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点云学习笔记16——提取点云的边界，填充边界

时间：2024-11-12 21:18:16浏览次数：3  
标签：边界 16 points pcl 点云 boundary PointCloud id cloud 

#include <iostream>
#include <algorithm>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/visualization/pcl_visualizer.h>

void BoundaryExtraction(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud, pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_boundary, int resolution)
{
	pcl::PointXYZ px_min = *std::min_element(cloud->begin(), cloud->end(), [](pcl::PointXYZ pt1, pcl::PointXYZ pt2) {return pt1.x < pt2.x; });
	pcl::PointXYZ px_max = *std::max_element(cloud->begin(), cloud->end(), [](pcl::PointXYZ pt1, pcl::PointXYZ pt2) {return pt1.x < pt2.x; });

	float delta_x = (px_max.x - px_min.x) / resolution;
	float min_y = INT_MAX, max_y = -INT_MAX;
	std::vector<int> indexs_x(2 * resolution + 2);
	std::vector<std::pair<float, float>> minmax_x(resolution + 1, { INT_MAX,-INT_MAX });
	for (size_t i = 0; i < cloud->size(); ++i)
	{
		int id = (cloud->points[i].x - px_min.x) / delta_x;
		if (cloud->points[i].y < minmax_x[id].first)
		{
			minmax_x[id].first = cloud->points[i].y;
			indexs_x[id] = i;
		}
		else if (cloud->points[i].y > minmax_x[id].second)
		{
			minmax_x[id].second = cloud->points[i].y;
			indexs_x[id + resolution + 1] = i;
		}
	}

	pcl::PointXYZ py_min = *std::min_element(cloud->begin(), cloud->end(), [](pcl::PointXYZ pt1, pcl::PointXYZ pt2) {return pt1.y < pt2.y; });
	pcl::PointXYZ py_max = *std::max_element(cloud->begin(), cloud->end(), [](pcl::PointXYZ pt1, pcl::PointXYZ pt2) {return pt1.y < pt2.y; });

	float delta_y = (py_max.y - py_min.y) / resolution;
	float min_x = INT_MAX, max_x = -INT_MAX;
	std::vector<int> indexs_y(2 * resolution + 2);
	std::vector<std::pair<float, float>> minmax_y(resolution + 1, { INT_MAX,-INT_MAX });
	for (size_t i = 0; i < cloud->size(); ++i)
	{
		int id = (cloud->points[i].y - py_min.y) / delta_y;
		if (cloud->points[i].x < minmax_y[id].first)
		{
			minmax_y[id].first = cloud->points[i].x;
			indexs_y[id] = i;
		}
		else if (cloud->points[i].x > minmax_y[id].second)
		{
			minmax_y[id].second = cloud->points[i].x;
			indexs_y[id + resolution + 1] = i;
		}
	}

	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_xboundary(new pcl::PointCloud<pcl::PointXYZ>);
	pcl::copyPointCloud(*cloud, indexs_x, *cloud_xboundary);
	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_yboundary(new pcl::PointCloud<pcl::PointXYZ>);
	pcl::copyPointCloud(*cloud, indexs_y, *cloud_yboundary);
	*cloud_boundary = *cloud_xboundary + *cloud_yboundary;
}

int main(int argc, char* argv[])
{
	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>) ;
	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_boundary(new pcl::PointCloud<pcl::PointXYZ>);
	pcl::io::loadPCDFile("F:\\19.pcd", *cloud);

	BoundaryExtraction(cloud, cloud_boundary, 200);
	pcl::io::savePCDFile("F:\\boundary1.pcd", *cloud_boundary);

	pcl::visualization::PCLVisualizer viewer("boundary");
    viewer.setBackgroundColor(0, 0, 0);//三个参数，分别代表红、绿、蓝颜色
    pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> target_color(cloud_boundary->makeShared(), 128, 128, 0);//类的实例 target_color，它用于自定义点云的颜色
    pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> result_color(cloud_boundary->makeShared(), 0, 255, 0);
    viewer.addPointCloud(cloud_boundary, result_color, "result_cloud");//用于

    while (!viewer.wasStopped())
    {
        viewer.spinOnce(100);
    }


	return EXIT_SUCCESS;
}

填充边界

#include <iostream>
#include <algorithm>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/visualization/pcl_visualizer.h>

// 边界提取函数
void BoundaryExtraction(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud, pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_boundary, int resolution) {
    pcl::PointXYZ px_min = *std::min_element(cloud->begin(), cloud->end(), [](pcl::PointXYZ pt1, pcl::PointXYZ pt2) {return pt1.x < pt2.x; });
    pcl::PointXYZ px_max = *std::max_element(cloud->begin(), cloud->end(), [](pcl::PointXYZ pt1, pcl::PointXYZ pt2) {return pt1.x < pt2.x; });

    float delta_x = (px_max.x - px_min.x) / resolution;
    std::vector<int> indexs_x(2 * resolution + 2);
    std::vector<std::pair<float, float>> minmax_x(resolution + 1, { INT_MAX, -INT_MAX });
    for (size_t i = 0; i < cloud->size(); ++i) {
        int id = (cloud->points[i].x - px_min.x) / delta_x;
        if (cloud->points[i].y < minmax_x[id].first) {
            minmax_x[id].first = cloud->points[i].y;
            indexs_x[id] = i;
        } else if (cloud->points[i].y > minmax_x[id].second) {
            minmax_x[id].second = cloud->points[i].y;
            indexs_x[id + resolution + 1] = i;
        }
    }

    pcl::PointXYZ py_min = *std::min_element(cloud->begin(), cloud->end(), [](pcl::PointXYZ pt1, pcl::PointXYZ pt2) {return pt1.y < pt2.y; });
    pcl::PointXYZ py_max = *std::max_element(cloud->begin(), cloud->end(), [](pcl::PointXYZ pt1, pcl::PointXYZ pt2) {return pt1.y < pt2.y; });

    float delta_y = (py_max.y - py_min.y) / resolution;
    std::vector<int> indexs_y(2 * resolution + 2);
    std::vector<std::pair<float, float>> minmax_y(resolution + 1, { INT_MAX, -INT_MAX });
    for (size_t i = 0; i < cloud->size(); ++i) {
        int id = (cloud->points[i].y - py_min.y) / delta_y;
        if (cloud->points[i].x < minmax_y[id].first) {
            minmax_y[id].first = cloud->points[i].x;
            indexs_y[id] = i;
        } else if (cloud->points[i].x > minmax_y[id].second) {
            minmax_y[id].second = cloud->points[i].x;
            indexs_y[id + resolution + 1] = i;
        }
    }

    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_xboundary(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::copyPointCloud(*cloud, indexs_x, *cloud_xboundary);
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_yboundary(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::copyPointCloud(*cloud, indexs_y, *cloud_yboundary);
    *cloud_boundary = *cloud_xboundary + *cloud_yboundary;
}

// 边界点云填充函数
void FillBoundary(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_boundary, pcl::PointCloud<pcl::PointXYZ>::Ptr filled_boundary, float step) {
    for (size_t i = 0; i < cloud_boundary->size() - 1; ++i) {
        pcl::PointXYZ pt1 = cloud_boundary->points[i];
        pcl::PointXYZ pt2 = cloud_boundary->points[i + 1];
        filled_boundary->push_back(pt1);

        // 插值生成填充点
        int num_steps = std::ceil(std::sqrt(std::pow(pt2.x - pt1.x, 2) + std::pow(pt2.y - pt1.y, 2)) / step);
        for (int j = 1; j <= num_steps; ++j) {
            pcl::PointXYZ interpolated_pt;
            interpolated_pt.x = pt1.x + j * (pt2.x - pt1.x) / num_steps;
            interpolated_pt.y = pt1.y + j * (pt2.y - pt1.y) / num_steps;
            interpolated_pt.z = pt1.z + j * (pt2.z - pt1.z) / num_steps;
            filled_boundary->push_back(interpolated_pt);
        }
    }
    filled_boundary->push_back(cloud_boundary->back()); // 添加最后一个边界点
}

int main(int argc, char* argv[]) {
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_boundary(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::PointCloud<pcl::PointXYZ>::Ptr filled_boundary(new pcl::PointCloud<pcl::PointXYZ>);

    pcl::io::loadPCDFile("F:\\19.pcd", *cloud);
    BoundaryExtraction(cloud, cloud_boundary, 200);
    
    // 插值填充边界
    FillBoundary(cloud_boundary, filled_boundary, 0.01); // 0.01表示插值步长

    pcl::io::savePCDFile("F:\\boundary1_filled.pcd", *filled_boundary);
    std::cout << "边界点云已保存为 boundary1_filled.pcd" << std::endl;

    // 可视化边界
    pcl::visualization::PCLVisualizer viewer("boundary");
    viewer.setBackgroundColor(0, 0, 0);
    pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> target_color(filled_boundary->makeShared(), 128, 128, 0);
    pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> result_color(filled_boundary->makeShared(), 0, 255, 0);
    viewer.addPointCloud(filled_boundary, result_color, "result_cloud");

    while (!viewer.wasStopped()) {
        viewer.spinOnce(100);
    }

    return EXIT_SUCCESS;
}

ransac拟合边界直线

#include <iostream>
#include <algorithm>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/filters/extract_indices.h>

// 边界提取函数
void BoundaryExtraction(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud, pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_boundary, int resolution) {
    // (边界提取代码保持不变)
    pcl::PointXYZ px_min = *std::min_element(cloud->begin(), cloud->end(), [](pcl::PointXYZ pt1, pcl::PointXYZ pt2) { return pt1.x < pt2.x; });
    pcl::PointXYZ px_max = *std::max_element(cloud->begin(), cloud->end(), [](pcl::PointXYZ pt1, pcl::PointXYZ pt2) { return pt1.x < pt2.x; });

    float delta_x = (px_max.x - px_min.x) / resolution;
    std::vector<int> indexs_x(2 * resolution + 2);
    std::vector<std::pair<float, float>> minmax_x(resolution + 1, { INT_MAX,-INT_MAX });
    for (size_t i = 0; i < cloud->size(); ++i) {
        int id = (cloud->points[i].x - px_min.x) / delta_x;
        if (cloud->points[i].y < minmax_x[id].first) {
            minmax_x[id].first = cloud->points[i].y;
            indexs_x[id] = i;
        }
        else if (cloud->points[i].y > minmax_x[id].second) {
            minmax_x[id].second = cloud->points[i].y;
            indexs_x[id + resolution + 1] = i;
        }
    }

    pcl::PointXYZ py_min = *std::min_element(cloud->begin(), cloud->end(), [](pcl::PointXYZ pt1, pcl::PointXYZ pt2) { return pt1.y < pt2.y; });
    pcl::PointXYZ py_max = *std::max_element(cloud->begin(), cloud->end(), [](pcl::PointXYZ pt1, pcl::PointXYZ pt2) { return pt1.y < pt2.y; });

    float delta_y = (py_max.y - py_min.y) / resolution;
    std::vector<int> indexs_y(2 * resolution + 2);
    std::vector<std::pair<float, float>> minmax_y(resolution + 1, { INT_MAX,-INT_MAX });
    for (size_t i = 0; i < cloud->size(); ++i) {
        int id = (cloud->points[i].y - py_min.y) / delta_y;
        if (cloud->points[i].x < minmax_y[id].first) {
            minmax_y[id].first = cloud->points[i].x;
            indexs_y[id] = i;
        }
        else if (cloud->points[i].x > minmax_y[id].second) {
            minmax_y[id].second = cloud->points[i].x;
            indexs_y[id + resolution + 1] = i;
        }
    }

    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_xboundary(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::copyPointCloud(*cloud, indexs_x, *cloud_xboundary);
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_yboundary(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::copyPointCloud(*cloud, indexs_y, *cloud_yboundary);
    *cloud_boundary = *cloud_xboundary + *cloud_yboundary;
}

// RANSAC 直线拟合函数
void FitBoundaryLines(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_boundary) {
    pcl::SACSegmentation<pcl::PointXYZ> seg;
    pcl::ExtractIndices<pcl::PointXYZ> extract;
    pcl::PointIndices::Ptr inliers(new pcl::PointIndices);
    pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients);

    seg.setOptimizeCoefficients(true);
    seg.setModelType(pcl::SACMODEL_LINE);
    seg.setMethodType(pcl::SAC_RANSAC);
    seg.setDistanceThreshold(0.01);

    pcl::PointCloud<pcl::PointXYZ>::Ptr remaining_points(new pcl::PointCloud<pcl::PointXYZ>(*cloud_boundary));

    int line_count = 0;
    while (remaining_points->size() > 10) {
        seg.setInputCloud(remaining_points);
        seg.segment(*inliers, *coefficients);

        if (inliers->indices.empty()) break;

        std::cout << "直线 " << ++line_count << " 系数: "
            << coefficients->values[0] << " " << coefficients->values[1] << " " << coefficients->values[2] << " "
            << coefficients->values[3] << " " << coefficients->values[4] << " " << coefficients->values[5] << std::endl;

        pcl::PointCloud<pcl::PointXYZ>::Ptr line_points(new pcl::PointCloud<pcl::PointXYZ>);
        extract.setInputCloud(remaining_points);
        extract.setIndices(inliers);
        extract.setNegative(false);
        extract.filter(*line_points);

        // 将已拟合的直线点从 remaining_points 中删除
        extract.setNegative(true);
        pcl::PointCloud<pcl::PointXYZ>::Ptr temp_points(new pcl::PointCloud<pcl::PointXYZ>);
        extract.filter(*temp_points);
        remaining_points.swap(temp_points);
    }
}

int main(int argc, char* argv[]) {
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_boundary(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::io::loadPCDFile("F:\\111.pcd", *cloud);

    BoundaryExtraction(cloud, cloud_boundary, 200);
    pcl::io::savePCDFile("F:\\boundary2.pcd", *cloud_boundary);

    // 使用 RANSAC 拟合直线
    FitBoundaryLines(cloud_boundary);

    // 可视化边界点云
    pcl::visualization::PCLVisualizer viewer("Boundary with Lines");
    viewer.setBackgroundColor(0, 0, 0);
    pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> color_handler(cloud_boundary, 0, 255, 0);
    viewer.addPointCloud(cloud_boundary, color_handler, "Boundary Points");

    while (!viewer.wasStopped()) {
        viewer.spinOnce(100);
    }

    return EXIT_SUCCESS;
}

标签：边界,16,points,pcl,点云,boundary,PointCloud,id,cloud
From： https://blog.csdn.net/qq_64095888/article/details/143700117

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