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dtu_eval.py
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from ast import Store
import numpy as np
from numpy import *
import open3d as o3d
import sklearn.neighbors as skln
from tqdm import tqdm
from scipy.io import loadmat
import multiprocessing as mp
import argparse
import os
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('--outdir', type=str, default='./outputs')
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='./MVS_Data')
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)
parser.add_argument('--vis_error', action='store_true')
parser.add_argument('--scan_id', type=int, default=None)
args = parser.parse_args()
thresh = args.downsample_density
mean_d2s_list, mean_s2d_list, over_all_list = [], [], []
if args.scan_id:
scans = [args.scan_id]
else:
scans = [24, 37, 40, 55, 63, 65, 69, 83, 97, 105, 106, 110, 114, 118, 122]
for scan in scans:
if args.mode == 'mesh':
data = os.path.join(args.outdir, "mesh", "final", "scan{}.ply".format(scan))
pbar = tqdm(total=9)
pbar.set_description('read data mesh')
data_mesh = o3d.io.read_triangle_mesh(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':
data = os.path.join(args.outdir, "pc_result", "itermvs{:0>3}_l3.ply".format(scan))
# data = os.path.join('/home/haohe/project/Reconformer/pc_result', "itermvs{:0>3}_l3.ply".format(scan))
pbar = tqdm(total=8)
pbar.set_description('read data pcd')
data_pcd_o3d = o3d.io.read_point_cloud(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{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{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{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()
if args.vis_error:
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.outdir}/mesh/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.outdir}/mesh/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(scan, mean_d2s, mean_s2d, over_all)
mean_d2s_list.append(mean_d2s)
mean_s2d_list.append(mean_s2d)
over_all_list.append(over_all)
print("final result")
print(mean(mean_d2s_list), mean(mean_s2d_list), mean(over_all_list))
# from ast import Store
# import numpy as np
# from numpy import *
# import open3d as o3d
# import sklearn.neighbors as skln
# from tqdm import tqdm
# from scipy.io import loadmat
# import multiprocessing as mp
# import argparse
# import os
# 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('--outdir', type=str, default='./outputs')
# 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='./MVS_Data')
# 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)
# parser.add_argument('--vis_error', action='store_true')
# parser.add_argument('--scan_id', type=int, default=None)
# args = parser.parse_args()
# thresh = args.downsample_density
# mean_d2s_list, mean_s2d_list, over_all_list = [], [], []
# if args.scan_id:
# scans = [args.scan_id]
# else:
# scans = [24, 37, 40, 55, 63, 65, 69, 83, 97, 105, 106, 110, 114, 118, 122]
# for scan in scans:
# if args.mode == 'mesh':
# data = os.path.join(args.outdir, "mesh", "final", "scan{}.ply".format(scan))
# pbar = tqdm(total=9)
# pbar.set_description('read data mesh')
# data_mesh = o3d.io.read_triangle_mesh(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':
# data = os.path.join(args.outdir, "pcd", "scan{}.ply".format(scan))
# pbar = tqdm(total=8)
# pbar.set_description('read data pcd')
# data_pcd_o3d = o3d.io.read_point_cloud(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{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{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()
# precision_1 = len(dist_d2s[dist_d2s < dist_thred1]) / len(dist_d2s)
# precision_2 = len(dist_d2s[dist_d2s < dist_thred2]) / len(dist_d2s)
# precision_3 = len(dist_d2s[dist_d2s < dist_thred3]) / len(dist_d2s)
# pbar.update(1)
# pbar.set_description('compute stl2data')
# ground_plane = loadmat(f'{args.dataset_dir}/ObsMask/Plane{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()
# if args.vis_error:
# 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.outdir}/mesh/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.outdir}/mesh/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
# fscore_1 = 2 * precision_1 * recall_1 / (precision_1 + recall_1 + 1e-6)
# fscore_2 = 2 * precision_2 * recall_2 / (precision_2 + recall_2 + 1e-6)
# fscore_3 = 2 * precision_3 * recall_3 / (precision_3 + recall_3 + 1e-6)
# print(scan, mean_d2s, mean_s2d, over_all)
# print(fscore_1, fscore_2, fscore_3)
# print(precision_1, precision_2, precision_3)
# print(recall_1, recall_2, recall_3)
# mean_d2s_list.append(mean_d2s)
# mean_s2d_list.append(mean_s2d)
# over_all_list.append(over_all)
# depth_mean_error_1.append(fscore_1)
# depth_mean_error_2.append(fscore_2)
# depth_mean_error_3.append(fscore_3)
# print("final result")
# print(mean(mean_d2s_list), mean(mean_s2d_list), mean(over_all_list))
# print(mean(depth_mean_error_1), mean(depth_mean_error_2), mean(depth_mean_error_3))