opencv GPU HoughLines直线

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原创最新推荐文章于 2026-10-13 20:47:30 发布 1.3k 阅读 AI 写同款· GEO 优化 ›

opencv基础59-霍夫变换原理讲解及示例-cv2.HoughLines()-＞(直线，圆形检测) 霍夫变换是一种在图像中寻找直线、圆形以及其他简单形状的方法。霍夫变换采用类似于投票的方式来获取当前图像内的形状集合，该变换由 Paul Hough（霍夫）于 1962 年首次提出。最初的霍夫变换只能用于检测直线，经过发展后，霍夫变换不仅能够识别直线，还能识别其他简单的图形结构，常见的有圆、椭圆等。阅读详情

#include <opencv2/objdetect/objdetect.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <cv.h>
//#include "d:\Program Files\opencv_build11\install\include\opencv2\gpu\gpu.hpp"
    using namespace cv;
#define ROI_rows 178
#define ROI_cols 100

int main (int argc, char* argv[])
{    // CvCapture *Frame=cvCaptureFromCAM(-1);
     VideoCapture cap("Load.avi");
     if(!cap.isOpened())  
    return -1;  
     cv::Mat Image_frame,Lines_out;
      //Mat element = getStructuringElement(MORPH_RECT, Size(15, 15));
while (1)
  {    cap>>Image_frame;
     // imshow("Cam",Image_frame);


     // cv::Mat Image = cv::imread("load.jpg" );
         cv::Mat  ROI_Mat=Image_frame(Rect(ROI_cols,376-ROI_rows,672-2*ROI_cols,ROI_rows));
         cv::Mat copy_mat(ROI_Mat);


          cv::gpu::GpuMat Image_Src,Image_Dst,Lins ;
          Image_Src.upload (ROI_Mat);

          cv::gpu::cvtColor(Image_Src,Image_Dst,CV_BGR2GRAY);
        // cv::erode(Image_Dst,Image_Dst,element);
        //   cv::gpu::Canny(Image_Dst,Image_Dst,120,255);
         cv::gpu::Sobel(Image_Dst,Image_Dst,CV_8U,1,1);
          vector<Vec2f> lines1;
          //show line
          cv::gpu::HoughLines(Image_Dst,Lins,1,CV_PI/180,50,1,5);    //5 line long   7number of line
          cv::gpu::HoughLinesDownload(Lins,lines1);

          std::cout<<"Lines_out"<<lines1.size()<<std::endl;
          for( int i = 0; i < lines1.size(); i++ )
  {
    float rho = lines1[i][0], theta = lines1[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( copy_mat, pt1, pt2, Scalar(55,100,195), 1, CV_AA);
  }


          cv::Mat  Image_sobel(Image_Dst);  
          std::cout<<"宽"<<Image_sobel.cols <<std::endl;
          std::cout<<"高"<<Image_sobel.rows <<std::endl;
          rectangle( Image_frame,cvPoint(ROI_cols,376-ROI_rows),cvPoint(672-ROI_cols,376),cvScalar(0,0255),1);

          //show Image
          cv::imshow("Sobel",Image_sobel);
          cv::imshow("源图像",Image_frame);


      cv::waitKey(5); 

}