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# ----------------------------------------------------------------------------
# - Open3D: www.open3d.org -
# ----------------------------------------------------------------------------
# Copyright (c) 2018-2024 www.open3d.org
# SPDX-License-Identifier: MIT
# ----------------------------------------------------------------------------
# examples/python/reconstruction_system/register_fragments.py
import multiprocessing
import os
import sys
import numpy as np
import open3d as o3d
pyexample_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(pyexample_path)
from open3d_example import join, get_file_list, make_clean_folder, draw_registration_result
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from optimize_posegraph import optimize_posegraph_for_scene
from refine_registration import multiscale_icp
def preprocess_point_cloud(pcd, config):
voxel_size = config["voxel_size"]
pcd_down = pcd.voxel_down_sample(voxel_size)
pcd_down.estimate_normals(
o3d.geometry.KDTreeSearchParamHybrid(radius=voxel_size * 2.0,
max_nn=30))
pcd_fpfh = o3d.pipelines.registration.compute_fpfh_feature(
pcd_down,
o3d.geometry.KDTreeSearchParamHybrid(radius=voxel_size * 5.0,
max_nn=100))
return (pcd_down, pcd_fpfh)
def register_point_cloud_fpfh(source, target, source_fpfh, target_fpfh, config):
o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Debug)
distance_threshold = config["voxel_size"] * 1.4
if config["global_registration"] == "fgr":
result = o3d.pipelines.registration.registration_fgr_based_on_feature_matching(
source, target, source_fpfh, target_fpfh,
o3d.pipelines.registration.FastGlobalRegistrationOption(
maximum_correspondence_distance=distance_threshold))
if config["global_registration"] == "ransac":
# Fallback to preset parameters that works better
result = o3d.pipelines.registration.registration_ransac_based_on_feature_matching(
source, target, source_fpfh, target_fpfh, False, distance_threshold,
o3d.pipelines.registration.TransformationEstimationPointToPoint(
False), 4,
[
o3d.pipelines.registration.
CorrespondenceCheckerBasedOnEdgeLength(0.9),
o3d.pipelines.registration.CorrespondenceCheckerBasedOnDistance(
distance_threshold)
],
o3d.pipelines.registration.RANSACConvergenceCriteria(
1000000, 0.999))
if (result.transformation.trace() == 4.0):
return (False, np.identity(4), np.zeros((6, 6)))
information = o3d.pipelines.registration.get_information_matrix_from_point_clouds(
source, target, distance_threshold, result.transformation)
if information[5, 5] / min(len(source.points), len(target.points)) < 0.3:
return (False, np.identity(4), np.zeros((6, 6)))
return (True, result.transformation, information)
def compute_initial_registration(s, t, source_down, target_down, source_fpfh,
target_fpfh, path_dataset, config):
if t == s + 1: # odometry case
print("Using RGBD odometry")
pose_graph_frag = o3d.io.read_pose_graph(
join(path_dataset,
config["template_fragment_posegraph_optimized"] % s))
n_nodes = len(pose_graph_frag.nodes)
transformation_init = np.linalg.inv(pose_graph_frag.nodes[n_nodes -
1].pose)
(transformation, information) = \
multiscale_icp(source_down, target_down,
[config["voxel_size"]], [50], config, transformation_init)
else: # loop closure case
(success, transformation,
information) = register_point_cloud_fpfh(source_down, target_down,
source_fpfh, target_fpfh,
config)
if not success:
print("No reasonable solution. Skip this pair")
return (False, np.identity(4), np.zeros((6, 6)))
print(transformation)
if config["debug_mode"]:
draw_registration_result(source_down, target_down, transformation)
return (True, transformation, information)
def update_posegraph_for_scene(s, t, transformation, information, odometry,
pose_graph):
if t == s + 1: # odometry case
odometry = np.dot(transformation, odometry)
odometry_inv = np.linalg.inv(odometry)
pose_graph.nodes.append(
o3d.pipelines.registration.PoseGraphNode(odometry_inv))
pose_graph.edges.append(
o3d.pipelines.registration.PoseGraphEdge(s,
t,
transformation,
information,
uncertain=False))
else: # loop closure case
pose_graph.edges.append(
o3d.pipelines.registration.PoseGraphEdge(s,
t,
transformation,
information,
uncertain=True))
return (odometry, pose_graph)
def register_point_cloud_pair(ply_file_names, s, t, config):
print("reading %s ..." % ply_file_names[s])
source = o3d.io.read_point_cloud(ply_file_names[s])
print("reading %s ..." % ply_file_names[t])
target = o3d.io.read_point_cloud(ply_file_names[t])
(source_down, source_fpfh) = preprocess_point_cloud(source, config)
(target_down, target_fpfh) = preprocess_point_cloud(target, config)
(success, transformation, information) = \
compute_initial_registration(
s, t, source_down, target_down,
source_fpfh, target_fpfh, config["path_dataset"], config)
if t != s + 1 and not success:
return (False, np.identity(4), np.identity(6))
if config["debug_mode"]:
print(transformation)
print(information)
return (True, transformation, information)
# other types instead of class?
class matching_result:
def __init__(self, s, t):
self.s = s
self.t = t
self.success = False
self.transformation = np.identity(4)
self.infomation = np.identity(6)
def make_posegraph_for_scene(ply_file_names, config):
pose_graph = o3d.pipelines.registration.PoseGraph()
odometry = np.identity(4)
pose_graph.nodes.append(o3d.pipelines.registration.PoseGraphNode(odometry))
n_files = len(ply_file_names)
matching_results = {}
for s in range(n_files):
for t in range(s + 1, n_files):
matching_results[s * n_files + t] = matching_result(s, t)
if config["python_multi_threading"] is True:
os.environ['OMP_NUM_THREADS'] = '1'
max_workers = max(
1, min(multiprocessing.cpu_count() - 1, len(matching_results)))
mp_context = multiprocessing.get_context('spawn')
with mp_context.Pool(processes=max_workers) as pool:
args = [(ply_file_names, v.s, v.t, config)
for k, v in matching_results.items()]
results = pool.starmap(register_point_cloud_pair, args)
for i, r in enumerate(matching_results):
matching_results[r].success = results[i][0]
matching_results[r].transformation = results[i][1]
matching_results[r].information = results[i][2]
else:
for r in matching_results:
(matching_results[r].success, matching_results[r].transformation,
matching_results[r].information) = \
register_point_cloud_pair(ply_file_names,
matching_results[r].s, matching_results[r].t, config)
for r in matching_results:
if matching_results[r].success:
(odometry, pose_graph) = update_posegraph_for_scene(
matching_results[r].s, matching_results[r].t,
matching_results[r].transformation,
matching_results[r].information, odometry, pose_graph)
o3d.io.write_pose_graph(
join(config["path_dataset"], config["template_global_posegraph"]),
pose_graph)
def run(config):
print("register fragments.")
o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Debug)
ply_file_names = get_file_list(
join(config["path_dataset"], config["folder_fragment"]), ".ply")
make_clean_folder(join(config["path_dataset"], config["folder_scene"]))
make_posegraph_for_scene(ply_file_names, config)
optimize_posegraph_for_scene(config["path_dataset"], config)
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