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| # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. | |
| # SPDX-License-Identifier: Apache-2.0 | |
| # | |
| # 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. | |
| from typing import IO | |
| import numpy as np | |
| import torch | |
| from cosmos_predict1.utils.easy_io.handlers.base import BaseFileHandler | |
| try: | |
| import imageio | |
| except ImportError: | |
| imageio = None | |
| class ImageioVideoHandler(BaseFileHandler): | |
| str_like = False | |
| def load_from_fileobj(self, file: IO[bytes], format: str = "mp4", mode: str = "rgb", **kwargs): | |
| """ | |
| Load video from a file-like object using imageio with specified format and color mode. | |
| Parameters: | |
| file (IO[bytes]): A file-like object containing video data. | |
| format (str): Format of the video file (default 'mp4'). | |
| mode (str): Color mode of the video, 'rgb' or 'gray' (default 'rgb'). | |
| Returns: | |
| tuple: A tuple containing an array of video frames and metadata about the video. | |
| """ | |
| file.seek(0) | |
| video_reader = imageio.get_reader(file, format, **kwargs) | |
| video_frames = [] | |
| for frame in video_reader: | |
| if mode == "gray": | |
| import cv2 # Convert frame to grayscale if mode is gray | |
| frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) | |
| frame = np.expand_dims(frame, axis=2) # Keep frame dimensions consistent | |
| video_frames.append(frame) | |
| return np.array(video_frames), video_reader.get_meta_data() | |
| def dump_to_fileobj( | |
| self, | |
| obj: np.ndarray | torch.Tensor, | |
| file: IO[bytes], | |
| format: str = "mp4", # pylint: disable=redefined-builtin | |
| fps: int = 17, | |
| quality: int = 5, | |
| **kwargs, | |
| ): | |
| """ | |
| Save an array of video frames to a file-like object using imageio. | |
| Parameters: | |
| obj (np.ndarray): An array of frames to be saved as video. | |
| file (IO[bytes]): A file-like object to which the video data will be written. | |
| format (str): Format of the video file (default 'mp4'). | |
| fps (int): Frames per second of the output video (default 30). | |
| """ | |
| if isinstance(obj, torch.Tensor): | |
| assert obj.dtype == torch.uint8 | |
| obj = obj.cpu().numpy() | |
| h, w = obj.shape[1:-1] | |
| kwargs = { | |
| "fps": fps, | |
| "quality": quality, | |
| "macro_block_size": 1, | |
| "ffmpeg_params": ["-s", f"{w}x{h}"], | |
| "output_params": ["-f", "mp4"], | |
| } | |
| imageio.mimsave(file, obj, format, **kwargs) | |
| def dump_to_str(self, obj, **kwargs): | |
| raise NotImplementedError | |