How to use the mtcnn.mtcnn.StageStatus function in mtcnn

pick = self.__nms(total_boxes.copy(), 0.7, 'Union')
            total_boxes = total_boxes[pick, :]

            regw = total_boxes[:, 2] - total_boxes[:, 0]
            regh = total_boxes[:, 3] - total_boxes[:, 1]

            qq1 = total_boxes[:, 0] + total_boxes[:, 5] * regw
            qq2 = total_boxes[:, 1] + total_boxes[:, 6] * regh
            qq3 = total_boxes[:, 2] + total_boxes[:, 7] * regw
            qq4 = total_boxes[:, 3] + total_boxes[:, 8] * regh

            total_boxes = np.transpose(np.vstack([qq1, qq2, qq3, qq4, total_boxes[:, 4]]))
            total_boxes = self.__rerec(total_boxes.copy())

            total_boxes[:, 0:4] = np.fix(total_boxes[:, 0:4]).astype(np.int32)
            status = StageStatus(self.__pad(total_boxes.copy(), stage_status.width, stage_status.height),
                                 width=stage_status.width, height=stage_status.height)

        return total_boxes, status

def __stage3(self, img, total_boxes, stage_status: StageStatus):
        """
        Third stage of the MTCNN.

        :param img:
        :param total_boxes:
        :param stage_status:
        :return:
        """
        num_boxes = total_boxes.shape[0]
        if num_boxes == 0:
            return total_boxes, np.empty(shape=(0,))

        total_boxes = np.fix(total_boxes).astype(np.int32)

        status = StageStatus(self.__pad(total_boxes.copy(), stage_status.width, stage_status.height),
                             width=stage_status.width, height=stage_status.height)

        tempimg = np.zeros((48, 48, 3, num_boxes))

        for k in range(0, num_boxes):

            tmp = np.zeros((int(status.tmph[k]), int(status.tmpw[k]), 3))

            tmp[status.dy[k] - 1:status.edy[k], status.dx[k] - 1:status.edx[k], :] = \
                img[status.y[k] - 1:status.ey[k], status.x[k] - 1:status.ex[k], :]

            if tmp.shape[0] > 0 and tmp.shape[1] > 0 or tmp.shape[0] == 0 and tmp.shape[1] == 0:
                tempimg[:, :, :, k] = cv2.resize(tmp, (48, 48), interpolation=cv2.INTER_AREA)
            else:
                return np.empty(shape=(0,)), np.empty(shape=(0,))

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 = []

pick = self.__nms(total_boxes.copy(), 0.7, 'Union')
            total_boxes = total_boxes[pick, :]

            regw = total_boxes[:, 2] - total_boxes[:, 0]
            regh = total_boxes[:, 3] - total_boxes[:, 1]

            qq1 = total_boxes[:, 0] + total_boxes[:, 5] * regw
            qq2 = total_boxes[:, 1] + total_boxes[:, 6] * regh
            qq3 = total_boxes[:, 2] + total_boxes[:, 7] * regw
            qq4 = total_boxes[:, 3] + total_boxes[:, 8] * regh

            total_boxes = np.transpose(np.vstack([qq1, qq2, qq3, qq4, total_boxes[:, 4]]))
            total_boxes = self.__rerec(total_boxes.copy())

            total_boxes[:, 0:4] = np.fix(total_boxes[:, 0:4]).astype(np.int32)
            status = StageStatus(self.__pad(total_boxes.copy(), stage_status.width, stage_status.height),
                                 width=stage_status.width, height=stage_status.height)

        return total_boxes, status

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 = []

def __stage3(self, img, total_boxes, stage_status: StageStatus):
        """
        Third stage of the MTCNN.

        :param img:
        :param total_boxes:
        :param stage_status:
        :return:
        """
        num_boxes = total_boxes.shape[0]
        if num_boxes == 0:
            return total_boxes, np.empty(shape=(0,))

        total_boxes = np.fix(total_boxes).astype(np.int32)

        status = StageStatus(self.__pad(total_boxes.copy(), stage_status.width, stage_status.height),
                             width=stage_status.width, height=stage_status.height)

        tempimg = np.zeros((48, 48, 3, num_boxes))

        for k in range(0, num_boxes):

            tmp = np.zeros((int(status.tmph[k]), int(status.tmpw[k]), 3))

            tmp[status.dy[k] - 1:status.edy[k], status.dx[k] - 1:status.edx[k], :] = \
                img[status.y[k] - 1:status.ey[k], status.x[k] - 1:status.ex[k], :]

            if tmp.shape[0] > 0 and tmp.shape[1] > 0 or tmp.shape[0] == 0 and tmp.shape[1] == 0:
                tempimg[:, :, :, k] = cv2.resize(tmp, (48, 48), interpolation=cv2.INTER_AREA)
            else:
                return np.empty(shape=(0,)), np.empty(shape=(0,))