# Copyright (c) 2024-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md). # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause """ Script to create a new dataset by combining existing HDF5 demonstrations with visually augmented MP4 videos. This script takes an existing HDF5 dataset containing demonstrations and a directory of MP4 videos that are visually augmented versions of the original demonstration videos (e.g., with different lighting, color schemes, or visual effects). It creates a new HDF5 dataset that preserves all the original demonstration data (actions, robot state, etc.) but replaces the video frames with the augmented versions. required arguments: --input_file Path to the input HDF5 file containing original demonstrations. --output_file Path to save the new HDF5 file with augmented videos. --videos_dir Directory containing the visually augmented MP4 videos. """ import argparse import glob import os import cv2 import h5py import numpy as np def parse_args(): """Parse command line arguments.""" parser = argparse.ArgumentParser(description="Create a new dataset with visually augmented videos.") parser.add_argument( "--input_file", type=str, required=True, help="Path to the input HDF5 file containing original demonstrations.", ) parser.add_argument( "--videos_dir", type=str, required=True, help="Directory containing the visually augmented MP4 videos.", ) parser.add_argument( "--output_file", type=str, required=True, help="Path to save the new HDF5 file with augmented videos.", ) args = parser.parse_args() return args def get_frames_from_mp4(video_path, target_height=None, target_width=None): """Extract frames from an MP4 video file. Args: video_path (str): Path to the MP4 video file. target_height (int, optional): Target height for resizing frames. If None, no resizing is done. target_width (int, optional): Target width for resizing frames. If None, no resizing is done. Returns: np.ndarray: Array of frames from the video in RGB format. """ # Open the video file video = cv2.VideoCapture(video_path) # Get video properties frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) # Read all frames into a numpy array frames = [] for _ in range(frame_count): ret, frame = video.read() if not ret: break frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) if target_height is not None and target_width is not None: frame = cv2.resize(frame, (target_width, target_height), interpolation=cv2.INTER_LINEAR) frames.append(frame) # Convert to numpy array frames = np.array(frames).astype(np.uint8) # Release the video object video.release() return frames def process_video_and_demo(f_in, f_out, video_path, orig_demo_id, new_demo_id): """Process a single video and create a new demo with augmented video frames. Args: f_in (h5py.File): Input HDF5 file. f_out (h5py.File): Output HDF5 file. video_path (str): Path to the augmented video file. orig_demo_id (int): ID of the original demo to copy. new_demo_id (int): ID for the new demo. """ # Get original demo data actions = f_in[f"data/demo_{str(orig_demo_id)}/actions"] eef_pos = f_in[f"data/demo_{str(orig_demo_id)}/obs/eef_pos"] eef_quat = f_in[f"data/demo_{str(orig_demo_id)}/obs/eef_quat"] gripper_pos = f_in[f"data/demo_{str(orig_demo_id)}/obs/gripper_pos"] wrist_cam = f_in[f"data/demo_{str(orig_demo_id)}/obs/wrist_cam"] # Get original video resolution orig_video = f_in[f"data/demo_{str(orig_demo_id)}/obs/table_cam"] target_height, target_width = orig_video.shape[1:3] # Extract frames from video with original resolution frames = get_frames_from_mp4(video_path, target_height, target_width) # Create new datasets f_out.create_dataset(f"data/demo_{str(new_demo_id)}/actions", data=actions, compression="gzip") f_out.create_dataset(f"data/demo_{str(new_demo_id)}/obs/eef_pos", data=eef_pos, compression="gzip") f_out.create_dataset(f"data/demo_{str(new_demo_id)}/obs/eef_quat", data=eef_quat, compression="gzip") f_out.create_dataset(f"data/demo_{str(new_demo_id)}/obs/gripper_pos", data=gripper_pos, compression="gzip") f_out.create_dataset( f"data/demo_{str(new_demo_id)}/obs/table_cam", data=frames.astype(np.uint8), compression="gzip" ) f_out.create_dataset(f"data/demo_{str(new_demo_id)}/obs/wrist_cam", data=wrist_cam, compression="gzip") # Copy attributes f_out[f"data/demo_{str(new_demo_id)}"].attrs["num_samples"] = f_in[f"data/demo_{str(orig_demo_id)}"].attrs[ "num_samples" ] def main(): """Main function to create a new dataset with augmented videos.""" # Parse command line arguments args = parse_args() # Get list of MP4 videos search_path = os.path.join(args.videos_dir, "*.mp4") video_paths = glob.glob(search_path) video_paths.sort() print(f"Found {len(video_paths)} MP4 videos in {args.videos_dir}") # Create output directory if it doesn't exist os.makedirs(os.path.dirname(args.output_file), exist_ok=True) with h5py.File(args.input_file, "r") as f_in, h5py.File(args.output_file, "w") as f_out: # Copy all data from input to output f_in.copy("data", f_out) # Get the largest demo ID to start new demos from demo_ids = [int(key.split("_")[1]) for key in f_in["data"].keys()] next_demo_id = max(demo_ids) + 1 # noqa: SIM113 print(f"Starting new demos from ID: {next_demo_id}") # Process each video and create new demo for video_path in video_paths: # Extract original demo ID from video filename video_filename = os.path.basename(video_path) orig_demo_id = int(video_filename.split("_")[1]) process_video_and_demo(f_in, f_out, video_path, orig_demo_id, next_demo_id) next_demo_id += 1 print(f"Augmented data saved to {args.output_file}") if __name__ == "__main__": main()