opencv之7.3霍夫变换

霍夫变换可以实现任何由参数方程描述的几何体的检测。
1.检测直线
原理：霍夫变换基于二值图像，寻找经过每个单独像素点的所有直线，当直线经过足够多的像素点，则这个直线的存在足够明显。
void HoughLines( InputArray image, OutputArray lines, double rho, double theta, int threshold, double srn = 0, double stn = 0, double min_theta = 0, double max_theta = CV_PI );
代码：

Mat image, result, result2;    image = imread("D:/road.jpg", 0);    Canny(image, result, 120, 200);    namedWindow("Canny");    imshow("Canny", result);    /*vector<Vec2f> lines;                                      //lines为二维向量，vector<Vec2f>定义二维浮点向量    HoughLines(result, lines, 1, PI/ 180, 120);                 //霍夫变换    vector<Vec2f>::iterator it = lines.begin();    while (it != lines.end())    {        float rho = (*it)[0];        float theta = (*it)[1];        if (theta < PI / 4 || theta>3 * PI / 4)                 //这个是优化        {            Point ptr1(rho / cos(theta), 0);            Point ptr2((rho - result.rows*sin(theta))             / cos(theta), result.rows);            line(result, ptr1, ptr2, Scalar(255,0,0));          //        }        else        {            Point ptr1(0, rho / sin(theta));            Point ptr2(result.cols, (rho - result.cols*sin(theta)) / cos(theta));            line(result, ptr1, ptr2, Scalar(255,255,0));        }        it++;    }    namedWindow("hough");    imshow("hough", result);
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概率霍夫检测：不再逐行扫描图像，随机选择像素。一旦累加器达到阈值，则移除直线所经过的像素点，并接受线段的长度。void HoughLinesP( InputArray image, OutputArray lines,double rho, double theta, int threshold,double minLineLength = 0, double maxLineGap = 0 );minLineLength表示接受线段的最小长度，maxLineGap表示像素点的最大距离。代码：LineFinder.h头文件：

#include <opencv2/core/core.hpp>#include <opencv2/imgproc/imgproc.hpp>#include <opencv2/highgui/highgui.hpp>#define PI 3.141591653using namespace std;using namespace cv;class LineFinder{private:    Mat image;    vector<Vec4f> lines;    double deltaRho, deltatheta;    int minVote;    double minLength;    double maxGap;public:    LineFinder() :deltaRho(1), deltatheta(PI / 180), minVote(10), minLength(0.), maxGap(0.)    {    }    //设置分辨率    void SetAccResolusion(double dRho, double dTheta)    {        deltaRho = dRho;        deltatheta = dTheta;    }    void SetLengthandGap(double Length, double Gap)    {        minLength = Length;        maxGap = Gap;    }    void SetMinVote(int minv)    {        minVote = minv;    }    vector<Vec4f>findLines(Mat &binary)    {        lines.clear();        HoughLinesP(binary, lines, deltaRho, deltatheta, minVote, minLength, maxGap);        return lines;    }    void drawDetectedLines(Mat &image, Scalar color = Scalar(255,255,255))    {        vector<Vec4f>::iterator it = lines.begin();        for (;it != lines.end();++it)        {            Point ptr1((*it)[0], (*it)[1]);            Point ptr2((*it)[2], (*it)[3]);            line(image, ptr1, ptr2, color);        }    }};
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main.cpp文件

#include <opencv2/core/core.hpp>#include <opencv2/imgproc/imgproc.hpp>#include <opencv2/highgui/highgui.hpp>#include "LineFinder.h"#define PI 3.141591653using namespace std;using namespace cv;int main(){    Mat image, result, result2;    image = imread("D:/road.jpg", 0);    Canny(image, result, 120, 200);    namedWindow("Canny");    imshow("Canny", result);    LineFinder finder;    finder.SetLengthandGap(100, 20);    finder.SetMinVote(200);    vector<Vec4f> lines = finder.findLines(result);    finder.drawDetectedLines(image);    namedWindow("HoughP");    imshow("HoughP", image);    waitKey(0);    return 0;}
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检测圆：
三个参数确定一个圆。
HoughCircles( InputArray image, OutputArray circles, int method, double dp,double minDist, double param1 = 100, double param2 = 100, int minRadius = 0, int maxRadius = 0 );
src_gray: 输入图像 (灰度图)，无需canny变换
circles: 存储下面三个参数: x_{c}, y_{c}, r 集合的容器来表示每个检测到的圆.
CV_HOUGH_GRADIENT: 指定检测方法. 现在OpenCV中只有霍夫梯度法
dp = 1: 累加器图像的反比分辨率
min_dist = src_gray.rows/8: 检测到圆心之间的最小距离
param_1 = 200: Canny边缘函数的高阈值
param_2 = 100: 圆心检测阈值.投票数
min_radius = 0: 能检测到的最小圆半径, 默认为0.
max_radius = 0: 能检测到的最大圆半径, 默认为0
代码：
Mat image, result, result2;
image = imread(“D:/round.jpg”, 0);
namedWindow(“image”);
imshow(“image”, image);
GaussianBlur(image, image, Size(5, 5), 1.5);
vector circles;
HoughCircles(image, circles, HOUGH_GRADIENT, 2, 50, 200, 200, 25, 200);
vector::iterator it = circles.begin();
for (;it != circles.end();++it)
{
circle(image, Point((*it)[0], (*it)[1]), (*it)[2], Scalar(255), 2);
}
namedWindow(“circle”);
imshow(“circle”, image);

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