diff --git a/bob/paper/nir_patch_pooling/database/mlfp.py b/bob/paper/nir_patch_pooling/database/mlfp.py
index 74b9bcc056af6fdfccd7a1bccbc850485a29ff9a..b1c84acdeac7d2a50b5eddc836fe1f212239eb92 100644
--- a/bob/paper/nir_patch_pooling/database/mlfp.py
+++ b/bob/paper/nir_patch_pooling/database/mlfp.py
@@ -50,7 +50,6 @@ class File(VideoPadFile):
#------------------------------------------------------------------------------
-#------------------------------------------------------------------------------
class MLFPDatabase(FileListPadDatabase):
@@ -61,7 +60,7 @@ class MLFPDatabase(FileListPadDatabase):
def __init__(
self,
- name = "MLFP",
+ name = "MLFP NIR",
original_directory = None,
original_extension = ".hdf5",
annotation_directory = None,
@@ -98,22 +97,35 @@ class MLFPDatabase(FileListPadDatabase):
file_path = os.path.join(self.annotation_directory, f.path + ".json")
- # if file exists, load the annotations
+ # if annotations exist, load from file_path
if os.path.isfile(file_path):
with open(file_path, "r") as json_file:
annotations = json.load(json_file)
- if not annotations: # if dictionary is empty
- logger.warning("Empty annotations for %s", f.path)
- return None
-
- return annotations
+ else:
+
+ # find and save annotations
+ annotations = {}
+ video = f.load(directory=self.original_directory, extension=self.original_extension)
+
+ for idx, image in enumerate(video.as_array()):
+ frame_annotations = detect_face_landmarks_in_image(image, method=self.landmark_detect_method)
+ if frame_annotations:
+ annotations[str(idx)] = frame_annotations
- else:
+ # save to file_path
+ create_directories_safe(directory=os.path.split(file_path)[0], dryrun=False)
+ with open(file_path, 'w+') as json_file:
+ json_file.write(json.dumps(annotations))
- logger.warning("Annotation file for %s does not exist. (Overall path: %s)", f.path, file_path)
+
+ if not annotations: # if dictionary is empty
+ logger.warning("Empty annotations for {}".format(f.path))
return None
+ return annotations
+
+
#------------------------------------------------------------------------------
diff --git a/bob/paper/nir_patch_pooling/database/wmca_mask.py b/bob/paper/nir_patch_pooling/database/wmca_mask.py
index 5fa5a185da756294425f962b1cffe714e13ead25..ded9a6f94f342134946ead614b8f1f8b4047b5e9 100644
--- a/bob/paper/nir_patch_pooling/database/wmca_mask.py
+++ b/bob/paper/nir_patch_pooling/database/wmca_mask.py
@@ -7,16 +7,17 @@ Implementation of dataset interface of WMCA Masks for PAD.
"""
# Imports
-from bob.pad.base.database import FileListPadDatabase, PadFile
+from bob.pad.base.database import FileListPadDatabase
from bob.pad.face.database.batl import BatlPadFile
from bob.db.batl.models import VideoFile
from bob.extension import rc
-from bob.bio.video.utils import FrameSelector, FrameContainer
+from bob.bio.video.utils import FrameSelector
from bob.pad.face.preprocessor.FaceCropAlign import detect_face_landmarks_in_image
-import json
import os
-import bob.io.base
+import json
+from bob.io.base import create_directories_safe
+
import pkg_resources
import logging
@@ -42,26 +43,6 @@ class File(VideoFile):
#------------------------------------------------------------------------------
- # auxiliary function to load existing preprocessed files stored as *.hdf5
-
- def load_aux(self, directory=None, extension=None,
- frame_selector=FrameSelector()):
-
- path = self.make_path(directory, extension)
-
- # if loading a preprocessed data
- if path.endswith('hdf5'):
- with bob.io.base.HDF5File(path) as f:
- return FrameContainer(hdf5=f)
- else:
- raise NotImplementedError("No loading method for {} extension"\
- .format(extension))
-
-
-#------------------------------------------------------------------------------
-#------------------------------------------------------------------------------
-
-
class WMCAMask(FileListPadDatabase):
"""
@@ -76,6 +57,7 @@ class WMCAMask(FileListPadDatabase):
original_extension = ".h5",
protocol = "grandtest",
annotation_directory = None,
+ landmark_detect_method = "mtcnn",
pad_file_class = BatlPadFile,
low_level_pad_file_class = File,
**kwargs,
@@ -139,14 +121,10 @@ class WMCAMask(FileListPadDatabase):
type_id = int(info.split("_")[3])
pai_id = int(info.split("_")[4])
- video_file = self.low_level_pad_file_class(
- path = path,
- client_id = client_id,
- session_id = session_id,
- presenter_id = presenter_id,
- type_id = type_id,
- pai_id = pai_id
- )
+ video_file = self.low_level_pad_file_class(path = path,
+ client_id = client_id, session_id = session_id,
+ presenter_id = presenter_id, type_id = type_id,
+ pai_id = pai_id)
video_pad_files.append(video_file)
@@ -164,16 +142,16 @@ class WMCAMask(FileListPadDatabase):
if groups is None:
groups = ["train", "dev", "eval"]
- # parse the protocol to extract necessary information
- protocol = protocol
- stream_type = "nir"
- num_frames = 50
-
# obtain the file list using the parent class's functionality
files = super(WMCAMask, self).objects(groups=groups, protocol=protocol,
purposes=purposes, model_ids=model_ids, **kwargs)
# create objects for each file where the class is BATLPadFile
+
+ # aux information
+ stream_type = "nir"
+ num_frames = 50
+
files = [self.pad_file_class(f=f, stream_type=stream_type,
max_frames=num_frames) for f in files]
@@ -184,8 +162,8 @@ class WMCAMask(FileListPadDatabase):
def annotations(self, f):
"""
Returns annotations for a given file object ``f``.
- The annotations must be precomputed using the script provided with the
- package.
+ If annotations do not exist, these will be computed and saved in annotation_
+ directory.
**Parameters:**
@@ -209,21 +187,34 @@ class WMCAMask(FileListPadDatabase):
file_path = os.path.join(self.annotation_directory, f.f.path + ".json")
+ # if annotations exist, load from file_path
if os.path.isfile(file_path):
with open(file_path, "r") as json_file:
annotations = json.load(json_file)
- if not annotations: # if dictionary is empty
- logger.warning("Empty annotations for %s", f.path)
- return None
+ else:
- return annotations
+ # find and save annotations
+ annotations = {}
+ video = f.load(directory=self.original_directory, extension=self.original_extension)
- else:
- logger.warning("Annotation file for %s does not exist. (Overall path: %s)", f.path, file_path)
+ for idx, image in enumerate(video.as_array()):
+ frame_annotations = detect_face_landmarks_in_image(image, method=self.landmark_detect_method)
+ if frame_annotations:
+ annotations[str(idx)] = frame_annotations
+
+ # save to file_path
+ create_directories_safe(directory=os.path.split(file_path)[0], dryrun=False)
+ with open(file_path, 'w+') as json_file:
+ json_file.write(json.dumps(annotations))
+
+
+ if not annotations: # if dictionary is empty
+ logger.warning("Empty annotations for {}".format(f.path))
return None
+ return annotations
#------------------------------------------------------------------------------