Upload hdf5_to_mp4.py with huggingface_hub
Browse files- hdf5_to_mp4.py +209 -0
hdf5_to_mp4.py
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# Copyright (c) 2024-2025, The Isaac Lab Project Developers.
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# All rights reserved.
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#
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# SPDX-License-Identifier: BSD-3-Clause
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"""
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Script to convert HDF5 demonstration files to MP4 videos.
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This script converts camera frames stored in HDF5 demonstration files to MP4 videos.
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It supports multiple camera modalities including RGB, segmentation, and normal maps.
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The output videos are saved in the specified directory with appropriate naming.
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required arguments:
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--input_file Path to the input HDF5 file.
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--output_dir Directory to save the output MP4 files.
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optional arguments:
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--input_keys List of input keys to process from the HDF5 file. (default: ["table_cam", "wrist_cam", "table_cam_segmentation", "table_cam_normals", "table_cam_shaded_segmentation"])
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--video_height Height of the output video in pixels. (default: 704)
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--video_width Width of the output video in pixels. (default: 1280)
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--framerate Frames per second for the output video. (default: 30)
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"""
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# Standard library imports
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import argparse
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import h5py
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import numpy as np
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# Third-party imports
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import os
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import cv2
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# Constants
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DEFAULT_VIDEO_HEIGHT = 704
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DEFAULT_VIDEO_WIDTH = 1280
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DEFAULT_INPUT_KEYS = [
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"table_cam",
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"wrist_cam",
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"table_cam_segmentation",
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"table_cam_normals",
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"table_cam_shaded_segmentation",
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"table_cam_depth",
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]
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DEFAULT_FRAMERATE = 30
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LIGHT_SOURCE = np.array([0.0, 0.0, 1.0])
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MIN_DEPTH = 0.0
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MAX_DEPTH = 1.5
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def parse_args():
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"""Parse command line arguments."""
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parser = argparse.ArgumentParser(description="Convert HDF5 demonstration files to MP4 videos.")
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parser.add_argument(
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"--input_file",
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type=str,
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required=True,
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help="Path to the input HDF5 file containing demonstration data.",
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)
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parser.add_argument(
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"--output_dir",
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type=str,
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required=True,
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help="Directory path where the output MP4 files will be saved.",
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)
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parser.add_argument(
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"--input_keys",
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type=str,
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nargs="+",
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default=DEFAULT_INPUT_KEYS,
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help="List of input keys to process.",
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)
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parser.add_argument(
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"--video_height",
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type=int,
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default=DEFAULT_VIDEO_HEIGHT,
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help="Height of the output video in pixels.",
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)
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parser.add_argument(
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"--video_width",
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type=int,
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default=DEFAULT_VIDEO_WIDTH,
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help="Width of the output video in pixels.",
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)
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parser.add_argument(
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"--framerate",
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type=int,
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default=DEFAULT_FRAMERATE,
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help="Frames per second for the output video.",
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)
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args = parser.parse_args()
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return args
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def write_demo_to_mp4(
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hdf5_file,
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demo_id,
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frames_path,
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input_key,
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output_dir,
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video_height,
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video_width,
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framerate=DEFAULT_FRAMERATE,
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):
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"""Convert frames from an HDF5 file to an MP4 video.
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Args:
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hdf5_file (str): Path to the HDF5 file containing the frames.
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demo_id (int): ID of the demonstration to convert.
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frames_path (str): Path to the frames data in the HDF5 file.
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input_key (str): Name of the input key to convert.
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output_dir (str): Directory to save the output MP4 file.
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video_height (int): Height of the output video in pixels.
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video_width (int): Width of the output video in pixels.
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framerate (int, optional): Frames per second for the output video. Defaults to 30.
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"""
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with h5py.File(hdf5_file, "r") as f:
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# Get frames based on input key type
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if "shaded_segmentation" in input_key:
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temp_key = input_key.replace("shaded_segmentation", "segmentation")
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frames = f[f"data/demo_{demo_id}/obs/{temp_key}"]
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else:
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frames = f[frames_path + "/" + input_key]
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# Setup video writer
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output_path = os.path.join(output_dir, f"demo_{demo_id}_{input_key}.mp4")
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fourcc = cv2.VideoWriter_fourcc(*"mp4v")
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if "depth" in input_key:
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video = cv2.VideoWriter(output_path, fourcc, framerate, (video_width, video_height), isColor=False)
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else:
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video = cv2.VideoWriter(output_path, fourcc, framerate, (video_width, video_height))
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# Process and write frames
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for ix, frame in enumerate(frames):
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# Convert normal maps to uint8 if needed
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if "normals" in input_key:
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frame = (frame * 255.0).astype(np.uint8)
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# Process shaded segmentation frames
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elif "shaded_segmentation" in input_key:
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seg = frame[..., :-1]
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normals_key = input_key.replace("shaded_segmentation", "normals")
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normals = f[f"data/demo_{demo_id}/obs/{normals_key}"][ix]
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shade = 0.5 + (normals * LIGHT_SOURCE[None, None, :]).sum(axis=-1) * 0.5
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shaded_seg = (shade[..., None] * seg).astype(np.uint8)
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frame = np.concatenate((shaded_seg, frame[..., -1:]), axis=-1)
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# Convert RGB to BGR
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if "depth" not in input_key:
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frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
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else:
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frame = (frame[..., 0] - MIN_DEPTH) / (MAX_DEPTH - MIN_DEPTH)
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frame = np.where(frame < 0.01, 1.0, frame)
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frame = 1.0 - frame
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frame = (frame * 255.0).astype(np.uint8)
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# Resize to video resolution
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frame = cv2.resize(frame, (video_width, video_height), interpolation=cv2.INTER_CUBIC)
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video.write(frame)
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video.release()
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def get_num_demos(hdf5_file):
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"""Get the number of demonstrations in the HDF5 file.
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Args:
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hdf5_file (str): Path to the HDF5 file.
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Returns:
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int: Number of demonstrations found in the file.
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"""
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with h5py.File(hdf5_file, "r") as f:
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return len(f["data"].keys())
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+
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def main():
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"""Main function to convert all demonstrations to MP4 videos."""
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# Parse command line arguments
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args = parse_args()
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# Create output directory if it doesn't exist
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os.makedirs(args.output_dir, exist_ok=True)
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# Get number of demonstrations from the file
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num_demos = get_num_demos(args.input_file)
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print(f"Found {num_demos} demonstrations in {args.input_file}")
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# Convert each demonstration
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for i in range(num_demos):
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frames_path = f"data/demo_{str(i)}/obs"
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for input_key in args.input_keys:
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write_demo_to_mp4(
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args.input_file,
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i,
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frames_path,
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input_key,
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+
args.output_dir,
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args.video_height,
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args.video_width,
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args.framerate,
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)
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+
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if __name__ == "__main__":
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main()
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