Third stage of the MTCNN.

        :param img:
        :param total_boxes:
        :param stage_status:
        :return:

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,))

        tempimg = (tempimg - 127.5) * 0.0078125
        tempimg1 = np.transpose(tempimg, (3, 1, 0, 2))

        out = self.__onet.feed(tempimg1)
        out0 = np.transpose(out[0])
        out1 = np.transpose(out[1])
        out2 = np.transpose(out[2])

        score = out2[1, :]

        points = out1

        ipass = np.where(score > self.__steps_threshold[2])

        points = points[:, ipass[0]]

        total_boxes = np.hstack([total_boxes[ipass[0], 0:4].copy(), np.expand_dims(score[ipass].copy(), 1)])

        mv = out0[:, ipass[0]]

        w = total_boxes[:, 2] - total_boxes[:, 0] + 1
        h = total_boxes[:, 3] - total_boxes[:, 1] + 1

        points[0:5, :] = np.tile(w, (5, 1)) * points[0:5, :] + np.tile(total_boxes[:, 0], (5, 1)) - 1
        points[5:10, :] = np.tile(h, (5, 1)) * points[5:10, :] + np.tile(total_boxes[:, 1], (5, 1)) - 1

        if total_boxes.shape[0] > 0:
            total_boxes = self.__bbreg(total_boxes.copy(), np.transpose(mv))
            pick = self.__nms(total_boxes.copy(), 0.7, 'Min')
            total_boxes = total_boxes[pick, :]
            points = points[:, pick]

        return total_boxes, points