def detect_faces(self, img) -> list:
"""
Detects bounding boxes from the specified image.
:param img: image to process
:return: list containing all the bounding boxes detected with their keypoints.
"""
if img is None or not hasattr(img, "shape"):
raise InvalidImage("Image not valid.")
height, width, _ = img.shape
stage_status = StageStatus(width=width, height=height)
m = 12 / self.__min_face_size
min_layer = np.amin([height, width]) * m
scales = self.__compute_scale_pyramid(m, min_layer)
stages = [self.__stage1, self.__stage2, self.__stage3]
result = [scales, stage_status]
# We pipe here each of the stages
for stage in stages:
result = stage(img, result[0], result[1])
[total_boxes, points] = result
bounding_boxes = []
for bounding_box in total_boxes:
bounding_boxes.append([
int(bounding_box[0]),
int(bounding_box[1]),
int(bounding_box[2] - bounding_box[0]),
int(bounding_box[3] - bounding_box[1])
])
return bounding_boxes
def detect_faces(self, img) -> list:
"""
Detects bounding boxes from the specified image.
:param img: image to process
:return: list containing all the bounding boxes detected with their keypoints.
"""
if img is None or not hasattr(img, "shape"):
raise InvalidImage("Image not valid.")
height, width, _ = img.shape
stage_status = StageStatus(width=width, height=height)
m = 12 / self._min_face_size
min_layer = np.amin([height, width]) * m
scales = self.__compute_scale_pyramid(m, min_layer)
stages = [self.__stage1, self.__stage2, self.__stage3]
result = [scales, stage_status]
# We pipe here each of the stages
for stage in stages:
result = stage(img, result[0], result[1])
if self.topless:
# return result of last convolutional layer
return result
[total_boxes, points] = result
bounding_boxes = []
for bounding_box, keypoints in zip(total_boxes, points.T):
bounding_boxes.append({
'box': [
max(0, int(bounding_box[0])),
max(0, int(bounding_box[1])),
int(bounding_box[2] - bounding_box[0]),
int(bounding_box[3] - bounding_box[1])
],
'confidence':
bounding_box[-1],
'keypoints': {
'left_eye': (int(keypoints[0]), int(keypoints[5])),
'right_eye': (int(keypoints[1]), int(keypoints[6])),
'nose': (int(keypoints[2]), int(keypoints[7])),
'mouth_left': (int(keypoints[3]), int(keypoints[8])),
'mouth_right': (int(keypoints[4]), int(keypoints[9])),
}
})
return bounding_boxes