def process_image(self, filename):
# TODO move the model load and the converter creation in a method called on init, but after the arg parsing
(face_A,
face_B) = ('/decoder_A.h5',
'/decoder_B.h5') if not self.arguments.swap_model else (
'/decoder_B.h5', '/decoder_A.h5')
model_dir = self.arguments.model_dir
encoder.load_weights(model_dir + "/encoder.h5")
decoder_A.load_weights(model_dir + face_A)
decoder_B.load_weights(model_dir + face_B)
converter = PluginLoader.get_converter("Masked")(autoencoder_B)
try:
image = cv2.imread(filename)
for (idx, face) in enumerate(detect_faces(image)):
if idx > 0 and self.arguments.verbose:
print('- Found more than one face!')
self.verify_output = True
image = converter.patch_image(image, face)
self.faces_detected = self.faces_detected + 1
output_file = self.output_dir / Path(filename).name
cv2.imwrite(str(output_file), image)
except Exception as e:
print('Failed to convert image: {}. Reason: {}'.format(
filename, e))
def _convert_frame(frame, convert_colors = True):
if convert_colors:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # Swap RGB to BGR to work with OpenCV
for face in detect_faces(frame, "cnn"):
if (not face_filter) or (face_filter and filter.check(face)):
frame = converter.patch_image(frame, face)
frame = frame.astype(numpy.float32)
if convert_colors:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # Swap RGB to BGR to work with OpenCV
return frame
def get_image_face(self, filename):
try:
image = cv2.imread(filename)
for (idx, face) in enumerate(detect_faces(image)):
if idx > 0 and self.arguments.verbose:
print('- Found more than one face!')
self.verify_output = True
return image, face
except Exception as e:
print('Failed to convert image: {}. Reason: {}'.format(
filename, e))
def get_faces(self, image):
faces_count = 0
for face in detect_faces(image):
# if self.filter is not None and not self.filter.check(face):
# print('Skipping not recognized face!')
# continue
yield faces_count, face
self.faces_detected = self.faces_detected + 1
faces_count += 1
def get_faces(self, image):
faces_count = 0
for face in detect_faces(image, self.arguments.detector):
if self.filter is not None and not self.filter.check(face):
print('Skipping not recognized face!')
continue
yield faces_count, face
self.faces_detected = self.faces_detected + 1
faces_count += 1
print("DEBUG : ")
if faces_count > 1 and self.arguments.verbose:
print('Note: Found more than one face in an image!')
self.verify_output = True
def get_faces(self, image, rotation=0):
faces_count = 0
faces = detect_faces(image, rotation, self.arguments.detector)
for face in faces:
if self.filter is not None and not self.filter.check(face):
print('Skipping not recognized face!')
continue
yield faces_count, face
self.num_faces_detected += 1
faces_count += 1
if faces_count > 1 and self.arguments.verbose:
self.verify_output = True
def get_faces(self, image):
faces_count = 0
for face in detect_faces(image):
if self.filter is not None and not self.filter.check(face):
print('Skipping not recognized face!')
continue
yield faces_count, face
self.faces_detected = self.faces_detected + 1
faces_count +=1
if faces_count > 0 and self.arguments.verbose:
print('Note: Found more than one face in an image!')
self.verify_output = True
def process_image(self, filename):
try:
image = cv2.imread(filename)
for (idx, face) in enumerate(detect_faces(image)):
if idx > 0 and self.arguments.verbose:
print('- Found more than one face!')
self.verify_output = True
image = self.converter.patch_image(image, face)
self.faces_detected = self.faces_detected + 1
output_file = self.output_dir / Path(filename).name
cv2.imwrite(str(output_file), image)
except Exception as e:
print('Failed to convert image: {}. Reason: {}'.format(
filename, e))
def get_faces(self, image, rotation=0):
""" Extract the faces from an image """
faces_count = 0
faces = detect_faces(image, self.args.detector, self.args.verbose, rotation)
for face in faces:
if self.filter and not self.filter.check(face):
if self.args.verbose:
print("Skipping not recognized face!")
continue
yield faces_count, face
self.num_faces_detected += 1
faces_count += 1
if faces_count > 1 and self.args.verbose:
self.verify_output = True
def get_faces(self, image, rotation=0):
""" Extract the faces from an image """
faces_count = 0
faces = detect_faces(image, self.args.detector, self.args.verbose, rotation)
for face in faces:
if self.filter and not self.filter.check(face):
if self.args.verbose:
print("Skipping not recognized face!")
continue
yield faces_count, face
self.num_faces_detected += 1
faces_count += 1
if faces_count > 1 and self.args.verbose:
self.verify_output = True
def process_image(self, filename):
extractor = PluginLoader.get_extractor("Align")()
try:
image = cv2.imread(filename)
for (idx, face) in enumerate(detect_faces(image)):
if idx > 0 and self.arguments.verbose:
print('- Found more than one face!')
self.verify_output = True
resized_image = extractor.extract(image, face, 256)
output_file = self.output_dir / Path(filename).stem
cv2.imwrite(
str(output_file) + str(idx) + Path(filename).suffix,
resized_image)
self.faces_detected = self.faces_detected + 1
except Exception as e:
print('Failed to extract from image: {}. Reason: {}'.format(
filename, e))
def extract(input_path, output_path):
files = os.listdir(input_path)
if not len(files):
raise Exception("no files inside {0}!!!".format(input_path))
extractor = PluginLoader.get_extractor("Align")()
for n, _file in enumerate(files):
_file_id = os.path.join(input_path, _file)
#print (_file_id)
output = os.path.join(
output_path, _file) #"{0}.{1}".format(n, _file.split(".")[1]))
if not os.path.exists(output):
if os.path.isfile(_file_id):
print("file {0}/{1}".format(n, len(files)), _file_id)
image = cv2.imread(_file_id)
try:
for (idx, face) in enumerate(detect_faces(image)):
resized_image = extractor.extract(image, face, 256)
cv2.imwrite(output, resized_image)
except Exception as e:
print(
'Failed to extract from image: {}. Reason: {}'.format(
_file, e))
else:
print("Jump ", output)
