def __call__(self, results):
        """
        Performs resize operations.
        Args:
            imgs (Sequence[PIL.Image]): List where each item is a PIL.Image.
            For example, [PIL.Image0, PIL.Image1, PIL.Image2, ...]
        return:
            resized_imgs: List where each item is a PIL.Image after scaling.
        """
        imgs = results['imgs']
        resized_imgs = []
        for i in range(len(imgs)):
            img = imgs[i]
            if isinstance(img, np.ndarray):
                h, w, _ = img.shape
            elif isinstance(img, Image.Image):
                w, h = img.size
            else:
                raise NotImplementedError

            if w <= h:
                ow = self.short_size
                if self.fixed_ratio:  # default is True
                    oh = int(self.short_size * 4.0 / 3.0)
                elif not self.keep_ratio:  # no
                    oh = self.short_size
                else:
                    scale_factor = self.short_size / w
                    oh = int(h * float(scale_factor) +
                             0.5) if self.do_round else int(h *
                                                            self.short_size / w)
                    ow = int(w * float(scale_factor) +
                             0.5) if self.do_round else int(w *
                                                            self.short_size / h)
            else:
                oh = self.short_size
                if self.fixed_ratio:
                    ow = int(self.short_size * 4.0 / 3.0)
                elif not self.keep_ratio:  # no
                    ow = self.short_size
                else:
                    scale_factor = self.short_size / h
                    oh = int(h * float(scale_factor) +
                             0.5) if self.do_round else int(h *
                                                            self.short_size / w)
                    ow = int(w * float(scale_factor) +
                             0.5) if self.do_round else int(w *
                                                            self.short_size / h)

            if type(img) == np.ndarray:
                img = Image.fromarray(img, mode='RGB')

            if self.backend == 'pillow':
                resized_imgs.append(img.resize((ow, oh), Image.BILINEAR))
            elif self.backend == 'cv2' and (self.keep_ratio is not None):
                resized_imgs.append(
                    cv2.resize(
                        img, (ow, oh), interpolation=cv2.INTER_LINEAR))
            else:
                resized_imgs.append(
                    Image.fromarray(
                        cv2.resize(
                            np.asarray(img), (ow, oh),
                            interpolation=cv2.INTER_LINEAR)))
        results['imgs'] = resized_imgs
        return results

        Performs resize operations.
        Args:
            imgs (Sequence[PIL.Image]): List where each item is a PIL.Image.
            For example, [PIL.Image0, PIL.Image1, PIL.Image2, ...]
        return:
            resized_imgs: List where each item is a PIL.Image after scaling.
        
deploy/pipeline/pphuman/video_action_preprocess.py