| import argparse |
| import random |
| import json |
| from pathlib import Path |
|
|
| import numpy as np |
| import torch |
| import open3d as o3d |
|
|
|
|
| def convert_pc_to_box(obj_pc): |
| xmin = np.min(obj_pc[:,0]) |
| ymin = np.min(obj_pc[:,1]) |
| zmin = np.min(obj_pc[:,2]) |
| xmax = np.max(obj_pc[:,0]) |
| ymax = np.max(obj_pc[:,1]) |
| zmax = np.max(obj_pc[:,2]) |
| center = [(xmin+xmax)/2, (ymin+ymax)/2, (zmin+zmax)/2] |
| box_size = [xmax-xmin, ymax-ymin, zmax-zmin] |
| return center, box_size |
|
|
|
|
| def load_scan(pcd_path, inst2label_path, scene_name): |
| pcd_data = torch.load(pcd_path / f'{scene_name}.pth') |
| inst_to_label = torch.load(inst2label_path / f"{scene_name}.pth") |
| points, colors, instance_labels = pcd_data[0], pcd_data[1], pcd_data[-1] |
| pcds = np.concatenate([points, colors], 1) |
| return points, colors, pcds, instance_labels, inst_to_label |
|
|
|
|
| def visualize_one_scene(obj_pcds, points, colors, caption): |
| |
| o3d_pcd = o3d.geometry.PointCloud() |
| o3d_pcd.points = o3d.utility.Vector3dVector(points) |
| o3d_pcd.colors = o3d.utility.Vector3dVector(colors / 255.0) |
| |
| for idx, (obj, obj_label) in enumerate(obj_pcds): |
| if idx > 3: |
| break |
| gt_center, gt_size = convert_pc_to_box(obj) |
| gt_o3d_box = o3d.geometry.OrientedBoundingBox(gt_center, np.eye(3,3), gt_size) |
| gt_o3d_box.color = [0, 1, 0] |
| mesh_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=0.6, origin=[-0, -0, -0]) |
| o3d.visualization.draw_geometries([o3d_pcd, gt_o3d_box, mesh_frame], window_name=obj_label+'_'+caption) |
|
|
|
|
| def visualize_data(save_root, scene_name, vis_obj=True): |
| inst2label_path = save_root / 'instance_id_to_label' |
| pcd_path = save_root / 'pcd_with_global_alignment' |
|
|
| points, colors, pcds, instance_labels, inst_to_label = load_scan(pcd_path, inst2label_path, scene_name) |
|
|
| if not vis_obj: |
| o3d_pcd = o3d.geometry.PointCloud() |
| mesh_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=0.6, origin=[-0, -0, -0]) |
| o3d_pcd.points = o3d.utility.Vector3dVector(points) |
| o3d_pcd.colors = o3d.utility.Vector3dVector(colors / 255.0) |
| o3d.visualization.draw_geometries([mesh_frame, o3d_pcd]) |
| return |
|
|
| obj_pcds = [] |
| for i in inst_to_label.keys(): |
| mask = instance_labels == i |
| if np.sum(mask) == 0: |
| continue |
| obj_pcds.append((pcds[mask], inst_to_label[i])) |
|
|
| visualize_one_scene(obj_pcds, points, colors, scene_name) |
|
|
| def visualize_refer(save_root, anno_file): |
| inst2label_path = save_root / 'instance_id_to_label' |
| pcd_path = save_root / 'pcd_with_global_alignment' |
| json_data = json.load(open(anno_file, 'r', encoding='utf8')) |
| for item in json_data: |
| scan_id = item['scan_id'] |
| inst2label_path = save_root / 'instance_id_to_label' |
| pcd_path = save_root / 'pcd_with_global_alignment' |
|
|
| inst_to_label = torch.load(inst2label_path / f"{scan_id}.pth") |
| pcd_data = torch.load(pcd_path / f'{scan_id}.pth') |
| points, colors, instance_labels = pcd_data[0], pcd_data[1], pcd_data[-1] |
| pcds = np.concatenate([points, colors], 1) |
|
|
| target_id = int(item['target_id']) |
| mask = instance_labels == target_id |
| if np.sum(mask) == 0: |
| continue |
|
|
| obj_pcds = [(pcds[mask], inst_to_label[target_id])] |
| visualize_one_scene(obj_pcds, points, colors, scan_id+'-'+item['utterance']) |
|
|
|
|
| if __name__ == "__main__": |
| parser = argparse.ArgumentParser() |
| parser.add_argument("-r", "--root", required=True, type=str, help="path of dataset dir") |
| parser.add_argument("-d", "--dataset", type=str, |
| help="available datasets in ['ARKitScenes', 'HM3D', 'MultiScan', 'ProcThor', \ |
| 'Structured3D', 'ScanNet', '3RScan']") |
| parser.add_argument("--vis_refer", action="store_true", |
| help="visualize reference data") |
| parser.add_argument("-a", "--anno", type=str, default="ssg_ref_rel2_template.json", |
| help="the annotation file for reference") |
| args = parser.parse_args() |
| dataset = args.dataset |
| assert dataset in ['ARKitScenes', 'HM3D', 'MultiScan', 'ProcThor', 'Structured3D', 'ScanNet', '3RScan'] |
| print(dataset) |
| data_root = Path(args.root) / dataset |
| if args.vis_refer: |
| if dataset == 'ScanNet': |
| anno_file = data_root / 'annotations/refer' / args.anno |
| else: |
| anno_file = data_root / 'annotations' / args.anno |
|
|
| visualize_refer(data_root / 'scan_data', anno_file) |
| else: |
| all_scans = (data_root / 'scan_data' / 'pcd_with_global_alignment').glob('*.pth') |
| scene_id = Path(random.choice(list(all_scans))).stem |
| visualize_data(data_root / 'scan_data', scene_id) |
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