Commit 5eb1b4d6 authored by Amir MOHAMMADI's avatar Amir MOHAMMADI

Remove traces of dlib and menpo

parent a260437b
Pipeline #29316 failed with stage
in 6 minutes and 55 seconds
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
This file contains configurations to run LBP and SVM based face PAD baseline.
The settings are tuned for the Replay-attack database.
The idea of the algorithm is introduced in the following paper: [CAM12]_.
However some settings are different from the ones introduced in the paper.
"""
#=======================================================================================
sub_directory = 'lbp_svm'
"""
Sub-directory where results will be placed.
You may change this setting using the ``--sub-directory`` command-line option
or the attribute ``sub_directory`` in a configuration file loaded **after**
this resource.
"""
#=======================================================================================
# define preprocessor:
from ..preprocessor import FaceCropAlign
from bob.bio.video.preprocessor import Wrapper
from bob.bio.video.utils import FrameSelector
from ..preprocessor.FaceCropAlign import auto_norm_image as _norm_func
FACE_SIZE = 64 # The size of the resulting face
RGB_OUTPUT_FLAG = False # Gray-scale output
USE_FACE_ALIGNMENT = False # use annotations
MAX_IMAGE_SIZE = None # no limiting here
FACE_DETECTION_METHOD = None # use annotations
MIN_FACE_SIZE = 50 # skip small faces
NORMALIZATION_FUNCTION = _norm_func
NORMALIZATION_FUNCTION_KWARGS = {}
NORMALIZATION_FUNCTION_KWARGS = {'n_sigma':3.0, 'norm_method':'MAD'}
_image_preprocessor = FaceCropAlign(face_size = FACE_SIZE,
rgb_output_flag = RGB_OUTPUT_FLAG,
use_face_alignment = USE_FACE_ALIGNMENT,
max_image_size = MAX_IMAGE_SIZE,
face_detection_method = FACE_DETECTION_METHOD,
min_face_size = MIN_FACE_SIZE,
normalization_function = NORMALIZATION_FUNCTION,
normalization_function_kwargs = NORMALIZATION_FUNCTION_KWARGS)
_frame_selector = FrameSelector(selection_style = "all")
preprocessor = Wrapper(preprocessor = _image_preprocessor,
frame_selector = _frame_selector)
"""
In the preprocessing stage the face is cropped in each frame of the input video given facial annotations.
The size of the face is normalized to ``FACE_SIZE`` dimensions. The faces with the size
below ``MIN_FACE_SIZE`` threshold are discarded. The preprocessor is similar to the one introduced in
[CAM12]_, which is defined by ``FACE_DETECTION_METHOD = None``.
"""
#=======================================================================================
# define extractor:
from ..extractor import LBPHistogram
from bob.bio.video.extractor import Wrapper
LBPTYPE = 'uniform'
ELBPTYPE = 'regular'
RAD = 1
NEIGHBORS = 8
CIRC = False
DTYPE = None
extractor = Wrapper(LBPHistogram(
lbptype=LBPTYPE,
elbptype=ELBPTYPE,
rad=RAD,
neighbors=NEIGHBORS,
circ=CIRC,
dtype=DTYPE))
"""
In the feature extraction stage the LBP histograms are extracted from each frame of the preprocessed video.
The parameters are similar to the ones introduced in [CAM12]_.
"""
#=======================================================================================
# define algorithm:
from bob.pad.base.algorithm import LogRegr
C = 1. # The regularization parameter for the LR classifier
FRAME_LEVEL_SCORES_FLAG = True # Return one score per frame
algorithm = LogRegr(
C=C, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
"""
The Logistic Regression is used to classify the data into *real* and *attack* classes.
One score is produced for each frame of the input video, ``frame_level_scores_flag = True``.
The sub-sampling of training data is not used here, sub-sampling flags have default ``False``
values.
"""
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
This file contains configurations to run LBP and SVM based face PAD baseline.
The settings are tuned for the Replay-attack database.
The idea of the algorithm is introduced in the following paper: [CAM12]_.
However some settings are different from the ones introduced in the paper.
"""
#=======================================================================================
sub_directory = 'lbp_svm'
"""
Sub-directory where results will be placed.
You may change this setting using the ``--sub-directory`` command-line option
or the attribute ``sub_directory`` in a configuration file loaded **after**
this resource.
"""
#=======================================================================================
# define preprocessor:
from ..preprocessor import FaceCropAlign
from bob.bio.video.preprocessor import Wrapper
from bob.bio.video.utils import FrameSelector
FACE_SIZE = 64 # The size of the resulting face
RGB_OUTPUT_FLAG = False # Gray-scale output
USE_FACE_ALIGNMENT = False # use annotations
MAX_IMAGE_SIZE = None # no limiting here
FACE_DETECTION_METHOD = None # use annotations
MIN_FACE_SIZE = 50 # skip small faces
_image_preprocessor = FaceCropAlign(face_size = FACE_SIZE,
rgb_output_flag = RGB_OUTPUT_FLAG,
use_face_alignment = USE_FACE_ALIGNMENT,
max_image_size = MAX_IMAGE_SIZE,
face_detection_method = FACE_DETECTION_METHOD,
min_face_size = MIN_FACE_SIZE)
_frame_selector = FrameSelector(selection_style = "all")
preprocessor = Wrapper(preprocessor = _image_preprocessor,
frame_selector = _frame_selector)
"""
In the preprocessing stage the face is cropped in each frame of the input video given facial annotations.
The size of the face is normalized to ``FACE_SIZE`` dimensions. The faces with the size
below ``MIN_FACE_SIZE`` threshold are discarded. The preprocessor is similar to the one introduced in
[CAM12]_, which is defined by ``FACE_DETECTION_METHOD = None``.
"""
#=======================================================================================
# define extractor:
from ..extractor import LBPHistogram
from bob.bio.video.extractor import Wrapper
LBPTYPE = 'uniform'
ELBPTYPE = 'regular'
RAD = 1
NEIGHBORS = 8
CIRC = False
DTYPE = None
extractor = Wrapper(LBPHistogram(
lbptype=LBPTYPE,
elbptype=ELBPTYPE,
rad=RAD,
neighbors=NEIGHBORS,
circ=CIRC,
dtype=DTYPE))
"""
In the feature extraction stage the LBP histograms are extracted from each frame of the preprocessed video.
The parameters are similar to the ones introduced in [CAM12]_.
"""
#=======================================================================================
# define algorithm:
from bob.pad.base.algorithm import SVM
MACHINE_TYPE = 'C_SVC'
KERNEL_TYPE = 'RBF'
N_SAMPLES = 10000
TRAINER_GRID_SEARCH_PARAMS = {
'cost': [2**P for P in range(-3, 14, 2)],
'gamma': [2**P for P in range(-15, 0, 2)]
}
MEAN_STD_NORM_FLAG = True # enable mean-std normalization
FRAME_LEVEL_SCORES_FLAG = True # one score per frame(!) in this case
algorithm = SVM(
machine_type=MACHINE_TYPE,
kernel_type=KERNEL_TYPE,
n_samples=N_SAMPLES,
trainer_grid_search_params=TRAINER_GRID_SEARCH_PARAMS,
mean_std_norm_flag=MEAN_STD_NORM_FLAG,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
"""
The SVM algorithm with RBF kernel is used to classify the data into *real* and *attack* classes.
One score is produced for each frame of the input video, ``frame_level_scores_flag = True``.
In contrast to [CAM12]_, the grid search of SVM parameters is used to select the
successful settings. The grid search is done on the subset of training data. The size
of this subset is defined by ``n_samples`` parameter.
The data is also mean-std normalized, ``mean_std_norm_flag = True``.
"""
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
This file contains configurations to run LBP and SVM based face PAD baseline.
The settings of the preprocessor and extractor are tuned for the Replay-attack database.
In the SVM algorithm the amount of training data is reduced speeding-up the training for
large data sets, such as Aggregated PAD database.
The idea of the algorithm is introduced in the following paper: [CAM12]_.
However some settings are different from the ones introduced in the paper.
"""
#=======================================================================================
sub_directory = 'lbp_svm_aggregated_db'
"""
Sub-directory where results will be placed.
You may change this setting using the ``--sub-directory`` command-line option
or the attribute ``sub_directory`` in a configuration file loaded **after**
this resource.
"""
#=======================================================================================
# define preprocessor:
from ..preprocessor import FaceCropAlign
from bob.bio.video.preprocessor import Wrapper
from bob.bio.video.utils import FrameSelector
FACE_SIZE = 64 # The size of the resulting face
RGB_OUTPUT_FLAG = False # Gray-scale output
USE_FACE_ALIGNMENT = False # use annotations
MAX_IMAGE_SIZE = None # no limiting here
FACE_DETECTION_METHOD = None # use annotations
MIN_FACE_SIZE = 50 # skip small faces
_image_preprocessor = FaceCropAlign(face_size = FACE_SIZE,
rgb_output_flag = RGB_OUTPUT_FLAG,
use_face_alignment = USE_FACE_ALIGNMENT,
max_image_size = MAX_IMAGE_SIZE,
face_detection_method = FACE_DETECTION_METHOD,
min_face_size = MIN_FACE_SIZE)
_frame_selector = FrameSelector(selection_style = "all")
preprocessor = Wrapper(preprocessor = _image_preprocessor,
frame_selector = _frame_selector)
"""
In the preprocessing stage the face is cropped in each frame of the input video given facial annotations.
The size of the face is normalized to ``FACE_SIZE`` dimensions. The faces with the size
below ``MIN_FACE_SIZE`` threshold are discarded. The preprocessor is similar to the one introduced in
[CAM12]_, which is defined by ``FACE_DETECTION_METHOD = None``.
"""
#=======================================================================================
# define extractor:
from ..extractor import LBPHistogram
from bob.bio.video.extractor import Wrapper
LBPTYPE = 'uniform'
ELBPTYPE = 'regular'
RAD = 1
NEIGHBORS = 8
CIRC = False
DTYPE = None
extractor = Wrapper(LBPHistogram(
lbptype=LBPTYPE,
elbptype=ELBPTYPE,
rad=RAD,
neighbors=NEIGHBORS,
circ=CIRC,
dtype=DTYPE))
"""
In the feature extraction stage the LBP histograms are extracted from each frame of the preprocessed video.
The parameters are similar to the ones introduced in [CAM12]_.
"""
#=======================================================================================
# define algorithm:
from bob.pad.base.algorithm import SVM
MACHINE_TYPE = 'C_SVC'
KERNEL_TYPE = 'RBF'
N_SAMPLES = 10000
TRAINER_GRID_SEARCH_PARAMS = {
'cost': [2**P for P in range(-3, 14, 2)],
'gamma': [2**P for P in range(-15, 0, 2)]
}
MEAN_STD_NORM_FLAG = True # enable mean-std normalization
FRAME_LEVEL_SCORES_FLAG = True # one score per frame(!) in this case
SAVE_DEBUG_DATA_FLAG = True # save the data, which might be useful for debugging
REDUCED_TRAIN_DATA_FLAG = True # reduce the amount of training data in the final training stage
N_TRAIN_SAMPLES = 50000 # number of training samples per class in the final SVM training stage
algorithm = SVM(
machine_type=MACHINE_TYPE,
kernel_type=KERNEL_TYPE,
n_samples=N_SAMPLES,
trainer_grid_search_params=TRAINER_GRID_SEARCH_PARAMS,
mean_std_norm_flag=MEAN_STD_NORM_FLAG,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG,
save_debug_data_flag=SAVE_DEBUG_DATA_FLAG,
reduced_train_data_flag=REDUCED_TRAIN_DATA_FLAG,
n_train_samples=N_TRAIN_SAMPLES)
"""
The SVM algorithm with RBF kernel is used to classify the data into *real* and *attack* classes.
One score is produced for each frame of the input video, ``frame_level_scores_flag = True``.
The grid search of SVM parameters is used to select the successful settings.
The grid search is done on the subset of training data.
The size of this subset is defined by ``n_samples`` parameter.
The final training of the SVM is done on the subset of training data ``reduced_train_data_flag = True``.
The size of the subset for the final training stage is defined by the ``n_train_samples`` argument.
The data is also mean-std normalized, ``mean_std_norm_flag = True``.
"""
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
from bob.pad.face.preprocessor import FaceCropAlign
from bob.bio.video.preprocessor import Wrapper
from bob.bio.video.utils import FrameSelector
# =======================================================================================
# Define instances here:
FACE_SIZE = 64 # The size of the resulting face
RGB_OUTPUT_FLAG = True # RGB output
USE_FACE_ALIGNMENT = False #
MAX_IMAGE_SIZE = None # no limiting here
FACE_DETECTION_METHOD = "dlib" # use dlib face detection
MIN_FACE_SIZE = 50 # skip small faces
_image_preprocessor = FaceCropAlign(face_size=FACE_SIZE,
rgb_output_flag=RGB_OUTPUT_FLAG,
use_face_alignment=USE_FACE_ALIGNMENT,
max_image_size=MAX_IMAGE_SIZE,
face_detection_method=FACE_DETECTION_METHOD,
min_face_size=MIN_FACE_SIZE)
_frame_selector = FrameSelector(selection_style = "all")
rgb_face_detector_dlib = Wrapper(preprocessor = _image_preprocessor,
frame_selector = _frame_selector)
# =======================================================================================
FACE_DETECTION_METHOD = "mtcnn" # use mtcnn face detection
_image_preprocessor = FaceCropAlign(face_size=FACE_SIZE,
rgb_output_flag=RGB_OUTPUT_FLAG,
use_face_alignment=USE_FACE_ALIGNMENT,
max_image_size=MAX_IMAGE_SIZE,
face_detection_method=FACE_DETECTION_METHOD,
min_face_size=MIN_FACE_SIZE)
rgb_face_detector_mtcnn = Wrapper(preprocessor = _image_preprocessor,
frame_selector = _frame_selector)
# =======================================================================================
FACE_SIZE = 64 # The size of the resulting face
RGB_OUTPUT_FLAG = False # Gray-scale output
USE_FACE_ALIGNMENT = True # detect face landmarks locally and align the face
MAX_IMAGE_SIZE = 1920 # the largest possible dimension of the input image
FACE_DETECTION_METHOD = "mtcnn" # face landmarks detection method
MIN_FACE_SIZE = 50 # skip faces smaller than this value
NORMALIZATION_FUNCTION = None # no normalization
NORMALIZATION_FUNCTION_KWARGS = None
_image_preprocessor = FaceCropAlign(face_size=FACE_SIZE,
rgb_output_flag=RGB_OUTPUT_FLAG,
use_face_alignment=USE_FACE_ALIGNMENT,
max_image_size=MAX_IMAGE_SIZE,
face_detection_method=FACE_DETECTION_METHOD,
min_face_size=MIN_FACE_SIZE,
normalization_function=NORMALIZATION_FUNCTION,
normalization_function_kwargs=NORMALIZATION_FUNCTION_KWARGS)
bw_face_detect_mtcnn = Wrapper(preprocessor=_image_preprocessor,
frame_selector=_frame_selector)
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
# =============================================================================
# Import here:
from bob.pad.face.preprocessor import VideoFaceCropAlignBlockPatch
from bob.pad.face.preprocessor import FaceCropAlign
from bob.bio.video.preprocessor import Wrapper
from bob.bio.video.utils import FrameSelector
from bob.pad.face.preprocessor.FaceCropAlign import auto_norm_image as _norm_func
from bob.pad.face.preprocessor import BlockPatch
# =============================================================================
# names of the channels to process:
_channel_names = ['color', 'infrared', 'depth']
# =============================================================================
# dictionary containing preprocessors for all channels:
_preprocessors = {}
"""
Preprocessor to be used for Color channel.
"""
FACE_SIZE = 128 # The size of the resulting face
RGB_OUTPUT_FLAG = False # BW output
USE_FACE_ALIGNMENT = True # use annotations
MAX_IMAGE_SIZE = None # no limiting here
FACE_DETECTION_METHOD = None # use ANNOTATIONS
MIN_FACE_SIZE = 50 # skip small faces
_image_preprocessor = FaceCropAlign(face_size = FACE_SIZE,
rgb_output_flag = RGB_OUTPUT_FLAG,
use_face_alignment = USE_FACE_ALIGNMENT,
max_image_size = MAX_IMAGE_SIZE,
face_detection_method = FACE_DETECTION_METHOD,
min_face_size = MIN_FACE_SIZE)
_frame_selector = FrameSelector(selection_style = "all")
_preprocessor_rgb = Wrapper(preprocessor = _image_preprocessor,
frame_selector = _frame_selector)
_preprocessors[_channel_names[0]] = _preprocessor_rgb
"""
Preprocessor to be used for Infrared (or Thermal) channels:
"""
FACE_SIZE = 128 # The size of the resulting face
RGB_OUTPUT_FLAG = False # Gray-scale output
USE_FACE_ALIGNMENT = True # use annotations
MAX_IMAGE_SIZE = None # no limiting here
FACE_DETECTION_METHOD = None # use annotations
MIN_FACE_SIZE = 50 # skip small faces
NORMALIZATION_FUNCTION = _norm_func
NORMALIZATION_FUNCTION_KWARGS = {}
NORMALIZATION_FUNCTION_KWARGS = {'n_sigma':3.0, 'norm_method':'MAD'}
_image_preprocessor_ir = FaceCropAlign(face_size = FACE_SIZE,
rgb_output_flag = RGB_OUTPUT_FLAG,
use_face_alignment = USE_FACE_ALIGNMENT,
max_image_size = MAX_IMAGE_SIZE,
face_detection_method = FACE_DETECTION_METHOD,
min_face_size = MIN_FACE_SIZE,
normalization_function = NORMALIZATION_FUNCTION,
normalization_function_kwargs = NORMALIZATION_FUNCTION_KWARGS)
_preprocessor_ir = Wrapper(preprocessor = _image_preprocessor_ir,
frame_selector = _frame_selector)
_preprocessors[_channel_names[1]] = _preprocessor_ir
"""
Preprocessor to be used for Depth channel:
"""
FACE_SIZE = 128 # The size of the resulting face
RGB_OUTPUT_FLAG = False # Gray-scale output
USE_FACE_ALIGNMENT = True # use annotations
MAX_IMAGE_SIZE = None # no limiting here
FACE_DETECTION_METHOD = None # use annotations
MIN_FACE_SIZE = 50 # skip small faces
NORMALIZATION_FUNCTION = _norm_func
NORMALIZATION_FUNCTION_KWARGS = {}
NORMALIZATION_FUNCTION_KWARGS = {'n_sigma':6.0, 'norm_method':'MAD'}
_image_preprocessor_d = FaceCropAlign(face_size = FACE_SIZE,
rgb_output_flag = RGB_OUTPUT_FLAG,
use_face_alignment = USE_FACE_ALIGNMENT,
max_image_size = MAX_IMAGE_SIZE,
face_detection_method = FACE_DETECTION_METHOD,
min_face_size = MIN_FACE_SIZE,
normalization_function = NORMALIZATION_FUNCTION,
normalization_function_kwargs = NORMALIZATION_FUNCTION_KWARGS)
_preprocessor_d = Wrapper(preprocessor = _image_preprocessor_d,
frame_selector = _frame_selector)
_preprocessors[_channel_names[2]] = _preprocessor_d
# =============================================================================
# define parameters and an instance of the patch extractor:
PATCH_SIZE = 128
STEP = 1
_block_patch_128x128 = BlockPatch(patch_size = PATCH_SIZE,
step = STEP,
use_annotations_flag = False)
# =============================================================================
"""
Define an instance for extraction of one (**whole face**) multi-channel
(BW-NIR-D) face patch of the size (3 x 128 x 128).
"""
video_face_crop_align_bw_ir_d_channels_3x128x128 = VideoFaceCropAlignBlockPatch(preprocessors = _preprocessors,
channel_names = _channel_names,
return_multi_channel_flag = True,
block_patch_preprocessor = _block_patch_128x128)
# This instance is similar to above, but will return a **vectorized** patch:
video_face_crop_align_bw_ir_d_channels_3x128x128_vect = VideoFaceCropAlignBlockPatch(preprocessors = _preprocessors,
channel_names = _channel_names,
return_multi_channel_flag = False,
block_patch_preprocessor = _block_patch_128x128)
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
This file contains configurations to run Image Quality Measures (IQM) and LR based face PAD algorithm.
The settings of the preprocessor and extractor are tuned for the Replay-attack database.
The IQM features used in this algorithm/resource are introduced in the following papers: [WHJ15]_ and [CBVM16]_.
"""
#=======================================================================================
sub_directory = 'qm_lr'
"""
Sub-directory where results will be placed.
You may change this setting using the ``--sub-directory`` command-line option
or the attribute ``sub_directory`` in a configuration file loaded **after**
this resource.
"""
#=======================================================================================
# define preprocessor:
from ..preprocessor import FaceCropAlign
from bob.bio.video.preprocessor import Wrapper
from bob.bio.video.utils import FrameSelector
FACE_SIZE = 64 # The size of the resulting face
RGB_OUTPUT_FLAG = True # RGB output
USE_FACE_ALIGNMENT = False # use annotations
MAX_IMAGE_SIZE = None # no limiting here
FACE_DETECTION_METHOD = None # use annotations
MIN_FACE_SIZE = 50 # skip small faces
_image_preprocessor = FaceCropAlign(face_size = FACE_SIZE,
rgb_output_flag = RGB_OUTPUT_FLAG,
use_face_alignment = USE_FACE_ALIGNMENT,
max_image_size = MAX_IMAGE_SIZE,
face_detection_method = FACE_DETECTION_METHOD,
min_face_size = MIN_FACE_SIZE)
_frame_selector = FrameSelector(selection_style = "all")
preprocessor = Wrapper(preprocessor = _image_preprocessor,
frame_selector = _frame_selector)
"""
In the preprocessing stage the face is cropped in each frame of the input video given facial annotations.
The size of the face is normalized to ``FACE_SIZE`` dimensions. The faces of the size
below ``MIN_FACE_SIZE`` threshold are discarded. The preprocessor is similar to the one introduced in
[CAM12]_, which is defined by ``FACE_DETECTION_METHOD = None``. The preprocessed frame is the RGB
facial image, which is defined by ``RGB_OUTPUT_FLAG = True``.
"""
#=======================================================================================
# define extractor:
from ..extractor import ImageQualityMeasure
from bob.bio.video.extractor import Wrapper
GALBALLY = True
MSU = True
DTYPE = None
extractor = Wrapper(ImageQualityMeasure(galbally=GALBALLY, msu=MSU, dtype=DTYPE))
"""
In the feature extraction stage the Image Quality Measures are extracted from each frame of the preprocessed RGB video.
The features to be computed are introduced in the following papers: [WHJ15]_ and [CBVM16]_.
"""
#=======================================================================================
# define algorithm:
from bob.pad.base.algorithm import LogRegr
C = 1. # The regularization parameter for the LR classifier
FRAME_LEVEL_SCORES_FLAG = True # Return one score per frame
algorithm = LogRegr(
C=C, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
"""
The Logistic Regression is used to classify the data into *real* and *attack* classes.
One score is produced for each frame of the input video, ``frame_level_scores_flag = True``.
The sub-sampling of training data is not used here, sub-sampling flags have default ``False``
values.
"""
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
This file contains configurations to run Image Quality Measures (IQM) and one-class GMM based face PAD algorithm.
The settings of the preprocessor and extractor are tuned for the Replay-attack database.
The IQM features used in this algorithm/resource are introduced in the following papers: [WHJ15]_ and [CBVM16]_.
"""
#=======================================================================================
sub_directory = 'qm_one_class_gmm'
"""
Sub-directory where results will be placed.
You may change this setting using the ``--sub-directory`` command-line option
or the attribute ``sub_directory`` in a configuration file loaded **after**
this resource.
"""
#=======================================================================================
# define preprocessor:
from ..preprocessor import FaceCropAlign
from bob.bio.video.preprocessor import Wrapper
from bob.bio.video.utils import FrameSelector
FACE_SIZE = 64 # The size of the resulting face
RGB_OUTPUT_FLAG = True # RGB output
USE_FACE_ALIGNMENT = False # use annotations
MAX_IMAGE_SIZE = None # no limiting here
FACE_DETECTION_METHOD = None # use annotations
MIN_FACE_SIZE = 50 # skip small faces
_image_preprocessor = FaceCropAlign(face_size = FACE_SIZE,
rgb_output_flag = RGB_OUTPUT_FLAG,
use_face_alignment = USE_FACE_ALIGNMENT,
max_image_size = MAX_IMAGE_SIZE,
face_detection_method = FACE_DETECTION_METHOD,
min_face_size = MIN_FACE_SIZE)
_frame_selector = FrameSelector(selection_style = "all")
preprocessor = Wrapper(preprocessor = _image_preprocessor,
frame_selector = _frame_selector)
"""
In the preprocessing stage the face is cropped in each frame of the input video given facial annotations.
The size of the face is normalized to ``FACE_SIZE`` dimensions. The faces of the size
below ``MIN_FACE_SIZE`` threshold are discarded. The preprocessor is similar to the one introduced in
[CAM12]_, which is defined by ``FACE_DETECTION_METHOD = None``. The preprocessed frame is the RGB
facial image, which is defined by ``RGB_OUTPUT_FLAG = True``.
"""
#=======================================================================================
# define extractor:
from ..extractor import ImageQualityMeasure
from bob.bio.video.extractor import Wrapper
GALBALLY = True
MSU = True
DTYPE = None
extractor = Wrapper(ImageQualityMeasure(galbally=GALBALLY, msu=MSU, dtype=DTYPE))
"""
In the feature extraction stage the Image Quality Measures are extracted from each frame of the preprocessed RGB video.
The features to be computed are introduced in the following papers: [WHJ15]_ and [CBVM16]_.
"""
#=======================================================================================
# define algorithm:
from bob.pad.base.algorithm import OneClassGMM
N_COMPONENTS = 50
RANDOM_STATE = 3
FRAME_LEVEL_SCORES_FLAG = True
algorithm = OneClassGMM(
n_components=N_COMPONENTS,
random_state=RANDOM_STATE,
frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
"""
The GMM with 50 clusters is trained using samples from the real class only. The pre-trained
GMM is next used to classify the data into *real* and *attack* classes.
One score is produced for each frame of the input video, ``frame_level_scores_flag = True``.
"""
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""