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# ----------------------------------------------------------------------------
# - Open3D: www.open3d.org -
# ----------------------------------------------------------------------------
# Copyright (c) 2018-2024 www.open3d.org
# SPDX-License-Identifier: MIT
# ----------------------------------------------------------------------------
# examples/python/reconstruction_system/make_fragments.py
import math
import multiprocessing
import os, 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 *
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from optimize_posegraph import optimize_posegraph_for_fragment
# check opencv python package
with_opencv = initialize_opencv()
if with_opencv:
from opencv_pose_estimation import pose_estimation
def register_one_rgbd_pair(s, t, color_files, depth_files, intrinsic,
with_opencv, config):
source_rgbd_image = read_rgbd_image(color_files[s], depth_files[s], True,
config)
target_rgbd_image = read_rgbd_image(color_files[t], depth_files[t], True,
config)
option = o3d.pipelines.odometry.OdometryOption()
option.depth_diff_max = config["depth_diff_max"]
if abs(s - t) != 1:
if with_opencv:
success_5pt, odo_init = pose_estimation(source_rgbd_image,
target_rgbd_image,
intrinsic, False)
if success_5pt:
[success, trans, info
] = o3d.pipelines.odometry.compute_rgbd_odometry(
source_rgbd_image, target_rgbd_image, intrinsic, odo_init,
o3d.pipelines.odometry.RGBDOdometryJacobianFromHybridTerm(),
option)
return [success, trans, info]
return [False, np.identity(4), np.identity(6)]
else:
odo_init = np.identity(4)
[success, trans, info] = o3d.pipelines.odometry.compute_rgbd_odometry(
source_rgbd_image, target_rgbd_image, intrinsic, odo_init,
o3d.pipelines.odometry.RGBDOdometryJacobianFromHybridTerm(), option)
return [success, trans, info]
def make_posegraph_for_fragment(path_dataset, sid, eid, color_files,
depth_files, fragment_id, n_fragments,
intrinsic, with_opencv, config):
o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Error)
pose_graph = o3d.pipelines.registration.PoseGraph()
trans_odometry = np.identity(4)
pose_graph.nodes.append(
o3d.pipelines.registration.PoseGraphNode(trans_odometry))
for s in range(sid, eid):
for t in range(s + 1, eid):
# odometry
if t == s + 1:
print(
"Fragment %03d / %03d :: RGBD matching between frame : %d and %d"
% (fragment_id, n_fragments - 1, s, t))
[success, trans,
info] = register_one_rgbd_pair(s, t, color_files, depth_files,
intrinsic, with_opencv, config)
trans_odometry = np.dot(trans, trans_odometry)
trans_odometry_inv = np.linalg.inv(trans_odometry)
pose_graph.nodes.append(
o3d.pipelines.registration.PoseGraphNode(
trans_odometry_inv))
pose_graph.edges.append(
o3d.pipelines.registration.PoseGraphEdge(s - sid,
t - sid,
trans,
info,
uncertain=False))
# keyframe loop closure
if s % config['n_keyframes_per_n_frame'] == 0 \
and t % config['n_keyframes_per_n_frame'] == 0:
print(
"Fragment %03d / %03d :: RGBD matching between frame : %d and %d"
% (fragment_id, n_fragments - 1, s, t))
[success, trans,
info] = register_one_rgbd_pair(s, t, color_files, depth_files,
intrinsic, with_opencv, config)
if success:
pose_graph.edges.append(
o3d.pipelines.registration.PoseGraphEdge(
s - sid, t - sid, trans, info, uncertain=True))
o3d.io.write_pose_graph(
join(path_dataset, config["template_fragment_posegraph"] % fragment_id),
pose_graph)
def integrate_rgb_frames_for_fragment(color_files, depth_files, fragment_id,
n_fragments, pose_graph_name, intrinsic,
config):
pose_graph = o3d.io.read_pose_graph(pose_graph_name)
volume = o3d.pipelines.integration.ScalableTSDFVolume(
voxel_length=config["tsdf_cubic_size"] / 512.0,
sdf_trunc=0.04,
color_type=o3d.pipelines.integration.TSDFVolumeColorType.RGB8)
for i in range(len(pose_graph.nodes)):
i_abs = fragment_id * config['n_frames_per_fragment'] + i
print(
"Fragment %03d / %03d :: integrate rgbd frame %d (%d of %d)." %
(fragment_id, n_fragments - 1, i_abs, i + 1, len(pose_graph.nodes)))
rgbd = read_rgbd_image(color_files[i_abs], depth_files[i_abs], False,
config)
pose = pose_graph.nodes[i].pose
volume.integrate(rgbd, intrinsic, np.linalg.inv(pose))
mesh = volume.extract_triangle_mesh()
mesh.compute_vertex_normals()
return mesh
def make_pointcloud_for_fragment(path_dataset, color_files, depth_files,
fragment_id, n_fragments, intrinsic, config):
mesh = integrate_rgb_frames_for_fragment(
color_files, depth_files, fragment_id, n_fragments,
join(path_dataset,
config["template_fragment_posegraph_optimized"] % fragment_id),
intrinsic, config)
pcd = o3d.geometry.PointCloud()
pcd.points = mesh.vertices
pcd.colors = mesh.vertex_colors
pcd_name = join(path_dataset,
config["template_fragment_pointcloud"] % fragment_id)
o3d.io.write_point_cloud(pcd_name,
pcd,
format='auto',
write_ascii=False,
compressed=True)
def process_single_fragment(fragment_id, color_files, depth_files, n_files,
n_fragments, config):
if config["path_intrinsic"]:
intrinsic = o3d.io.read_pinhole_camera_intrinsic(
config["path_intrinsic"])
else:
intrinsic = o3d.camera.PinholeCameraIntrinsic(
o3d.camera.PinholeCameraIntrinsicParameters.PrimeSenseDefault)
sid = fragment_id * config['n_frames_per_fragment']
eid = min(sid + config['n_frames_per_fragment'], n_files)
make_posegraph_for_fragment(config["path_dataset"], sid, eid, color_files,
depth_files, fragment_id, n_fragments,
intrinsic, with_opencv, config)
optimize_posegraph_for_fragment(config["path_dataset"], fragment_id, config)
make_pointcloud_for_fragment(config["path_dataset"], color_files,
depth_files, fragment_id, n_fragments,
intrinsic, config)
def run(config):
print("making fragments from RGBD sequence.")
make_clean_folder(join(config["path_dataset"], config["folder_fragment"]))
[color_files, depth_files] = get_rgbd_file_lists(config["path_dataset"])
n_files = len(color_files)
n_fragments = int(
math.ceil(float(n_files) / config['n_frames_per_fragment']))
if config["python_multi_threading"] is True:
max_workers = min(max(1, multiprocessing.cpu_count() - 1), n_fragments)
# Prevent over allocation of open mp threads in child processes
os.environ['OMP_NUM_THREADS'] = '1'
mp_context = multiprocessing.get_context('spawn')
with mp_context.Pool(processes=max_workers) as pool:
args = [(fragment_id, color_files, depth_files, n_files,
n_fragments, config) for fragment_id in range(n_fragments)]
pool.starmap(process_single_fragment, args)
else:
for fragment_id in range(n_fragments):
process_single_fragment(fragment_id, color_files, depth_files,
n_files, n_fragments, config)
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