KD Tree¶
kd_tree_feature_matching.py¶
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | # ----------------------------------------------------------------------------
# - Open3D: www.open3d.org -
# ----------------------------------------------------------------------------
# The MIT License (MIT)
#
# Copyright (c) 2018-2021 www.open3d.org
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
# ----------------------------------------------------------------------------
import numpy as np
import open3d as o3d
if __name__ == "__main__":
print("Load two aligned point clouds.")
demo_data = o3d.data.DemoFeatureMatchingPointClouds()
pcd0 = o3d.io.read_point_cloud(demo_data.point_cloud_paths[0])
pcd1 = o3d.io.read_point_cloud(demo_data.point_cloud_paths[1])
pcd0.paint_uniform_color([1, 0.706, 0])
pcd1.paint_uniform_color([0, 0.651, 0.929])
o3d.visualization.draw_geometries([pcd0, pcd1])
print("Load their FPFH feature and evaluate.")
print("Black : matching distance > 0.2")
print("White : matching distance = 0")
feature0 = o3d.io.read_feature(demo_data.fpfh_feature_paths[0])
feature1 = o3d.io.read_feature(demo_data.fpfh_feature_paths[1])
fpfh_tree = o3d.geometry.KDTreeFlann(feature1)
for i in range(len(pcd0.points)):
[_, idx, _] = fpfh_tree.search_knn_vector_xd(feature0.data[:, i], 1)
dis = np.linalg.norm(pcd0.points[i] - pcd1.points[idx[0]])
c = (0.2 - np.fmin(dis, 0.2)) / 0.2
pcd0.colors[i] = [c, c, c]
o3d.visualization.draw_geometries([pcd0])
print("")
print("Load their L32D feature and evaluate.")
print("Black : matching distance > 0.2")
print("White : matching distance = 0")
feature0 = o3d.io.read_feature(demo_data.l32d_feature_paths[0])
feature1 = o3d.io.read_feature(demo_data.l32d_feature_paths[1])
fpfh_tree = o3d.geometry.KDTreeFlann(feature1)
for i in range(len(pcd0.points)):
[_, idx, _] = fpfh_tree.search_knn_vector_xd(feature0.data[:, i], 1)
dis = np.linalg.norm(pcd0.points[i] - pcd1.points[idx[0]])
c = (0.2 - np.fmin(dis, 0.2)) / 0.2
pcd0.colors[i] = [c, c, c]
o3d.visualization.draw_geometries([pcd0])
print("")
|
kd_tree_search.py¶
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | # ----------------------------------------------------------------------------
# - Open3D: www.open3d.org -
# ----------------------------------------------------------------------------
# The MIT License (MIT)
#
# Copyright (c) 2018-2021 www.open3d.org
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
# ----------------------------------------------------------------------------
"""Build a KDTree and use it for neighbour search"""
import open3d as o3d
import numpy as np
def radius_search():
print("Loading pointcloud ...")
sample_pcd_data = o3d.data.PCDPointCloud()
pcd = o3d.io.read_point_cloud(sample_pcd_data.path)
pcd_tree = o3d.geometry.KDTreeFlann(pcd)
print(
"Find the neighbors of 50000th point with distance less than 0.2, and painting them green ..."
)
[k, idx, _] = pcd_tree.search_radius_vector_3d(pcd.points[50000], 0.2)
np.asarray(pcd.colors)[idx[1:], :] = [0, 1, 0]
print("Displaying the final point cloud ...\n")
o3d.visualization.draw([pcd])
def knn_search():
print("Loading pointcloud ...")
sample_pcd = o3d.data.PCDPointCloud()
pcd = o3d.io.read_point_cloud(sample_pcd.path)
pcd_tree = o3d.geometry.KDTreeFlann(pcd)
print(
"Find the 2000 nearest neighbors of 50000th point, and painting them red ..."
)
[k, idx, _] = pcd_tree.search_knn_vector_3d(pcd.points[50000], 2000)
np.asarray(pcd.colors)[idx[1:], :] = [1, 0, 0]
print("Displaying the final point cloud ...\n")
o3d.visualization.draw([pcd])
if __name__ == "__main__":
knn_search()
radius_search()
|