# adapted from https://github.com/jzhangbs/DTUeval-python import numpy as np import open3d as o3d import sklearn.neighbors as skln from tqdm import tqdm from scipy.io import loadmat import multiprocessing as mp import argparse def sample_single_tri(input_): n1, n2, v1, v2, tri_vert = input_ c = np.mgrid[:n1+1, :n2+1] c += 0.5 c[0] /= max(n1, 1e-7) c[1] /= max(n2, 1e-7) c = np.transpose(c, (1,2,0)) k = c[c.sum(axis=-1) < 1] # m2 q = v1 * k[:,:1] + v2 * k[:,1:] + tri_vert return q def write_vis_pcd(file, points, colors): pcd = o3d.geometry.PointCloud() pcd.points = o3d.utility.Vector3dVector(points) pcd.colors = o3d.utility.Vector3dVector(colors) o3d.io.write_point_cloud(file, pcd) if __name__ == '__main__': mp.freeze_support() parser = argparse.ArgumentParser() parser.add_argument('--data', type=str, default='data_in.ply') parser.add_argument('--scan', type=int, default=1) parser.add_argument('--mode', type=str, default='mesh', choices=['mesh', 'pcd']) parser.add_argument('--dataset_dir', type=str, default='.') parser.add_argument('--vis_out_dir', type=str, default='.') parser.add_argument('--downsample_density', type=float, default=0.2) parser.add_argument('--patch_size', type=float, default=60) parser.add_argument('--max_dist', type=float, default=20) parser.add_argument('--visualize_threshold', type=float, default=10) args = parser.parse_args() thresh = args.downsample_density if args.mode == 'mesh': pbar = tqdm(total=9) pbar.set_description('read data mesh') data_mesh = o3d.io.read_triangle_mesh(args.data) vertices = np.asarray(data_mesh.vertices) triangles = np.asarray(data_mesh.triangles) tri_vert = vertices[triangles] pbar.update(1) pbar.set_description('sample pcd from mesh') v1 = tri_vert[:,1] - tri_vert[:,0] v2 = tri_vert[:,2] - tri_vert[:,0] l1 = np.linalg.norm(v1, axis=-1, keepdims=True) l2 = np.linalg.norm(v2, axis=-1, keepdims=True) area2 = np.linalg.norm(np.cross(v1, v2), axis=-1, keepdims=True) non_zero_area = (area2 > 0)[:,0] l1, l2, area2, v1, v2, tri_vert = [ arr[non_zero_area] for arr in [l1, l2, area2, v1, v2, tri_vert] ] thr = thresh * np.sqrt(l1 * l2 / area2) n1 = np.floor(l1 / thr) n2 = np.floor(l2 / thr) with mp.Pool() as mp_pool: new_pts = mp_pool.map(sample_single_tri, ((n1[i,0], n2[i,0], v1[i:i+1], v2[i:i+1], tri_vert[i:i+1,0]) for i in range(len(n1))), chunksize=1024) new_pts = np.concatenate(new_pts, axis=0) data_pcd = np.concatenate([vertices, new_pts], axis=0) elif args.mode == 'pcd': pbar = tqdm(total=8) pbar.set_description('read data pcd') data_pcd_o3d = o3d.io.read_point_cloud(args.data) data_pcd = np.asarray(data_pcd_o3d.points) pbar.update(1) pbar.set_description('random shuffle pcd index') shuffle_rng = np.random.default_rng() shuffle_rng.shuffle(data_pcd, axis=0) pbar.update(1) pbar.set_description('downsample pcd') nn_engine = skln.NearestNeighbors(n_neighbors=1, radius=thresh, algorithm='kd_tree', n_jobs=-1) nn_engine.fit(data_pcd) rnn_idxs = nn_engine.radius_neighbors(data_pcd, radius=thresh, return_distance=False) mask = np.ones(data_pcd.shape[0], dtype=np.bool_) for curr, idxs in enumerate(rnn_idxs): if mask[curr]: mask[idxs] = 0 mask[curr] = 1 data_down = data_pcd[mask] pbar.update(1) pbar.set_description('masking data pcd') obs_mask_file = loadmat(f'{args.dataset_dir}/ObsMask/ObsMask{args.scan}_10.mat') ObsMask, BB, Res = [obs_mask_file[attr] for attr in ['ObsMask', 'BB', 'Res']] BB = BB.astype(np.float32) patch = args.patch_size inbound = ((data_down >= BB[:1]-patch) & (data_down < BB[1:]+patch*2)).sum(axis=-1) ==3 data_in = data_down[inbound] data_grid = np.around((data_in - BB[:1]) / Res).astype(np.int32) grid_inbound = ((data_grid >= 0) & (data_grid < np.expand_dims(ObsMask.shape, 0))).sum(axis=-1) ==3 data_grid_in = data_grid[grid_inbound] in_obs = ObsMask[data_grid_in[:,0], data_grid_in[:,1], data_grid_in[:,2]].astype(np.bool_) data_in_obs = data_in[grid_inbound][in_obs] pbar.update(1) pbar.set_description('read STL pcd') stl_pcd = o3d.io.read_point_cloud(f'{args.dataset_dir}/Points/stl/stl{args.scan:03}_total.ply') stl = np.asarray(stl_pcd.points) pbar.update(1) pbar.set_description('compute data2stl') nn_engine.fit(stl) dist_d2s, idx_d2s = nn_engine.kneighbors(data_in_obs, n_neighbors=1, return_distance=True) max_dist = args.max_dist mean_d2s = dist_d2s[dist_d2s < max_dist].mean() pbar.update(1) pbar.set_description('compute stl2data') ground_plane = loadmat(f'{args.dataset_dir}/ObsMask/Plane{args.scan}.mat')['P'] stl_hom = np.concatenate([stl, np.ones_like(stl[:,:1])], -1) above = (ground_plane.reshape((1,4)) * stl_hom).sum(-1) > 0 stl_above = stl[above] nn_engine.fit(data_in) dist_s2d, idx_s2d = nn_engine.kneighbors(stl_above, n_neighbors=1, return_distance=True) mean_s2d = dist_s2d[dist_s2d < max_dist].mean() pbar.update(1) pbar.set_description('visualize error') vis_dist = args.visualize_threshold R = np.array([[1,0,0]], dtype=np.float64) G = np.array([[0,1,0]], dtype=np.float64) B = np.array([[0,0,1]], dtype=np.float64) W = np.array([[1,1,1]], dtype=np.float64) data_color = np.tile(B, (data_down.shape[0], 1)) data_alpha = dist_d2s.clip(max=vis_dist) / vis_dist data_color[ np.where(inbound)[0][grid_inbound][in_obs] ] = R * data_alpha + W * (1-data_alpha) data_color[ np.where(inbound)[0][grid_inbound][in_obs][dist_d2s[:,0] >= max_dist] ] = G write_vis_pcd(f'{args.vis_out_dir}/vis_{args.scan:03}_d2s.ply', data_down, data_color) stl_color = np.tile(B, (stl.shape[0], 1)) stl_alpha = dist_s2d.clip(max=vis_dist) / vis_dist stl_color[ np.where(above)[0] ] = R * stl_alpha + W * (1-stl_alpha) stl_color[ np.where(above)[0][dist_s2d[:,0] >= max_dist] ] = G write_vis_pcd(f'{args.vis_out_dir}/vis_{args.scan:03}_s2d.ply', stl, stl_color) pbar.update(1) pbar.set_description('done') pbar.close() over_all = (mean_d2s + mean_s2d) / 2 print(mean_d2s, mean_s2d, over_all) import json with open(f'{args.vis_out_dir}/results.json', 'w') as fp: json.dump({ 'mean_d2s': mean_d2s, 'mean_s2d': mean_s2d, 'overall': over_all, }, fp, indent=True)