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| # Copyright 2024 The HuggingFace Team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import warnings | |
| from typing import List, Optional, Union | |
| import numpy as np | |
| import PIL | |
| import torch | |
| from .image_processor import VaeImageProcessor, is_valid_image, is_valid_image_imagelist | |
| class VideoProcessor(VaeImageProcessor): | |
| r"""Simple video processor.""" | |
| def preprocess_video(self, video, height: Optional[int] = None, width: Optional[int] = None) -> torch.Tensor: | |
| r""" | |
| Preprocesses input video(s). | |
| Args: | |
| video (`List[PIL.Image]`, `List[List[PIL.Image]]`, `torch.Tensor`, `np.array`, `List[torch.Tensor]`, `List[np.array]`): | |
| The input video. It can be one of the following: | |
| * List of the PIL images. | |
| * List of list of PIL images. | |
| * 4D Torch tensors (expected shape for each tensor `(num_frames, num_channels, height, width)`). | |
| * 4D NumPy arrays (expected shape for each array `(num_frames, height, width, num_channels)`). | |
| * List of 4D Torch tensors (expected shape for each tensor `(num_frames, num_channels, height, | |
| width)`). | |
| * List of 4D NumPy arrays (expected shape for each array `(num_frames, height, width, num_channels)`). | |
| * 5D NumPy arrays: expected shape for each array `(batch_size, num_frames, height, width, | |
| num_channels)`. | |
| * 5D Torch tensors: expected shape for each array `(batch_size, num_frames, num_channels, height, | |
| width)`. | |
| height (`int`, *optional*, defaults to `None`): | |
| The height in preprocessed frames of the video. If `None`, will use the `get_default_height_width()` to | |
| get default height. | |
| width (`int`, *optional*`, defaults to `None`): | |
| The width in preprocessed frames of the video. If `None`, will use get_default_height_width()` to get | |
| the default width. | |
| """ | |
| if isinstance(video, list) and isinstance(video[0], np.ndarray) and video[0].ndim == 5: | |
| warnings.warn( | |
| "Passing `video` as a list of 5d np.ndarray is deprecated." | |
| "Please concatenate the list along the batch dimension and pass it as a single 5d np.ndarray", | |
| FutureWarning, | |
| ) | |
| video = np.concatenate(video, axis=0) | |
| if isinstance(video, list) and isinstance(video[0], torch.Tensor) and video[0].ndim == 5: | |
| warnings.warn( | |
| "Passing `video` as a list of 5d torch.Tensor is deprecated." | |
| "Please concatenate the list along the batch dimension and pass it as a single 5d torch.Tensor", | |
| FutureWarning, | |
| ) | |
| video = torch.cat(video, axis=0) | |
| # ensure the input is a list of videos: | |
| # - if it is a batch of videos (5d torch.Tensor or np.ndarray), it is converted to a list of videos (a list of 4d torch.Tensor or np.ndarray) | |
| # - if it is is a single video, it is convereted to a list of one video. | |
| if isinstance(video, (np.ndarray, torch.Tensor)) and video.ndim == 5: | |
| video = list(video) | |
| elif isinstance(video, list) and is_valid_image(video[0]) or is_valid_image_imagelist(video): | |
| video = [video] | |
| elif isinstance(video, list) and is_valid_image_imagelist(video[0]): | |
| video = video | |
| else: | |
| raise ValueError( | |
| "Input is in incorrect format. Currently, we only support numpy.ndarray, torch.Tensor, PIL.Image.Image" | |
| ) | |
| video = torch.stack([self.preprocess(img, height=height, width=width) for img in video], dim=0) | |
| # move the number of channels before the number of frames. | |
| video = video.permute(0, 2, 1, 3, 4) | |
| return video | |
| def postprocess_video( | |
| self, video: torch.Tensor, output_type: str = "np" | |
| ) -> Union[np.ndarray, torch.Tensor, List[PIL.Image.Image]]: | |
| r""" | |
| Converts a video tensor to a list of frames for export. | |
| Args: | |
| video (`torch.Tensor`): The video as a tensor. | |
| output_type (`str`, defaults to `"np"`): Output type of the postprocessed `video` tensor. | |
| """ | |
| batch_size = video.shape[0] | |
| outputs = [] | |
| for batch_idx in range(batch_size): | |
| batch_vid = video[batch_idx].permute(1, 0, 2, 3) | |
| batch_output = self.postprocess(batch_vid, output_type) | |
| outputs.append(batch_output) | |
| if output_type == "np": | |
| outputs = np.stack(outputs) | |
| elif output_type == "pt": | |
| outputs = torch.stack(outputs) | |
| elif not output_type == "pil": | |
| raise ValueError(f"{output_type} does not exist. Please choose one of ['np', 'pt', 'pil']") | |
| return outputs | |