class Extract(object):
""" The extract process. """
def __init__(self, arguments):
self.args = arguments
self.images = Images(self.args)
self.faces = Faces(self.args)
self.alignments = Alignments(self.args)
self.output_dir = self.faces.output_dir
self.export_face = True
self.save_interval = self.args.save_interval if hasattr(
self.args, "save_interval") else None
def process(self):
""" Perform the extraction process """
print('Starting, this may take a while...')
Utils.set_verbosity(self.args.verbose)
if (hasattr(self.args, 'multiprocess') and self.args.multiprocess
and GPUStats().device_count == 0):
# TODO Checking that there is no available GPU is not
# necessarily an indicator of whether the user is actually
# using the CPU. Maybe look to implement further checks on
# dlib/tensorflow compilations
self.extract_multi_process()
else:
self.extract_single_process()
self.write_alignments()
images, faces = Utils.finalize(self.images.images_found,
self.faces.num_faces_detected,
self.faces.verify_output)
self.images.images_found = images
self.faces.num_faces_detected = faces
def write_alignments(self):
self.alignments.write_alignments(self.faces.faces_detected)
def extract_single_process(self):
""" Run extraction in a single process """
frame_no = 0
for filename in tqdm(self.images.input_images, file=sys.stdout):
filename, faces = self.process_single_image(filename)
self.faces.faces_detected[os.path.basename(filename)] = faces
frame_no += 1
if frame_no == self.save_interval:
self.write_alignments()
frame_no = 0
def extract_multi_process(self):
""" Run the extraction on the correct number of processes """
frame_no = 0
for filename, faces in tqdm(pool_process(self.process_single_image,
self.images.input_images),
total=self.images.images_found,
file=sys.stdout):
self.faces.num_faces_detected += 1
self.faces.faces_detected[os.path.basename(filename)] = faces
frame_no += 1
if frame_no == self.save_interval:
self.write_alignments()
frame_no = 0
def process_single_image(self, filename):
""" Detect faces in an image. Rotate the image the specified amount
until at least one face is found, or until image rotations are
depleted.
Once at least one face has been detected, pass to
process_single_face to process the individual faces """
retval = filename, list()
try:
image = Utils.cv2_read_write('read', filename)
for angle in self.images.rotation_angles:
currentimage = Utils.rotate_image_by_angle(image, angle)
faces = self.faces.get_faces(currentimage, angle)
process_faces = [(idx, face) for idx, face in faces]
if process_faces and angle != 0 and self.args.verbose:
print("found face(s) by rotating image "
"{} degrees".format(angle))
if process_faces:
break
final_faces = [
self.process_single_face(idx, face, filename, currentimage)
for idx, face in process_faces
]
retval = filename, final_faces
except Exception as err:
if self.args.verbose:
print("Failed to extract from image: "
"{}. Reason: {}".format(filename, err))
return retval
def process_single_face(self, idx, face, filename, image):
""" Perform processing on found faces """
output_file = self.output_dir / Path(
filename).stem if self.export_face else None
self.faces.draw_landmarks_on_face(face, image)
resized_face, t_mat = self.faces.extractor.extract(
image, face, 256, self.faces.align_eyes)
blurry_file = self.faces.detect_blurry_faces(face, t_mat, resized_face,
filename)
output_file = blurry_file if blurry_file else output_file
if self.export_face:
filename = "{}_{}{}".format(str(output_file), str(idx),
Path(filename).suffix)
Utils.cv2_read_write('write', filename, resized_face)
return {
"r": face.r,
"x": face.x,
"w": face.w,
"y": face.y,
"h": face.h,
"landmarksXY": face.landmarks_as_xy()
}
class Extract(object):
""" The extract process. """
def __init__(self, arguments):
self.args = arguments
self.images = Images(self.args)
self.faces = Faces(self.args)
self.alignments = Alignments(self.args)
self.output_dir = self.faces.output_dir
self.export_face = True
def process(self):
""" Perform the extraction process """
print('Starting, this may take a while...')
Utils.set_verbosity(self.args.verbose)
if hasattr(self.args, 'processes') and self.args.processes > 1:
self.extract_multi_process()
else:
self.extract_single_process()
self.alignments.write_alignments(self.faces.faces_detected)
images, faces = Utils.finalize(self.images.images_found,
self.faces.num_faces_detected,
self.faces.verify_output)
self.images.images_found = images
self.faces.num_faces_detected = faces
def extract_single_process(self):
""" Run extraction in a single process """
for filename in tqdm(self.images.input_images):
filename, faces = self.process_single_image(filename)
self.faces.faces_detected[os.path.basename(filename)] = faces
def extract_multi_process(self):
""" Run the extraction on the correct number of processes """
for filename, faces in tqdm(pool_process(
self.process_single_image,
self.images.input_images,
processes=self.args.processes),
total=self.images.images_found):
self.faces.num_faces_detected += 1
self.faces.faces_detected[os.path.basename(filename)] = faces
def process_single_image(self, filename):
""" Detect faces in an image. Rotate the image the specified amount
until at least one face is found, or until image rotations are
depleted.
Once at least one face has been detected, pass to process_single_face
to process the individual faces """
retval = filename, list()
try:
image = Utils.cv2_read_write('read', filename)
for angle in self.images.rotation_angles:
image = Utils.rotate_image_by_angle(image, angle)
faces = self.faces.get_faces(image, angle)
process_faces = [(idx, face) for idx, face in faces]
if process_faces and angle != 0 and self.args.verbose:
print("found face(s) by rotating image {} degrees".format(
angle))
if process_faces:
break
final_faces = [
self.process_single_face(idx, face, filename, image)
for idx, face in process_faces
]
retval = filename, final_faces
except Exception as err:
if self.args.verbose:
print("Failed to extract from image: {}. Reason: {}".format(
filename, err))
return retval
def process_single_face(self, idx, face, filename, image):
""" Perform processing on found faces """
output_file = self.output_dir / Path(
filename).stem if self.export_face else None
self.faces.draw_landmarks_on_face(face, image)
resized_face, t_mat = self.faces.extractor.extract(
image, face, 256, self.faces.align_eyes)
blurry_file = self.faces.detect_blurry_faces(face, t_mat, resized_face,
filename)
output_file = blurry_file if blurry_file else output_file
if self.export_face:
filename = "{}_{}{}".format(str(output_file), str(idx),
Path(filename).suffix)
Utils.cv2_read_write('write', filename, resized_face)
return {
"r": face.r,
"x": face.x,
"w": face.w,
"y": face.y,
"h": face.h,
"landmarksXY": face.landmarksAsXY()
}
class Extract(object):
""" The extract process. """
def __init__(self, arguments):
self.args = arguments
self.images = Images(self.args)
self.faces = Faces(self.args)
self.alignments = Alignments(self.args)
self.output_dir = self.faces.output_dir
self.export_face = True
def process(self):
""" Perform the extraction process """
print('Starting, this may take a while...')
Utils.set_verbosity(self.args.verbose)
if hasattr(self.args, 'processes') and self.args.processes > 1:
self.extract_multi_process()
else:
self.extract_single_process()
self.alignments.write_alignments(self.faces.faces_detected)
images, faces = Utils.finalize(self.images.images_found,
self.faces.num_faces_detected,
self.faces.verify_output)
self.images.images_found = images
self.faces.num_faces_detected = faces
def extract_single_process(self):
""" Run extraction in a single process """
for filename in tqdm(self.images.input_images, file=sys.stdout):
filename, faces = self.process_single_image(filename)
self.faces.faces_detected[os.path.basename(filename)] = faces
def extract_multi_process(self):
""" Run the extraction on the correct number of processes """
for filename, faces in tqdm(pool_process(self.process_single_image,
self.images.input_images,
processes=self.args.processes),
total=self.images.images_found,
file=sys.stdout):
self.faces.num_faces_detected += 1
self.faces.faces_detected[os.path.basename(filename)] = faces
def process_single_image(self, filename):
""" Detect faces in an image. Rotate the image the specified amount
until at least one face is found, or until image rotations are
depleted.
Once at least one face has been detected, pass to
process_single_face to process the individual faces """
retval = filename, list()
try:
image = Utils.cv2_read_write('read', filename)
for angle in self.images.rotation_angles:
currentimage = Utils.rotate_image_by_angle(image, angle)
faces = self.faces.get_faces(currentimage, angle)
process_faces = [(idx, face) for idx, face in faces]
if process_faces and angle != 0 and self.args.verbose:
print("found face(s) by rotating image {} degrees".format(angle))
if process_faces:
break
final_faces = [self.process_single_face(idx, face, filename, currentimage)
for idx, face in process_faces]
retval = filename, final_faces
except Exception as err:
if self.args.verbose:
print("Failed to extract from image: {}. Reason: {}".format(filename, err))
return retval
def process_single_face(self, idx, face, filename, image):
""" Perform processing on found faces """
output_file = self.output_dir / Path(filename).stem if self.export_face else None
self.faces.draw_landmarks_on_face(face, image)
resized_face, t_mat = self.faces.extractor.extract(image,
face,
256,
self.faces.align_eyes)
blurry_file = self.faces.detect_blurry_faces(face, t_mat, resized_face, filename)
output_file = blurry_file if blurry_file else output_file
if self.export_face:
filename = "{}_{}{}".format(str(output_file), str(idx), Path(filename).suffix)
Utils.cv2_read_write('write', filename, resized_face)
return {"r": face.r,
"x": face.x,
"w": face.w,
"y": face.y,
"h": face.h,
"landmarksXY": face.landmarksAsXY()}