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import os |
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import io |
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import av |
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import json |
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from pickle import dumps, loads |
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import numpy as np |
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import torch |
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from torchvision.transforms.functional import resize |
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import tensorflow as tf |
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import tensorflow_datasets as tfds |
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from einops import rearrange |
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def decode_inst(insts): |
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decoded_insts = [] |
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for inst in insts: |
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decoded_insts.append(bytes(inst[np.where(inst != 0)].tolist()).decode("utf-8")) |
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return decoded_insts |
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def save_video(file, video): |
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container = av.open(file, 'w', 'mp4') |
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stream = container.add_stream('libx264', rate=30) |
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stream.height = video[0].shape[0] |
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stream.width = video[0].shape[1] |
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stream.bit_rate = 2000000 |
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stream.pix_fmt = 'yuv420p' |
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for i in range(len(video)): |
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frame = av.VideoFrame.from_ndarray(video[i], format='rgb24') |
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frame = frame.reformat(format=stream.pix_fmt) |
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for packet in stream.encode(frame): |
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container.mux(packet) |
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for packet in stream.encode(): |
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container.mux(packet) |
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container.close() |
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if __name__ == '__main__': |
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tf_builder = tfds.builder_from_directory('./droid/1.0.0/') |
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tf_dataset = tf_builder.as_dataset(split="train") |
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skip_episode = 78663 |
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js_path = 'index.json' |
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if os.path.exists(js_path): |
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js_data = json.load(open(js_path, 'r')) |
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else: |
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js_data = [] |
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for episode_id, episode in enumerate(tf_dataset): |
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file_path = episode['episode_metadata']['file_path'].numpy().decode('utf-8') |
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recording_folderpath = episode['episode_metadata']['recording_folderpath'].numpy().decode('utf-8') |
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if episode_id <= skip_episode or 'success' not in file_path: |
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print(f'skipping {episode_id}/{len(tf_dataset)}') |
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continue |
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left_camera = [] |
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arm_camera = [] |
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right_camera = [] |
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inst = [] |
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skip_episode = False |
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for step_id, single_step in enumerate(episode['steps']): |
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if single_step['language_instruction'].numpy().decode('utf-8') not in inst: |
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inst.append(single_step['language_instruction'].numpy().decode('utf-8')) |
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if single_step['language_instruction_2'].numpy().decode('utf-8') not in inst: |
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inst.append(single_step['language_instruction_2'].numpy().decode('utf-8')) |
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if single_step['language_instruction_3'].numpy().decode('utf-8') not in inst: |
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inst.append(single_step['language_instruction_3'].numpy().decode('utf-8')) |
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if len(inst) == 1 and inst[0] == '': |
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skip_episode = True |
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break |
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left_camera.append(single_step['observation']['exterior_image_1_left'].numpy()) |
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right_camera.append(single_step['observation']['exterior_image_2_left'].numpy()) |
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arm_camera.append(single_step['observation']['wrist_image_left'].numpy()) |
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if skip_episode: |
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print(f'skipping {episode_id}/{len(tf_dataset)}') |
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continue |
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print(f'saving {episode_id}/{len(tf_dataset)}') |
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save_video(f'droid_videos/episode_{episode_id}_left_camera.mp4', left_camera) |
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save_video(f'droid_videos/episode_{episode_id}_right_camera.mp4', right_camera) |
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save_video(f'droid_videos/episode_{episode_id}_arm_camera.mp4', arm_camera) |
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for i in range(len(inst)): |
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if inst[i] == '': |
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continue |
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js_data.append({"path": f'droid_videos/episode_{episode_id}_left_camera.mp4', "recording_folder": recording_folderpath, "cap": [inst[i]]}) |
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js_data.append({"path": f'droid_videos/episode_{episode_id}_right_camera.mp4', "recording_folder": recording_folderpath, "cap": [inst[i]]}) |
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js_data.append({"path": f'droid_videos/episode_{episode_id}_arm_camera.mp4', "recording_folder": recording_folderpath, "cap": [inst[i]]}) |
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if episode_id % 1000 < 10: |
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json.dump(js_data, open(js_path, 'w'), indent=4) |
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json.dump(js_data, open(js_path, 'w'), indent=4) |
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