【OpenCV】Hough检测

Hough直线检测

  使用 HoughLines()和HoughLinesP() 检测图像中的直线。

void cv::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 )   void cv::HoughLinesP(   InputArray  image,  OutputArray     lines,                        double  rho,        double  theta,                        int     threshold,  double  minLineLength = 0,                        double  maxLineGap = 0 )   说明：image是输入的8位单通道二进制图像，lines是线的输出向量，     rho是累加器的距离分辨率，theta是累加器角度分辨率弧度，threshold是阈值。

  在Hough变换中，我们使用极坐标而非笛卡尔坐标。

  原理参考：https://docs.opencv.org/master/d9/db0/tutorial_hough_lines.html

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代码示例

#include "opencv2/imgcodecs.hpp"#include "opencv2/highgui.hpp"#include "opencv2/imgproc.hpp"using namespace cv;using namespace std;int main(int argc, char** argv){    Mat src, dst, cdst, cdstP;    char* filename = "../data/sudoku.png";    src = imread(filename, IMREAD_GRAYSCALE);    if (src.empty()) { return -1; }    Canny(src, dst, 50, 200, 3);                // Canny算子边缘检测    cvtColor(dst, cdst, COLOR_GRAY2BGR);        // 转换灰度图像    cdstP = cdst.clone();    vector<Vec2f> lines;    HoughLines(dst, lines, 1, CV_PI / 180, 150, 0, 0);  // hough检测    for (size_t i = 0; i < lines.size(); i++)           // 绘制线条    {        float rho = lines[i][0], theta = lines[i][1];        Point pt1, pt2;        double a = cos(theta), b = sin(theta);        double x0 = a*rho, y0 = b*rho;        pt1.x = cvRound(x0 + 1000 * (-b));        pt1.y = cvRound(y0 + 1000 * (a));        pt2.x = cvRound(x0 - 1000 * (-b));        pt2.y = cvRound(y0 - 1000 * (a));        line(cdst, pt1, pt2, Scalar(0, 0, 255), 3, CV_AA);    }    vector<Vec4i> linesP;    HoughLinesP(dst, linesP, 1, CV_PI / 180, 50, 50, 10);    for (size_t i = 0; i < linesP.size(); i++)    {        Vec4i l = linesP[i];        line(cdstP, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0, 0, 255), 3, LINE_AA);    }    imshow("Source", src);    imshow("HoughLines", cdst);    imshow("HoughLinesP", cdstP);    waitKey();    return 0;}

运行结果

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Hough圆检测

void cv::HoughCircles   (   InputArray  image,    OutputArray   circles,                            int    method,        double    dp,                            double minDist,                                double param1 = 100,  double    param2 = 100,                            int   minRadius = 0,  int maxRadius = 0 )   说明：dp是累加器分辨率与图像分辨率的反比。例如，如果dp = 1，则累加器具有与输入图像相同的分辨率。如果dp = 2，则累加器的宽度和高度只有一半。     minDist指检测到的圆圈中心之间的最小距离。     param1：用于Canny的边缘阀值上限，下限被置为上限的一半。     param2：累加器的阀值。

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代码示例

#include "opencv2/imgcodecs.hpp"#include "opencv2/highgui.hpp"#include "opencv2/imgproc.hpp"using namespace cv;using namespace std;int main(int argc, char** argv){    char* filename = "../data/smarties.png";    Mat src = imread(filename, IMREAD_COLOR);    if (src.empty()) { return -1; }    Mat gray;    cvtColor(src, gray, COLOR_BGR2GRAY);    medianBlur(gray, gray, 5);              // 中值滤波，滤除噪声，避免错误检测    vector<Vec3f> circles;    HoughCircles(gray, circles, HOUGH_GRADIENT, 1, gray.rows / 16, 100, 30, 1, 30); // Hough圆检测    for (size_t i = 0; i < circles.size(); i++)    {        Vec3i c = circles[i];        Point center = Point(c[0], c[1]);        circle(src, center, 1, Scalar(0, 100, 100), 3, LINE_AA);                    // 画圆        int radius = c[2];        circle(src, center, radius, Scalar(255, 0, 255), 3, LINE_AA);    }    imshow("detected circles", src);    waitKey();    return 0;}

运行结果

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