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