《PCL点云库学习&VS2010(X64)》Part 45 点云压缩算法—扫描线(DouglasPeuckerAlgorithm)

《PCL点云库学习&VS2010(X64)》Part 45 点云压缩算法—扫描线(DouglasPeuckerAlgorithm)

道格拉斯-普克算法主要应用有点云滤波、点云压缩、点云分割、轮廓线提取等，还可用于曲线拟合、曲线平滑、轨迹线压缩等。前期在做滤波算法，查阅论文时发现这个算法的介绍，出于好奇就在网上搜了一下，资源蛮多，找到了一个与点云相关的算法，将其稍微修改了下，贴出来供大家参考。

1、main.cpp:

---

#include <vector>
#include <iostream>
#include <string>
#include <stdio.h>
#include "DouglasPeucker.h"

using namespace std;

void readin(vector<MyPointStruct> &Points, const char * filename)
{
    MyPointStruct SinglePoint;
    FILE *fp = fopen(filename, "r");
    while (fscanf(fp, "%lf%lf", &SinglePoint.X, &SinglePoint.Y) != EOF)
    {
        Points.push_back(SinglePoint);
    }
}

void DouglasPeuckerAlgorithm(vector<MyPointStruct> &Points, int &tolerance, const char*filename)
{
    DouglasPeucker Instance(Points, tolerance);
    Instance.WriteData(filename);
}

void DumpOut1()
{
    printf("done!\n");
}

void DumpOut2()
{
    printf("need 3 command line parameter:"
        "\n[0]executable file name;"
        "\n[1] file name of the input data;"
        "\n[2] file name of the output data; "
        "\n[3] threshold.\n");
}

int main(int argc, char** argv)
{
    if (argc == 4)
    {
        vector<MyPointStruct> Points;
        readin(Points, argv[1]);//argv[1]-第二个参数为文件名
        int threshold = atoi(argv[3]);//argv[3]-第四个参数为距离阈值
        DouglasPeuckerAlgorithm(Points, threshold, argv[2]);//argv[2]-第三个参数为输出文件
        DumpOut1();
    }
    else
    {
        DumpOut2();
    }
}

2、DouglasPeucker.h:

---

#pragma once
#include <vector>
#include <stdio.h>
#include <iostream>

using namespace std;

struct MyPointStruct//点云结构体 
{
public:
    double X;
    double Y;
    double Z;
    MyPointStruct()
    {
        this->X = 0;
        this->Y = 0;
        this->Z = 0;
    };

    MyPointStruct(double x, double y, double z)
    {
        this->X = x;
        this->Y = y;
        this->Z = z;
    };

    ~MyPointStruct(){};
};



class DouglasPeucker:public MyPointStruct
{
public:
    //MyPointStruct pointXYZ;
    vector<MyPointStruct> PointStruct;
    vector<bool> myTag; // 标记特征点的一个bool数组
    vector<int> PointNum;//离散化得到的点号

public:
    DouglasPeucker(void);
    DouglasPeucker(vector<MyPointStruct>& Points, int tolerance);
    ~DouglasPeucker();

    void WriteData(const char *filename);

private:
    void DouglasPeuckerReduction(int firstPoint, int lastPoint, double tolerance);
    double PerpendicularDistance(MyPointStruct &point1, MyPointStruct &point2, MyPointStruct &point3);
    MyPointStruct& myConvert(int index);
};

3、DouglasPeucker.cpp:

---

#include "DouglasPeucker.h"
#include <stdio.h>

DouglasPeucker::DouglasPeucker()
{

}

DouglasPeucker::DouglasPeucker(vector<MyPointStruct>& Points, int tolerance)
{
    PointStruct = Points;
    int totalPointNum = Points.size();

    myTag.resize(totalPointNum, 0);

    DouglasPeuckerReduction(0, totalPointNum - 1, tolerance);

    for (int index = 0; index<totalPointNum;index++)
    {
        if (myTag[index])PointNum.push_back(index);
    }
}


DouglasPeucker::~DouglasPeucker()
{

}

void DouglasPeucker::WriteData(const char *filename)
{
    FILE *fp = fopen(filename, "w");
    int pSize = PointNum.size();
    for (int index = 0; index < pSize; index++)
    {
        fprintf(fp, "%lf\t%lf\n", PointStruct[PointNum[index]].X, PointStruct[PointNum[index]].Y);
    }
}

void DouglasPeucker::DouglasPeuckerReduction(int firstPoint, int lastPoint, double tolerance)
{
    double maxDistance = 0;
    int indexFarthest = 0; // 记录最大值时点元素在数组中的下标

    for (int index = firstPoint; index < lastPoint; index++)
    {
        double distance = PerpendicularDistance(myConvert(firstPoint),

            myConvert(lastPoint), myConvert(index));

        if (distance > maxDistance)
        {
            maxDistance = distance;
            indexFarthest = index;
        }
    }
    if (maxDistance > tolerance && indexFarthest != 0)
    {
        myTag[indexFarthest] = true; // 记录特征点的索引信息

        DouglasPeuckerReduction(firstPoint, indexFarthest, tolerance);
        DouglasPeuckerReduction(indexFarthest, lastPoint, tolerance);
    }
}

double DouglasPeucker::PerpendicularDistance(MyPointStruct &point1, MyPointStruct &point2, MyPointStruct &point3)
{
    // 点到直线的距离公式法
    double A, B, C, maxDist = 0;
    A = point2.Y - point1.Y;
    B = point1.X - point2.X;
    C = point2.X * point1.Y - point1.X * point2.Y;
    maxDist = fabs((A * point3.X + B * point3.Y + C) / sqrt(A * A + B *B));
    return maxDist;
}

MyPointStruct& DouglasPeucker::myConvert(int index)
{
    return PointStruct[index];
}

4、总结

---

程序运行后后得到三个点块，分别切开后会发现对应着数据的三个视图面，数据的存储内存确实减少了。但是二维数据的利用还没有新的思路，在二维点云上做形态学滤波或者其他聚类分割，获取点云的索引，能分割点云。

根据个人的理解，这种全局递归计算的方式对数据压缩可能有用，要想实现线扫描滤波分割，则需要对对数据切片或栅格化处理，最后将数据合并，则可实现分割，当然，中间需要加入一些辅助的条件。