75 lines
2.5 KiB
Python
75 lines
2.5 KiB
Python
#!/usr/bin/env python
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import inspect
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from concurrent.futures import ThreadPoolExecutor
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from pathlib import Path
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import numpy
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import PIL
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import torch
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from lerobot.common.datasets.video_utils import encode_video_frames
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def concatenate_episodes(ep_dicts):
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data_dict = {}
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keys = ep_dicts[0].keys()
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for key in keys:
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if torch.is_tensor(ep_dicts[0][key][0]):
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data_dict[key] = torch.cat([ep_dict[key] for ep_dict in ep_dicts])
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else:
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if key not in data_dict:
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data_dict[key] = []
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for ep_dict in ep_dicts:
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for x in ep_dict[key]:
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data_dict[key].append(x)
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total_frames = data_dict["frame_index"].shape[0]
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data_dict["index"] = torch.arange(0, total_frames, 1)
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return data_dict
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def save_images_concurrently(imgs_array: numpy.array, out_dir: Path, max_workers: int = 4):
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out_dir = Path(out_dir)
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out_dir.mkdir(parents=True, exist_ok=True)
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def save_image(img_array, i, out_dir):
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img = PIL.Image.fromarray(img_array)
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img.save(str(out_dir / f"frame_{i:06d}.png"), quality=100)
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num_images = len(imgs_array)
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with ThreadPoolExecutor(max_workers=max_workers) as executor:
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[executor.submit(save_image, imgs_array[i], i, out_dir) for i in range(num_images)]
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def get_default_encoding() -> dict:
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"""Returns the default ffmpeg encoding parameters used by `encode_video_frames`."""
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signature = inspect.signature(encode_video_frames)
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return {
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k: v.default
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for k, v in signature.parameters.items()
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if v.default is not inspect.Parameter.empty and k in ["vcodec", "pix_fmt", "g", "crf"]
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}
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def check_repo_id(repo_id: str) -> None:
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if len(repo_id.split("/")) != 2:
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raise ValueError(
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f"""`repo_id` is expected to contain a community or user id `/` the name of the dataset
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(e.g. 'lerobot/pusht'), but contains '{repo_id}'."""
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)
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