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benchmark_3dmatch.py
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benchmark_3dmatch.py
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"""
A collection of unrefactored functions.
"""
import os
import sys
import numpy as np
import argparse
import logging
import open3d as o3d
from lib.timer import Timer, AverageMeter
from util.misc import extract_features
from model import load_model
from util.file import ensure_dir, get_folder_list, get_file_list
from util.trajectory import read_trajectory, write_trajectory
from util.pointcloud import make_open3d_point_cloud, evaluate_feature_3dmatch
from scripts.benchmark_util import do_single_pair_matching, gen_matching_pair, gather_results
import torch
import MinkowskiEngine as ME
ch = logging.StreamHandler(sys.stdout)
logging.getLogger().setLevel(logging.INFO)
logging.basicConfig(
format='%(asctime)s %(message)s', datefmt='%m/%d %H:%M:%S', handlers=[ch])
o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Error)
def extract_features_batch(model, config, source_path, target_path, voxel_size, device):
folders = get_folder_list(source_path)
assert len(folders) > 0, f"Could not find 3DMatch folders under {source_path}"
logging.info(folders)
list_file = os.path.join(target_path, "list.txt")
f = open(list_file, "w")
timer, tmeter = Timer(), AverageMeter()
num_feat = 0
model.eval()
for fo in folders:
if 'evaluation' in fo:
continue
files = get_file_list(fo, ".ply")
fo_base = os.path.basename(fo)
f.write("%s %d\n" % (fo_base, len(files)))
for i, fi in enumerate(files):
# Extract features from a file
pcd = o3d.io.read_point_cloud(fi)
save_fn = "%s_%03d" % (fo_base, i)
if i % 100 == 0:
logging.info(f"{i} / {len(files)}: {save_fn}")
timer.tic()
xyz_down, feature = extract_features(
model,
xyz=np.array(pcd.points),
rgb=None,
normal=None,
voxel_size=voxel_size,
device=device,
skip_check=True)
t = timer.toc()
if i > 0:
tmeter.update(t)
num_feat += len(xyz_down)
np.savez_compressed(
os.path.join(target_path, save_fn),
points=np.array(pcd.points),
xyz=xyz_down,
feature=feature.detach().cpu().numpy())
if i % 20 == 0 and i > 0:
logging.info(
f'Average time: {tmeter.avg}, FPS: {num_feat / tmeter.sum}, time / feat: {tmeter.sum / num_feat}, '
)
f.close()
def registration(feature_path, voxel_size):
"""
Gather .log files produced in --target folder and run this Matlab script
https://github.com/andyzeng/3dmatch-toolbox#geometric-registration-benchmark
(see Geometric Registration Benchmark section in
http://3dmatch.cs.princeton.edu/)
"""
# List file from the extract_features_batch function
with open(os.path.join(feature_path, "list.txt")) as f:
sets = f.readlines()
sets = [x.strip().split() for x in sets]
for s in sets:
set_name = s[0]
pts_num = int(s[1])
matching_pairs = gen_matching_pair(pts_num)
results = []
for m in matching_pairs:
results.append(do_single_pair_matching(feature_path, set_name, m, voxel_size))
traj = gather_results(results)
logging.info(f"Writing the trajectory to {feature_path}/{set_name}.log")
write_trajectory(traj, "%s.log" % (os.path.join(feature_path, set_name)))
def do_single_pair_evaluation(feature_path,
set_name,
traj,
voxel_size,
tau_1=0.1,
tau_2=0.05,
num_rand_keypoints=-1):
trans_gth = np.linalg.inv(traj.pose)
i = traj.metadata[0]
j = traj.metadata[1]
name_i = "%s_%03d" % (set_name, i)
name_j = "%s_%03d" % (set_name, j)
# coord and feat form a sparse tensor.
data_i = np.load(os.path.join(feature_path, name_i + ".npz"))
coord_i, points_i, feat_i = data_i['xyz'], data_i['points'], data_i['feature']
data_j = np.load(os.path.join(feature_path, name_j + ".npz"))
coord_j, points_j, feat_j = data_j['xyz'], data_j['points'], data_j['feature']
# use the keypoints in 3DMatch
if num_rand_keypoints > 0:
# Randomly subsample N points
Ni, Nj = len(points_i), len(points_j)
inds_i = np.random.choice(Ni, min(Ni, num_rand_keypoints), replace=False)
inds_j = np.random.choice(Nj, min(Nj, num_rand_keypoints), replace=False)
sample_i, sample_j = points_i[inds_i], points_j[inds_j]
key_points_i = ME.utils.fnv_hash_vec(np.floor(sample_i / voxel_size))
key_points_j = ME.utils.fnv_hash_vec(np.floor(sample_j / voxel_size))
key_coords_i = ME.utils.fnv_hash_vec(np.floor(coord_i / voxel_size))
key_coords_j = ME.utils.fnv_hash_vec(np.floor(coord_j / voxel_size))
inds_i = np.where(np.isin(key_coords_i, key_points_i))[0]
inds_j = np.where(np.isin(key_coords_j, key_points_j))[0]
coord_i, feat_i = coord_i[inds_i], feat_i[inds_i]
coord_j, feat_j = coord_j[inds_j], feat_j[inds_j]
coord_i = make_open3d_point_cloud(coord_i)
coord_j = make_open3d_point_cloud(coord_j)
hit_ratio = evaluate_feature_3dmatch(coord_i, coord_j, feat_i, feat_j, trans_gth,
tau_1)
# logging.info(f"Hit ratio of {name_i}, {name_j}: {hit_ratio}, {hit_ratio >= tau_2}")
if hit_ratio >= tau_2:
return True
else:
return False
def feature_evaluation(source_path, feature_path, voxel_size, num_rand_keypoints=-1):
with open(os.path.join(feature_path, "list.txt")) as f:
sets = f.readlines()
sets = [x.strip().split() for x in sets]
assert len(
sets
) > 0, "Empty list file. Makesure to run the feature extraction first with --do_extract_feature."
tau_1 = 0.1 # 10cm
tau_2 = 0.05 # 5% inlier
logging.info("%f %f" % (tau_1, tau_2))
recall = []
for s in sets:
set_name = s[0]
traj = read_trajectory(os.path.join(source_path, set_name + "_gt.log"))
assert len(traj) > 0, "Empty trajectory file"
results = []
for i in range(len(traj)):
results.append(
do_single_pair_evaluation(feature_path, set_name, traj[i], voxel_size, tau_1,
tau_2, num_rand_keypoints))
mean_recall = np.array(results).mean()
std_recall = np.array(results).std()
recall.append([set_name, mean_recall, std_recall])
logging.info(f'{set_name}: {mean_recall} +- {std_recall}')
for r in recall:
logging.info("%s : %.4f" % (r[0], r[1]))
scene_r = np.array([r[1] for r in recall])
logging.info("average : %.4f +- %.4f" % (scene_r.mean(), scene_r.std()))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--source', default=None, type=str, help='path to 3dmatch test dataset')
parser.add_argument(
'--source_high_res',
default=None,
type=str,
help='path to high_resolution point cloud')
parser.add_argument(
'--target', default=None, type=str, help='path to produce generated data')
parser.add_argument(
'-m',
'--model',
default=None,
type=str,
help='path to latest checkpoint (default: None)')
parser.add_argument(
'--voxel_size',
default=0.05,
type=float,
help='voxel size to preprocess point cloud')
parser.add_argument('--extract_features', action='store_true')
parser.add_argument('--evaluate_feature_match_recall', action='store_true')
parser.add_argument(
'--evaluate_registration',
action='store_true',
help='The target directory must contain extracted features')
parser.add_argument('--with_cuda', action='store_true')
parser.add_argument(
'--num_rand_keypoints',
type=int,
default=5000,
help='Number of random keypoints for each scene')
args = parser.parse_args()
device = torch.device('cuda' if args.with_cuda else 'cpu')
if args.extract_features:
assert args.model is not None
assert args.source is not None
assert args.target is not None
ensure_dir(args.target)
checkpoint = torch.load(args.model)
config = checkpoint['config']
num_feats = 1
Model = load_model(config.model)
model = Model(
num_feats,
config.model_n_out,
bn_momentum=0.05,
normalize_feature=config.normalize_feature,
conv1_kernel_size=config.conv1_kernel_size,
D=3)
model.load_state_dict(checkpoint['state_dict'])
model.eval()
model = model.to(device)
with torch.no_grad():
extract_features_batch(model, config, args.source, args.target, config.voxel_size,
device)
if args.evaluate_feature_match_recall:
assert (args.target is not None)
with torch.no_grad():
feature_evaluation(args.source, args.target, args.voxel_size,
args.num_rand_keypoints)
if args.evaluate_registration:
assert (args.target is not None)
with torch.no_grad():
registration(args.target, args.voxel_size)