python 如何找点云的边缘点云边界提取

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网络安全战士 2024-01-03 09:34:34

文章标签 python 如何找点云的边缘计算机视觉 #include 点云搜索 文章分类 Python 后端开发

经纬线扫描法

经纬线扫描法的基本思想是：将经过坐标变换后的二维数据点集按照 x值的大小排序后存储在一个链表中，在二维平面建立点集的最小包围盒并分别计算出 x 和 y 的最大、最小值；令经线从 x 的最小值开始，取步长为dx，在 x 的取值范围内分别计算出每根经线的最大和最小 y 值，并将它们的索引值放在一个新建的链表中，扫描完整个 x 区间；同样的方法扫描纬线，最后将重复的索引值删掉。
该算法的运算速度受点云点数以及搜索分辨率影响。下面代码在点云形状近似为平面，且在xoy平面上投影面积最大时表现较好，其他情况需要修改。

#include <iostream>
#include <algorithm>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.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("test.pcd", *cloud);
	
	BoundaryExtraction(cloud, cloud_boundary, 200);
	pcl::io::savePCDFile("boundary.pcd", *cloud_boundary);

	return EXIT_SUCCESS;
}

提取结果：

python 如何找点云的边缘点云边界提取_搜索_02

网格划分法

网格划分法分为三个步骤：（1）网格划分（2）寻找边界网格（3）提取边界线。首先建立数据点集的最小包围盒，并用给定间隔的矩形网格将其分割；然后找出边界网格，将这些边界网格按顺序连接起来形成一条由边界网格组成的“粗边界”；再对每个边界网格按照某种规则判断其内的点是否为边界点，连接初始边界，并对此边界线进一步处理使其光滑。

python 如何找点云的边缘点云边界提取_#include_03

python 如何找点云的边缘点云边界提取_#include_04

该算法的运算速度受点云点数以及搜索分辨率影响。下面代码在点云形状近似为平面，且在xoy平面上投影面积最大时表现较好，其他情况需要修改。

#include <iostream>
#include <vector>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/common/transforms.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; });
	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 x_min = px_min.x, x_max = px_max.x, y_min = py_min.y, y_max = py_max.y;

	float L = sqrt((x_max - x_min)*(y_max - y_min) / resolution);
	int x_num = (x_max - x_min) / L + 1;
	int y_num = (y_max - y_min) / L + 1;

	std::vector<std::vector< pcl::PointCloud<pcl::PointXYZ>::Ptr>> uv(x_num + 1, std::vector< pcl::PointCloud<pcl::PointXYZ>::Ptr>(y_num + 1));
	for (int i = 0; i <= x_num; ++i)
	{
		for (int j = 0; j <= y_num; ++j)
		{
			 pcl::PointCloud<pcl::PointXYZ>::Ptr ptcloud(new  pcl::PointCloud<pcl::PointXYZ>);
			 uv[i][j] = ptcloud;
		}
	}
	for (int i = 0; i < cloud->size(); ++i)
	{
		int x_id = (cloud->points[i].x - x_min) / L;
		int y_id = (cloud->points[i].y - y_min) / L;
		uv[x_id][y_id]->push_back(cloud->points[i]);
	}

	std::vector<std::vector<bool>> boundary_index(x_num + 1, std::vector<bool>(y_num + 1, false));
	for (int i = 0; i <= x_num; ++i)
	{
		if (uv[i][0]->size())
			boundary_index[i][0] = true;
		if (uv[i][y_num]->size())
			boundary_index[i][y_num] = true;
	}		
	for (int i = 0; i <= y_num; ++i)
	{
		if(uv[0][i]->size())
			boundary_index[0][i] = true;
		if (uv[x_num][i]->size())
			boundary_index[x_num][i] = true;
	}
	for (int i = 1; i < x_num - 1; ++i)
	{
		for (int j = 1; j < y_num - 1; ++j)
		{
			if (uv[i][j]->size())
			{
				if (!uv[i - 1][j - 1]->size() || !uv[i][j - 1]->size() || !uv[i + 1][j - 1]->size() || !uv[i][j - 1]->size() ||
					!uv[i + 1][j - 1]->size() || !uv[i + 1][j]->size() || !uv[i + 1][j + 1]->size() || !uv[i][j + 1]->size())
					boundary_index[i][j] = true;
			}
		}
	}

	for (int i = 0; i <= x_num; ++i)
	{
		for (int j = 0; j <= y_num; ++j)
		{
			if (boundary_index[i][j])
			{
				 pcl::PointCloud<pcl::PointXYZ>::Ptr ptcloud(new  pcl::PointCloud<pcl::PointXYZ>);
				ptcloud = uv[i][j];
				Eigen::Vector4f cloud_centroid;
				pcl::compute3DCentroid(*ptcloud, cloud_centroid);
				cloud_boundary->push_back(pcl::PointXYZ(cloud_centroid(0), cloud_centroid(1), cloud_centroid(2)));
			}
		}
	}
}

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("test.pcd", *cloud);
	
	BoundaryExtraction(cloud, cloud_boundary, 200*200);
	pcl::io::savePCDFile("boundary.pcd", *cloud_boundary);

	return EXIT_SUCCESS;
}

提取结果：

python 如何找点云的边缘点云边界提取_python 如何找点云的边缘_05

法线估计法

可参考PCL：点云数据基于法线的边界提取(从最初的法线估计理论推导到最终的边界提取) 该算法的运算速度受点云点数以及搜索半径影响。

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/features/normal_3d.h>
#include <pcl/features/boundary.h>


int main(int argc, char* argv[])
{

	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
	pcl::io::loadPCDFile("test.pcd", *cloud);

	pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);
	pcl::PointCloud<pcl::Boundary> boundaries;
	pcl::BoundaryEstimation<pcl::PointXYZ, pcl::Normal, pcl::Boundary> est;
	pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>());

	pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normEst;
	normEst.setInputCloud(cloud);
	normEst.setSearchMethod(tree);
	normEst.setRadiusSearch(3);  //法向估计的半径  3
	//normEst.setKSearch(9);  //法向估计的点数
	normEst.compute(*normals);

	est.setInputCloud(cloud);
	est.setInputNormals(normals);
	est.setSearchMethod(tree);
	est.setKSearch(500);  //一般这里的数值越高，最终边界识别的精度越好
	est.compute(boundaries);

	pcl::PointCloud<pcl::PointXYZ>::Ptr boundPoints(new pcl::PointCloud<pcl::PointXYZ>);
	pcl::PointCloud<pcl::PointXYZ> noBoundPoints;
	int countBoundaries = 0;
	for (int i = 0; i < cloud->size(); i++)
	{
		uint8_t x = (boundaries.points[i].boundary_point);
		int a = static_cast<int>(x); //该函数的功能是强制类型转换
		if (a == 1)
		{
			(*boundPoints).push_back(cloud->points[i]);
			countBoundaries++;
		}
		else
			noBoundPoints.push_back(cloud->points[i]);
	}

	pcl::io::savePCDFile("boundary.pcd", *boundPoints);

	return EXIT_SUCCESS;
}

提取结果：

python 如何找点云的边缘点云边界提取_点云_06

alpha shapes算法

原理参考：alpha shapes提取平面点云边界点该算法的运算速度受点云点数以及搜索半径影响。
手写实现：在alpha-shape计算二维点云边界–c++实现基础上精简优化

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/common/distances.h>
#include <pcl/kdtree/kdtree_flann.h>


int main()
{
	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
	pcl::io::loadPCDFile("test.pcd", *cloud);
	for (auto& point : *cloud)
		point.z = 0.0;

	pcl::PointCloud<pcl::PointXYZ>::Ptr boundary(new pcl::PointCloud<pcl::PointXYZ>);
	float alpha = 10.0;
	pcl::KdTreeFLANN<pcl::PointXYZ>kdtree;
	kdtree.setInputCloud(cloud);
	std::vector<int> indices;
	std::vector<float>dist;
	for (size_t i = 0; i < cloud->size(); ++i)
	{
		pcl::PointXYZ p = cloud->points[i];
		kdtree.radiusSearch(cloud->points[i], 2*alpha, indices, dist); //搜索在点云中到p点距离小于2*alpha的点
		for (size_t j = 1; j < indices.size(); ++j)
		{
			pcl::PointXYZ p1 = cloud->points[indices[j]];
			float s_2 = std::pow((p.x - p1.x), 2) + std::pow((p.y - p1.y), 2);
			float h = std::pow((alpha * alpha / s_2 - 0.25), 0.5);
			float x2 = p.x + 0.5 * (p1.x - p.x) - h * (p1.y - p.y);
			float y2 = p.y + 0.5 * (p1.y - p.y) - h * (p.x - p1.x);
			float x3 = p.x + 0.5 * (p1.x - p.x) + h * (p1.y - p.y);
			float y3 = p.y + 0.5 * (p1.y - p.y) + h * (p.x - p1.x);
			pcl::PointXYZ p2(x2, y2, 0.0);
			pcl::PointXYZ p3(x3, y3, 0.0);

			//计算邻域内除了p1之外的点到p2、p3的距离
			std::vector<bool> distp2_bool, distp3_bool;
			int count = 0;
			for (size_t k = 1; k < indices.size(); ++k)
			{
				pcl::PointXYZ p_other = cloud->points[indices[k]];
				if (k != j)
				{
					++count;
					//比较剩余的点到p2、p3距离与r的大小
					if (pcl::euclideanDistance(p_other, p2) > alpha)
						distp2_bool.push_back(true);
					if (pcl::euclideanDistance(p_other, p3) > alpha)
						distp3_bool.push_back(true);
				}
			}
			//如果邻域内所有点到p2，或者p3的距离均大于alpha，则表明p是边界点，退出循环
			if (count == distp2_bool.size() || count == distp3_bool.size())
			{
				boundary->push_back(cloud->points[i]);
				break;
			}
		}
	}

	pcl::io::savePCDFile("boundary.pcd", *boundary);

	return EXIT_SUCCESS;
}

调用api：

#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/surface/concave_hull.h>


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

	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_hull(new pcl::PointCloud<pcl::PointXYZ>); 
	pcl::ConcaveHull<pcl::PointXYZ> chull;
	chull.setInputCloud(cloud); //输入点云为投影后的点云
	chull.setAlpha(10); //设置aLpha值
	chull.reconstruct(*cloud_hull);
	pcl::io::savePCDFile("boundary.pcd", *cloud_hull);

	return EXIT_SUCCESS;
}

提取结果：

python 如何找点云的边缘点云边界提取_#include_07