def detect_blurry_faces(self, face, t_mat, resized_image, filename):
""" Detect and move blurry face """
if not hasattr(self.args, 'blur_thresh') or not self.args.blur_thresh:
return None
blurry_file = None
aligned_landmarks = self.extractor.transform_points(
face.landmarks_as_xy(),
t_mat,
256,
48)
feature_mask = self.extractor.get_feature_mask(aligned_landmarks / 256,
256,
48)
feature_mask = cv2.blur(feature_mask, (10, 10))
isolated_face = cv2.multiply(
feature_mask,
resized_image.astype(float)).astype(np.uint8)
blurry, focus_measure = is_blurry(isolated_face, self.args.blur_thresh)
if blurry:
print("{}'s focus measure of {} was below the blur threshold, "
"moving to \"blurry\"".format(Path(filename).stem,
focus_measure))
blurry_file = get_folder(Path(self.output_dir) /
Path("blurry")) / Path(filename).stem
return blurry_file
def detect_blurry_faces(self, face, t_mat, resized_image, filename):
""" Detect and move blurry face """
if not hasattr(self.args, 'blur_thresh') or not self.args.blur_thresh:
return None
blurry_file = None
aligned_landmarks = self.extractor.transform_points(
face.landmarks_as_xy(),
t_mat,
256,
48)
feature_mask = self.extractor.get_feature_mask(aligned_landmarks / 256,
256,
48)
feature_mask = cv2.blur(feature_mask, (10, 10))
isolated_face = cv2.multiply(
feature_mask,
resized_image.astype(float)).astype(np.uint8)
blurry, focus_measure = is_blurry(isolated_face, self.args.blur_thresh)
if blurry:
print("{}'s focus measure of {} was below the blur threshold, "
"moving to \"blurry\"".format(Path(filename).stem,
focus_measure))
blurry_file = get_folder(Path(self.output_dir) /
Path("blurry")) / Path(filename).stem
return blurry_file
def process(self, output_item):
""" Detect and move blurry face """
extractor = AlignerExtract()
for idx, face in enumerate(output_item["detected_faces"]):
resized_face = output_item["resized_faces"][idx]
dims = resized_face.shape[:2]
size = dims[0]
t_mat = output_item["t_mats"][idx]
aligned_landmarks = extractor.transform_points(
face.landmarksXY, t_mat, size, 48)
feature_mask = extractor.get_feature_mask(aligned_landmarks / size,
size, 48)
feature_mask = cv2.blur(feature_mask, (10, 10))
isolated_face = cv2.multiply(feature_mask,
resized_face.astype(float)).astype(
np.uint8)
blurry, focus_measure = is_blurry(isolated_face, self.blur_thresh)
if blurry:
blur_folder = output_item["output_file"].parts[:-1]
blur_folder = get_folder(Path(*blur_folder) / Path("blurry"))
frame_name = output_item["output_file"].parts[-1]
output_item["output_file"] = blur_folder / Path(frame_name)
if self.verbose:
print("{}'s focus measure of {} was below the blur "
"threshold, moving to \"blurry\"".format(
frame_name, focus_measure))
def process(self, output_item):
""" Detect and move blurry face """
extractor = AlignerExtract()
for face in output_item["detected_faces"]:
aligned_landmarks = face.aligned_landmarks
resized_face = face.aligned_face
size = face.aligned["size"]
feature_mask = extractor.get_feature_mask(
aligned_landmarks / size,
size, 48)
feature_mask = cv2.blur( # pylint: disable=no-member
feature_mask, (10, 10))
isolated_face = cv2.multiply( # pylint: disable=no-member
feature_mask,
resized_face.astype(float)).astype(np.uint8)
blurry, focus_measure = is_blurry(isolated_face, self.blur_thresh)
if blurry:
blur_folder = output_item["output_file"].parts[:-1]
blur_folder = get_folder(Path(*blur_folder) / Path("blurry"))
frame_name = output_item["output_file"].parts[-1]
output_item["output_file"] = blur_folder / Path(frame_name)
if self.verbose:
print("{}'s focus measure of {} was below the blur "
"threshold, moving to \"blurry\"".format(
frame_name, focus_measure))
def handleImage(self, image, filename):
faces = self.get_faces(image)
process_faces = [(idx, face) for idx, face in faces]
# Run image rotator if requested and no faces found
if self.arguments.rotate_images.lower() == 'on' and len(
process_faces) == 0:
process_faces, image = self.imageRotator(image)
rvals = []
for idx, face in process_faces:
# Draws landmarks for debug
if self.arguments.debug_landmarks:
for (x, y) in face.landmarksAsXY():
cv2.circle(image, (x, y), 2, (0, 0, 255), -1)
resized_image, t_mat = self.extractor.extract(
image, face, 256, self.arguments.align_eyes)
output_file = get_folder(self.output_dir) / Path(filename).stem
# Detect blurry images
if self.arguments.blur_thresh is not None:
aligned_landmarks = self.extractor.transform_points(
face.landmarksAsXY(), t_mat, 256, 48)
feature_mask = self.extractor.get_feature_mask(
aligned_landmarks / 256, 256, 48)
feature_mask = cv2.blur(feature_mask, (10, 10))
isolated_face = cv2.multiply(
feature_mask, resized_image.astype(float)).astype(np.uint8)
blurry, focus_measure = is_blurry(isolated_face,
self.arguments.blur_thresh)
# print("{} focus measure: {}".format(Path(filename).stem, focus_measure))
# cv2.imshow("Isolated Face", isolated_face)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
if blurry:
print(
"{}'s focus measure of {} was below the blur threshold, moving to \"blurry\""
.format(Path(filename).stem, focus_measure))
output_file = get_folder(
Path(self.output_dir) /
Path("blurry")) / Path(filename).stem
cv2.imwrite(
'{}_{}{}'.format(str(output_file), str(idx),
Path(filename).suffix), resized_image)
f = {
"r": face.r,
"x": face.x,
"w": face.w,
"y": face.y,
"h": face.h,
"landmarksXY": face.landmarksAsXY()
}
rvals.append(f)
return rvals