Python API

This section lists all the functionality available in this library allowing to run face PAD experiments.

Database Interfaces

REPLAY-ATTACK Database

bob.pad.face.database.replay_attack.ReplayAttackPadDatabase(protocol='grandtest', selection_style=None, max_number_of_frames=None, step_size=None, annotation_directory=None, annotation_type=None, fixed_positions=None, **kwargs)

REPLAY-MOBILE Database

bob.pad.face.database.replay_mobile.ReplayMobilePadDatabase(protocol='grandtest', selection_style=None, max_number_of_frames=None, step_size=None, annotation_directory=None, annotation_type=None, fixed_positions=None, **kwargs)

Transformers

Pre-processors

class bob.pad.face.preprocessor.ImagePatches(block_size, block_overlap=(0, 0), n_random_patches=None, **kwargs)

Bases: TransformerMixin, BaseEstimator

Extracts patches of images and returns it in a VideoLikeContainer. You need to wrap the further blocks (extractor and algorithm) that come after this in bob.bio.video wrappers.

transform(images)[source]
transform_one_image(image)[source]
class bob.pad.face.preprocessor.VideoPatches(face_cropper, block_size, block_overlap=(0, 0), n_random_patches=None, normalizer=None, **kwargs)

Bases: TransformerMixin, BaseEstimator

Extracts patches of images from video containers and returns it in a VideoLikeContainer.

transform(videos, annotations=None)[source]
transform_one_video(frames, annotations=None)[source]

Feature Extractors

Utilities

bob.pad.face.utils.bbx_cropper(frame, ...)

bob.pad.face.utils.blocks(data, block_size)

Extracts patches of an image

bob.pad.face.utils.blocks_generator(data, ...)

Yields patches of an image

bob.pad.face.utils.color_augmentation(image)

Converts an RGB image to different color channels.

bob.pad.face.utils.frames(path)

Yields the frames of a video file.

bob.pad.face.utils.min_face_size_normalizer(...)

bob.pad.face.utils.number_of_frames(path)

returns the number of frames of a video file.

bob.pad.face.utils.scale_face(face, face_height)

Scales a face image to the given size.

bob.pad.face.utils.the_giant_video_loader(...)

Loads a video pad file frame by frame and optionally applies transformations.

bob.pad.face.utils.yield_faces(pad_sample, ...)

Yields face images of a padfile.
bob.pad.face.utils.bbx_cropper(frame, annotations)[source]
bob.pad.face.utils.blocks(data, block_size, block_overlap=(0, 0))[source]

Extracts patches of an image

Parameters

  • data (numpy.ndarray) – The image in gray-scale, color, or color video format.

  • block_size ((int, int)) – The size of patches

  • block_overlap ((int, int), optional) – The size of overlap of patches

Returns

The patches.

Return type

numpy.ndarray

Raises

ValueError – If data dimension is not between 2 and 4 (inclusive).

bob.pad.face.utils.blocks_generator(data, block_size, block_overlap=(0, 0))[source]

Yields patches of an image

Parameters

  • data (numpy.ndarray) – The image in gray-scale, color, or color video format.

  • block_size ((int, int)) – The size of patches

  • block_overlap ((int, int), optional) – The size of overlap of patches

Yields

numpy.ndarray – The patches.

Raises

ValueError – If data dimension is not between 2 and 4 (inclusive).

bob.pad.face.utils.color_augmentation(image, channels=('rgb',))[source]

Converts an RGB image to different color channels.

Parameters

  • image (numpy.ndarray) – The image in RGB Bob format.

  • channels (tuple, optional) – List of channels to convert the image to. It can be any of rgb, yuv, hsv.

Returns

The image that contains several channels: (3*len(channels), height, width).

Return type

numpy.ndarray

bob.pad.face.utils.extract_patches(image, block_size, block_overlap=(0, 0), n_random_patches=None)[source]

Yields either all patches from an image or N random patches.

bob.pad.face.utils.frames(path)[source]

Yields the frames of a video file.

Parameters

path (str) – Path to the video file.

Yields

numpy.ndarray – A frame of the video. The size is (3, 240, 320).

bob.pad.face.utils.min_face_size_normalizer(annotations, max_age=15, **kwargs)[source]
bob.pad.face.utils.number_of_frames(path)[source]

returns the number of frames of a video file.

Parameters

path (str) – Path to the video file.

Returns

The number of frames. Then, it yields the frames.

Return type

int

bob.pad.face.utils.random_patches(image, block_size, n_random_patches=1)[source]

Extracts N random patches of block_size from an image

bob.pad.face.utils.random_sample(A, size)[source]

Randomly selects size samples from the array A

bob.pad.face.utils.scale_face(face, face_height, face_width=None)[source]

Scales a face image to the given size.

Parameters

  • face (numpy.ndarray) – The face image. It can be 2D or 3D in bob image format.

  • face_height (int) – The height of the scaled face.

  • face_width (None, optional) – The width of the scaled face. If None, face_height is used.

Returns

The scaled face.

Return type

numpy.ndarray

bob.pad.face.utils.the_giant_video_loader(pad_sample, region='whole', scaling_factor=None, cropper=None, normalizer=None, patches=False, block_size=(96, 96), block_overlap=(0, 0), random_patches_per_frame=None, augment=None, multiple_bonafide_patches=1, keep_pa_samples=None, keep_bf_samples=None)[source]

Loads a video pad file frame by frame and optionally applies transformations.

Parameters

  • pad_sample – The pad sample

  • region (str) – Either whole or crop. If whole, it will return the whole frame. Otherwise, you need to provide a cropper and a normalizer.

  • scaling_factor (float) – If given, will scale images to this factor.

  • cropper – The cropper to use

  • normalizer – The normalizer to use

  • patches (bool) – If true, will extract patches from images.

  • block_size (tuple) – Size of the patches

  • block_overlap (tuple) – Size of overlap of the patches

  • random_patches_per_frame (int) – If not None, will only take this much patches per frame

  • augment – If given, frames will be transformed using this function.

  • multiple_bonafide_patches (int) – Will use more random patches for bonafide samples

  • keep_pa_samples (float) – If given, will drop some PA samples.

  • keep_bf_samples (float) – If given, will drop some BF samples.

Returns

A generator that yields the samples.

Return type

object

Raises

ValueError – If region is not whole or crop.

bob.pad.face.utils.yield_faces(pad_sample, cropper, normalizer=None)[source]

Yields face images of a padfile. It uses the annotations from the database. The annotations are further normalized.

Parameters

  • pad_sample – The pad sample to return the faces.

  • cropper (collections.abc.Callable) – A face image cropper that works with database’s annotations.

  • normalizer (collections.abc.Callable) – If not None, it should be a function that takes all the annotations of the whole video and yields normalized annotations frame by frame. It should yield same as annotations.items().

Yields

numpy.ndarray – Face images

Raises

ValueError – If the database returns None for annotations.