scripts/visualize_dataset.py

# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
#
'''
Visualize the data with both the object mesh and its corresponding grasps, using meshcat.

Installation:
    pip install trimesh==4.5.3 objaverse==0.1.7 meshcat==0.0.12 webdataset==0.2.111

Usage:

Before running the script, start the meshcat server in a different terminal:
    meshcat-server

To visualize a single object from the dataset:
    python visualize_dataset.py --dataset_path /path/to/dataset --object_uuid {object_uuid} --object_file /path/to/mesh --gripper_name {choose from: franka, suction, robotiq2f140}

To visualize many objects (one at a time) from the dataset
    python visualize_dataset.py --dataset_path /path/to/dataset --uuid_list /path/to/uuid_list.json --gripper_name {choose from: franka, suction, robotiq2f140} --uuid_object_paths_file /path/to/uuid_object_paths_file.json

NOTE:
    - The uuid_object_paths_file is a json file, that contains a dictionary with a mapping from the UUID to the absolute path of the mesh file. if you are using the download_objaverse.py script, this file will be auto-generated.
    - The uuid_list can be the split json file from the GraspGen dataset
    - The gripper_name has to be one of the following: franka, suction, robotiq2f140
'''

import os
import argparse
import trimesh
import numpy as np
import json
from meshcat_utils import create_visualizer, visualize_mesh, visualize_grasp
from dataset import GraspWebDatasetReader, load_uuid_list

def visualize_mesh_with_grasps(
    mesh_path: str, 
    mesh_scale: float, 
    gripper_name: str = "franka_panda",
    grasps: list[np.ndarray] = None,
    color: list = [192, 192, 192], 
    transform: np.ndarray = None,
    max_grasps_to_visualize: int = 20
):
    """
    Visualize a single mesh with optional grasps using meshcat.
    
    Args:
        mesh_path (str): Path to the mesh file
        mesh_scale (float): Scale factor for the mesh
        gripper_name (str): Name of the gripper to visualize ("franka_panda", "suction", etc.)
        grasps (list[np.ndarray], optional): List of 4x4 grasp transforms
        color (list, optional): RGB color for the mesh. Defaults to gray if None
        transform (np.ndarray, optional): 4x4 transform matrix for the mesh. Defaults to identity if None
        max_grasps_to_visualize (int, optional): Maximum number of grasps to visualize. Defaults to 20
    """
    # Create visualizer
    vis = create_visualizer()
    vis.delete()
    
    # Default transform if none provided
    if transform is None:
        transform = np.eye(4)

    # Load and visualize the mesh
    try:
        transform = transform.astype(np.float64)
        mesh = trimesh.load(mesh_path)
        if type(mesh) == trimesh.Scene:
            mesh = mesh.dump(concatenate=True)
        mesh.apply_scale(mesh_scale)

        T_move_mesh_to_origin = np.eye(4)
        T_move_mesh_to_origin[:3, 3] = -mesh.centroid

        transform = transform @ T_move_mesh_to_origin

        visualize_mesh(vis, 'mesh', mesh, color=color, transform=transform)
    except Exception as e:
        print(f"Error loading mesh from {mesh_path}: {e}")
    
    # Visualize grasps if provided
    if grasps is not None:
        for i, grasp in enumerate(np.random.permutation(grasps)[:max_grasps_to_visualize]):
            visualize_grasp(
                vis, 
                f"grasps/{i:03d}",
                transform @ grasp.astype(np.float), 
                [0, 255, 0], 
                gripper_name=gripper_name, 
                linewidth=0.2
            )


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--dataset_path", type=str, required=True)
    parser.add_argument("--object_uuid", type=str, help="The UUID of the object to visualize", default=None)
    parser.add_argument("--uuid_list", type=str, help="Path to UUID list", default=None)
    parser.add_argument("--uuid_object_paths_file", type=str, help="Path to JSON file, mapping UUID to absolute path of the mesh file", default=None)
    parser.add_argument("--object_file", type=str, help="This has to be a .stl or .obj or .glb file", default=None)
    parser.add_argument("--gripper_name", type=str, required=True, help="Specify the gripper name", choices=["franka_panda", "single_suction_cup_30mm", "robotiq_2f_140"])
    parser.add_argument("--max_grasps_to_visualize", type=int, help="The max number of grasps to visualize", default=20)
    return parser.parse_args()

if __name__ == "__main__":
    args = parse_args()
    assert args.object_uuid is not None or args.uuid_list is not None, "Either object_uuid or uuid_list must be provided"

    if args.object_uuid is not None:
        webdataset_reader = GraspWebDatasetReader(os.path.join(args.dataset_path, args.gripper_name))
        uuid_list = [args.object_uuid,]
        object_paths = [args.object_file,]
        assert args.object_file is not None, "object_file must be provided if object_uuid is provided"
        assert os.path.exists(args.object_file), f"Object file {args.object_file} does not exist"
    else:
        assert os.path.exists(args.uuid_list), f"UUID list {args.uuid_list} does not exist"
        uuid_list = load_uuid_list(args.uuid_list)
        assert args.uuid_object_paths_file is not None, "uuid_object_paths_file must be provided if uuid_list is provided"
        assert os.path.exists(args.uuid_object_paths_file), f"UUID object paths file {args.uuid_object_paths_file} does not exist"
        object_paths = json.load(open(args.uuid_object_paths_file))
        object_paths = [object_paths[uuid] for uuid in uuid_list]
        webdataset_reader = GraspWebDatasetReader(os.path.join(args.dataset_path, args.gripper_name))

    for uuid, object_path in zip(uuid_list, object_paths):
        print(f"Visualizing object {uuid}")
        grasp_data = webdataset_reader.read_grasps_by_uuid(uuid)
        object_scale = grasp_data['object']['scale']
        grasps = grasp_data["grasps"]
        grasp_poses = np.array(grasps["transforms"])
        grasp_mask = np.array(grasps["object_in_gripper"])
        positive_grasps = grasp_poses[grasp_mask] # Visualizing only the positive grasps

        if len(positive_grasps) > 0:
            # Visualize the mesh with the grasps
            visualize_mesh_with_grasps(
                mesh_path=object_path,
                mesh_scale=object_scale,
                grasps=positive_grasps,
                gripper_name=args.gripper_name,
                max_grasps_to_visualize=args.max_grasps_to_visualize,
            )
            print("Press Enter to continue...")
            input()