用cpp调用pcl库写一个将xyz无序点云转变为有序点云,根据输入像素坐标形成锚框切割生成新的点云的程序
时间: 2024-05-02 07:16:13 浏览: 18
以下是一个可能的实现,使用PCL的半径搜索和KD-Tree来对无序点云进行排序,然后使用输入的像素坐标生成锚框,通过切割生成新的点云。
```cpp
#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/search/kdtree.h>
#include <pcl/filters/radius_outlier_removal.h>
#include <pcl/filters/conditional_removal.h>
#include <pcl/visualization/cloud_viewer.h>
int main(int argc, char** argv)
{
// Load input point cloud
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
if (pcl::io::loadPCDFile<pcl::PointXYZ>("/path/to/pointcloud.pcd", *cloud) == -1) //* load the file
{
PCL_ERROR("Couldn't read file test_pcd.pcd \n");
return (-1);
}
// Downsample the cloud
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_downsampled(new pcl::PointCloud<pcl::PointXYZ>);
pcl::VoxelGrid<pcl::PointXYZ> voxel_grid;
voxel_grid.setInputCloud(cloud);
voxel_grid.setLeafSize(0.01f, 0.01f, 0.01f);
voxel_grid.filter(*cloud_downsampled);
// Remove outliers
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered(new pcl::PointCloud<pcl::PointXYZ>);
pcl::RadiusOutlierRemoval<pcl::PointXYZ> outlier_removal;
outlier_removal.setInputCloud(cloud_downsampled);
outlier_removal.setRadiusSearch(0.05);
outlier_removal.setMinNeighborsInRadius(10);
outlier_removal.filter(*cloud_filtered);
// Sort the cloud using KD-Tree
pcl::search::KdTree<pcl::PointXYZ>::Ptr kd_tree(new pcl::search::KdTree<pcl::PointXYZ>);
kd_tree->setInputCloud(cloud_filtered);
pcl::IndicesPtr indices(new std::vector<int>);
std::vector<float> distances;
kd_tree->nearestKSearch(*cloud_filtered, 1, *indices, distances);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_sorted(new pcl::PointCloud<pcl::PointXYZ>);
pcl::copyPointCloud(*cloud_filtered, *indices, *cloud_sorted);
// Generate anchor boxes based on input pixel coordinates
int x = 100; // example pixel coordinate
int y = 200; // example pixel coordinate
float min_x = cloud_sorted->at(x).x;
float max_x = cloud_sorted->at(x).x;
float min_y = cloud_sorted->at(y).y;
float max_y = cloud_sorted->at(y).y;
// Cut the cloud using the anchor box
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cut(new pcl::PointCloud<pcl::PointXYZ>);
pcl::ConditionAnd<pcl::PointXYZ>::Ptr condition(new pcl::ConditionAnd<pcl::PointXYZ>());
condition->addComparison(pcl::FieldComparison<pcl::PointXYZ>::ConstPtr(new pcl::FieldComparison<pcl::PointXYZ>("x", pcl::ComparisonOps::GT, min_x)));
condition->addComparison(pcl::FieldComparison<pcl::PointXYZ>::ConstPtr(new pcl::FieldComparison<pcl::PointXYZ>("x", pcl::ComparisonOps::LT, max_x)));
condition->addComparison(pcl::FieldComparison<pcl::PointXYZ>::ConstPtr(new pcl::FieldComparison<pcl::PointXYZ>("y", pcl::ComparisonOps::GT, min_y)));
condition->addComparison(pcl::FieldComparison<pcl::PointXYZ>::ConstPtr(new pcl::FieldComparison<pcl::PointXYZ>("y", pcl::ComparisonOps::LT, max_y)));
pcl::ConditionalRemoval<pcl::PointXYZ> conditional_removal;
conditional_removal.setCondition(condition);
conditional_removal.setInputCloud(cloud_sorted);
conditional_removal.filter(*cloud_cut);
// Visualize the results
pcl::visualization::CloudViewer viewer("Cloud Viewer");
viewer.showCloud(cloud_cut);
while (!viewer.wasStopped()) {}
return 0;
}
```
请注意,这只是一个粗略的实现,可能需要根据具体情况进行调整。
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