def _detect_faces(image_container):
"""Detects faces in a given image and returns it's bounding boxes and\
facial landmarks.
### Parameters
image: image to be searched on.
### Returns
(bounding_box, facial_landmarks) for the face.
"""
#print('_detect_faces - PID: {}'.format(os.getpid()))
detected_faces = _mtcnn_detect_faces(image_container.image)
if not detected_faces:
return Result(
'Failure',
f' File: {image_container.image_path} had zero faces detected.')
if len(detected_faces) > 1:
_, keypoints = _extract_center_face(image_container.image.shape,
detected_faces)
else:
#bounding_box = detected_faces[0]['box']
keypoints = detected_faces[0]['keypoints']
facial_landmarks = (keypoints['left_eye'], keypoints['right_eye'],
keypoints['nose'], keypoints['mouth_left'],
keypoints['mouth_right'])
return Result('Success', image_container, args=facial_landmarks)
def _log_results(Result):
""""""
if Result.get_result() == "Success":
logger.info(Result.get_payload())
return True
else:
logger.error(Result.get_payload())
return False
def _align_face(image_container, facial_landmarks, crop_shape=(112, 112)):
"""Align faces using the facial landmarks or the bounding box and crops\
them.
### Parameters
image: face image to be aligned.
facial_landmarks: facial landmarks for the face in the image.
bounding_box: optional bounding box.
crop_shape: optional shape for the crop.
### Returns
Face image aligned and cropped.
"""
# print('_align_faces - PID: {}'.format(os.getpid()))
# if crop_shape == (112, 112):
# pass
# else:
# pass
source_landmarks = np.array(
[
[30.2946, 51.6963],
[65.5318, 51.5014],
[48.0252, 71.7366],
[33.5493, 92.3655],
[62.7299, 92.2041],
],
dtype=np.float32,
)
if crop_shape == (112, 112):
source_landmarks[:, 0] += 8.0
facial_landmarks = np.asfarray(facial_landmarks)
transformation = transform.SimilarityTransform()
transformation.estimate(facial_landmarks, source_landmarks)
transformation_matrix = transformation.params[0:2, :]
try:
aligned_image = cv2.warpAffine(image_container.image,
transformation_matrix,
crop_shape,
borderValue=0.0)
return Result(
"Success",
ImageContainer(image=aligned_image,
image_path=image_container.image_path),
)
except Exception as exception:
return Result(
"Failure",
f"An error occurred, probably transformation_matrix is None -\
Error: {str(exception)}",
)
def _save_image(image_container, destination_path):
""""""
# print('_save_image - PID: {}'.format(os.getpid()))
folder, file_name = _split_file_path(image_container.image_path)
destination_path = os.path.join(destination_path, folder)
if not os.path.exists(destination_path):
os.makedirs(destination_path)
destination_path = os.path.join(destination_path, file_name + ".jpg")
if cv2.imwrite(destination_path,
cv2.cvtColor(image_container.image, cv2.COLOR_RGB2BGR)):
return Result("Success", f" Created file - Path: {destination_path}")
return Result("Failure", f" File {destination_path} not saved")
def _read_image(file_path):
"""
"""
#print('_read_image - PID: {}'.format(os.getpid()))
#class_id, sample = _split_file_path(file_path)
#image = cv2.imread(file_path)
#if not image:
# print('ué')
# logger.error(
# " Image couldn't be loaded - path: {}".format(file_path)
# )
# return None
#print('foi')
#return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), '{}/{}'.format(class_id, sample)
try:
image = cv2.cvtColor(cv2.imread(file_path), cv2.COLOR_BGR2RGB)
return Result('Success',
ImageContainer(image=image, image_path=file_path))
except OSError as exception:
return Result(
'Failure',
f" Image couldn't be loaded - path: {file_path}, Exception: \
{str(exception)}")