",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "L_4",
+ "kind": "palm",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "L_4",
+ "kind": "palm",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "L_4",
+ "kind": "palm",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "palm-R_1",
+ "template": "advanced_vein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "R_1",
+ "kind": "palm",
+ "group": "train"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "R_1",
+ "kind": "palm",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "R_1",
+ "kind": "palm",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "R_1",
+ "kind": "palm",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "R_1",
+ "kind": "palm",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "palm-R_4",
+ "template": "advanced_vein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "R_4",
+ "kind": "palm",
+ "group": "train"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "R_4",
+ "kind": "palm",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "R_4",
+ "kind": "palm",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "R_4",
+ "kind": "palm",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "R_4",
+ "kind": "palm",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "palm-RL_1",
+ "template": "advanced_vein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "RL_1",
+ "kind": "palm",
+ "group": "train"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "RL_1",
+ "kind": "palm",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "RL_1",
+ "kind": "palm",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "RL_1",
+ "kind": "palm",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "RL_1",
+ "kind": "palm",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "palm-RL_4",
+ "template": "advanced_vein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "RL_4",
+ "kind": "palm",
+ "group": "train"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "RL_4",
+ "kind": "palm",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "RL_4",
+ "kind": "palm",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "RL_4",
+ "kind": "palm",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "RL_4",
+ "kind": "palm",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "palm-LR_1",
+ "template": "advanced_vein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "LR_1",
+ "kind": "palm",
+ "group": "train"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "LR_1",
+ "kind": "palm",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "LR_1",
+ "kind": "palm",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "LR_1",
+ "kind": "palm",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "LR_1",
+ "kind": "palm",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "palm-LR_4",
+ "template": "advanced_vein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "LR_4",
+ "kind": "palm",
+ "group": "train"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "LR_4",
+ "kind": "palm",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "LR_4",
+ "kind": "palm",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "LR_4",
+ "kind": "palm",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "LR_4",
+ "kind": "palm",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "palm-R_BEAT_1",
+ "template": "advanced_vein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "R_BEAT_1",
+ "kind": "palm",
+ "group": "train"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "R_BEAT_1",
+ "kind": "palm",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "R_BEAT_1",
+ "kind": "palm",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "R_BEAT_1",
+ "kind": "palm",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "R_BEAT_1",
+ "kind": "palm",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "palm-R_BEAT_4",
+ "template": "advanced_vein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "R_BEAT_4",
+ "kind": "palm",
+ "group": "train"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "R_BEAT_4",
+ "kind": "palm",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "R_BEAT_4",
+ "kind": "palm",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "R_BEAT_4",
+ "kind": "palm",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "R_BEAT_4",
+ "kind": "palm",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "wrist-L_1",
+ "template": "advanced_vein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "L_1",
+ "kind": "wrist",
+ "group": "train"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "L_1",
+ "kind": "wrist",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "L_1",
+ "kind": "wrist",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "L_1",
+ "kind": "wrist",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "L_1",
+ "kind": "wrist",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "wrist-L_4",
+ "template": "advanced_vein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "L_4",
+ "kind": "wrist",
+ "group": "train"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "L_4",
+ "kind": "wrist",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "L_4",
+ "kind": "wrist",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "L_4",
+ "kind": "wrist",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "L_4",
+ "kind": "wrist",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "wrist-R_1",
+ "template": "advanced_vein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "R_1",
+ "kind": "wrist",
+ "group": "train"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "R_1",
+ "kind": "wrist",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "R_1",
+ "kind": "wrist",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "R_1",
+ "kind": "wrist",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "R_1",
+ "kind": "wrist",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "wrist-R_4",
+ "template": "advanced_vein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "R_4",
+ "kind": "wrist",
+ "group": "train"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "R_4",
+ "kind": "wrist",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "R_4",
+ "kind": "wrist",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "R_4",
+ "kind": "wrist",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "R_4",
+ "kind": "wrist",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "wrist-RL_1",
+ "template": "advanced_vein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "RL_1",
+ "kind": "wrist",
+ "group": "train"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "RL_1",
+ "kind": "wrist",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "RL_1",
+ "kind": "wrist",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "RL_1",
+ "kind": "wrist",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "RL_1",
+ "kind": "wrist",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "wrist-RL_4",
+ "template": "advanced_vein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "RL_4",
+ "kind": "wrist",
+ "group": "train"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "RL_4",
+ "kind": "wrist",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "RL_4",
+ "kind": "wrist",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "RL_4",
+ "kind": "wrist",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "RL_4",
+ "kind": "wrist",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "wrist-LR_1",
+ "template": "advanced_vein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "LR_1",
+ "kind": "wrist",
+ "group": "train"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "LR_1",
+ "kind": "wrist",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "LR_1",
+ "kind": "wrist",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "LR_1",
+ "kind": "wrist",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "LR_1",
+ "kind": "wrist",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "wrist-LR_4",
+ "template": "advanced_vein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "LR_4",
+ "kind": "wrist",
+ "group": "train"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "LR_4",
+ "kind": "wrist",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "LR_4",
+ "kind": "wrist",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "LR_4",
+ "kind": "wrist",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "LR_4",
+ "kind": "wrist",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "wrist-R_BEAT_1",
+ "template": "advanced_vein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "R_BEAT_1",
+ "kind": "wrist",
+ "group": "train"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "R_BEAT_1",
+ "kind": "wrist",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "R_BEAT_1",
+ "kind": "wrist",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "R_BEAT_1",
+ "kind": "wrist",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "R_BEAT_1",
+ "kind": "wrist",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ },
+ {
+ "name": "wrist-R_BEAT_4",
+ "template": "advanced_vein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "template": "train",
+ "parameters": {
+ "protocol": "R_BEAT_4",
+ "kind": "wrist",
+ "group": "train"
+ },
+ "name": "train"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "dev_templates",
+ "parameters": {
+ "protocol": "R_BEAT_4",
+ "kind": "wrist",
+ "group": "dev",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "dev_probes",
+ "parameters": {
+ "protocol": "R_BEAT_4",
+ "kind": "wrist",
+ "group": "dev",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ },
+ {
+ "outputs": {
+ "model_id": "{{ system_user.username }}/text/1",
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "TemplateView",
+ "name": "eval_templates",
+ "parameters": {
+ "protocol": "R_BEAT_4",
+ "kind": "wrist",
+ "group": "eval",
+ "purpose": "enroll"
+ },
+ "template": "enroll"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "client_id": "{{ system_user.username }}/uint64/1"
+ },
+ "view": "View",
+ "name": "eval_probes",
+ "parameters": {
+ "protocol": "R_BEAT_4",
+ "kind": "wrist",
+ "group": "eval",
+ "purpose": "probe"
+ },
+ "template": "probe"
+ }
+ ]
+ }
+ ]
+}
diff --git a/advanced/databases/putvein/4.py b/advanced/databases/putvein/4.py
new file mode 100644
index 0000000..e7e8152
--- /dev/null
+++ b/advanced/databases/putvein/4.py
@@ -0,0 +1,222 @@
+###############################################################################
+# #
+# Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import os
+import numpy as np
+from collections import namedtuple
+
+from beat.backend.python.database import View as BaseView
+
+import bob.db.putvein
+import bob.ip.color
+
+
+#----------------------------------------------------------
+
+
+class View(BaseView):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_2d_uint8/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ Several "image" are associated with a given "client_id"
+
+ --------- --------- --------- --------- --------- ---------
+ | image | | image | | image | | image | | image | | image |
+ --------- --------- --------- --------- --------- ---------
+ ----------------------------- -----------------------------
+ | client_id | | client_id |
+ ----------------------------- -----------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.putvein.Database()
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ purposes=parameters.get('purpose', None),
+ groups=[parameters['group']],
+ kinds=[parameters['kind']]),
+ key=lambda x: x.client_id)
+
+ return [ Entry(x.client_id, x.make_path(root_folder))
+ for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'image':
+ """
+ The image returned by the ``bob.db.putvein`` is RGB (with shape
+ (3, 768, 1024)). This method converts image to a grayscale
+ (shape (768, 1024)) and then rotates image by 270 deg so that
+ images can be used with ``bob.bio.vein`` algorithms designed for
+ the ``bob.db.biowave_v1`` database.
+ Output images dimensions: (1024, 768).
+ """
+ color_image = bob.io.base.load(obj.image)
+ grayscale_image = bob.ip.color.rgb_to_gray(color_image)
+ grayscale_image = np.rot90(grayscale_image, k=3)
+
+ return {
+ 'value': grayscale_image
+ }
+
+
+#----------------------------------------------------------
+
+
+class TemplateView(BaseView):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_2d_uint8/1"
+ - model_id: "{{ system_user.username }}/text/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ Several "image" are associated with a given "model_id".
+ Several "model_id" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | model_id | | model_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | client_id |
+ -----------------------------------------------------------------------------------------------
+
+ Note: for this particular database, there is only one "image"
+ per "model_id".
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'model_id', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.putvein.Database()
+
+ model_ids = db.model_ids(protocol=parameters['protocol'],
+ groups=[parameters['group']],
+ kinds=[parameters['kind']])
+
+ entries = []
+
+ for model_id in model_ids:
+ objs = db.objects(protocol=parameters['protocol'],
+ purposes=parameters.get('purpose', None),
+ groups=[parameters['group']],
+ kinds=[parameters['kind']],
+ model_ids=[model_id])
+
+ entries.extend([ Entry(x.client_id, model_id, x.make_path(root_folder))
+ for x in objs ])
+
+ return sorted(entries, key=lambda x: (x.client_id, x.model_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'model_id':
+ return {
+ 'text': str(obj.model_id)
+ }
+
+ elif output == 'image':
+ """
+ The image returned by the ``bob.db.putvein`` is RGB (with shape
+ (3, 768, 1024)). This method converts image to a grayscale
+ (shape (768, 1024)) and then rotates image by 270 deg so that
+ images can be used with ``bob.bio.vein`` algorithms designed for
+ the ``bob.db.biowave_v1`` database.
+ Output images dimensions: (1024, 768).
+ """
+ color_image = bob.io.base.load(obj.image)
+ grayscale_image = bob.ip.color.rgb_to_gray(color_image)
+ grayscale_image = np.rot90(grayscale_image, k=3)
+
+ return {
+ 'value': grayscale_image
+ }
+
+
+#----------------------------------------------------------
+
+
+def setup_tests():
+ # Install a mock methods
+ def mock_load(filename):
+ return np.ndarray((3, 10, 20), dtype=np.uint8)
+
+ bob.io.base.load = mock_load
+
+
+#----------------------------------------------------------
+
+
+# Test the behavior of the views (on fake data)
+if __name__ == '__main__':
+
+ setup_tests()
+
+ view = View()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol = 'LR_4',
+ kind = 'wrist',
+ group = 'dev',
+ purpose = 'probe',
+ )
+ )
+ view.get('client_id', 0)
+ view.get('image', 0)
+
+
+ view = TemplateView()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol = 'LR_4',
+ kind = 'wrist',
+ group = 'dev',
+ purpose = 'enroll',
+ )
+ )
+ view.get('client_id', 0)
+ view.get('model_id', 0)
+ view.get('image', 0)
diff --git a/advanced/databases/putvein/4.rst b/advanced/databases/putvein/4.rst
new file mode 100644
index 0000000..54a642e
--- /dev/null
+++ b/advanced/databases/putvein/4.rst
@@ -0,0 +1,138 @@
+.. Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ ..
+.. Contact: beat.support@idiap.ch ..
+.. ..
+.. This file is part of the beat.examples module of the BEAT platform. ..
+.. ..
+.. Commercial License Usage ..
+.. Licensees holding valid commercial BEAT licenses may use this file in ..
+.. accordance with the terms contained in a written agreement between you ..
+.. and Idiap. For further information contact tto@idiap.ch ..
+.. ..
+.. Alternatively, this file may be used under the terms of the GNU Affero ..
+.. Public License version 3 as published by the Free Software and appearing ..
+.. in the file LICENSE.AGPL included in the packaging of this file. ..
+.. The BEAT platform is distributed in the hope that it will be useful, but ..
+.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
+.. or FITNESS FOR A PARTICULAR PURPOSE. ..
+.. ..
+.. You should have received a copy of the GNU Affero Public License along ..
+.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
+
+
+The PUT Vein Database
+---------------------
+
+Changelog
+=========
+
+* **Version 4**, 30/Jan/2018:
+
+ - Port to beat.backend.python v1.5.0
+
+* **Version 3**, 02/Nov/2017:
+
+ - Port to beat.backend.python v1.4.2
+
+* **Version 2**, 10/Feb/2017:
+
+ - Major update to make database compatible with BIOWAVE database toolchains
+
+* **Version 1**, 10/Feb/2016:
+
+ - Initial release
+
+
+Description
+===========
+
+PUT Vein pattern database consists of 2400 images presenting human vein
+patterns. Half of images contains a palmar vein pattern (1200 images) and
+another half contains a wrist vein pattern (another 1200 images). Data was
+acquired from both hands of 50 students, with means it has a 100 different
+patterns for palm and wrist region. Pictures ware taken in 3 series, 4 pictures
+each, with at least one week interval between each series. In case of palm
+region volunteers were asked to put his/her hand on the device to cover
+acquisition window, in way that line below their fingers coincident with its
+edge. No additional positioning systems ware used. In case of wrist region only
+construction allowing to place palm and wrist in comfortable way was used to
+help position a hand.
+
+Original images have **768 x 1024** resolution and are saved as 24-bit bitmap.
+Database consist of 2 main sections: hand and wrist, with are further divided
+in persons. Each person folder has sub-sections for left and right hand, which
+are separated into series.
+
+More information about the original database - `PUT Vein database
+<http://biometrics.put.poznan.pl/vein-dataset/>`_.
+
+In order to enable to use the database with BIOWAVE toolchains, this database
+implementation converts image to a grey-scale (shape (768, 1024)) and then
+rotates images by 270 deg so that vein pattern in wrist images would appear
+vertical (wrist's part closer to the palm would appear at the top of the image)
+
+In this implementation we use both - original 50 client x 2 hand data - in the
+database clients with IDs between ``1`` and ``50`` - and also - mirrored
+file representations (left hand / palm data mirrored to look like right hand
+data and vice versa) - clients with IDs between ``51`` and ``100``.
+
+
+Protocols
+=========
+
+Currently (as on 10.02.2017) there are 10x2 protocols, they also match the
+protocols in ``bob.bio.vein``:
+
+- ``palm-L_4``,
+- ``palm-R_4``,
+- ``palm-RL_4``,
+- ``palm-LR_4``,
+- ``palm-R_BEAT_4``,
+- ``palm-L_1``,
+- ``palm-R_1``,
+- ``palm-RL_1``,
+- ``palm-LR_1``,
+- ``palm-R_BEAT_1``,
+- ``wrist-L_4``,
+- ``wrist-R_4``,
+- ``wrist-RL_4``,
+- ``wrist-LR_4``,
+- ``wrist-R_BEAT_4``,
+- ``wrist-L_1``,
+- ``wrist-R_1``,
+- ``wrist-RL_1``,
+- ``wrist-LR_1``,
+- ``wrist-R_BEAT_1``.
+
+Protocols (except the ``BEAT`` protocols) still contains the original protocol
+(``L``, ``R``, ``RL``, ``LR``) data, the difference is, whether each enroll
+model is constructed using all 4 hand's images (protocol name ends with ``4``),
+or each enroll image is used as a model (corresponding protocol names ends with
+``1``).
+
+The original protocols consists of following data, ``world`` purpose dataset
+consists of the **same** data, as ``dev`` purpose dataset, so won't be
+separately described:
+
++-------------+-----------------------------------------+-------------------------------------------+
+|**protocol** | ``dev`` | ``eval`` |
++-------------+-----------------------------------------+-------------------------------------------+
+| L | IDs 1-25, un-mirrored left hand images | IDs 26-50, un-mirrored left hand images |
++-------------+-----------------------------------------+-------------------------------------------+
+| R | IDs 1-25, un-mirrored right hand images | IDs 26-50, un-mirrored right hand images |
++-------------+-----------------------------------------+-------------------------------------------+
+| RL | IDs 1-50, un-mirrored right hand images |IDs 51-100, mirrored left hand images |
+| | |(to represent right hand) |
++-------------+-----------------------------------------+-------------------------------------------+
+| LR | IDs 1-50, un-mirrored left hand images |IDs 51-100, mirrored right hand images |
+| | |(to represent left hand) |
++-------------+-----------------------------------------+-------------------------------------------+
+
+The new test protocols (ends with ``R_BEAT_1`` and ``R_BEAT_4``) are intended
+for use with ``bob.bio.vein`` and ``BEAT`` platform for quick tests, if
+necessary. Both protocols consist of such data:
+
++-------------+---------------------------------------------------------+------------------------------------------------------------+
+|**protocol** | ``dev`` | ``eval`` |
++-------------+---------------------------------------------------------+------------------------------------------------------------+
+| R_BEAT | IDs ``1``, ``2``, un-mirrored right hand / wrist images | IDs ``26``, ``27``, un-mirrored right hand / wrist images |
++-------------+---------------------------------------------------------+------------------------------------------------------------+
diff --git a/advanced/databases/replay/4.json b/advanced/databases/replay/4.json
new file mode 100644
index 0000000..45edb1d
--- /dev/null
+++ b/advanced/databases/replay/4.json
@@ -0,0 +1,1122 @@
+{
+ "description": "The Replay Database",
+ "root_folder": "/idiap/group/replay/database/protocols/replayattack-database",
+ "protocols": [
+ {
+ "name": "grandtest",
+ "template": "simple_face_antispoofing",
+ "sets": [
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "enroll": false,
+ "protocol": "grandtest"
+ },
+ "name": "train",
+ "template": "train",
+ "view": "All"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "enroll": false,
+ "protocol": "grandtest"
+ },
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "All"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "test",
+ "enroll": false,
+ "protocol": "grandtest"
+ },
+ "name": "test_probes",
+ "template": "probes",
+ "view": "All"
+ }
+ ]
+ },
+ {
+ "name": "print",
+ "template": "simple_face_antispoofing",
+ "sets": [
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "enroll": false,
+ "protocol": "print"
+ },
+ "name": "train",
+ "template": "train",
+ "view": "All"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "enroll": false,
+ "protocol": "print"
+ },
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "All"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "test",
+ "enroll": false,
+ "protocol": "print"
+ },
+ "name": "test_probes",
+ "template": "probes",
+ "view": "All"
+ }
+ ]
+ },
+ {
+ "name": "photo",
+ "template": "simple_face_antispoofing",
+ "sets": [
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "enroll": false,
+ "protocol": "photo"
+ },
+ "name": "train",
+ "template": "train",
+ "view": "All"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "enroll": false,
+ "protocol": "photo"
+ },
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "All"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "test",
+ "enroll": false,
+ "protocol": "photo"
+ },
+ "name": "test_probes",
+ "template": "probes",
+ "view": "All"
+ }
+ ]
+ },
+ {
+ "name": "video",
+ "template": "simple_face_antispoofing",
+ "sets": [
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "enroll": false,
+ "protocol": "video"
+ },
+ "name": "train",
+ "template": "train",
+ "view": "All"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "enroll": false,
+ "protocol": "video"
+ },
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "All"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "test",
+ "enroll": false,
+ "protocol": "video"
+ },
+ "name": "test_probes",
+ "template": "probes",
+ "view": "All"
+ }
+ ]
+ },
+ {
+ "name": "mobile",
+ "template": "simple_face_antispoofing",
+ "sets": [
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "enroll": false,
+ "protocol": "mobile"
+ },
+ "name": "train",
+ "template": "train",
+ "view": "All"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "enroll": false,
+ "protocol": "mobile"
+ },
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "All"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "test",
+ "enroll": false,
+ "protocol": "mobile"
+ },
+ "name": "test_probes",
+ "template": "probes",
+ "view": "All"
+ }
+ ]
+ },
+ {
+ "name": "highdef",
+ "template": "simple_face_antispoofing",
+ "sets": [
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "enroll": false,
+ "protocol": "highdef"
+ },
+ "name": "train",
+ "template": "train",
+ "view": "All"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "enroll": false,
+ "protocol": "highdef"
+ },
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "All"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "test",
+ "enroll": false,
+ "protocol": "highdef"
+ },
+ "name": "test_probes",
+ "template": "probes",
+ "view": "All"
+ }
+ ]
+ },
+ {
+ "name": "verification_grandtest",
+ "template": "advanced_face_antispoofing",
+ "sets": [
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "enroll": true,
+ "protocol": "grandtest"
+ },
+ "name": "train",
+ "template": "train",
+ "view": "All"
+ },
+ {
+ "outputs": {
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "grandtest"
+ },
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "grandtest"
+ },
+ "name": "dev_probes_real",
+ "template": "probes_real",
+ "view": "ProbesReal"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "grandtest"
+ },
+ "name": "dev_probes_attack",
+ "template": "probes_attack",
+ "view": "ProbesAttack"
+ },
+ {
+ "outputs": {
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "grandtest"
+ },
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "grandtest"
+ },
+ "name": "test_probes_real",
+ "template": "probes_real",
+ "view": "ProbesReal"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "grandtest"
+ },
+ "name": "test_probes_attack",
+ "template": "probes_attack",
+ "view": "ProbesAttack"
+ }
+ ]
+ },
+ {
+ "name": "verification_print",
+ "template": "advanced_face_antispoofing",
+ "sets": [
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "enroll": true,
+ "protocol": "print"
+ },
+ "name": "train",
+ "template": "train",
+ "view": "All"
+ },
+ {
+ "outputs": {
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "print"
+ },
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "print"
+ },
+ "name": "dev_probes_real",
+ "template": "probes_real",
+ "view": "ProbesReal"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "print"
+ },
+ "name": "dev_probes_attack",
+ "template": "probes_attack",
+ "view": "ProbesAttack"
+ },
+ {
+ "outputs": {
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "print"
+ },
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "print"
+ },
+ "name": "test_probes_real",
+ "template": "probes_real",
+ "view": "ProbesReal"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "print"
+ },
+ "name": "test_probes_attack",
+ "template": "probes_attack",
+ "view": "ProbesAttack"
+ }
+ ]
+ },
+ {
+ "name": "verification_photo",
+ "template": "advanced_face_antispoofing",
+ "sets": [
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "enroll": true,
+ "protocol": "photo"
+ },
+ "name": "train",
+ "template": "train",
+ "view": "All"
+ },
+ {
+ "outputs": {
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "photo"
+ },
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "photo"
+ },
+ "name": "dev_probes_real",
+ "template": "probes_real",
+ "view": "ProbesReal"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "photo"
+ },
+ "name": "dev_probes_attack",
+ "template": "probes_attack",
+ "view": "ProbesAttack"
+ },
+ {
+ "outputs": {
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "photo"
+ },
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "photo"
+ },
+ "name": "test_probes_real",
+ "template": "probes_real",
+ "view": "ProbesReal"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "photo"
+ },
+ "name": "test_probes_attack",
+ "template": "probes_attack",
+ "view": "ProbesAttack"
+ }
+ ]
+ },
+ {
+ "name": "verification_video",
+ "template": "advanced_face_antispoofing",
+ "sets": [
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "enroll": true,
+ "protocol": "video"
+ },
+ "name": "train",
+ "template": "train",
+ "view": "All"
+ },
+ {
+ "outputs": {
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "video"
+ },
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "video"
+ },
+ "name": "dev_probes_real",
+ "template": "probes_real",
+ "view": "ProbesReal"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "video"
+ },
+ "name": "dev_probes_attack",
+ "template": "probes_attack",
+ "view": "ProbesAttack"
+ },
+ {
+ "outputs": {
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "video"
+ },
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "video"
+ },
+ "name": "test_probes_real",
+ "template": "probes_real",
+ "view": "ProbesReal"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "video"
+ },
+ "name": "test_probes_attack",
+ "template": "probes_attack",
+ "view": "ProbesAttack"
+ }
+ ]
+ },
+ {
+ "name": "verification_mobile",
+ "template": "advanced_face_antispoofing",
+ "sets": [
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "enroll": true,
+ "protocol": "mobile"
+ },
+ "name": "train",
+ "template": "train",
+ "view": "All"
+ },
+ {
+ "outputs": {
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "mobile"
+ },
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "mobile"
+ },
+ "name": "dev_probes_real",
+ "template": "probes_real",
+ "view": "ProbesReal"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "mobile"
+ },
+ "name": "dev_probes_attack",
+ "template": "probes_attack",
+ "view": "ProbesAttack"
+ },
+ {
+ "outputs": {
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "mobile"
+ },
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "mobile"
+ },
+ "name": "test_probes_real",
+ "template": "probes_real",
+ "view": "ProbesReal"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "mobile"
+ },
+ "name": "test_probes_attack",
+ "template": "probes_attack",
+ "view": "ProbesAttack"
+ }
+ ]
+ },
+ {
+ "name": "verification_highdef",
+ "template": "advanced_face_antispoofing",
+ "sets": [
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "class": "{{ system_user.username }}/text/1"
+ },
+ "parameters": {
+ "group": "train",
+ "enroll": true,
+ "protocol": "highdef"
+ },
+ "name": "train",
+ "template": "train",
+ "view": "All"
+ },
+ {
+ "outputs": {
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "highdef"
+ },
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "highdef"
+ },
+ "name": "dev_probes_real",
+ "template": "probes_real",
+ "view": "ProbesReal"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "devel",
+ "protocol": "highdef"
+ },
+ "name": "dev_probes_attack",
+ "template": "probes_attack",
+ "view": "ProbesAttack"
+ },
+ {
+ "outputs": {
+ "annotations": "{{ system_user.username }}/bounding_box_video/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "highdef"
+ },
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "highdef"
+ },
+ "name": "test_probes_real",
+ "template": "probes_real",
+ "view": "ProbesReal"
+ },
+ {
+ "outputs": {
+ "attack_support": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "video": "{{ system_user.username }}/array_4d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "annotations": "{{ system_user.username }}/bounding_box_video/1"
+ },
+ "parameters": {
+ "group": "test",
+ "protocol": "highdef"
+ },
+ "name": "test_probes_attack",
+ "template": "probes_attack",
+ "view": "ProbesAttack"
+ }
+ ]
+ }
+ ]
+}
diff --git a/advanced/databases/replay/4.py b/advanced/databases/replay/4.py
new file mode 100644
index 0000000..ae1733d
--- /dev/null
+++ b/advanced/databases/replay/4.py
@@ -0,0 +1,571 @@
+###############################################################################
+# #
+# Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import numpy as np
+from collections import namedtuple
+
+from beat.backend.python.database import View
+
+import bob.io.base
+import bob.io.video
+import bob.db.replay
+
+from bob.db.replay.driver import Interface
+
+INFO = Interface()
+SQLITE_FILE = INFO.files()[0]
+
+
+#----------------------------------------------------------
+
+
+class All(View):
+ """Outputs:
+ - video: "{{ system_user.username }}/array_4d_uint8/1"
+ - annotations: "{{ system_user.username }}/bounding_box_video/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+ - attack_support: "{{ system_user.username }}/text/1"
+ - class: "{{ system_user.username }}/text/1"
+
+ One "file_id" is associated with a given "video".
+ One "annotations" is associated with a given "video".
+ Several "video" are associated with a given "client_id".
+ Several "client_id" are associated with a given "class".
+ Several "attack_support" are associated with a given "class".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | video | | video | | video | | video | | video | | video |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | annotations | | annotations | | annotations | | annotations | | annotations | | annotations |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ------------------------------- ------------------------------- -------------------------------
+ | client_id | | client_id | | client_id |
+ ------------------------------- ------------------------------- -------------------------------
+ --------------------------------------------------------------- -------------------------------
+ | attack_support | | attack_support |
+ --------------------------------------------------------------- -------------------------------
+ -----------------------------------------------------------------------------------------------
+ | class |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['cls', 'attack_support', 'client_id', 'file_id',
+ 'annotations', 'video'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.replay.Database()
+
+ objs = []
+
+ if parameters['enroll']:
+ objs.extend([ ('enroll', '', x)
+ for x in sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ cls='enroll'),
+ key=lambda x: (x.client_id, x.id))
+ ])
+
+ objs.extend([ ('real', '', x)
+ for x in sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ cls='real'),
+ key=lambda x: (x.client_id, x.id))
+ ])
+
+ objs.extend([ ('attack', 'fixed', x)
+ for x in sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ cls='attack',
+ support='fixed'),
+ key=lambda x: (x.client_id, x.id))
+ ])
+
+ objs.extend([ ('attack', 'hand', x)
+ for x in sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ cls='attack',
+ support='hand'),
+ key=lambda x: (x.client_id, x.id))
+ ])
+
+
+ return [ Entry(x[0], x[1], x[2].client_id, x[2].id, x[2].bbx(root_folder),
+ x[2].videofile(root_folder))
+ for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'class':
+ return {
+ 'text': str(obj.cls)
+ }
+
+ elif output == 'attack_support':
+ return {
+ 'text': str(obj.attack_support)
+ }
+
+ elif output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'annotations':
+ annotations_list = []
+ for i in range(obj.annotations.shape[0]):
+ annotations_list.append({
+ 'frame_id': np.uint64(obj.annotations[i][0]),
+ 'top-left-x': np.int32(obj.annotations[i][1]),
+ 'top-left-y': np.int32(obj.annotations[i][2]),
+ 'width': np.int32(obj.annotations[i][3]),
+ 'height': np.int32(obj.annotations[i][4])
+ })
+
+ return {
+ 'value': annotations_list
+ }
+
+ elif output == 'video':
+ return {
+ 'value': bob.io.base.load(obj.video)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Templates(View):
+ """Outputs:
+ - video: "{{ system_user.username }}/array_4d_uint8/1"
+ - annotations: "{{ system_user.username }}/bounding_box_video/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - template_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ One "file_id" is associated with a given "video".
+ One "annotations" is associated with a given "video".
+ Several "video" are associated with a given "template_id".
+ Several "template_id" are associated with a given "client_id".
+
+ --------------- --------------- --------------- ---------------
+ | video | | video | | video | | video |
+ --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- ---------------
+ | annotations | | annotations | | annotations | | annotations |
+ --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- ---------------
+ ------------------------------- -------------------------------
+ | template_id | | template_id |
+ ------------------------------- -------------------------------
+ ---------------------------------------------------------------
+ | client_id |
+ ---------------------------------------------------------------
+
+ Note: for this particular database, there is only one "template_id"
+ per "client_id".
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'template_id', 'file_id',
+ 'annotations', 'video'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.replay.Database()
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups=[parameters['group']],
+ cls='enroll'),
+ key=lambda x: (x.client_id, x.id))
+
+
+ return [ Entry(x.client_id, x.client_id, x.id, x.bbx(root_folder),
+ x.videofile(root_folder))
+ for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'template_id':
+ return {
+ 'value': np.uint64(obj.template_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'annotations':
+ annotations_list = []
+ for i in range(obj.annotations.shape[0]):
+ annotations_list.append({
+ 'frame_id': np.uint64(obj.annotations[i][0]),
+ 'top-left-x': np.int32(obj.annotations[i][1]),
+ 'top-left-y': np.int32(obj.annotations[i][2]),
+ 'width': np.int32(obj.annotations[i][3]),
+ 'height': np.int32(obj.annotations[i][4])
+ })
+
+ return {
+ 'value': annotations_list
+ }
+
+ elif output == 'video':
+ return {
+ 'value': bob.io.base.load(obj.video)
+ }
+
+
+#----------------------------------------------------------
+
+
+class ProbesReal(View):
+ """Outputs:
+ - video: "{{ system_user.username }}/array_4d_uint8/1"
+ - annotations: "{{ system_user.username }}/bounding_box_video/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - probe_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+ - template_ids: "{{ system_user.username }}/array_1d_uint64/1"
+
+ One "file_id" is associated with a given "video".
+ One "annotations" is associated with a given "video".
+ One "probe_id" is associated with a given "video".
+ Several "video" are associated with a given "client_id".
+ Several "client_id" are associated with a given "template_ids".
+
+ --------------- --------------- --------------- ---------------
+ | video | | video | | video | | video |
+ --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- ---------------
+ | annotations | | annotations | | annotations | | annotations |
+ --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- ---------------
+ | probe_id | | probe_id | | probe_id | | probe_id |
+ --------------- --------------- --------------- ---------------
+ ------------------------------- -------------------------------
+ | client_id | | client_id |
+ ------------------------------- -------------------------------
+ ---------------------------------------------------------------
+ | template_ids |
+ ---------------------------------------------------------------
+
+ Note: for this particular database, there is only one "template_ids"
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['template_ids', 'client_id', 'probe_id', 'file_id',
+ 'annotations', 'video'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.replay.Database()
+
+ template_ids = sorted([c.id for c in db.clients() if c.set == parameters['group']])
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups=[parameters['group']],
+ cls='real'),
+ key=lambda x: (x.client_id, x.id))
+
+
+ return [ Entry(template_ids, x.client_id, x.id, x.id, x.bbx(root_folder),
+ x.videofile(root_folder))
+ for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'template_ids':
+ return {
+ 'value': np.uint64(obj.template_ids)
+ }
+
+ elif output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'probe_id':
+ return {
+ 'value': np.uint64(obj.probe_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'annotations':
+ annotations_list = []
+ for i in range(obj.annotations.shape[0]):
+ annotations_list.append({
+ 'frame_id': np.uint64(obj.annotations[i][0]),
+ 'top-left-x': np.int32(obj.annotations[i][1]),
+ 'top-left-y': np.int32(obj.annotations[i][2]),
+ 'width': np.int32(obj.annotations[i][3]),
+ 'height': np.int32(obj.annotations[i][4])
+ })
+
+ return {
+ 'value': annotations_list
+ }
+
+ elif output == 'video':
+ return {
+ 'value': bob.io.base.load(obj.video)
+ }
+
+
+#----------------------------------------------------------
+
+
+class ProbesAttack(View):
+ """Outputs:
+ - video: "{{ system_user.username }}/array_4d_uint8/1"
+ - annotations: "{{ system_user.username }}/bounding_box_video/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - probe_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+ - attack_support: "{{ system_user.username }}/text/1"
+ - template_ids: "{{ system_user.username }}/array_1d_uint64/1"
+
+ One "file_id" is associated with a given "video".
+ One "annotations" is associated with a given "video".
+ One "probe_id" is associated with a given "video".
+ Several "video" are associated with a given "client_id".
+ Several "client_id" are associated with a given "template_ids".
+ Several "template_ids" are associated with a given "attack_support".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | video | | video | | video | | video | | video | | video |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | annotations | | annotations | | annotations | | annotations | | annotations | | annotations |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | probe_id | | probe_id | | probe_id | | probe_id | | probe_id | | probe_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ------------------------------- ------------------------------- -------------------------------
+ | client_id | | client_id | | client_id |
+ ------------------------------- ------------------------------- -------------------------------
+ --------------------------------------------------------------- -------------------------------
+ | template_ids | | template_ids |
+ --------------------------------------------------------------- -------------------------------
+ -----------------------------------------------------------------------------------------------
+ | attack_support |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['attack_support', 'template_ids', 'client_id',
+ 'probe_id', 'file_id', 'annotations', 'video'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.replay.Database()
+
+ objs = []
+
+ objs.extend([ ('fixed', x)
+ for x in sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ cls='attack',
+ support='fixed'),
+ key=lambda x: (x.client_id, x.id))
+ ])
+
+ objs.extend([ ('hand', x)
+ for x in sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ cls='attack',
+ support='hand'),
+ key=lambda x: (x.client_id, x.id))
+ ])
+
+ return [ Entry(x[0], [ x[1].client_id ], x[1].client_id, x[1].id, x[1].id,
+ x[1].bbx(root_folder), x[1].videofile(root_folder))
+ for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'attack_support':
+ return {
+ 'text': str(obj.attack_support)
+ }
+
+ elif output == 'template_ids':
+ return {
+ 'value': np.uint64(obj.template_ids)
+ }
+
+ elif output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'probe_id':
+ return {
+ 'value': np.uint64(obj.probe_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'annotations':
+ annotations_list = []
+ for i in range(obj.annotations.shape[0]):
+ annotations_list.append({
+ 'frame_id': np.uint64(obj.annotations[i][0]),
+ 'top-left-x': np.int32(obj.annotations[i][1]),
+ 'top-left-y': np.int32(obj.annotations[i][2]),
+ 'width': np.int32(obj.annotations[i][3]),
+ 'height': np.int32(obj.annotations[i][4])
+ })
+
+ return {
+ 'value': annotations_list
+ }
+
+ elif output == 'video':
+ return {
+ 'value': bob.io.base.load(obj.video)
+ }
+
+
+#----------------------------------------------------------
+
+
+def setup_tests():
+ def mock_load(filename):
+ return np.ndarray((5, 3, 10, 20), dtype=np.uint8)
+
+ def mock_bbx(obj, directory):
+ return np.array([(0, 1, 2, 3, 4, 5), (1, 10, 20, 30, 40, 50)])
+
+ bob.io.base.load = mock_load
+ bob.db.replay.File.bbx = mock_bbx
+
+
+#----------------------------------------------------------
+
+
+# Test the behavior of the views (on fake data)
+if __name__ == '__main__':
+
+ setup_tests()
+
+ view = All()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol="grandtest",
+ group="train",
+ enroll=False,
+ )
+ )
+ view.get('class', 0)
+ view.get('attack_support', 0)
+ view.get('client_id', 0)
+ view.get('file_id', 0)
+ view.get('annotations', 0)
+ view.get('video', 0)
+
+
+ view = Templates()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol="grandtest",
+ group="devel",
+ )
+ )
+ view.get('client_id', 0)
+ view.get('template_id', 0)
+ view.get('file_id', 0)
+ view.get('annotations', 0)
+ view.get('video', 0)
+
+
+ view = ProbesReal()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol="grandtest",
+ group="devel",
+ )
+ )
+ view.get('template_ids', 0)
+ view.get('client_id', 0)
+ view.get('probe_id', 0)
+ view.get('file_id', 0)
+ view.get('annotations', 0)
+ view.get('video', 0)
+
+
+ view = ProbesAttack()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol="grandtest",
+ group="devel",
+ )
+ )
+ view.get('attack_support', 0)
+ view.get('template_ids', 0)
+ view.get('client_id', 0)
+ view.get('probe_id', 0)
+ view.get('file_id', 0)
+ view.get('annotations', 0)
+ view.get('video', 0)
diff --git a/advanced/databases/replay/4.rst b/advanced/databases/replay/4.rst
new file mode 100644
index 0000000..18ed891
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+++ b/advanced/databases/replay/4.rst
@@ -0,0 +1,225 @@
+.. Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ ..
+.. Contact: beat.support@idiap.ch ..
+.. ..
+.. This file is part of the beat.examples module of the BEAT platform. ..
+.. ..
+.. Commercial License Usage ..
+.. Licensees holding valid commercial BEAT licenses may use this file in ..
+.. accordance with the terms contained in a written agreement between you ..
+.. and Idiap. For further information contact tto@idiap.ch ..
+.. ..
+.. Alternatively, this file may be used under the terms of the GNU Affero ..
+.. Public License version 3 as published by the Free Software and appearing ..
+.. in the file LICENSE.AGPL included in the packaging of this file. ..
+.. The BEAT platform is distributed in the hope that it will be useful, but ..
+.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
+.. or FITNESS FOR A PARTICULAR PURPOSE. ..
+.. ..
+.. You should have received a copy of the GNU Affero Public License along ..
+.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
+
+
+The Replay Database
+-------------------
+
+Changelog
+=========
+
+* **Version 4**, 30/Jan/2018:
+
+ - Port to beat.backend.python v1.5.0
+
+* **Version 3**, 02/Nov/2017:
+
+ - Port to beat.backend.python v1.4.2
+
+* **Version 2**, 20/Jan/2016:
+
+ - Port to Bob v2
+ - Support all protocols of Replay Attack
+
+* **Version 1**, 21/Oct/2014:
+
+ - Initial release
+
+
+Description
+===========
+
+The `Replay-Attack Database <https://www.idiap.ch/dataset/replayattack>`_ for
+face spoofing consists of 1300 video clips of photo and video attack attempts
+to 50 clients, under different lighting conditions. This Database was produced
+at the Idiap Research Institute, in Switzerland.
+
+
+Acknowledgements
+================
+
+If you use this database, please cite the following publication on your paper::
+
+ @INPROCEEDINGS{Chingovska_BIOSIG-2012,
+ author = {Chingovska, Ivana and Anjos, Andr{\'{e}} and Marcel, S{\'{e}}bastien},
+ keywords = {biometric, Counter-Measures, Local Binary Patterns, Spoofing Attacks},
+ month = september,
+ title = {On the Effectiveness of Local Binary Patterns in Face Anti-spoofing},
+ journal = {IEEE BIOSIG 2012},
+ year = {2012},
+ }
+
+
+Spoofing Attacks Description
+============================
+
+The 2D face spoofing attack database consists of 1,300 video clips of photo and
+video attack attempts of 50 clients, under different lighting conditions.
+
+The data is split into 4 sub-groups comprising:
+
+ * Training data ("train"), to be used for training your anti-spoof classifier;
+ * Development data ("devel"), to be used for threshold estimation;
+ * Test data ("test"), with which to report error figures;
+ * Enrollment data ("enroll"), that can be used to verify spoofing sensitivity on face detection algorithms.
+
+Clients that appear in one of the data sets (train, devel or test) do not appear
+in any other set.
+
+Database Description
+====================
+
+All videos are generated by either having a (real) client trying to access a
+laptop through a built-in webcam or by displaying a photo or a video recording
+of the same client for at least 9 seconds. The webcam produces colour videos
+with a resolution of 320 pixels (width) by 240 pixels (height). The movies were
+recorded on a Macbook laptop using the QuickTime framework (codec: Motion JPEG)
+and saved into ".mov" files. The frame rate is about 25 Hz. Besides the native
+support on Apple computers, these files are *easily* readable using mplayer,
+ffmpeg or any other video utilities available under Linux or MS Windows systems.
+
+Real client accesses as well as data collected for the attacks are taken under
+two different lighting conditions:
+
+* **controlled**: The office light was turned on, blinds are down, background is homogeneous;
+* **adverse**: Blinds up, more complex background, office lights are out.
+
+To produce the attacks, high-resolution photos and videos from each client were
+taken under the same conditions as in their authentication sessions, using a
+Canon PowerShot SX150 IS camera, which records both 12.1 Mpixel photographs and
+720p high-definition video clips. The way to perform the attacks can be divided
+into two subsets: the first subset is composed of videos generated using a stand
+to hold the client biometry ("fixed"). For the second set, the attacker holds
+the device used for the attack with their own hands. In total, 20 attack videos
+were registered for each client, 10 for each of the attacking modes just
+described:
+
+* 4 x mobile attacks using an iPhone 3GS screen (with resolution 480x320 pixels) displaying:
+
+ * 1 x mobile photo/controlled
+ * 1 x mobile photo/adverse
+ * 1 x mobile video/controlled
+ * 1 x mobile video/adverse
+
+* 4 x high-resolution screen attacks using an iPad (first generation, with a screen resolution of 1024x768 pixels) displaying:
+
+ * 1 x high-resolution photo/controlled
+ * 1 x high-resolution photo/adverse
+ * 1 x high-resolution video/controlled
+ * 1 x high-resolution video/adverse
+
+* 2 x hard-copy print attacks (produced on a Triumph-Adler DCC 2520 color laser printer) occupying the whole available printing surface on A4 paper for the following samples:
+
+ * 1 x high-resolution print of photo/controlled
+ * 1 x high-resolution print of photo/adverse
+
+The 1300 real-accesses and attacks videos were then divided in the following
+way:
+
+* **Training set**: contains 60 real-accesses and 300 attacks under different lighting conditions;
+* **Development set**: contains 60 real-accesses and 300 attacks under different lighting conditions;
+* **Test set**: contains 80 real-accesses and 400 attacks under different lighting conditions;
+* **Enrollment set**: contains 100 real-accesses under different lighting conditions, to be used **exclusively** for studying the baseline performance of face recognition systems.
+
+
+Face Locations
+==============
+
+We also provide face locations automatically annotated by a cascade of
+classifiers based on a variant of Local Binary Patterns (LBP) referred as
+Modified Census Transform (MCT) [Face Detection with the Modified Census
+Transform, Froba, B. and Ernst, A., 2004, IEEE International Conference on
+Automatic Face and Gesture Recognition, pp. 91-96]. The automatic face
+localisation procedure works in more than 99% of the total number of frames
+acquired. This means that less than 1% of the total set of frames for all videos
+do not possess annotated faces. User algorithms must account for this fact.
+
+
+Protocol for Licit Biometric Transactions
+=========================================
+
+It is possible to measure the performance of baseline face recognition systems
+on the 2D Face spoofing database and evaluate how well the attacks pass such
+systems or how, otherwise robust they are to attacks. Here we describe how to
+use the available data at the enrollment set to create a background model,
+client models and how to perform scoring using the available data.
+
+1. Universal Background Model (UBM): To generate the UBM, subselect the
+training-set client videos from the enrollment videos. There should be 2 per
+client, which means you get 30 videos, each with 375 frames to create the model;
+
+2. Client models: To generate client models, use the enrollment data for clients
+at the development and test groups. There should be 2 videos per client (one for
+each light condition) once more. At the end of the enrollment procedure, the
+development set must have 1 model for each of the 15 clients available in that
+set. Similarly, for the test set, 1 model for each of the 20 clients available;
+
+3. For a simple baseline verification, generate scores **exhaustively** for all
+videos from the development and test **real-accesses** respectively, but
+**without** intermixing accross development and test sets. The scores generated
+against matched client videos and models (within the subset, i.e. development or
+test) should be considered true client accesses, while all others impostors;
+
+4. If you are looking for a single number to report on the performance do the
+following: exclusively using the scores from the development set, tune your
+baseline face recognition system on the EER of the development set and use this
+threshold to find the HTER on the test set scores.
+
+
+Protocols for Spoofing Attacks
+==============================
+
+Attack protocols are used to evaluate the (binary classification) performance of
+counter-measures to spoof attacks. The database can be split into 6 different
+protocols according to the type of device used to generate the attack: print,
+mobile (phone), high-definition (tablet), photo, video or grand test (all
+types). Furthermore, subsetting can be achieved on the top of the previous 6
+groups by classifying attacks as performed by the attacker bare hands or using a
+fixed support. This classification scheme makes-up a total of 18 protocols that
+can be used for studying the performance of counter-measures to 2D face spoofing
+attacks.
+
++------------+-------+-----+------+-------+-----+------+-------+-----+------+
+| | Hand-Attack | Fixed-Support | All Supports |
++------------+-------+-----+------+-------+-----+------+-------+-----+------+
+| Protocol | train | dev | test | train | dev | test | train | dev | test |
++============+=======+=====+======+=======+=====+======+=======+=====+======+
+| print | 30 | 30 | 40 | 30 | 30 | 40 | 60 | 60 | 80 |
++------------+-------+-----+------+-------+-----+------+-------+-----+------+
+| mobile | 60 | 60 | 80 | 60 | 60 | 80 | 120 | 120 | 160 |
++------------+-------+-----+------+-------+-----+------+-------+-----+------+
+| highdef | 60 | 60 | 80 | 60 | 60 | 80 | 120 | 120 | 160 |
++------------+-------+-----+------+-------+-----+------+-------+-----+------+
+| video | 60 | 60 | 80 | 60 | 60 | 80 | 120 | 120 | 160 |
++------------+-------+-----+------+-------+-----+------+-------+-----+------+
+| photo | 90 | 90 | 120 | 90 | 90 | 120 | 180 | 180 | 240 |
++------------+-------+-----+------+-------+-----+------+-------+-----+------+
+| grandtest | 150 | 150 | 200 | 150 | 150 | 200 | 300 | 300 | 400 |
++------------+-------+-----+------+-------+-----+------+-------+-----+------+
+
+
+Specificities to the BEAT View
+==============================
+
+Spoofing and genuine samples are each labelled with a text field that defines
+the class of the sample: ``"attack"`` or ``"real"``, for simple anti-spoofing
+binary classification systems and ``"attack"``, ``"real"`` and ``"enroll"`` for
+systems merging anti-spoofing with face verification systems. Code using this
+database views may use the ``class`` field to differentiate samples.
diff --git a/advanced/databases/utfvp/4.json b/advanced/databases/utfvp/4.json
new file mode 100644
index 0000000..90b6218
--- /dev/null
+++ b/advanced/databases/utfvp/4.json
@@ -0,0 +1,623 @@
+{
+ "description": "Finger-Vein database from the University of Twente",
+ "root_folder": "/idiap/resource/database/UTFVP/data",
+ "protocols": [
+ {
+ "name": "1vsall",
+ "template": "simple_fingervein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "train",
+ "template": "train",
+ "parameters": {
+ "protocol": "1vsall"
+ },
+ "view": "Train"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "dev_templates",
+ "template": "templates",
+ "parameters": {
+ "group": "dev",
+ "protocol": "1vsall"
+ },
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "name": "dev_probes",
+ "template": "probes",
+ "parameters": {
+ "group": "dev",
+ "protocol": "1vsall"
+ },
+ "view": "Probes"
+ }
+ ]
+ },
+ {
+ "name": "nom",
+ "template": "advanced_fingervein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "train",
+ "template": "train",
+ "parameters": {
+ "protocol": "nom"
+ },
+ "view": "Train"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "dev_templates",
+ "template": "templates",
+ "parameters": {
+ "group": "dev",
+ "protocol": "nom"
+ },
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "name": "dev_probes",
+ "template": "probes",
+ "parameters": {
+ "group": "dev",
+ "protocol": "nom"
+ },
+ "view": "Probes"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "test_templates",
+ "template": "templates",
+ "parameters": {
+ "group": "eval",
+ "protocol": "nom"
+ },
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "name": "test_probes",
+ "template": "probes",
+ "parameters": {
+ "group": "eval",
+ "protocol": "nom"
+ },
+ "view": "Probes"
+ }
+ ]
+ },
+ {
+ "name": "nomLeftRing",
+ "template": "advanced_fingervein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "train",
+ "template": "train",
+ "parameters": {
+ "protocol": "nomLeftRing"
+ },
+ "view": "Train"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "dev_templates",
+ "template": "templates",
+ "parameters": {
+ "group": "dev",
+ "protocol": "nomLeftRing"
+ },
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "name": "dev_probes",
+ "template": "probes",
+ "parameters": {
+ "group": "dev",
+ "protocol": "nomLeftRing"
+ },
+ "view": "Probes"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "test_templates",
+ "template": "templates",
+ "parameters": {
+ "group": "eval",
+ "protocol": "nomLeftRing"
+ },
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "name": "test_probes",
+ "template": "probes",
+ "parameters": {
+ "group": "eval",
+ "protocol": "nomLeftRing"
+ },
+ "view": "Probes"
+ }
+ ]
+ },
+ {
+ "name": "nomLeftMiddle",
+ "template": "advanced_fingervein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "train",
+ "template": "train",
+ "parameters": {
+ "protocol": "nomLeftMiddle"
+ },
+ "view": "Train"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "dev_templates",
+ "template": "templates",
+ "parameters": {
+ "group": "dev",
+ "protocol": "nomLeftMiddle"
+ },
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "name": "dev_probes",
+ "template": "probes",
+ "parameters": {
+ "group": "dev",
+ "protocol": "nomLeftMiddle"
+ },
+ "view": "Probes"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "test_templates",
+ "template": "templates",
+ "parameters": {
+ "group": "eval",
+ "protocol": "nomLeftMiddle"
+ },
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "name": "test_probes",
+ "template": "probes",
+ "parameters": {
+ "group": "eval",
+ "protocol": "nomLeftMiddle"
+ },
+ "view": "Probes"
+ }
+ ]
+ },
+ {
+ "name": "nomLeftIndex",
+ "template": "advanced_fingervein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "train",
+ "template": "train",
+ "parameters": {
+ "protocol": "nomLeftIndex"
+ },
+ "view": "Train"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "dev_templates",
+ "template": "templates",
+ "parameters": {
+ "group": "dev",
+ "protocol": "nomLeftIndex"
+ },
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "name": "dev_probes",
+ "template": "probes",
+ "parameters": {
+ "group": "dev",
+ "protocol": "nomLeftIndex"
+ },
+ "view": "Probes"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "test_templates",
+ "template": "templates",
+ "parameters": {
+ "group": "eval",
+ "protocol": "nomLeftIndex"
+ },
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "name": "test_probes",
+ "template": "probes",
+ "parameters": {
+ "group": "eval",
+ "protocol": "nomLeftIndex"
+ },
+ "view": "Probes"
+ }
+ ]
+ },
+ {
+ "name": "nomRightIndex",
+ "template": "advanced_fingervein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "train",
+ "template": "train",
+ "parameters": {
+ "protocol": "nomRightIndex"
+ },
+ "view": "Train"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "dev_templates",
+ "template": "templates",
+ "parameters": {
+ "group": "dev",
+ "protocol": "nomRightIndex"
+ },
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "name": "dev_probes",
+ "template": "probes",
+ "parameters": {
+ "group": "dev",
+ "protocol": "nomRightIndex"
+ },
+ "view": "Probes"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "test_templates",
+ "template": "templates",
+ "parameters": {
+ "group": "eval",
+ "protocol": "nomRightIndex"
+ },
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "name": "test_probes",
+ "template": "probes",
+ "parameters": {
+ "group": "eval",
+ "protocol": "nomRightIndex"
+ },
+ "view": "Probes"
+ }
+ ]
+ },
+ {
+ "name": "nomRightMiddle",
+ "template": "advanced_fingervein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "train",
+ "template": "train",
+ "parameters": {
+ "protocol": "nomRightMiddle"
+ },
+ "view": "Train"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "dev_templates",
+ "template": "templates",
+ "parameters": {
+ "group": "dev",
+ "protocol": "nomRightMiddle"
+ },
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "name": "dev_probes",
+ "template": "probes",
+ "parameters": {
+ "group": "dev",
+ "protocol": "nomRightMiddle"
+ },
+ "view": "Probes"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "test_templates",
+ "template": "templates",
+ "parameters": {
+ "group": "eval",
+ "protocol": "nomRightMiddle"
+ },
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "name": "test_probes",
+ "template": "probes",
+ "parameters": {
+ "group": "eval",
+ "protocol": "nomRightMiddle"
+ },
+ "view": "Probes"
+ }
+ ]
+ },
+ {
+ "name": "nomRightRing",
+ "template": "advanced_fingervein_recognition",
+ "sets": [
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "train",
+ "template": "train",
+ "parameters": {
+ "protocol": "nomRightRing"
+ },
+ "view": "Train"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "dev_templates",
+ "template": "templates",
+ "parameters": {
+ "group": "dev",
+ "protocol": "nomRightRing"
+ },
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "name": "dev_probes",
+ "template": "probes",
+ "parameters": {
+ "group": "dev",
+ "protocol": "nomRightRing"
+ },
+ "view": "Probes"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1"
+ },
+ "name": "test_templates",
+ "template": "templates",
+ "parameters": {
+ "group": "eval",
+ "protocol": "nomRightRing"
+ },
+ "view": "Templates"
+ },
+ {
+ "outputs": {
+ "image": "{{ system_user.username }}/array_2d_uint8/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1"
+ },
+ "name": "test_probes",
+ "template": "probes",
+ "parameters": {
+ "group": "eval",
+ "protocol": "nomRightRing"
+ },
+ "view": "Probes"
+ }
+ ]
+ }
+ ]
+}
diff --git a/advanced/databases/utfvp/4.py b/advanced/databases/utfvp/4.py
new file mode 100644
index 0000000..f9c5c88
--- /dev/null
+++ b/advanced/databases/utfvp/4.py
@@ -0,0 +1,308 @@
+###############################################################################
+# #
+# Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import numpy as np
+from collections import namedtuple
+
+from beat.backend.python.database import View
+
+import bob.io.base
+import bob.io.image
+import bob.db.utfvp
+
+
+#----------------------------------------------------------
+
+
+class Train(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_2d_uint8/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/text/1"
+
+ One "file_id" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'file_id', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.utfvp.Database()
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups='world',
+ purposes='train'),
+ key=lambda x: (x.client_id, x.id))
+
+ return [ Entry(x.client_id, x.id, x.make_path(root_folder, '.png')) for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'text': str(obj.client_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Templates(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_2d_uint8/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - template_id: "{{ system_user.username }}/text/1"
+ - client_id: "{{ system_user.username }}/text/1"
+
+ One "file_id" is associated with a given "image".
+ Several "image" are associated with a given "template_id".
+ Several "template_id" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | template_id | | template_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | client_id |
+ -----------------------------------------------------------------------------------------------
+
+ Note: for this particular database, there is only one "template_id"
+ per "client_id".
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'template_id', 'file_id', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.utfvp.Database()
+
+ template_ids = db.model_ids(protocol=parameters['protocol'],
+ groups=parameters['group'])
+
+ entries = []
+
+ for template_id in template_ids:
+ objs = db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ purposes='enroll',
+ model_ids=[template_id])
+
+ entries.extend([ Entry(x.client_id, template_id, x.id, x.make_path(root_folder, '.png'))
+ for x in objs ])
+
+ return sorted(entries, key=lambda x: (x.client_id, x.template_id, x.file_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'text': str(obj.client_id)
+ }
+
+ elif output == 'template_id':
+ return {
+ 'text': str(obj.template_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Probes(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_2d_uint8/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - probe_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/text/1"
+ - template_ids: "{{ system_user.username }}/array_1d_text/1"
+
+ One "file_id" is associated with a given "image".
+ One "probe_id" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+ Several "client_id" are associated with a given "template_ids".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | probe_id | | probe_id | | probe_id | | probe_id | | probe_id | | probe_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | template_ids |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['template_ids', 'client_id', 'probe_id', 'file_id', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.utfvp.Database()
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ purposes='probe'),
+ key=lambda x: (x.client_id, x.id))
+
+ objs = [ (sorted([ model.name for model in obj.models_probe
+ if model.sgroup == parameters['group'] ]), obj)
+ for obj in objs ]
+
+ entries = [ Entry(x[0], x[1].client_id, x[1].id, x[1].id, x[1].make_path(root_folder, '.png'))
+ for x in objs ]
+
+ return sorted(entries, key=lambda x: (len(x.template_ids), x.template_ids,
+ x.client_id, x.file_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'template_ids':
+ return {
+ 'text': obj.template_ids
+ }
+
+ elif output == 'client_id':
+ return {
+ 'text': str(obj.client_id)
+ }
+
+ elif output == 'probe_id':
+ return {
+ 'value': np.uint64(obj.probe_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+def setup_tests():
+ # Install a mock load function for the images
+ def mock_load(root_folder):
+ return np.ndarray((10, 20), dtype=np.uint8)
+
+ bob.io.base.load = mock_load
+
+
+#----------------------------------------------------------
+
+
+# Test the behavior of the views (on fake data)
+if __name__ == '__main__':
+
+ setup_tests()
+
+ view = Train()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='1vsall',
+ )
+ )
+ view.get('client_id', 0)
+ view.get('file_id', 0)
+ view.get('image', 0)
+
+ view = Templates()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='nomLeftRing',
+ group='dev',
+ )
+ )
+ view.get('client_id', 0)
+ view.get('template_id', 0)
+ view.get('file_id', 0)
+ view.get('image', 0)
+
+ view = Probes()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='nomLeftRing',
+ group='dev',
+ )
+ )
+ view.get('template_ids', 0)
+ view.get('client_id', 0)
+ view.get('probe_id', 0)
+ view.get('file_id', 0)
+ view.get('image', 0)
diff --git a/advanced/databases/utfvp/4.rst b/advanced/databases/utfvp/4.rst
new file mode 100644
index 0000000..93f19c3
--- /dev/null
+++ b/advanced/databases/utfvp/4.rst
@@ -0,0 +1,108 @@
+.. Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ ..
+.. Contact: beat.support@idiap.ch ..
+.. ..
+.. This file is part of the beat.examples module of the BEAT platform. ..
+.. ..
+.. Commercial License Usage ..
+.. Licensees holding valid commercial BEAT licenses may use this file in ..
+.. accordance with the terms contained in a written agreement between you ..
+.. and Idiap. For further information contact tto@idiap.ch ..
+.. ..
+.. Alternatively, this file may be used under the terms of the GNU Affero ..
+.. Public License version 3 as published by the Free Software and appearing ..
+.. in the file LICENSE.AGPL included in the packaging of this file. ..
+.. The BEAT platform is distributed in the hope that it will be useful, but ..
+.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
+.. or FITNESS FOR A PARTICULAR PURPOSE. ..
+.. ..
+.. You should have received a copy of the GNU Affero Public License along ..
+.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
+
+
+The UTFVP Database of Finger vein
+---------------------------------
+
+Changelog
+=========
+
+* **Version 4**, 30/Jan/2018:
+
+ - Port to beat.backend.python v1.5.0
+
+* **Version 3**, 02/Nov/2017:
+
+ - Port to beat.backend.python v1.4.2
+
+* **Version 2**, 20/Jan/2016:
+
+ - Port to Bob v2
+
+* **Version 1**, 06/Nov/2014:
+
+ - Initial release
+
+
+Description
+===========
+
+The University of Twente Finger Vascular Pattern (UTFVP) database is a
+realistic and challenging finger vein database to support and stimulate
+research efforts in the area of developing, testing and evaluating algorithms
+for vascular pattern recognition.
+
+The collected dataset contains 1440 finger vascular pattern images in total
+which have been collected from 60 volunteers at Twente University during the
+2011-2012 academic year. Images were captured in two identical sessions with an
+average time lapse of 15 days. For each volunteer the vascular pattern of the
+index, ring and middle finger of both hands has been collected twice at each
+session. This means that each individual finger has been captured four times in
+total. The captured images have a resolution of 672 x 380 pixels and have a
+pixel density of 126 pixels per centimetre (ppcm). The images are stored using
+the lossless 8 bit grey scale Portable Network Graphics(PNG) format.
+
+The percentage of male volunteers was 73% and the percentage of right handed
+volunteers was 87%. The dataset represents a young population with 82% of the
+volunteers falling in the age range of 19-30, the remaining volunteers were
+older than this. The quality of the collected images varies from person to
+person, but the variation in quality of the images from the same person is
+small. The width of the visible blood vessels range from 4-20 pixels which
+corresponds to vessel widths of approximately 0.3-1.6 mm. These vessel widths
+are approximate numbers because the pixel density was determined assuming a
+flat surface.
+
+Associated with the database is the UTFVP protocol called **1vsall**, which is
+based on the database reference paper [Ton+13].
+
+The `Idiap Research Institue <http://www.idiap.ch/>`_ and `The Swiss Center for
+Biometrics Research and Testing <http://www.biometrics-center.ch/>`_ define the
+Normal Operation Mode (NOM) protocols which set of data to use for training,
+evaluation and testing. Performing experiments according to the protocol allows
+institutions to easily compare their results to others.
+
+Description: The nom protocols divide the database on three subsets: world
+(subjects 1-10), development (subjects 11-28) and test (subjects 29-60). Only
+the images in world set should be used to train system components such as
+world/background models, PCA/LDA subspaces, etc., or to otherwise use as
+background data, for example for score normalisation, etc. The development set
+only should be used to train system hyper-parameters such as the decision
+threshold, number of dimensions in a subspace, feature extraction and
+preprocessing hyper-parameters, coefficients for linear fusion, etc., to
+minimise the chosen error rate metric. Finally, the test set should be used to
+test finger vein verification accuracy. The decision threshold must be
+determined by tuning on the development set, and then blindly applied to finger
+vein verification scores produced on the test set.
+
+In the **nom** protocol the different fingers of one subject are considered
+different subjects. Therefore, a total number of 60 x 6 = 360 subjects will be
+considered for the experiments. The remaining protocols: **nomLeftRing**,
+**nomLeftMiddle**, **nomLeftIndex**, **nomRightIndex**, **nomRightMiddle**,
+**nomRightRing** consider just one finger per subject. Therefore, a total
+number of 60 subjects will be considered for the experiments in these cases.
+In all these protocols, the two finger vein images from the first session are
+used for enrolment and the two from the second session as probe samples.
+
+Citation: All documents and papers that report on research that uses the UTFVP
+database must acknowledge the use of the database by including a citation of
+the paper [Ton+13].
+
+.. [Ton+13] *B. Ton and R.N.J. Veldhuis*. **A High Quality Finger Vascular Pattern Dataset Collected Using a Custom Designed Capturing Device**. In: 6th IAPR International Conference on Biometrics (ICB), pp. 1-5, June 4-7, Madrid, Spain, 2013.
diff --git a/advanced/databases/voxforge/4.json b/advanced/databases/voxforge/4.json
new file mode 100644
index 0000000..31b4360
--- /dev/null
+++ b/advanced/databases/voxforge/4.json
@@ -0,0 +1,80 @@
+{
+ "description": "The VoxForge Database",
+ "root_folder": "/idiap/resource/database/VoxForge/dbase/SpeechCorpus/Trunk/Audio/Main/16kHz_16bit",
+ "protocols": [
+ {
+ "name": "default",
+ "template": "advanced_speaker_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_id": "{{ system_user.username }}/text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/text/1",
+ "probe_id": "{{ system_user.username }}/text/1",
+ "client_id": "{{ system_user.username }}/text/1",
+ "template_ids": "{{ system_user.username }}/array_1d_text/1",
+ "speech": "{{ system_user.username }}/array_1d_floats/1"
+ }
+ }
+ ]
+ }
+ ]
+}
diff --git a/advanced/databases/voxforge/4.py b/advanced/databases/voxforge/4.py
new file mode 100644
index 0000000..ac3e698
--- /dev/null
+++ b/advanced/databases/voxforge/4.py
@@ -0,0 +1,323 @@
+###############################################################################
+# #
+# Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import numpy as np
+from collections import namedtuple
+
+from beat.backend.python.database import View
+
+import bob.db.voxforge
+import bob.io.base
+import bob.io.audio
+
+
+#----------------------------------------------------------
+
+
+class Train(View):
+ """Outputs:
+ - speech: "{{ system_user.username }}/array_1d_floats/1"
+ - file_id: "{{ system_user.username }}/text/1"
+ - client_id: "{{ system_user.username }}/text/1"
+
+ One "file_id" is associated with a given "speech".
+ Several "speech" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | speech | | speech | | speech | | speech | | speech | | speech |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'file_id', 'speech'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.voxforge.Database()
+
+ objs = sorted(db.objects(groups='world'),
+ key=lambda x: (x.client_id, x.id))
+
+ return [ Entry(x.client_id, x.id, x.make_path(root_folder, '.wav')) for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'text': str(obj.client_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'text': str(obj.file_id)
+ }
+
+ elif output == 'speech':
+ audio = bob.io.base.load(obj.speech)
+
+ return {
+ 'value': np.cast['float'](audio[0] * pow(2, 15))
+ }
+
+
+#----------------------------------------------------------
+
+
+class Templates(View):
+ """Outputs:
+ - speech: "{{ system_user.username }}/array_1d_floats/1"
+ - file_id: "{{ system_user.username }}/text/1"
+ - template_id: "{{ system_user.username }}/text/1"
+ - client_id: "{{ system_user.username }}/text/1"
+
+ One "file_id" is associated with a given "speech".
+ Several "speech" are associated with a given "template_id".
+ Several "template_id" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | speech | | speech | | speech | | speech | | speech | | speech |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | template_id | | template_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | client_id |
+ -----------------------------------------------------------------------------------------------
+
+ Note: for this particular database, there is only one "template_id"
+ per "client_id".
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'template_id', 'file_id', 'speech'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.voxforge.Database()
+
+ template_ids = db.model_ids(groups=parameters['group'])
+
+ entries = []
+
+ for template_id in template_ids:
+ objs = db.objects(groups=parameters['group'],
+ purposes='enroll',
+ model_ids=[template_id])
+
+ entries.extend([ Entry(x.client_id, template_id, x.id, x.make_path(root_folder, '.wav'))
+ for x in objs ])
+
+ return sorted(entries, key=lambda x: (x.client_id, x.template_id, x.file_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'text': str(obj.client_id)
+ }
+
+ elif output == 'template_id':
+ return {
+ 'text': str(obj.template_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'text': str(obj.file_id)
+ }
+
+ elif output == 'speech':
+ audio = bob.io.base.load(obj.speech)
+
+ return {
+ 'value': np.cast['float'](audio[0] * pow(2, 15))
+ }
+
+
+#----------------------------------------------------------
+
+
+class Probes(View):
+ """Outputs:
+ - speech: "{{ system_user.username }}/array_1d_floats/1"
+ - file_id: "{{ system_user.username }}/text/1"
+ - probe_id: "{{ system_user.username }}/text/1",
+ - client_id: "{{ system_user.username }}/text/1"
+ - template_ids: "{{ system_user.username }}/array_1d_text/1",
+
+ One "file_id" is associated with a given "speech".
+ One "probe_id" is associated with a given "speech".
+ Several "speech" are associated with a given "client_id".
+ Several "client_id" are associated with a given "template_ids".
+
+ Each probe must be matched against a number of templates defined by a list of
+ client identifiers.
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | speech | | speech | | speech | | speech | | speech | | speech |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | probe_id | | probe_id | | probe_id | | probe_id | | probe_id | | probe_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | template_ids |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['template_ids', 'client_id', 'probe_id', 'file_id', 'speech'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.voxforge.Database()
+
+ template_ids = db.model_ids(groups=parameters['group'])
+
+ template_probes = {}
+ for template_id in template_ids:
+ objs = sorted(db.objects(groups=parameters['group'],
+ purposes='probe',
+ model_ids=[template_id]),
+ key=lambda x: (x.client_id, x.id))
+
+ template_probes[template_id] = [ p.id for p in objs ]
+
+ objs = sorted(db.objects(groups=parameters['group'],
+ purposes='probe'),
+ key=lambda x: (x.client_id, x.id))
+
+ entries = []
+ for obj in objs:
+ templates = [ template_id for template_id in template_ids
+ if obj.id in template_probes[template_id] ]
+ entries.append( (templates, obj) )
+
+ return sorted([ Entry(x[0], x[1].client_id, x[1].id, x[1].id,
+ x[1].make_path(root_folder, '.wav'))
+ for x in entries ],
+ key=lambda x: (len(x.template_ids), x.template_ids, x.client_id, x.file_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'template_ids':
+ return {
+ 'text': [ str(x) for x in obj.template_ids ]
+ }
+
+ elif output == 'client_id':
+ return {
+ 'text': str(obj.client_id)
+ }
+
+ elif output == 'probe_id':
+ return {
+ 'text': str(obj.probe_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'text': str(obj.file_id)
+ }
+
+ elif output == 'speech':
+ audio = bob.io.base.load(obj.speech)
+
+ return {
+ 'value': np.cast['float'](audio[0] * pow(2, 15))
+ }
+
+
+#----------------------------------------------------------
+
+
+def setup_tests():
+ # Install a mock load function for the images
+ def mock_load(root_folder):
+ return np.ndarray((1, 512), dtype=np.uint8)
+
+ bob.io.base.load = mock_load
+
+
+#----------------------------------------------------------
+
+
+# Test the behavior of the views (on fake data)
+if __name__ == '__main__':
+
+ setup_tests()
+
+ view = Train()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ )
+ )
+ view.get('client_id', 0)
+ view.get('file_id', 0)
+ view.get('speech', 0)
+
+
+ view = Templates()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ group='dev',
+ )
+ )
+ view.get('client_id', 0)
+ view.get('template_id', 0)
+ view.get('file_id', 0)
+ view.get('speech', 0)
+
+
+ view = Probes()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ group='dev',
+ )
+ )
+ view.get('template_ids', 0)
+ view.get('client_id', 0)
+ view.get('probe_id', 0)
+ view.get('file_id', 0)
+ view.get('speech', 0)
diff --git a/advanced/databases/voxforge/4.rst b/advanced/databases/voxforge/4.rst
new file mode 100644
index 0000000..175d4eb
--- /dev/null
+++ b/advanced/databases/voxforge/4.rst
@@ -0,0 +1,56 @@
+.. Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ ..
+.. Contact: beat.support@idiap.ch ..
+.. ..
+.. This file is part of the beat.examples module of the BEAT platform. ..
+.. ..
+.. Commercial License Usage ..
+.. Licensees holding valid commercial BEAT licenses may use this file in ..
+.. accordance with the terms contained in a written agreement between you ..
+.. and Idiap. For further information contact tto@idiap.ch ..
+.. ..
+.. Alternatively, this file may be used under the terms of the GNU Affero ..
+.. Public License version 3 as published by the Free Software and appearing ..
+.. in the file LICENSE.AGPL included in the packaging of this file. ..
+.. The BEAT platform is distributed in the hope that it will be useful, but ..
+.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
+.. or FITNESS FOR A PARTICULAR PURPOSE. ..
+.. ..
+.. You should have received a copy of the GNU Affero Public License along ..
+.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
+
+
+-----------------------
+ The VoxForge Database
+-----------------------
+
+Changelog
+=========
+
+* **Version 4**, 30/Jan/2018:
+
+ - Port to beat.backend.python v1.5.0
+
+* **Version 3**, 02/Nov/2017:
+
+ - Port to beat.backend.python v1.4.2
+
+* **Version 2**, 26/Jan/2016:
+
+ - Port to Bob v2
+
+* **Version 1**, 21/Oct/2014:
+
+ - Initial release
+
+
+Description
+===========
+
+`VoxForge <http://www.voxforge.org/>`_ offers a collection of transcribed
+speech for use with Free and Open Source Speech Recognition Engines.
+
+`bob.db.voxforge <https://pypi.python.org/pypi/bob.db.voxforge>`_ defines
+speaker recognition protocol that uses a small subset of the english audio
+files (only 6561 files) belonging to 30 speakers randomly selected. This subset
+is split into three equivalent parts: Training (10 speakers), Development (10
+speakers) and Test (10 speakers) sets.
diff --git a/advanced/databases/xm2vts/4.json b/advanced/databases/xm2vts/4.json
new file mode 100644
index 0000000..d505d4d
--- /dev/null
+++ b/advanced/databases/xm2vts/4.json
@@ -0,0 +1,350 @@
+{
+ "description": "The XM2VTS Database of Faces",
+ "root_folder": "/idiap/resource/database/xm2vtsdb/images",
+ "protocols": [
+ {
+ "name": "lp1",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "lp1"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "lp1",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "lp1",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "lp1",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "lp1",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "lp2",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "lp2"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "lp2",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "lp2",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "lp2",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "lp2",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "darkened-lp1",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "darkened-lp1"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "darkened-lp1",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "darkened-lp1",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "darkened-lp1",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "darkened-lp1",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "darkened-lp2",
+ "template": "advanced_face_recognition",
+ "sets": [
+ {
+ "name": "train",
+ "template": "train",
+ "view": "Train",
+ "parameters": {
+ "protocol": "darkened-lp2"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "darkened-lp2",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "dev_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "darkened-lp2",
+ "group": "dev"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_templates",
+ "template": "templates",
+ "view": "Templates",
+ "parameters": {
+ "protocol": "darkened-lp2",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_id": "{{ system_user.username }}/uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ },
+ {
+ "name": "test_probes",
+ "template": "probes",
+ "view": "Probes",
+ "parameters": {
+ "protocol": "darkened-lp2",
+ "group": "eval"
+ },
+ "outputs": {
+ "file_id": "{{ system_user.username }}/uint64/1",
+ "probe_id": "{{ system_user.username }}/uint64/1",
+ "client_id": "{{ system_user.username }}/uint64/1",
+ "template_ids": "{{ system_user.username }}/array_1d_uint64/1",
+ "image": "{{ system_user.username }}/array_3d_uint8/1",
+ "eye_centers": "{{ system_user.username }}/eye_positions/1"
+ }
+ }
+ ]
+ }
+ ]
+}
diff --git a/advanced/databases/xm2vts/4.py b/advanced/databases/xm2vts/4.py
new file mode 100644
index 0000000..3a084c5
--- /dev/null
+++ b/advanced/databases/xm2vts/4.py
@@ -0,0 +1,382 @@
+###############################################################################
+# #
+# Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import numpy as np
+from collections import namedtuple
+
+from beat.backend.python.database import View
+
+import bob.io.base
+import bob.io.image
+import bob.db.xm2vts
+
+
+#----------------------------------------------------------
+
+
+class Train(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'file_id', 'eye_centers', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.xm2vts.Database()
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups='world'),
+ key=lambda x: (x.client_id, x.id))
+
+ return [ Entry(x.client_id, x.id, db.annotations(x), x.make_path(root_folder, '.ppm'))
+ for x in objs ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'eye_centers':
+ return {
+ 'left': {
+ 'y': np.int32(obj.eye_centers['leye'][0]),
+ 'x': np.int32(obj.eye_centers['leye'][1]),
+ },
+ 'right': {
+ 'y': np.int32(obj.eye_centers['reye'][0]),
+ 'x': np.int32(obj.eye_centers['reye'][1]),
+ }
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Templates(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - template_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ Several "image" are associated with a given "template_id".
+ Several "template_id" are associated with a given "client_id".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | template_id | | template_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | client_id |
+ -----------------------------------------------------------------------------------------------
+
+ Note: for this particular database, there is only one "template_id"
+ per "client_id".
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['client_id', 'template_id', 'file_id', 'eye_centers', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.xm2vts.Database()
+
+ template_ids = db.model_ids(protocol=parameters['protocol'],
+ groups=parameters['group'])
+
+ entries = []
+
+ for template_id in template_ids:
+ objs = db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ purposes='enroll',
+ model_ids=[template_id])
+
+ entries.extend([ Entry(x.client_id, template_id, x.id, db.annotations(x),
+ x.make_path(root_folder, '.ppm'))
+ for x in objs ])
+
+ return sorted(entries, key=lambda x: (x.client_id, x.template_id, x.file_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'template_id':
+ return {
+ 'value': np.uint64(obj.template_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'eye_centers':
+ return {
+ 'left': {
+ 'y': np.int32(obj.eye_centers['leye'][0]),
+ 'x': np.int32(obj.eye_centers['leye'][1]),
+ },
+ 'right': {
+ 'y': np.int32(obj.eye_centers['reye'][0]),
+ 'x': np.int32(obj.eye_centers['reye'][1]),
+ }
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+class Probes(View):
+ """Outputs:
+ - image: "{{ system_user.username }}/array_3d_uint8/1"
+ - eye_centers: "{{ system_user.username }}/eye_positions/1"
+ - file_id: "{{ system_user.username }}/uint64/1"
+ - probe_id: "{{ system_user.username }}/uint64/1"
+ - client_id: "{{ system_user.username }}/uint64/1"
+ - template_ids: "{{ system_user.username }}/array_1d_uint64/1"
+
+ One "file_id" is associated with a given "image".
+ One "eye_centers" is associated with a given "image".
+ One "probe_id" is associated with a given "image".
+ Several "image" are associated with a given "client_id".
+ Several "client_id" are associated with a given "template_ids".
+
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | image | | image | | image | | image | | image | | image |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers | | eye_centers |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | file_id | | file_id | | file_id | | file_id | | file_id | | file_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ | probe_id | | probe_id | | probe_id | | probe_id | | probe_id | | probe_id |
+ --------------- --------------- --------------- --------------- --------------- ---------------
+ ----------------------------------------------- -----------------------------------------------
+ | client_id | | client_id |
+ ----------------------------------------------- -----------------------------------------------
+ -----------------------------------------------------------------------------------------------
+ | template_ids |
+ -----------------------------------------------------------------------------------------------
+ """
+
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['template_ids', 'client_id', 'probe_id', 'file_id',
+ 'eye_centers', 'image'])
+
+ # Open the database and load the objects to provide via the outputs
+ db = bob.db.xm2vts.Database()
+
+ template_ids = sorted(db.model_ids(protocol=parameters['protocol'],
+ groups=parameters['group']))
+
+
+ template_probes = {}
+ for template_id in template_ids:
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ purposes='probe',
+ model_ids=[template_id]),
+ key=lambda x: (x.client_id, x.id))
+
+ template_probes[template_id] = [ p.id for p in objs ]
+
+
+ objs = sorted(db.objects(protocol=parameters['protocol'],
+ groups=parameters['group'],
+ purposes='probe'),
+ key=lambda x: (x.client_id, x.id))
+
+ entries = []
+ for obj in objs:
+ templates = [ template_id for template_id in template_ids
+ if obj.id in template_probes[template_id] ]
+
+ entries.append( Entry(templates, obj.client_id, obj.id, obj.id,
+ db.annotations(obj), obj.make_path(root_folder, '.ppm')) )
+
+ return sorted(entries, key=lambda x: (len(x.template_ids), x.template_ids,
+ x.client_id, x.probe_id))
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'template_ids':
+ return {
+ 'value': np.uint64(obj.template_ids)
+ }
+
+ elif output == 'client_id':
+ return {
+ 'value': np.uint64(obj.client_id)
+ }
+
+ elif output == 'probe_id':
+ return {
+ 'value': np.uint64(obj.probe_id)
+ }
+
+ elif output == 'file_id':
+ return {
+ 'value': np.uint64(obj.file_id)
+ }
+
+ elif output == 'eye_centers':
+ return {
+ 'left': {
+ 'y': np.int32(obj.eye_centers['leye'][0]),
+ 'x': np.int32(obj.eye_centers['leye'][1]),
+ },
+ 'right': {
+ 'y': np.int32(obj.eye_centers['reye'][0]),
+ 'x': np.int32(obj.eye_centers['reye'][1]),
+ }
+ }
+
+ elif output == 'image':
+ return {
+ 'value': bob.io.base.load(obj.image)
+ }
+
+
+#----------------------------------------------------------
+
+
+def setup_tests():
+ # Install a mock load function for the images
+ def mock_load(root_folder):
+ return np.ndarray((3, 10, 20), dtype=np.uint8)
+
+ bob.io.base.load = mock_load
+
+
+#----------------------------------------------------------
+
+
+# Test the behavior of the views (on fake data)
+if __name__ == '__main__':
+
+ setup_tests()
+
+ view = Train()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='lp1',
+ )
+ )
+ view.get('client_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
+
+
+ view = Templates()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='lp1',
+ group='dev',
+ )
+ )
+ view.get('client_id', 0)
+ view.get('template_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
+
+
+ view = Probes()
+ view.objs = view.index(
+ root_folder='',
+ parameters=dict(
+ protocol='lp1',
+ group='dev',
+ )
+ )
+ view.get('template_ids', 0)
+ view.get('client_id', 0)
+ view.get('probe_id', 0)
+ view.get('file_id', 0)
+ view.get('eye_centers', 0)
+ view.get('image', 0)
diff --git a/advanced/databases/xm2vts/4.rst b/advanced/databases/xm2vts/4.rst
new file mode 100644
index 0000000..f907727
--- /dev/null
+++ b/advanced/databases/xm2vts/4.rst
@@ -0,0 +1,103 @@
+.. Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/ ..
+.. Contact: beat.support@idiap.ch ..
+.. ..
+.. This file is part of the beat.examples module of the BEAT platform. ..
+.. ..
+.. Commercial License Usage ..
+.. Licensees holding valid commercial BEAT licenses may use this file in ..
+.. accordance with the terms contained in a written agreement between you ..
+.. and Idiap. For further information contact tto@idiap.ch ..
+.. ..
+.. Alternatively, this file may be used under the terms of the GNU Affero ..
+.. Public License version 3 as published by the Free Software and appearing ..
+.. in the file LICENSE.AGPL included in the packaging of this file. ..
+.. The BEAT platform is distributed in the hope that it will be useful, but ..
+.. WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY ..
+.. or FITNESS FOR A PARTICULAR PURPOSE. ..
+.. ..
+.. You should have received a copy of the GNU Affero Public License along ..
+.. with the BEAT platform. If not, see http://www.gnu.org/licenses/. ..
+
+
+The XM2VTS Database of Faces
+----------------------------
+
+Changelog
+=========
+
+* **Version 4**, 30/Jan/2018:
+
+ - Port to beat.backend.python v1.5.0
+
+* **Version 3**, 02/Nov/2017:
+
+ - Port to beat.backend.python v1.4.2
+
+* **Version 2**, 20/Jan/2016:
+
+ - Port to Bob v2
+
+* **Version 1**, 21/Oct/2014:
+
+ - Initial release
+
+
+Description
+===========
+
+The `XM2VTS database <http://www.ee.surrey.ac.uk/CVSSP/xm2vtsdb/>`_ is a large
+multi-modal database that was captured onto high quality digital video. It
+contains four recordings of 295 subjects taken over a period of four months.
+Each recording contains a speaking head shot and a rotating head shot. Sets
+of data taken from this database are available including high quality colour
+images, 32 KHz 16-bit sound files, video sequences and a 3d Model.
+
+The database was aquired within the M2VTS project (Multi Modal Verification for
+Teleservices and Security applications), a part of the EU ACTS programme, which
+deals with access control by the use of multimodal identification of human faces.
+The goal of using a multimodal recognition scheme is to improve the recognition
+efficiency by combining single modalities, namely face and voice features.
+
+.. image:: http://www.ee.surrey.ac.uk/CVSSP/xm2vtsdb/images/000_1.jpg
+ :width: 150 px
+.. image:: http://www.ee.surrey.ac.uk/CVSSP/xm2vtsdb/images/001_1.jpg
+ :width: 150 px
+.. image:: http://www.ee.surrey.ac.uk/CVSSP/xm2vtsdb/images/002_1.jpg
+ :width: 150 px
+.. image:: http://www.ee.surrey.ac.uk/CVSSP/xm2vtsdb/images/003_1.jpg
+ :width: 150 px
+
+\
+
+.. image:: http://www.ee.surrey.ac.uk/CVSSP/xm2vtsdb/images/000_2.jpg
+ :width: 150 px
+.. image:: http://www.ee.surrey.ac.uk/CVSSP/xm2vtsdb/images/001_2.jpg
+ :width: 150 px
+.. image:: http://www.ee.surrey.ac.uk/CVSSP/xm2vtsdb/images/002_2.jpg
+ :width: 150 px
+.. image:: http://www.ee.surrey.ac.uk/CVSSP/xm2vtsdb/images/003_2.jpg
+ :width: 150 px
+
+\
+
+.. image:: http://www.ee.surrey.ac.uk/CVSSP/xm2vtsdb/images/000_3.jpg
+ :width: 150 px
+.. image:: http://www.ee.surrey.ac.uk/CVSSP/xm2vtsdb/images/001_3.jpg
+ :width: 150 px
+.. image:: http://www.ee.surrey.ac.uk/CVSSP/xm2vtsdb/images/002_3.jpg
+ :width: 150 px
+.. image:: http://www.ee.surrey.ac.uk/CVSSP/xm2vtsdb/images/003_3.jpg
+ :width: 150 px
+
+\
+
+.. image:: http://www.ee.surrey.ac.uk/CVSSP/xm2vtsdb/images/000_4.jpg
+ :width: 150 px
+.. image:: http://www.ee.surrey.ac.uk/CVSSP/xm2vtsdb/images/001_4.jpg
+ :width: 150 px
+.. image:: http://www.ee.surrey.ac.uk/CVSSP/xm2vtsdb/images/002_4.jpg
+ :width: 150 px
+.. image:: http://www.ee.surrey.ac.uk/CVSSP/xm2vtsdb/images/003_4.jpg
+ :width: 150 px
+
+This deployment of the XM2VTS database only consists of the images.
diff --git a/test/algorithms/username/integers_sum/1.json b/test/algorithms/username/integers_sum/1.json
new file mode 100644
index 0000000..079fa87
--- /dev/null
+++ b/test/algorithms/username/integers_sum/1.json
@@ -0,0 +1,26 @@
+{
+ "language": "python",
+ "splittable": true,
+ "groups": [
+ {
+ "name": "main",
+ "inputs": {
+ "a": {
+ "type": "{{ system_user.username }}/integer/1"
+ },
+ "b": {
+ "type": "{{ system_user.username }}/integer/1"
+ }
+ },
+ "outputs": {
+ "sum": {
+ "type": "{{ system_user.username }}/integer/1"
+ }
+ }
+ }
+ ],
+ "parameters": {
+ },
+ "uses": {
+ }
+}
diff --git a/test/algorithms/username/integers_sum/1.py b/test/algorithms/username/integers_sum/1.py
new file mode 100644
index 0000000..3d405d4
--- /dev/null
+++ b/test/algorithms/username/integers_sum/1.py
@@ -0,0 +1,37 @@
+###############################################################################
+# #
+# Copyright (c) 2016 Idiap Research Institute, http://www.idiap.ch/ #
+# Contact: beat.support@idiap.ch #
+# #
+# This file is part of the beat.examples module of the BEAT platform. #
+# #
+# Commercial License Usage #
+# Licensees holding valid commercial BEAT licenses may use this file in #
+# accordance with the terms contained in a written agreement between you #
+# and Idiap. For further information contact tto@idiap.ch #
+# #
+# Alternatively, this file may be used under the terms of the GNU Affero #
+# Public License version 3 as published by the Free Software and appearing #
+# in the file LICENSE.AGPL included in the packaging of this file. #
+# The BEAT platform is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
+# or FITNESS FOR A PARTICULAR PURPOSE. #
+# #
+# You should have received a copy of the GNU Affero Public License along #
+# with the BEAT platform. If not, see http://www.gnu.org/licenses/. #
+# #
+###############################################################################
+
+import numpy
+
+class Algorithm:
+
+ def __init__(self):
+ self.offset = 1
+
+ def process(self, inputs, outputs):
+ outputs['sum'].write({
+ 'value': numpy.int32(inputs['a'].data.value + inputs['b'].data.value),
+ })
+
+ return True
diff --git a/test/databases/simple/1.json b/test/databases/simple/1.json
index fac1117..46a799c 100644
--- a/test/databases/simple/1.json
+++ b/test/databases/simple/1.json
@@ -45,6 +45,35 @@
}
}
]
+ },
+ {
+ "name": "10_numbers",
+ "template": "one_block",
+ "sets": [
+ {
+ "name": "numbers",
+ "template": "numbers",
+ "view": "View10",
+ "outputs": {
+ "out": "{{ system_user.username }}/integer/1"
+ }
+ }
+ ]
+ },
+ {
+ "name": "duo",
+ "template": "one_block_two_inputs",
+ "sets": [
+ {
+ "name": "numbers",
+ "template": "numbers",
+ "view": "ViewDuo",
+ "outputs": {
+ "a": "{{ system_user.username }}/integer/1",
+ "b": "{{ system_user.username }}/integer/1"
+ }
+ }
+ ]
}
]
}
diff --git a/test/databases/simple/1.py b/test/databases/simple/1.py
index 4501e7e..d887ae8 100644
--- a/test/databases/simple/1.py
+++ b/test/databases/simple/1.py
@@ -23,12 +23,14 @@
###############################################################################
import numpy as np
+from collections import namedtuple
+from beat.backend.python.database import View as BaseView
#----------------------------------------------------------
-class View:
+class View(BaseView):
"""Outputs:
- out: "{{ system_user.username }}/integer/1"
@@ -37,47 +39,27 @@ class View:
--------------- --------------- --------------- ---------------
"""
- def setup(self, root_folder, outputs, parameters, force_start_index=None,
- force_end_index=None):
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['out'])
- # Initialisations
- self.outputs = outputs
+ return [
+ Entry(42),
+ ]
- # Determine the range of indices that must be provided
- self.start_index = force_start_index if force_start_index is not None else 0
- self.end_index = force_end_index if force_end_index is not None else 0
- self.next_index = self.start_index
+ def get(self, output, index):
+ obj = self.objs[index]
- return True
-
-
- def done(self, last_data_index):
- return last_data_index >= self.end_index
-
-
- def next(self):
- # Output: out (provide data at each iteration)
- if self.outputs['out'].isConnected():
- self.outputs['out'].write(
- {
- 'value': np.int32(42)
- },
- self.next_index
- )
-
- # Determine the next data index that must be provided
- self.next_index = 1 + min([ x.last_written_data_index for x in self.outputs
- if x.isConnected() ]
- )
-
- return True
+ if output == 'out':
+ return {
+ 'value': np.int32(obj.out)
+ }
#----------------------------------------------------------
-class View2:
+class View2(BaseView):
"""Outputs:
- out: "{{ system_user.username }}/integer/1"
@@ -86,47 +68,61 @@ class View2:
--------------- --------------- --------------- ---------------
"""
- def setup(self, root_folder, outputs, parameters, force_start_index=None,
- force_end_index=None):
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['out'])
- # Initialisations
- self.outputs = outputs
+ return [
+ Entry(53),
+ ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'out':
+ return {
+ 'value': np.int32(obj.out)
+ }
+
+
+#----------------------------------------------------------
- # Determine the range of indices that must be provided
- self.start_index = force_start_index if force_start_index is not None else 0
- self.end_index = force_end_index if force_end_index is not None else 0
- self.next_index = self.start_index
+class LargeView(BaseView):
- return True
+ """Outputs:
+ - out: "{{ system_user.username }}/integer/1"
+ --------------- --------------- --------------- ---------------
+ | out | | out | | out | | out |
+ --------------- --------------- --------------- ---------------
+ """
- def done(self, last_data_index):
- return last_data_index >= self.end_index
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['out'])
+ return [
+ Entry(0),
+ Entry(1),
+ Entry(2),
+ Entry(3),
+ Entry(4),
+ ]
- def next(self):
- # Output: out (provide data at each iteration)
- if self.outputs['out'].isConnected():
- self.outputs['out'].write(
- {
- 'value': np.int32(53)
- },
- self.next_index
- )
- # Determine the next data index that must be provided
- self.next_index = 1 + min([ x.last_written_data_index for x in self.outputs
- if x.isConnected() ]
- )
+ def get(self, output, index):
+ obj = self.objs[index]
- return True
+ if output == 'out':
+ return {
+ 'value': np.int32(obj.out)
+ }
#----------------------------------------------------------
-class LargeView:
+class View10(BaseView):
"""Outputs:
- out: "{{ system_user.username }}/integer/1"
@@ -136,41 +132,77 @@ class LargeView:
--------------- --------------- --------------- ---------------
"""
- def setup(self, root_folder, outputs, parameters, force_start_index=None,
- force_end_index=None):
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['out'])
- # Initialisations
- self.outputs = outputs
+ return [
+ Entry(0),
+ Entry(1),
+ Entry(2),
+ Entry(3),
+ Entry(4),
+ Entry(5),
+ Entry(6),
+ Entry(7),
+ Entry(8),
+ Entry(9),
+ ]
- # Determine the range of indices that must be provided
- self.start_index = force_start_index if force_start_index is not None else 0
- self.end_index = force_end_index if force_end_index is not None else 4
- self.next_index = self.start_index
+ def get(self, output, index):
+ obj = self.objs[index]
- return True
+ if output == 'out':
+ return {
+ 'value': np.int32(obj.out)
+ }
- def done(self, last_data_index):
- return last_data_index >= self.end_index
+#----------------------------------------------------------
+
+class ViewDuo(BaseView):
- def next(self):
- # Output: out (provide data at each iteration)
- if self.outputs['out'].isConnected():
- self.outputs['out'].write(
- {
- 'value': np.int32(self.next_index)
- },
- self.next_index
- )
+ """Outputs:
+ - a: "{{ system_user.username }}/integer/1"
+ - b: "{{ system_user.username }}/integer/1"
- # Determine the next data index that must be provided
- self.next_index = 1 + min([ x.last_written_data_index for x in self.outputs
- if x.isConnected() ]
- )
+ ------------------------------- -------------------------------
+ | a | | a |
+ ------------------------------- -------------------------------
+ --------------- --------------- --------------- ---------------
+ | b | | b | | b | | b |
+ --------------- --------------- --------------- ---------------
+ """
- return True
+ def index(self, root_folder, parameters):
+ Entry = namedtuple('Entry', ['a', 'b'])
+
+ return [
+ Entry(0, 0),
+ Entry(0, 1),
+ Entry(0, 2),
+ Entry(10, 3),
+ Entry(10, 4),
+ Entry(10, 5),
+ Entry(20, 6),
+ Entry(20, 7),
+ Entry(20, 8),
+ ]
+
+
+ def get(self, output, index):
+ obj = self.objs[index]
+
+ if output == 'a':
+ return {
+ 'value': np.int32(obj.a)
+ }
+
+ elif output == 'b':
+ return {
+ 'value': np.int32(obj.b)
+ }
#----------------------------------------------------------
diff --git a/test/experiments/username/username/duo/1/split_2.json b/test/experiments/username/username/duo/1/split_2.json
new file mode 100644
index 0000000..1c321e7
--- /dev/null
+++ b/test/experiments/username/username/duo/1/split_2.json
@@ -0,0 +1,37 @@
+{
+ "analyzers": {
+ "analysis": {
+ "algorithm": "{{ user.username }}/integers_echo_analyzer/1",
+ "inputs": {
+ "in_data": "in"
+ }
+ }
+ },
+ "blocks": {
+ "operation": {
+ "algorithm": "{{ user.username }}/integers_sum/1",
+ "nb_slots": 2,
+ "inputs": {
+ "a": "a",
+ "b": "b"
+ },
+ "outputs": {
+ "sum": "out"
+ }
+ }
+ },
+ "datasets": {
+ "set": {
+ "database": "simple/1",
+ "protocol": "duo",
+ "set": "numbers"
+ }
+ },
+ "globals": {
+ "queue": "{{ queue }}",
+ "environment": {
+ "name": "{{ environment.name }}",
+ "version": "{{ environment.version }}"
+ }
+ }
+}
diff --git a/test/experiments/username/username/single/1/single.json b/test/experiments/username/username/single/1/single.json
index 236549d..5ee0ee1 100644
--- a/test/experiments/username/username/single/1/single.json
+++ b/test/experiments/username/username/single/1/single.json
@@ -21,8 +21,8 @@
"datasets": {
"set": {
"database": "simple/1",
- "protocol": "protocol",
- "set": "set"
+ "protocol": "10_numbers",
+ "set": "numbers"
}
},
"globals": {
diff --git a/test/experiments/username/username/single/1/single_add.json b/test/experiments/username/username/single/1/single_add.json
index 0d46f10..a446d7e 100644
--- a/test/experiments/username/username/single/1/single_add.json
+++ b/test/experiments/username/username/single/1/single_add.json
@@ -20,9 +20,9 @@
},
"datasets": {
"set": {
- "database": "simple/1",
- "protocol": "protocol",
- "set": "set"
+ "database": "simple/1",
+ "protocol": "10_numbers",
+ "set": "numbers"
}
},
"globals": {
diff --git a/test/experiments/username/username/single/1/single_error.json b/test/experiments/username/username/single/1/single_error.json
index 86eddd2..b6d9f09 100644
--- a/test/experiments/username/username/single/1/single_error.json
+++ b/test/experiments/username/username/single/1/single_error.json
@@ -21,7 +21,7 @@
"datasets": {
"set": {
"database": "simple/1",
- "protocol": "protocol",
+ "protocol": "protocol2",
"set": "set"
}
},
diff --git a/test/experiments/username/username/single/1/single_error_split_2.json b/test/experiments/username/username/single/1/single_error_split_2.json
index 1824212..6463a7d 100644
--- a/test/experiments/username/username/single/1/single_error_split_2.json
+++ b/test/experiments/username/username/single/1/single_error_split_2.json
@@ -22,7 +22,7 @@
"datasets": {
"set": {
"database": "simple/1",
- "protocol": "protocol",
+ "protocol": "protocol2",
"set": "set"
}
},
diff --git a/test/experiments/username/username/single/1/single_split_10.json b/test/experiments/username/username/single/1/single_split_10.json
index 57587c5..856486e 100644
--- a/test/experiments/username/username/single/1/single_split_10.json
+++ b/test/experiments/username/username/single/1/single_split_10.json
@@ -22,7 +22,7 @@
"datasets": {
"set": {
"database": "simple/1",
- "protocol": "protocol",
+ "protocol": "protocol2",
"set": "set"
}
},
diff --git a/test/experiments/username/username/single/1/single_split_2.json b/test/experiments/username/username/single/1/single_split_2.json
index 4f3f96d..f9d3d42 100644
--- a/test/experiments/username/username/single/1/single_split_2.json
+++ b/test/experiments/username/username/single/1/single_split_2.json
@@ -22,8 +22,8 @@
"datasets": {
"set": {
"database": "simple/1",
- "protocol": "protocol",
- "set": "set"
+ "protocol": "10_numbers",
+ "set": "numbers"
}
},
"globals": {
diff --git a/test/toolchains/username/duo/1.json b/test/toolchains/username/duo/1.json
new file mode 100644
index 0000000..8959e73
--- /dev/null
+++ b/test/toolchains/username/duo/1.json
@@ -0,0 +1,60 @@
+{
+ "description": "",
+ "connections": [
+ {
+ "to": "operation.a",
+ "from": "set.a",
+ "channel": "set"
+ },
+ {
+ "to": "operation.b",
+ "from": "set.b",
+ "channel": "set"
+ },
+ {
+ "to": "analysis.in",
+ "from": "operation.out",
+ "channel": "set"
+ }
+ ],
+ "analyzers": [
+ {
+ "inputs": [
+ "in"
+ ],
+ "synchronized_channel": "set",
+ "name": "analysis"
+ }
+ ],
+ "datasets": [
+ {
+ "outputs": [
+ "a",
+ "b"
+ ],
+ "name": "set"
+ }
+ ],
+ "blocks": [
+ {
+ "inputs": [
+ "a",
+ "b"
+ ],
+ "synchronized_channel": "set",
+ "name": "operation",
+ "outputs": [
+ "out"
+ ]
+ }
+ ],
+ "representation": {
+ "connections": {
+ },
+ "blocks": {
+ },
+ "channel_colors": {
+ "set": "#0000FF"
+ }
+ }
+}
--
GitLab