def get_reproj_normal_error()

in lib/evaluator.py [0:0]

• 33 lines of code
• 8 McCabe index (conditional complexity)

    def get_reproj_normal_error(self, frontal=True, back=True, left=True, right=True, save_demo_img=None):
        # reproj error
        # if save_demo_img is not None, save a visualization at the given path (etc, "./test.png")
        if self._normal_render is None:
            print("In order to use normal render, "
                  "you have to call init_gl() before initialing any evaluator objects.")
            return -1

        side_cnt = 0
        total_error = 0
        demo_list = []
        if frontal:
            side_cnt += 1
            error, src_normal, tgt_normal = self._get_reproj_normal_error(0)
            total_error += error
            demo_list.append(np.concatenate([src_normal, tgt_normal], axis=0))
        if back:
            side_cnt += 1
            error, src_normal, tgt_normal = self._get_reproj_normal_error(180)
            total_error += error
            demo_list.append(np.concatenate([src_normal, tgt_normal], axis=0))
        if left:
            side_cnt += 1
            error, src_normal, tgt_normal = self._get_reproj_normal_error(90)
            total_error += error
            demo_list.append(np.concatenate([src_normal, tgt_normal], axis=0))
        if right:
            side_cnt += 1
            error, src_normal, tgt_normal = self._get_reproj_normal_error(270)
            total_error += error
            demo_list.append(np.concatenate([src_normal, tgt_normal], axis=0))
        if save_demo_img is not None:
            res_array = np.concatenate(demo_list, axis=1)
            res_img = Image.fromarray((res_array * 255).astype(np.uint8))
            res_img.save(save_demo_img)
        return total_error / side_cnt