from omnigibson.envs import DataPlaybackWrapper from omnigibson.utils.config_utils import TorchEncoder import torch as th import os import omnigibson as og from omnigibson.macros import gm import argparse import sys import json from gello.robots.sim_robot.og_teleop_utils import optimize_sim_settings from gello.utils.qa_utils import * from gello.utils.b1k_utils import ALL_QA_METRICS, COMMON_QA_METRICS, TASK_QA_METRICS import inspect RUN_QA = True gm.RENDER_VIEWER_CAMERA = False gm.DEFAULT_VIEWER_WIDTH = 128 gm.DEFAULT_VIEWER_HEIGHT = 128 def extract_arg_names(func): return list(inspect.signature(func).parameters.keys()) def replay_hdf5_file(hdf_input_path): """ Replays a single HDF5 file and saves videos to a new folder Args: hdf_input_path: Path to the HDF5 file to replay """ # Create folder with same name as HDF5 file (without extension) base_name = os.path.basename(hdf_input_path) folder_name = os.path.splitext(base_name)[0] folder_path = os.path.join(os.path.dirname(hdf_input_path), folder_name) # Create the folder if it doesn't exist os.makedirs(folder_path, exist_ok=True) # Define output paths hdf_output_path = os.path.join(folder_path, f"{folder_name}_replay.hdf5") video_dir = folder_path # Metrics path metrics_output_path = os.path.join(folder_path, f"qa_metrics.json") # Move original HDF5 file to the new folder new_hdf_input_path = os.path.join(folder_path, base_name) if hdf_input_path != new_hdf_input_path: # Avoid copying if already in target folder os.rename(hdf_input_path, new_hdf_input_path) hdf_input_path = new_hdf_input_path # Define resolution for consistency RESOLUTION_DEFAULT = 560 RESOLUTION_WRIST = 240 # This flag is needed to run data playback wrapper gm.ENABLE_TRANSITION_RULES = False # Define external camera positions and orientations external_camera_poses = [ # Camera 1 [[-0.4, 0, 2.0], [0.2706, -0.2706, -0.6533, 0.6533]], # # Camera 2 # [[-0.2, 0.6, 2.0], [-0.1930, 0.4163, 0.8062, -0.3734]], # # Camera 3 # [[-0.2, -0.6, 2.0], [0.4164, -0.1929, -0.3737, 0.8060]] ] # Robot sensor configuration robot_sensor_config = { "VisionSensor": { "modalities": ["rgb"], "sensor_kwargs": { "image_height": RESOLUTION_WRIST, "image_width": RESOLUTION_WRIST, }, }, } # Generate external sensors config automatically external_sensors_config = [] for i, (position, orientation) in enumerate(external_camera_poses): external_sensors_config.append({ "sensor_type": "VisionSensor", "name": f"external_sensor{i}", "relative_prim_path": f"/controllable__r1pro__robot_r1/base_link/external_sensor{i}", "modalities": ["rgb"], "sensor_kwargs": { "image_height": RESOLUTION_DEFAULT, "image_width": RESOLUTION_DEFAULT, "horizontal_aperture": 40.0, }, "position": th.tensor(position, dtype=th.float32), "orientation": th.tensor(orientation, dtype=th.float32), "pose_frame": "parent", }) # Replace normal head camera with custom config idx = len(external_sensors_config) external_sensors_config.append({ "sensor_type": "VisionSensor", "name": f"external_sensor{idx}", "relative_prim_path": f"/controllable__r1pro__robot_r1/zed_link/external_sensor{idx}", "modalities": ["rgb", "seg_instance_id"], "sensor_kwargs": { "image_height": RESOLUTION_DEFAULT, "image_width": RESOLUTION_DEFAULT, "horizontal_aperture": 40.0, }, "position": th.tensor([0.06, 0.0, 0.01], dtype=th.float32), "orientation": th.tensor([-1.0, 0.0, 0.0, 0.0], dtype=th.float32), "pose_frame": "parent", }) # Create the environment additional_wrapper_configs = [] if RUN_QA: additional_wrapper_configs.append({ "type": "MetricsWrapper", }) env = DataPlaybackWrapper.create_from_hdf5( input_path=hdf_input_path, output_path=hdf_output_path, robot_obs_modalities=["rgb"], robot_sensor_config=robot_sensor_config, external_sensors_config=external_sensors_config, exclude_sensor_names=["zed"], n_render_iterations=1, only_successes=False, additional_wrapper_configs=additional_wrapper_configs, include_task=True, include_task_obs=False, include_robot_control=False, include_contacts=True, ) # Optimize rendering for faster speeds og.sim.add_callback_on_play("optimize_rendering", optimize_sim_settings) if RUN_QA: # Add QA metrics metric_kwargs = dict( step_dt=1/30, vel_threshold=0.001, color_arms=False, # For ghost hand default_color=(0.8235, 0.8235, 1.0000), head_camera=env.external_sensors[f"external_sensor{len(env.external_sensors) - 1}"], head_camera_link_name="torso_link4", navigation_window=3.0, translation_threshold=0.1, rotation_threshold=0.05, camera_tilt_threshold=0.4, gripper_link_paths={ "left": set([ '/World/scene_0/controllable__r1pro__robot_r1/left_realsense_link/visuals', '/World/scene_0/controllable__r1pro__robot_r1/left_gripper_link/visuals', '/World/scene_0/controllable__r1pro__robot_r1/left_gripper_finger_link1/visuals', '/World/scene_0/controllable__r1pro__robot_r1/left_gripper_finger_link2/visuals' ]), "right": set([ '/World/scene_0/controllable__r1pro__robot_r1/right_realsense_link/visuals', '/World/scene_0/controllable__r1pro__robot_r1/right_gripper_link/visuals', '/World/scene_0/controllable__r1pro__robot_r1/right_gripper_finger_link1/visuals', '/World/scene_0/controllable__r1pro__robot_r1/right_gripper_finger_link2/visuals' ]) }, ) active_metrics_info = {metric_name: ALL_QA_METRICS[metric_name] for metric_name in COMMON_QA_METRICS} for metric_name, metric_info in active_metrics_info.items(): create_fcn = metric_info["cls"] if metric_info["init"] is None else metric_info["init"] init_kwargs = {arg: metric_kwargs[arg] for arg in extract_arg_names(create_fcn)} metric = create_fcn(**init_kwargs) env.add_metric(name=metric_name, metric=metric) env.reset() # Create a list to store video writers and RGB keys video_writers = [] video_rgb_keys = [] # Create video writer for robot cameras robot_camera_names = ['robot_r1::robot_r1:left_realsense_link:Camera:0::rgb', 'robot_r1::robot_r1:right_realsense_link:Camera:0::rgb'] for robot_camera_name in robot_camera_names: video_writers.append(env.create_video_writer( fpath=f"{video_dir}/{robot_camera_name}.mp4", resolution=(RESOLUTION_WRIST, RESOLUTION_WRIST), )) video_rgb_keys.append(robot_camera_name) # Create video writers for external cameras for i in range(len(external_sensors_config)): camera_name = f"external_sensor{i}" video_writers.append(env.create_pyav_writer( fpath=f"{video_dir}/{camera_name}.mp4", resolution=(RESOLUTION_DEFAULT, RESOLUTION_DEFAULT), )) video_rgb_keys.append(f"external::{camera_name}::rgb") # Playback the dataset with all video writers # We avoid calling playback_dataset and call playback_episode individually in order to manually # aggregate per-episode metrics metrics = dict() for episode_id in range(env.input_hdf5["data"].attrs["n_episodes"]): env.playback_episode( episode_id=episode_id, record_data=False, video_writers=video_writers, video_keys=video_rgb_keys, ) if RUN_QA: episode_metrics = env.aggregate_metrics(flatten=True) for k, v in episode_metrics.items(): print(f"Metric [{k}]: {v}") metrics[f"episode_{episode_id}"] = episode_metrics # Close all video writers for container, stream in video_writers: # Flush any remaining packets for packet in stream.encode(): container.mux(packet) # Close the container container.close() env.save_data() # Save metrics with open(metrics_output_path, "w+") as f: json.dump(metrics, f, cls=TorchEncoder, indent=4) # Always clear the environment to free resources og.clear() print(f"Successfully processed {hdf_input_path}") def main(): parser = argparse.ArgumentParser(description="Replay HDF5 files and save videos") parser.add_argument("--dir", help="Directory containing HDF5 files to process") parser.add_argument("--files", nargs="*", help="Individual HDF5 file(s) to process") args = parser.parse_args() if args.dir and os.path.isdir(args.dir): # Process all HDF5 files in the directory (non-recursively) hdf_files = [os.path.join(args.dir, f) for f in os.listdir(args.dir) if f.lower().endswith('.hdf5') and os.path.isfile(os.path.join(args.dir, f))] if not hdf_files: print(f"No HDF5 files found in directory: {args.dir}") else: print(f"Found {len(hdf_files)} HDF5 files to process") elif args.files: # Process individual files specified hdf_files = args.files else: parser.print_help() print("\nError: Either --dir or --files must be specified", file=sys.stderr) return # Process each file for hdf_file in hdf_files: if not os.path.exists(hdf_file): print(f"Error: File {hdf_file} does not exist", file=sys.stderr) continue replay_hdf5_file(hdf_file) og.shutdown() if __name__ == "__main__": main()