def get_face_bb(self, image):
faceboxes = []
start_time = time.time()
fraction = 4.0
image = cv2.resize(image, (0, 0), fx=1.0 / fraction, fy=1.0 / fraction)
detections = self.face_net.detect_from_image(image)
tqdm.write("Face Detector Frequency: {:.2f}Hz for {} Faces".format(
1 / (time.time() - start_time), len(detections)))
for result in detections:
# scale back up to image size
box = result[:4]
confidence = result[4]
if gaze_tools.box_in_image(box, image) and confidence > 0.8:
box = [x * fraction for x in box] # scale back up
diff_height_width = (box[3] - box[1]) - (box[2] - box[0])
offset_y = int(abs(diff_height_width / 2))
box_moved = gaze_tools.move_box(box, [0, offset_y])
# Make box square.
facebox = gaze_tools.get_square_box(box_moved)
faceboxes.append(facebox)
return faceboxes
def get_face_bb(self, image):
faceboxes = []
fraction = 4.0
image = cv2.resize(image, (0, 0), fx=1.0 / fraction, fy=1.0 / fraction)
detections = self.face_net.detect_from_image(image)
for result in detections:
# scale back up to image size
box = result[:4]
confidence = result[4]
if gaze_tools.box_in_image(box, image) and confidence > 0.6:
box = [x * fraction for x in box] # scale back up
diff_height_width = (box[3] - box[1]) - (box[2] - box[0])
offset_y = int(abs(diff_height_width / 2))
box_moved = gaze_tools.move_box(box, [0, offset_y])
# Make box square.
facebox = gaze_tools.get_square_box(box_moved)
faceboxes.append(facebox)
return faceboxes