OpenCV2.4.10之samples_cpp_tutorial-code_learn-----ImgTrans(Hough变换)
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本系列学习笔记参考自OpenCV2.4.10之opencv\sources\samples\cpp\tutorial_code和http://www.opencv.org.cn/opencvdoc/2.3.2/html/genindex.html
在图像中我们往往需要检测出一定形状的图形,比如圆等。霍夫变换就是用来检测图像中特定形状的变换,本文将介绍霍夫变换进行检测员和霍夫变换检测线的应用。
1.HoughCircle_Demo.cpp(霍夫圆变换)
示意demo源码及注释如下:
#include "stdafx.h" //预编译头文件 /**霍夫圆变换demo */#include "opencv2/highgui/highgui.hpp"#include "opencv2/imgproc/imgproc.hpp"#include <iostream>using namespace cv;namespace{ // 滑动条命名 const std::string windowName = "Hough Circle Detection Demo"; const std::string cannyThresholdTrackbarName = "Canny threshold"; const std::string accumulatorThresholdTrackbarName = "Accumulator Threshold"; const std::string usage = "Usage : tutorial_HoughCircle_Demo <path_to_input_image>\n"; // 初始值和最大值 const int cannyThresholdInitialValue = 200; const int accumulatorThresholdInitialValue = 50; const int maxAccumulatorThreshold = 200; const int maxCannyThreshold = 255;//霍夫圆检测主函数 void HoughDetection(const Mat& src_gray, const Mat& src_display, int cannyThreshold, int accumulatorThreshold) { // 存储检测到的圆 std::vector<Vec3f> circles; // 霍夫圆检测函数 HoughCircles( src_gray, circles, CV_HOUGH_GRADIENT, 1, src_gray.rows/8, cannyThreshold, accumulatorThreshold, 0, 0 ); // 显示 Mat display = src_display.clone(); for( size_t i = 0; i < circles.size(); i++ ) { Point center(cvRound(circles[i][0]), cvRound(circles[i][1])); int radius = cvRound(circles[i][2]); // 圆中心 circle( display, center, 3, Scalar(0,255,0), -1, 8, 0 ); // 圆周线 circle( display, center, radius, Scalar(0,0,255), 3, 8, 0 ); } // 显示检测结果 imshow( windowName, display); }}int main(int argc, char** argv){ Mat src, src_gray; // 读入图像 src = imread("D:\\opencv\\lena.png", 1 ); if( !src.data ) { std::cerr<<"Invalid input image\n"; std::cout<<usage; return -1; } // 转换成灰度图 cvtColor( src, src_gray, COLOR_BGR2GRAY ); // 减少图像噪声以避免错误的检测 GaussianBlur( src_gray, src_gray, Size(9, 9), 2, 2 ); //初始化 int cannyThreshold = cannyThresholdInitialValue; int accumulatorThreshold = accumulatorThresholdInitialValue; // 创建窗口和滑动条 namedWindow( windowName, WINDOW_AUTOSIZE ); createTrackbar(cannyThresholdTrackbarName, windowName, &cannyThreshold,maxCannyThreshold); createTrackbar(accumulatorThresholdTrackbarName, windowName, &accumulatorThreshold, maxAccumulatorThreshold); // 无限循环显示 // 更新检测图像直到输入q或者Q int key = 0; while(key != 'q' && key != 'Q') { //确保这些参数不为0 cannyThreshold = std::max(cannyThreshold, 1); accumulatorThreshold = std::max(accumulatorThreshold, 1); //检测与显示 HoughDetection(src_gray, src, cannyThreshold, accumulatorThreshold); key = waitKey(10); } return 0;}运行截图:
核心函数为HouguCircles,该函数用于使用霍夫曼变换在灰度图中检测圆,函数原型为:C++: void HoughCircles(InputArray image, OutputArray circles, int method, double dp, double minDist, double param1=100, double param2=100, int minRadius=0, int maxRadius=0 )
第一个参数image为待检测的8位单通道灰度图,第二个参数circles为检测到的圆,该参数为一个向量,其中向量每个元素为一个三个元素的向量(x,y,radius),x和y代表圆心坐标,radius代表半径。method为检测方式,当前的检测方式为CV_HOUGH_GRADIENT,即梯度检测。第三个参数dp为分辨率比率,一般为1。我的感觉是该值越大,检测到的圆越多。第四个参数minDist为检测到的圆的圆心之间的最小距离,该值太大会导致检测多个相邻的圆被错误的检测成一个。如果该值过大,会发生漏检情况。param1为canny边缘检测阈值,param2为蓄能器阈值,param3和param4为检测圆的最小半径和最大半径。
1.HoughLines_Demo.cpp(霍夫线变换)
实例Demo源码及注释如下:
#include "stdafx.h" //预编译头文件 /**霍夫线变化Demo */#include "opencv2/highgui/highgui.hpp"#include "opencv2/imgproc/imgproc.hpp"#include <iostream>#include <stdio.h>using namespace cv;using namespace std;/// 全局变量Mat src, edges;Mat src_gray;Mat standard_hough, probabilistic_hough;int min_threshold = 50;int max_trackbar = 150;const char* standard_name = "Standard Hough Lines Demo";const char* probabilistic_name = "Probabilistic Hough Lines Demo";int s_trackbar = max_trackbar;int p_trackbar = max_trackbar;/// 函数声明void Standard_Hough( int, void* );void Probabilistic_Hough( int, void* );/** 主函数 */int main( int, char** argv ){ ///读入图像 src = imread("D:\\opencv\\lena.png", 1 ); ///将图像转换为灰度图 cvtColor( src, src_gray, COLOR_RGB2GRAY ); ///进行Canny边缘检测 Canny( src_gray, edges, 50, 200, 3 ); ///创建阈值滑动条 char thresh_label[50]; sprintf( thresh_label, "Thres: %d + input", min_threshold ); namedWindow( standard_name, WINDOW_AUTOSIZE ); createTrackbar( thresh_label, standard_name, &s_trackbar, max_trackbar, Standard_Hough); namedWindow( probabilistic_name, WINDOW_AUTOSIZE ); createTrackbar( thresh_label, probabilistic_name, &p_trackbar, max_trackbar, Probabilistic_Hough); ///开始 Standard_Hough(0, 0); Probabilistic_Hough(0, 0); waitKey(0); return 0;}/** * 标准霍夫变换 */void Standard_Hough( int, void* ){ vector<Vec2f> s_lines; cvtColor( edges, standard_hough, CV_GRAY2BGR ); /// 标准霍夫变换 HoughLines( edges, s_lines, 1, CV_PI/180, min_threshold + s_trackbar, 0, 0 ); /// 显示 for( size_t i = 0; i < s_lines.size(); i++ ) { float r = s_lines[i][0], t = s_lines[i][1]; double cos_t = cos(t), sin_t = sin(t); double x0 = r*cos_t, y0 = r*sin_t; double alpha = 1000; Point pt1( cvRound(x0 + alpha*(-sin_t)), cvRound(y0 + alpha*cos_t) ); Point pt2( cvRound(x0 - alpha*(-sin_t)), cvRound(y0 - alpha*cos_t) ); line( standard_hough, pt1, pt2, Scalar(255,0,0), 3, CV_AA); } imshow( standard_name, standard_hough );}/** * @概率霍夫变换 */void Probabilistic_Hough( int, void* ){ vector<Vec4i> p_lines; cvtColor( edges, probabilistic_hough, CV_GRAY2BGR ); /// 概率霍夫变换 HoughLinesP( edges, p_lines, 1, CV_PI/180, min_threshold + p_trackbar, 30, 10 ); ///显示 for( size_t i = 0; i < p_lines.size(); i++ ) { Vec4i l = p_lines[i]; line( probabilistic_hough, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(255,0,0), 3, CV_AA); } imshow( probabilistic_name, probabilistic_hough );}运行结果如下:
HoughLines函数的功能使用标准霍夫变换在一张二值图像中检测直线。
函数原型:C++: void HoughLines(InputArray image, OutputArray lines, double rho, double theta, int threshold, double srn=0, double stn=0 )image表示输入的二值图像,lines为检测到的线向量,向量每个值用 极坐标表示。rho为像素的距离分辨率。theta为像素的角度分辨率,threshold为累加器阈值
HoughLinesP函数的功能使用概率霍夫变换在一张二值图像中检测直线。
函数原型为:C++: void HoughLinesP(InputArray image, OutputArray lines, double rho, double theta, int threshold, double minLineLength=0, doublemaxLineGap=0 )参数说明参照HoughLines。
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