def __call__(self, results):
        """
        Performs Center crop operations.
        Args:
            imgs: List where each item is a PIL.Image.
            For example, [PIL.Image0, PIL.Image1, PIL.Image2, ...]
        return:
            ccrop_imgs: List where each item is a PIL.Image after Center crop.
        """
        imgs = results['imgs']
        ccrop_imgs = []
        th, tw = self.target_size, self.target_size
        if isinstance(imgs, paddle.Tensor):
            h, w = imgs.shape[-2:]
            x1 = int(round((w - tw) / 2.0)) if self.do_round else (w - tw) // 2
            y1 = int(round((h - th) / 2.0)) if self.do_round else (h - th) // 2
            ccrop_imgs = imgs[:, :, y1:y1 + th, x1:x1 + tw]
        else:
            for img in imgs:
                if self.backend == 'pillow':
                    w, h = img.size
                elif self.backend == 'cv2':
                    h, w, _ = img.shape
                else:
                    raise NotImplementedError
                assert (w >= self.target_size) and (h >= self.target_size), \
                    "image width({}) and height({}) should be larger than crop size".format(
                        w, h, self.target_size)
                x1 = int(round((w - tw) / 2.0)) if self.do_round else (
                    w - tw) // 2
                y1 = int(round((h - th) / 2.0)) if self.do_round else (
                    h - th) // 2
                if self.backend == 'cv2':
                    ccrop_imgs.append(img[y1:y1 + th, x1:x1 + tw])
                elif self.backend == 'pillow':
                    ccrop_imgs.append(img.crop((x1, y1, x1 + tw, y1 + th)))
        results['imgs'] = ccrop_imgs
        return results

        Performs Center crop operations.
        Args:
            imgs: List where each item is a PIL.Image.
            For example, [PIL.Image0, PIL.Image1, PIL.Image2, ...]
        return:
            ccrop_imgs: List where each item is a PIL.Image after Center crop.
        
deploy/pipeline/pphuman/video_action_preprocess.py