From 96e1ff5952e76b2046afb2cde6b89cb363fbbd83 Mon Sep 17 00:00:00 2001
From: Andre Anjos <andre.dos.anjos@gmail.com>
Date: Wed, 23 Apr 2014 22:48:24 +0200
Subject: [PATCH] Testing with OpenCV implemented; All tests passing
---
buildout.cfg | 2 +
setup.py | 2 +
xbob/ip/flandmark/__init__.py | 143 +-
.../data/haarcascade_frontalface_alt.xml | 26161 ++++++++++++++++
xbob/ip/flandmark/data/lena.jpg | Bin 0 -> 49504 bytes
xbob/ip/flandmark/data/multi.jpg | Bin 0 -> 83359 bytes
xbob/ip/flandmark/flandmark.cpp | 18 +-
xbob/ip/flandmark/main.cpp | 34 +-
xbob/ip/flandmark/old.cpp | 173 -
xbob/ip/flandmark/test.py | 85 +-
10 files changed, 26313 insertions(+), 305 deletions(-)
create mode 100644 xbob/ip/flandmark/data/haarcascade_frontalface_alt.xml
create mode 100644 xbob/ip/flandmark/data/lena.jpg
create mode 100644 xbob/ip/flandmark/data/multi.jpg
delete mode 100644 xbob/ip/flandmark/old.cpp
diff --git a/buildout.cfg b/buildout.cfg
index 0913712..2455a14 100644
--- a/buildout.cfg
+++ b/buildout.cfg
@@ -11,6 +11,7 @@ auto-checkout = *
develop = src/xbob.extension
src/xbob.blitz
src/xbob.io
+ src/xbob.ip.color
.
; options for xbob.buildout extension
@@ -23,6 +24,7 @@ prefixes = /idiap/group/torch5spro/nightlies/last/bob/linux-x86_64-release
xbob.extension = git https://github.com/bioidiap/xbob.extension branch=prototype
xbob.blitz = git https://github.com/bioidiap/xbob.blitz
xbob.io = git https://github.com/bioidiap/xbob.io
+xbob.ip.color = git https://github.com/bioidiap/xbob.ip.color
[scripts]
recipe = xbob.buildout:scripts
diff --git a/setup.py b/setup.py
index 2b1195d..d4e837e 100644
--- a/setup.py
+++ b/setup.py
@@ -35,6 +35,7 @@ setup(
'setuptools',
'xbob.blitz',
'xbob.io', #for tests
+ 'xbob.ip.color', #for tests
],
namespace_packages=[
@@ -67,6 +68,7 @@ setup(
include_dirs = include_dirs,
version = version,
packages = packages,
+ boost_modules = ['system'],
),
],
diff --git a/xbob/ip/flandmark/__init__.py b/xbob/ip/flandmark/__init__.py
index 5a633a9..0b6f605 100644
--- a/xbob/ip/flandmark/__init__.py
+++ b/xbob/ip/flandmark/__init__.py
@@ -1,126 +1,33 @@
-from . import _flandmark
+from ._library import *
+from . import version
+from .version import module as __version__
-from pkg_resources import resource_filename
-
-def __filename__(f):
- """Returns the test file on the "data" subdirectory"""
- return resource_filename(__name__, f)
-
-class Localizer(_flandmark.Localizer):
- """A fast and effective facial landmark localization framework based on
- flandmark
-
- Consult http://cmp.felk.cvut.cz/~uricamic/flandmark/index.php for more
- information.
+def get_config():
+ """Returns a string containing the configuration information.
"""
- def __init__(self, detection_model=resource_filename(__name__,
- 'haarcascade_frontalface_alt.xml'),
- localization_model=resource_filename(__name__,
- 'flandmark_model.dat')):
- """Builds a new facial localization model.
-
- Raises RuntimeError's if the models either don't exist or can't be loaded.
-
- Keyword parameters:
-
- detection_model
- An OpenCV (xml) detection model file for a CvHaarClassifierCascade. If not
- specified, use a default installed with the package.
-
- localization_model
- A flandmark localization model file. If not specified, use a default
- installed with the package. The default model provides the following
- keypoints, in this order:
-
- [0]
- Face center
-
- [1]
- Canthus-rl (inner corner of the right eye). Note: The "right eye" means
- the right eye at face w.r.t. itself - that is the left eye in the image.
-
- [2]
- Canthus-lr (inner corder of the left eye)
-
- [3]
- Mouth-corner-r (right corner of the mouth)
-
- [4]
- Mouth-corner-l (left corner of the mouth)
-
- [5]
- Canthus-rr (outer corner of the right eye)
-
- [6]
- Canthus-ll (outer corner of the left eye)
-
- [7]
- Nose
- """
- super(Localizer, self).__init__(detection_model, localization_model)
+ import pkg_resources
+ from .version import externals
- def __call__(self, image):
- """Localizes facial keypoints on all faces detected at the input image.
+ packages = pkg_resources.require(__name__)
+ this = packages[0]
+ deps = packages[1:]
- Keyword parameters:
+ retval = "%s: %s (%s)\n" % (this.key, this.version, this.location)
+ retval += " - c/c++ dependencies:\n"
+ for k in sorted(externals): retval += " - %s: %s\n" % (k, externals[k])
+ retval += " - python dependencies:\n"
+ for d in deps: retval += " - %s: %s (%s)\n" % (d.key, d.version, d.location)
- image
- Either a gray-scale or colored image where to run the detection and
- localization.
+ return retval.strip()
- Returns a tuple composed of dictionaries. Each dictionary in the list has
- two entries: ``bbox`` and ``landmark``. The ``bbox`` entry corresponds to
- the OpenCV cascade face detected, whereas the ``landmark`` contains a list
- of tuples (representing x,y coordinates) with the landmarks.
+# gets sphinx autodoc done right - don't remove it
+__all__ = [_ for _ in dir() if not _.startswith('_')]
- If no faces are detected on the input image, than the returned tuple is
- empty.
- """
-
- if image.ndim == 3:
- from bob.ip import rgb_to_gray
- gray = rgb_to_gray(image)
- return super(Localizer, self).__call__(gray)
-
- elif image.ndim == 2:
- return super(Localizer, self).__call__(image)
-
- else:
- raise TypeError, "Localizer accepts images as numpy.ndarray objects with either 2 or 3 dimensions"
-
- def localize(self, image, b_y, b_x, b_height, b_width):
- """Localizes facial keypoints on all faces detected at the input image.
-
- Keyword parameters:
-
- image
- Either a gray-scale or colored image where to run the detection and
- localization.
-
- b_y
- The top-left y-coordinate of the bounding box
-
- b_x
- The top-left x-coordinate of the bounding box
-
- b_height
- The height of the bounding box
-
- b_width
- The width of the bounding box
-
- Returns the landmarks which are a list of tuples
- (representing x,y coordinates) with the landmarks.
- """
-
- if image.ndim == 3:
- from bob.ip import rgb_to_gray
- gray = rgb_to_gray(image)
- return super(Localizer, self).__call__(gray, b_y, b_x, b_height, b_width)
-
- elif image.ndim == 2:
- return super(Localizer, self).__call__(image, b_y, b_x, b_height, b_width)
-
- else:
- raise TypeError, "Localizer accepts images as numpy.ndarray objects with either 2 or 3 dimensions"
+# Setup default model for C-API
+from pkg_resources import resource_filename
+import os.path
+from ._library import __set_default_model__
+__set_default_model__(resource_filename(__name__,
+ os.path.join('data', 'flandmark_model.dat')))
+del resource_filename, __set_default_model__, os
diff --git a/xbob/ip/flandmark/data/haarcascade_frontalface_alt.xml b/xbob/ip/flandmark/data/haarcascade_frontalface_alt.xml
new file mode 100644
index 0000000..5a6f275
--- /dev/null
+++ b/xbob/ip/flandmark/data/haarcascade_frontalface_alt.xml
@@ -0,0 +1,26161 @@
+<?xml version="1.0"?>
+<!--
+ Stump-based 20x20 gentle adaboost frontal face detector.
+ Created by Rainer Lienhart.
+
+////////////////////////////////////////////////////////////////////////////////////////
+
+ IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+
+ By downloading, copying, installing or using the software you agree to this license.
+ If you do not agree to this license, do not download, install,
+ copy or use the software.
+
+
+ Intel License Agreement
+ For Open Source Computer Vision Library
+
+ Copyright (C) 2000, Intel Corporation, all rights reserved.
+ Third party copyrights are property of their respective owners.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistribution's of source code must retain the above copyright notice,
+ this list of conditions and the following disclaimer.
+
+ * Redistribution's in binary form must reproduce the above copyright notice,
+ this list of conditions and the following disclaimer in the documentation
+ and/or other materials provided with the distribution.
+
+ * The name of Intel Corporation may not be used to endorse or promote products
+ derived from this software without specific prior written permission.
+
+ This software is provided by the copyright holders and contributors "as is" and
+ any express or implied warranties, including, but not limited to, the implied
+ warranties of merchantability and fitness for a particular purpose are disclaimed.
+ In no event shall the Intel Corporation or contributors be liable for any direct,
+ indirect, incidental, special, exemplary, or consequential damages
+ (including, but not limited to, procurement of substitute goods or services;
+ loss of use, data, or profits; or business interruption) however caused
+ and on any theory of liability, whether in contract, strict liability,
+ or tort (including negligence or otherwise) arising in any way out of
+ the use of this software, even if advised of the possibility of such damage.
+-->
+<opencv_storage>
+<haarcascade_frontalface_alt type_id="opencv-haar-classifier">
+ <size>20 20</size>
+ <stages>
+ <_>
+ <!-- stage 0 -->
+ <trees>
+ <_>
+ <!-- tree 0 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>3 7 14 4 -1.</_>
+ <_>3 9 14 2 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>4.0141958743333817e-003</threshold>
+ <left_val>0.0337941907346249</left_val>
+ <right_val>0.8378106951713562</right_val></_></_>
+ <_>
+ <!-- tree 1 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>1 2 18 4 -1.</_>
+ <_>7 2 6 4 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>0.0151513395830989</threshold>
+ <left_val>0.1514132022857666</left_val>
+ <right_val>0.7488812208175659</right_val></_></_>
+ <_>
+ <!-- tree 2 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>1 7 15 9 -1.</_>
+ <_>1 10 15 3 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>4.2109931819140911e-003</threshold>
+ <left_val>0.0900492817163467</left_val>
+ <right_val>0.6374819874763489</right_val></_></_></trees>
+ <stage_threshold>0.8226894140243530</stage_threshold>
+ <parent>-1</parent>
+ <next>-1</next></_>
+ <_>
+ <!-- stage 1 -->
+ <trees>
+ <_>
+ <!-- tree 0 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>5 6 2 6 -1.</_>
+ <_>5 9 2 3 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>1.6227109590545297e-003</threshold>
+ <left_val>0.0693085864186287</left_val>
+ <right_val>0.7110946178436279</right_val></_></_>
+ <_>
+ <!-- tree 1 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>7 5 6 3 -1.</_>
+ <_>9 5 2 3 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>2.2906649392098188e-003</threshold>
+ <left_val>0.1795803010463715</left_val>
+ <right_val>0.6668692231178284</right_val></_></_>
+ <_>
+ <!-- tree 2 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>4 0 12 9 -1.</_>
+ <_>4 3 12 3 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>5.0025708042085171e-003</threshold>
+ <left_val>0.1693672984838486</left_val>
+ <right_val>0.6554006934165955</right_val></_></_>
+ <_>
+ <!-- tree 3 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>6 9 10 8 -1.</_>
+ <_>6 13 10 4 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>7.9659894108772278e-003</threshold>
+ <left_val>0.5866332054138184</left_val>
+ <right_val>0.0914145186543465</right_val></_></_>
+ <_>
+ <!-- tree 4 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>3 6 14 8 -1.</_>
+ <_>3 10 14 4 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-3.5227010957896709e-003</threshold>
+ <left_val>0.1413166970014572</left_val>
+ <right_val>0.6031895875930786</right_val></_></_>
+ <_>
+ <!-- tree 5 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>14 1 6 10 -1.</_>
+ <_>14 1 3 10 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>0.0366676896810532</threshold>
+ <left_val>0.3675672113895416</left_val>
+ <right_val>0.7920318245887756</right_val></_></_>
+ <_>
+ <!-- tree 6 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>7 8 5 12 -1.</_>
+ <_>7 12 5 4 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>9.3361474573612213e-003</threshold>
+ <left_val>0.6161385774612427</left_val>
+ <right_val>0.2088509947061539</right_val></_></_>
+ <_>
+ <!-- tree 7 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>1 1 18 3 -1.</_>
+ <_>7 1 6 3 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>8.6961314082145691e-003</threshold>
+ <left_val>0.2836230993270874</left_val>
+ <right_val>0.6360273957252502</right_val></_></_>
+ <_>
+ <!-- tree 8 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>1 8 17 2 -1.</_>
+ <_>1 9 17 1 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>1.1488880263641477e-003</threshold>
+ <left_val>0.2223580926656723</left_val>
+ <right_val>0.5800700783729553</right_val></_></_>
+ <_>
+ <!-- tree 9 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>16 6 4 2 -1.</_>
+ <_>16 7 4 1 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-2.1484689787030220e-003</threshold>
+ <left_val>0.2406464070081711</left_val>
+ <right_val>0.5787054896354675</right_val></_></_>
+ <_>
+ <!-- tree 10 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>5 17 2 2 -1.</_>
+ <_>5 18 2 1 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>2.1219060290604830e-003</threshold>
+ <left_val>0.5559654831886292</left_val>
+ <right_val>0.1362237036228180</right_val></_></_>
+ <_>
+ <!-- tree 11 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>14 2 6 12 -1.</_>
+ <_>14 2 3 12 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-0.0939491465687752</threshold>
+ <left_val>0.8502737283706665</left_val>
+ <right_val>0.4717740118503571</right_val></_></_>
+ <_>
+ <!-- tree 12 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>4 0 4 12 -1.</_>
+ <_>4 0 2 6 2.</_>
+ <_>6 6 2 6 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>1.3777789426967502e-003</threshold>
+ <left_val>0.5993673801422119</left_val>
+ <right_val>0.2834529876708984</right_val></_></_>
+ <_>
+ <!-- tree 13 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>2 11 18 8 -1.</_>
+ <_>8 11 6 8 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>0.0730631574988365</threshold>
+ <left_val>0.4341886043548584</left_val>
+ <right_val>0.7060034275054932</right_val></_></_>
+ <_>
+ <!-- tree 14 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>5 7 10 2 -1.</_>
+ <_>5 8 10 1 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>3.6767389974556863e-004</threshold>
+ <left_val>0.3027887940406799</left_val>
+ <right_val>0.6051574945449829</right_val></_></_>
+ <_>
+ <!-- tree 15 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>15 11 5 3 -1.</_>
+ <_>15 12 5 1 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-6.0479710809886456e-003</threshold>
+ <left_val>0.1798433959484100</left_val>
+ <right_val>0.5675256848335266</right_val></_></_></trees>
+ <stage_threshold>6.9566087722778320</stage_threshold>
+ <parent>0</parent>
+ <next>-1</next></_>
+ <_>
+ <!-- stage 2 -->
+ <trees>
+ <_>
+ <!-- tree 0 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>5 3 10 9 -1.</_>
+ <_>5 6 10 3 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-0.0165106896311045</threshold>
+ <left_val>0.6644225120544434</left_val>
+ <right_val>0.1424857974052429</right_val></_></_>
+ <_>
+ <!-- tree 1 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>9 4 2 14 -1.</_>
+ <_>9 11 2 7 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>2.7052499353885651e-003</threshold>
+ <left_val>0.6325352191925049</left_val>
+ <right_val>0.1288477033376694</right_val></_></_>
+ <_>
+ <!-- tree 2 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>3 5 4 12 -1.</_>
+ <_>3 9 4 4 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>2.8069869149476290e-003</threshold>
+ <left_val>0.1240288019180298</left_val>
+ <right_val>0.6193193197250366</right_val></_></_>
+ <_>
+ <!-- tree 3 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>4 5 12 5 -1.</_>
+ <_>8 5 4 5 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-1.5402400167658925e-003</threshold>
+ <left_val>0.1432143002748489</left_val>
+ <right_val>0.5670015811920166</right_val></_></_>
+ <_>
+ <!-- tree 4 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>5 6 10 8 -1.</_>
+ <_>5 10 10 4 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-5.6386279175058007e-004</threshold>
+ <left_val>0.1657433062791824</left_val>
+ <right_val>0.5905207991600037</right_val></_></_>
+ <_>
+ <!-- tree 5 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>8 0 6 9 -1.</_>
+ <_>8 3 6 3 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>1.9253729842603207e-003</threshold>
+ <left_val>0.2695507109165192</left_val>
+ <right_val>0.5738824009895325</right_val></_></_>
+ <_>
+ <!-- tree 6 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>9 12 1 8 -1.</_>
+ <_>9 16 1 4 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-5.0214841030538082e-003</threshold>
+ <left_val>0.1893538981676102</left_val>
+ <right_val>0.5782774090766907</right_val></_></_>
+ <_>
+ <!-- tree 7 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>0 7 20 6 -1.</_>
+ <_>0 9 20 2 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>2.6365420781075954e-003</threshold>
+ <left_val>0.2309329062700272</left_val>
+ <right_val>0.5695425868034363</right_val></_></_>
+ <_>
+ <!-- tree 8 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>7 0 6 17 -1.</_>
+ <_>9 0 2 17 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-1.5127769438549876e-003</threshold>
+ <left_val>0.2759602069854736</left_val>
+ <right_val>0.5956642031669617</right_val></_></_>
+ <_>
+ <!-- tree 9 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>9 0 6 4 -1.</_>
+ <_>11 0 2 4 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-0.0101574398577213</threshold>
+ <left_val>0.1732538044452667</left_val>
+ <right_val>0.5522047281265259</right_val></_></_>
+ <_>
+ <!-- tree 10 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>5 1 6 4 -1.</_>
+ <_>7 1 2 4 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-0.0119536602869630</threshold>
+ <left_val>0.1339409947395325</left_val>
+ <right_val>0.5559014081954956</right_val></_></_>
+ <_>
+ <!-- tree 11 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>12 1 6 16 -1.</_>
+ <_>14 1 2 16 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>4.8859491944313049e-003</threshold>
+ <left_val>0.3628703951835632</left_val>
+ <right_val>0.6188849210739136</right_val></_></_>
+ <_>
+ <!-- tree 12 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>0 5 18 8 -1.</_>
+ <_>0 5 9 4 2.</_>
+ <_>9 9 9 4 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-0.0801329165697098</threshold>
+ <left_val>0.0912110507488251</left_val>
+ <right_val>0.5475944876670837</right_val></_></_>
+ <_>
+ <!-- tree 13 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>8 15 10 4 -1.</_>
+ <_>13 15 5 2 2.</_>
+ <_>8 17 5 2 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>1.0643280111253262e-003</threshold>
+ <left_val>0.3715142905712128</left_val>
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diff --git a/xbob/ip/flandmark/flandmark.cpp b/xbob/ip/flandmark/flandmark.cpp
index 5dfd99e..9d9d316 100644
--- a/xbob/ip/flandmark/flandmark.cpp
+++ b/xbob/ip/flandmark/flandmark.cpp
@@ -146,7 +146,7 @@ static PyObject* call(PyBobIpFlandmarkObject* self,
Py_END_ALLOW_THREADS
PyObject* landmarks = 0;
- if (result == NO_ERR) {
+ if (result != NO_ERR) {
Py_INCREF(Py_None);
landmarks = Py_None;
}
@@ -156,7 +156,7 @@ static PyObject* call(PyBobIpFlandmarkObject* self,
auto landmarks_ = make_safe(landmarks);
for (int k = 0; k < (2*self->flandmark->data.options.M); k += 2) {
PyTuple_SET_ITEM(landmarks, k/2,
- Py_BuildValue("nn",
+ Py_BuildValue("dd",
self->landmarks[k+1], //y value
self->landmarks[k] //x value
)
@@ -241,7 +241,17 @@ static PyObject* PyBobIpFlandmark_call_single(PyBobIpFlandmarkObject* self,
bbx[2] = x + width;
bbx[3] = y + height;
- return call(self, cv_image, 1, bbx);
+ PyObject* retval = call(self, cv_image, 1, bbx);
+ if (!retval) return 0;
+
+ //gets the first entry, return it
+ PyObject* retval0 = PyTuple_GET_ITEM(retval, 0);
+ if (!retval0) return 0;
+
+ Py_INCREF(retval0);
+ Py_DECREF(retval);
+
+ return retval0;
};
@@ -263,7 +273,7 @@ PyObject* PyBobIpFlandmark_Repr(PyBobIpFlandmarkObject* self) {
* <xbob.ip.flandmark(model='...')>
*/
- PyObject* retval = PyUnicode_FromFormat("<%s(model=%s)>",
+ PyObject* retval = PyUnicode_FromFormat("<%s(model='%s')>",
Py_TYPE(self)->tp_name, self->filename.c_str());
#if PYTHON_VERSION_HEX < 0x03000000
diff --git a/xbob/ip/flandmark/main.cpp b/xbob/ip/flandmark/main.cpp
index 0604d3d..fedd774 100644
--- a/xbob/ip/flandmark/main.cpp
+++ b/xbob/ip/flandmark/main.cpp
@@ -10,11 +10,38 @@
#endif
#include <xbob.blitz/capi.h>
#include <xbob.blitz/cleanup.h>
+#include <xbob.io/api.h>
+#include <xbob.extension/documentation.h>
extern PyTypeObject PyBobIpFlandmark_Type;
+static auto s_setter = xbob::extension::FunctionDoc(
+ "__set_default_model__",
+ "Internal function to set the default model for the Flandmark class"
+ )
+ .add_prototype("path", "")
+ .add_parameter("path", "str", "The path to the new model file")
+ ;
+
+PyObject* set_flandmark_model(PyObject*, PyObject* o) {
+
+ int ok = PyDict_SetItemString(PyBobIpFlandmark_Type.tp_dict,
+ "__default_model__", o);
+
+ if (ok == -1) return 0;
+
+ Py_RETURN_NONE;
+
+}
+
static PyMethodDef module_methods[] = {
- {0} /* Sentinel */
+ {
+ s_setter.name(),
+ (PyCFunction)set_flandmark_model,
+ METH_O,
+ s_setter.doc()
+ },
+ {0} /* Sentinel */
};
PyDoc_STRVAR(module_docstr, "Flandmark keypoint localization library");
@@ -32,6 +59,10 @@ static PyModuleDef module_definition = {
static PyObject* create_module (void) {
+ //makes sure that PyBobIpFlandmark_Type has a dictionary on tp_dict
+ PyBobIpFlandmark_Type.tp_dict = PyDict_New();
+ if (!PyBobIpFlandmark_Type.tp_dict) return 0;
+
PyBobIpFlandmark_Type.tp_new = PyType_GenericNew;
if (PyType_Ready(&PyBobIpFlandmark_Type) < 0) return 0;
@@ -52,6 +83,7 @@ static PyObject* create_module (void) {
/* imports xbob.blitz C-API + dependencies */
if (import_xbob_blitz() < 0) return 0;
+ if (import_xbob_io() < 0) return 0;
Py_INCREF(m);
return m;
diff --git a/xbob/ip/flandmark/old.cpp b/xbob/ip/flandmark/old.cpp
deleted file mode 100644
index 7c67c68..0000000
--- a/xbob/ip/flandmark/old.cpp
+++ /dev/null
@@ -1,173 +0,0 @@
-/**
- * @author Andre Anjos <andre.anjos@idiap.ch>
- * @date Thu 20 Sep 2012 14:46:35 CEST
- *
- * @brief Boost.Python extension to flandmark
- */
-
-#include <boost/shared_ptr.hpp>
-#include <boost/shared_array.hpp>
-
-#include "flandmark_detector.h"
-
-using namespace boost::python;
-
-static void delete_cascade(CvHaarClassifierCascade* o) {
- cvReleaseHaarClassifierCascade(&o);
-}
-
-static void delete_flandmark(FLANDMARK_Model* o) {
- flandmark_free(o);
-}
-
-static void delete_image(IplImage* i) {
- i->imageData = 0; ///< never delete blitz::Array data
- cvReleaseImage(&i);
-}
-
-static void delete_storage(CvMemStorage* s) {
- cvClearMemStorage(s);
- cvReleaseMemStorage(&s);
-}
-
-/**
- * A simple wrapper to operate the flandmark library quickly in iterative
- * environments like Python.
- */
-class Localizer {
-
- public: //api
-
- /**
- * Constructor, has to load a boosted cascaded from OpenCV and the
- * flandmark model.
- */
- Localizer(const std::string& opencv_cascade,
- const std::string& flandmark_model) :
- m_cascade((CvHaarClassifierCascade*)cvLoad(opencv_cascade.c_str(), 0, 0, 0), std::ptr_fun(delete_cascade)),
- m_flandmark(flandmark_init(flandmark_model.c_str()), std::ptr_fun(delete_flandmark)),
- m_storage(cvCreateMemStorage(0), std::ptr_fun(delete_storage))
- {
- if( !m_cascade ) {
- PYTHON_ERROR(RuntimeError, "Couldnt load Face detector '%s'", opencv_cascade.c_str());
- }
-
- if ( !m_flandmark ) {
- PYTHON_ERROR(RuntimeError, "Structure model wasn't created. Corrupted file '%s'", flandmark_model.c_str());
- }
-
- m_landmarks.reset(new double[2*m_flandmark->data.options.M]);
- }
-
- /**
- * Detect and locates the landmarks from an input image
- */
- tuple call1(bob::python::const_ndarray input) {
- //checks type
- const bob::core::array::typeinfo& type = input.type();
- if ((type.dtype != bob::core::array::t_uint8) || (type.nd != 2)) {
- PYTHON_ERROR(TypeError, "Input data must be a 2D numpy.array with dtype=uint8 (i.e. a gray-scaled image), but you passed %s", type.str().c_str());
- }
-
- //converts to IplImage
- boost::shared_ptr<IplImage> ipl_image(cvCreateImageHeader(cvSize(type.shape[1], type.shape[0]), IPL_DEPTH_8U, 1), std::ptr_fun(delete_image));
- ipl_image->imageData = (char*)input.bz<uint8_t,2>().data();
-
- // Flags for OpenCV face detection
- CvSize minFeatureSize = cvSize(40, 40);
- int flags = CV_HAAR_DO_CANNY_PRUNING;
- float search_scale_factor = 1.1f;
-
- // Detect all the faces in the greyscale image.
- CvSeq* rects = cvHaarDetectObjects(ipl_image.get(), m_cascade.get(),
- m_storage.get(), search_scale_factor, 2, flags, minFeatureSize);
- int nFaces = rects->total;
-
- list retval;
- for (int iface = 0; iface < (rects ? nFaces : 0); ++iface) {
-
- CvRect* r = (CvRect*)cvGetSeqElem(rects, iface);
-
- dict det;
- det["bbox"] = make_tuple(r->x, r->y, r->width, r->height);
- int bbox[4] = {r->x, r->y, r->x + r->width, r->y + r->height};
-
- int flandmark_result;
- {
- bob::python::no_gil unlock;
- flandmark_result = flandmark_detect(ipl_image.get(), bbox, m_flandmark.get(),
- m_landmarks.get());
- }
-
- list lmlist; ///< landmark list
-
- // do not copy the results when the landmark detector indicates an error.
- // otherwise stale results (from a previous invocation) are returned
- if (flandmark_result == NO_ERR) {
- // The first point represents the center of the bounding box used by
- // the flandmark library.
- for (int i = 0; i < (2*m_flandmark->data.options.M); i += 2) {
- lmlist.append(make_tuple(m_landmarks[i+1], m_landmarks[i]));
- }
- }
- det["landmark"] = tuple(lmlist);
-
- retval.append(det);
- }
-
- return tuple(retval);
- }
-
-
- /**
- * Detect and locates the landmarks from an input image
- */
- object call2(bob::python::const_ndarray input, const int b_y, const int b_x, const int b_height, const int b_width)
- {
- //checks type
- const bob::core::array::typeinfo& type = input.type();
- if ((type.dtype != bob::core::array::t_uint8) || (type.nd != 2)) {
- PYTHON_ERROR(TypeError, "Input data must be a 2D numpy.array with dtype=uint8 (i.e. a gray-scaled image), but you passed %s", type.str().c_str());
- }
-
- //converts to IplImage
- boost::shared_ptr<IplImage> ipl_image(cvCreateImageHeader(cvSize(type.shape[1], type.shape[0]), IPL_DEPTH_8U, 1), std::ptr_fun(delete_image));
- ipl_image->imageData = (char*)input.bz<uint8_t,2>().data();
-
- int bbox[4] = {b_x, b_y, b_x + b_width, b_y + b_height};
- int flandmark_result;
- {
- bob::python::no_gil unlock;
- flandmark_result = flandmark_detect(ipl_image.get(), bbox, m_flandmark.get(),
- m_landmarks.get());
- }
-
- list lmlist; ///< landmark list
-
- if (flandmark_result == NO_ERR) {
- for (int i = 0; i < (2*m_flandmark->data.options.M); i += 2) {
- lmlist.append(make_tuple(m_landmarks[i+1], m_landmarks[i]));
- }
- }
-
- return object(lmlist);
- }
-
-
- private: //representation
-
- boost::shared_ptr<CvHaarClassifierCascade> m_cascade;
- boost::shared_ptr<FLANDMARK_Model> m_flandmark;
- boost::shared_ptr<CvMemStorage> m_storage;
- boost::shared_array<double> m_landmarks;
-
-};
-
-BOOST_PYTHON_MODULE(_flandmark) {
- bob::python::setup_python("bindings to flandmark - a library for the localization of facial landmarks");
-
- class_<Localizer>("Localizer", "A key-point localization for faces using flandmark", init<const std::string&, const std::string&>((arg("detector"), arg("localizer")), "Initializes with both an OpenCV face detector model and an flandmark model"))
- .def("__call__", &Localizer::call1, (arg("image")), "Locates (possibly multiple) key-points on the given input image. Returns a list of located faces (by OpenCV's model), each attached to a list of key-points.")
- .def("__call__", &Localizer::call2, (arg("image"), arg("b_y"), arg("b_x"), arg("b_height"), arg("b_width")), "Locates (possibly multiple) key-points on the given input image, given a bounding box and returns them as a list.")
- ;
-}
diff --git a/xbob/ip/flandmark/test.py b/xbob/ip/flandmark/test.py
index b634eb8..8c3ef1c 100644
--- a/xbob/ip/flandmark/test.py
+++ b/xbob/ip/flandmark/test.py
@@ -7,21 +7,88 @@
"""
import os
-import bob
import pkg_resources
import nose.tools
+import xbob.io
+import xbob.ip.color
-from . import Localizer
+from . import Flandmark
-def F(name, f):
+def F(f):
"""Returns the test file on the "data" subdirectory"""
- return pkg_resources.resource_filename(name, os.path.join('data', f))
+ return pkg_resources.resource_filename(__name__, os.path.join('data', f))
-INPUT_VIDEO = F('xbob.io', 'test.mov')
+LENA = F('lena.jpg')
+LENA_BBX = [
+ (214, 202, 183, 183)
+ ] #from OpenCV's cascade detector
-def test_video():
+MULTI = F('multi.jpg')
+MULTI_BBX = [
+ [326, 20, 31, 31],
+ [163, 25, 34, 34],
+ [253, 42, 28, 28],
+ ] #from OpenCV's cascade detector
- op = Localizer()
+def opencv_detect(image):
+ """Detects a face using OpenCV's cascade detector
- for f in bob.io.VideoReader(INPUT_VIDEO):
- assert op(f)
+ Returns a list of arrays containing (x, y, width, height) for each detected
+ face.
+ """
+
+ from cv2 import CascadeClassifier, cv
+
+ cc = CascadeClassifier(F('haarcascade_frontalface_alt.xml'))
+ return cc.detectMultiScale(
+ image,
+ 1.3, #scaleFactor (at each time the image is re-scaled)
+ 4, #minNeighbors (around candidate to be retained)
+ 0, #flags (normally, should be set to zero)
+ (20,20), #(minSize, maxSize) (of detected objects)
+ )
+
+@nose.tools.nottest
+def test_lena_opencv():
+
+ img = xbob.io.load(LENA)
+ gray = xbob.ip.color.rgb_to_gray(img)
+ (x, y, width, height) = opencv_detect(gray)[0]
+
+ flm = Flandmark()
+ keypoints = flm.locate(gray, y, x, height, width)
+ assert keypoints
+
+def test_lena():
+
+ img = xbob.io.load(LENA)
+ gray = xbob.ip.color.rgb_to_gray(img)
+ (x, y, width, height) = LENA_BBX[0]
+
+ flm = Flandmark()
+ keypoints = flm.locate(gray, y, x, height, width)
+ assert keypoints
+ nose.tools.eq_(len(keypoints), 8)
+
+@nose.tools.nottest
+def test_multi_opencv():
+
+ img = xbob.io.load(MULTI)
+ gray = xbob.ip.color.rgb_to_gray(img)
+ bbx = opencv_detect(gray)
+
+ flm = Flandmark()
+ for (x, y, width, height) in bbx:
+ keypoints = flm.locate(gray, y, x, height, width)
+ assert keypoints
+
+def test_multi():
+
+ img = xbob.io.load(MULTI)
+ gray = xbob.ip.color.rgb_to_gray(img)
+
+ flm = Flandmark()
+ for (x, y, width, height) in MULTI_BBX:
+ keypoints = flm.locate(gray, y, x, height, width)
+ assert keypoints
+ nose.tools.eq_(len(keypoints), 8)
--
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