diff --git a/bob/pad/face/utils/load_utils.py b/bob/pad/face/utils/load_utils.py
index 4bddd8830bfbe3a5fe92f957656fd6b4f095f2aa..47b79368494d0a109ad1d6c10b19767819265f90 100644
--- a/bob/pad/face/utils/load_utils.py
+++ b/bob/pad/face/utils/load_utils.py
@@ -45,14 +45,20 @@ def number_of_frames(path):
def bbx_cropper(frame, annotations):
- bbx = bounding_box_from_annotation(**annotations)
- return frame[..., bbx.top:bbx.bottom, bbx.left:bbx.right]
+ for source in ("direct", "eyes", None):
+ try:
+ bbx = bounding_box_from_annotation(source=source, **annotations)
+ break
+ except Exception:
+ if source is None:
+ raise
+ return frame[..., bbx.top : bbx.bottom, bbx.left : bbx.right]
def min_face_size_normalizer(annotations, max_age=15, **kwargs):
- return normalize_annotations(annotations,
- partial(min_face_size_validator, **kwargs),
- max_age=max_age)
+ return normalize_annotations(
+ annotations, partial(min_face_size_validator, **kwargs), max_age=max_age
+ )
def yield_faces(padfile, cropper, normalizer=None):
@@ -120,7 +126,7 @@ def scale_face(face, face_height, face_width=None):
face_width = face_height if face_width is None else face_width
shape = list(face.shape)
shape[-2:] = (face_height, face_width)
- scaled_face = numpy.empty(shape, dtype='float64')
+ scaled_face = numpy.empty(shape, dtype="float64")
scale(face, scaled_face)
return scaled_face
@@ -150,12 +156,12 @@ def blocks(data, block_size, block_overlap=(0, 0)):
data = numpy.asarray(data)
# if a gray scale image:
if data.ndim == 2:
- output = block(data, block_size, block_overlap,
- flat=True)
+ output = block(data, block_size, block_overlap, flat=True)
# if a color image:
elif data.ndim == 3:
- out_shape = list(data.shape[0:1]) + list(block_output_shape(
- data[0], block_size, block_overlap, flat=True))
+ out_shape = list(data.shape[0:1]) + list(
+ block_output_shape(data[0], block_size, block_overlap, flat=True)
+ )
output = numpy.empty(out_shape, dtype=data.dtype)
for i, img2d in enumerate(data):
@@ -163,8 +169,7 @@ def blocks(data, block_size, block_overlap=(0, 0)):
output = numpy.moveaxis(output, 0, 1)
# if a color video:
elif data.ndim == 4:
- output = [blocks(img3d, block_size, block_overlap)
- for img3d in data]
+ output = [blocks(img3d, block_size, block_overlap) for img3d in data]
output = numpy.concatenate(output, axis=0)
else:
raise ValueError("Unknown data dimension {}".format(data.ndim))
@@ -206,7 +211,7 @@ def blocks_generator(data, block_size, block_overlap=(0, 0)):
raise ValueError("Unknown data dimension {}".format(data.ndim))
-def color_augmentation(image, channels=('rgb',)):
+def color_augmentation(image, channels=("rgb",)):
"""Converts an RGB image to different color channels.
Parameters
@@ -225,13 +230,13 @@ def color_augmentation(image, channels=('rgb',)):
"""
final_image = []
- if 'rgb' in channels:
+ if "rgb" in channels:
final_image.append(image)
- if 'yuv' in channels:
+ if "yuv" in channels:
final_image.append(rgb_to_yuv(image))
- if 'hsv' in channels:
+ if "hsv" in channels:
final_image.append(rgb_to_hsv(image))
return numpy.concatenate(final_image, axis=0)
@@ -251,26 +256,33 @@ def random_patches(image, block_size, n_random_patches=1):
hl = numpy.random.randint(0, h - bh, size=n_random_patches)
wl = numpy.random.randint(0, w - bw, size=n_random_patches)
for ch, cw in zip(hl, wl):
- yield image[..., ch:ch + bh, cw:cw + bw]
+ yield image[..., ch : ch + bh, cw : cw + bw]
-def extract_patches(image, block_size, block_overlap=(0, 0),
- n_random_patches=None):
+def extract_patches(image, block_size, block_overlap=(0, 0), n_random_patches=None):
"""Yields either all patches from an image or N random patches."""
if n_random_patches is None:
return blocks_generator(image, block_size, block_overlap)
else:
- return random_patches(
- image, block_size, n_random_patches=n_random_patches)
-
-
-def the_giant_video_loader(paddb, padfile,
- 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):
+ return random_patches(image, block_size, n_random_patches=n_random_patches)
+
+
+def the_giant_video_loader(
+ paddb,
+ padfile,
+ 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,
+):
"""Loads a video pad file frame by frame and optionally applies
transformations.
@@ -316,41 +328,40 @@ def the_giant_video_loader(paddb, padfile,
ValueError
If region is not whole or crop.
"""
- if region == 'whole':
+ if region == "whole":
generator = padfile.frames
- elif region == 'crop':
- generator = yield_faces(
- padfile, cropper=cropper, normalizer=normalizer)
+ elif region == "crop":
+ generator = yield_faces(padfile, cropper=cropper, normalizer=normalizer)
else:
raise ValueError("Invalid region value: `{}'".format(region))
if scaling_factor is not None:
- generator = (scale(frame, scaling_factor)
- for frame in generator)
+ generator = (scale(frame, scaling_factor) for frame in generator)
if patches:
if random_patches_per_frame is None:
generator = (
- patch for frame in generator
- for patch in blocks_generator(
- frame, block_size, block_overlap))
+ patch
+ for frame in generator
+ for patch in blocks_generator(frame, block_size, block_overlap)
+ )
else:
if padfile.attack_type is None:
random_patches_per_frame *= multiple_bonafide_patches
generator = (
- patch for frame in generator
+ patch
+ for frame in generator
for patch in random_sample(
- blocks(frame, block_size, block_overlap),
- random_patches_per_frame))
+ blocks(frame, block_size, block_overlap), random_patches_per_frame
+ )
+ )
if augment is not None:
generator = (augment(frame) for frame in generator)
if keep_pa_samples is not None and padfile.attack_type is not None:
- generator = (frame for frame in generator
- if random.random() < keep_pa_samples)
+ generator = (frame for frame in generator if random.random() < keep_pa_samples)
if keep_bf_samples is not None and padfile.attack_type is None:
- generator = (frame for frame in generator
- if random.random() < keep_bf_samples)
+ generator = (frame for frame in generator if random.random() < keep_bf_samples)
return generator