测试环境：
pcl==1.12.1
python-pcl==0.3.1
python==3.7
代码：
# -*- coding: utf-8 -*- # Spatial change detection on unorganized point cloud data # https://pointclouds.org/documentation/tutorials/octree_change.php#octree-change-detection import pcl import numpy as np import random def main(): # // Octree resolution - side length of octree voxels resolution = 32.0 # // Instantiate octree-based point cloud change detection class # pcl::octree::OctreePointCloudChangeDetector<pcl::PointXYZ> octree (resolution); # pcl::PointCloud<pcl::PointXYZ>::Ptr cloudA (new pcl::PointCloud<pcl::PointXYZ> ); # // Generate pointcloud data for cloudA # cloudA->width = 128; # cloudA->height = 1; # cloudA->points.resize (cloudA->width * cloudA->height); # for (size_t i = 0; i < cloudA->points.size (); ++i) # { # cloudA->points[i].x = 64.0f * rand () / (RAND_MAX + 1.0f); # cloudA->points[i].y = 64.0f * rand () / (RAND_MAX + 1.0f); # cloudA->points[i].z = 64.0f * rand () / (RAND_MAX + 1.0f); # } # # // Add points from cloudA to octree # octree.setInputCloud (cloudA); # octree.addPointsFromInputCloud (); cloudA = pcl.PointCloud() points = np.zeros((128, 3), dtype=np.float32) RAND_MAX = 1.0 for i in range(0, 5): points[i][0] = 64.0 * random.random() / RAND_MAX points[i][1] = 64.0 * random.random() / RAND_MAX points[i][2] = 64.0 * random.random() / RAND_MAX cloudA.from_array(points) octree = cloudA.make_octreeChangeDetector(resolution) octree.add_points_from_input_cloud() ### # // Switch octree buffers: This resets octree but keeps previous tree structure in memory. # octree.switchBuffers (); octree.switchBuffers() # pcl::PointCloud<pcl::PointXYZ>::Ptr cloudB (new pcl::PointCloud<pcl::PointXYZ> ); cloudB = pcl.PointCloud() # // Generate pointcloud data for cloudB # cloudB->width = 128; # cloudB->height = 1; # cloudB->points.resize (cloudB->width * cloudB->height); # # for (size_t i = 0; i < cloudB->points.size (); ++i) # { # cloudB->points[i].x = 64.0f * rand () / (RAND_MAX + 1.0f); # cloudB->points[i].y = 64.0f * rand () / (RAND_MAX + 1.0f); # cloudB->points[i].z = 64.0f * rand () / (RAND_MAX + 1.0f); # } # // Add points from cloudB to octree # octree.setInputCloud (cloudB); # octree.addPointsFromInputCloud (); points2 = np.zeros((128, 3), dtype=np.float32) for i in range(0, 128): points2[i][0] = 64.0 * random.random() / RAND_MAX points2[i][1] = 64.0 * random.random() / RAND_MAX points2[i][2] = 64.0 * random.random() / RAND_MAX cloudB.from_array(points2) octree.set_input_cloud(cloudB) octree.add_points_from_input_cloud() # std::vector<int> newPointIdxVector; # // Get vector of point indices from octree voxels which did not exist in previous buffer # octree.getPointIndicesFromNewVoxels (newPointIdxVector); # // Output points # std::cout << "Output from getPointIndicesFromNewVoxels:" << std::endl; # for (size_t i = 0; i < newPointIdxVector.size (); ++i) # std::cout << i << "# Index:" << newPointIdxVector[i] # << " Point:" << cloudB->points[newPointIdxVector[i]].x << " " # << cloudB->points[newPointIdxVector[i]].y << " " # << cloudB->points[newPointIdxVector[i]].z << std::endl; newPointIdxVector = octree.get_PointIndicesFromNewVoxels() print('Output from getPointIndicesFromNewVoxels:') cloudB.extract(newPointIdxVector) # count = newPointIdxVector.size for i in range(0, len(newPointIdxVector)): # print(str(i) + '# Index:' + str(newPointIdxVector[i]) + ' Point:' + str(cloudB[i * 3 + 0]) + ' ' + str(cloudB[i * 3 + 1]) + ' ' + str(cloudB[i * 3 + 2]) ) # print(str(i) + '# Index:' + str(i) + ' Point:' + str(cloudB[i])) print(str(i) + '# Index:' + str(newPointIdxVector[i]) + ' Point:' + str(cloudB[newPointIdxVector[i]][0]) + ' ' + str( cloudB[newPointIdxVector[i]][1]) + ' ' + str(cloudB[newPointIdxVector[i]][2])) if __name__ == "__main__": # import cProfile # cProfile.run('main()', sort='time') main() #
