OpenCV2.4.10之samples_cpp_tutorial-code_learn-----ImgTrans(Hough變換)

標籤：

本系列學習筆記參考自OpenCV2.4.10之opencv\sources\samples\cpp\tutorial_code和http://www.opencv.org.cn/opencvdoc/2.3.2/html/genindex.html


在映像中我們往往需要檢測出一定形狀的圖形，比如圓等。霍夫變換就是用來檢測映像中特定形狀的變換，本文將介紹霍夫變換進行檢測員和霍夫變換檢測線的應用。
1.HoughCircle_Demo.cpp(霍夫圓變換)示意demo源碼及注釋如下:

#include "stdafx.h"    //先行編譯標頭檔    /**霍夫圓變換demo */#include "opencv2/highgui/highgui.hpp"#include "opencv2/imgproc/imgproc.hpp"#include <iostream>using namespace cv;namespace{    // 滑動條命名    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;//霍夫圓檢測主函數    void HoughDetection(const Mat& src_gray, const Mat& src_display, int cannyThreshold, int accumulatorThreshold)    {        // 儲存檢測到的圓        std::vector<Vec3f> circles;        // 霍夫圓檢測函數        HoughCircles( src_gray, circles, CV_HOUGH_GRADIENT, 1, src_gray.rows/8, cannyThreshold, accumulatorThreshold, 0, 0 );        // 顯示        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]);            // 圓中心            circle( display, center, 3, Scalar(0,255,0), -1, 8, 0 );            // 圓周線            circle( display, center, radius, Scalar(0,0,255), 3, 8, 0 );        }        // 顯示檢測結果        imshow( windowName, display);    }}int main(int argc, char** argv){    Mat src, src_gray;    // 讀入映像    src = imread("D:\\opencv\\lena.png", 1 );    if( !src.data )    {        std::cerr<<"Invalid input image\n";        std::cout<<usage;        return -1;    }    // 轉換成灰階圖    cvtColor( src, src_gray, COLOR_BGR2GRAY );    // 減少映像雜訊以避免錯誤的檢測    GaussianBlur( src_gray, src_gray, Size(9, 9), 2, 2 );    //初始化    int cannyThreshold = cannyThresholdInitialValue;    int accumulatorThreshold = accumulatorThresholdInitialValue;    // 建立視窗和滑動條    namedWindow( windowName, WINDOW_AUTOSIZE );    createTrackbar(cannyThresholdTrackbarName, windowName, &cannyThreshold,maxCannyThreshold);    createTrackbar(accumulatorThresholdTrackbarName, windowName, &accumulatorThreshold, maxAccumulatorThreshold);    // 無限迴圈顯示    // 更新檢測映像直到輸入q或者Q    int key = 0;    while(key != 'q' && key != 'Q')    {        //確保這些參數不為0        cannyThreshold = std::max(cannyThreshold, 1);        accumulatorThreshold = std::max(accumulatorThreshold, 1);        //檢測與顯示        HoughDetection(src_gray, src, cannyThreshold, accumulatorThreshold);        key = waitKey(10);    }    return 0;}

運行:
核心函數為HouguCircles，該函數用於使用霍夫曼變換在灰階圖中檢測圓，函數原型為: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 )第一個參數image為待檢測的8位單通道灰階圖，第二個參數circles為檢測到的圓，該參數為一個向量，其中向量每個元素為一個三個元素的向量(x,y,radius)，x和y代表圓心座標，radius代表半徑。method為檢測方式，當前的檢測方式為CV_HOUGH_GRADIENT，即梯度檢測。第三個參數dp為解析度比率，一般為1。我的感覺是該值越大，檢測到的圓越多。第四個參數minDist為檢測到的圓的圓心之間的最小距離，該值太大會導致檢測多個相鄰的圓被錯誤的檢測成一個。如果該值過大，會發生漏檢情況。param1為canny邊緣檢測閾值，param2為蓄能器閾值，param3和param4為檢測圓的最小半徑和最大半徑。


1.HoughLines_Demo.cpp(霍夫線變換)
執行個體Demo源碼及注釋如下:

#include "stdafx.h"    //先行編譯標頭檔    /**霍夫線變化Demo */#include "opencv2/highgui/highgui.hpp"#include "opencv2/imgproc/imgproc.hpp"#include <iostream>#include <stdio.h>using namespace cv;using namespace std;/// 全域變數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;/// 函式宣告void Standard_Hough( int, void* );void Probabilistic_Hough( int, void* );/** 主函數 */int main( int, char** argv ){   ///讀入映像   src = imread("D:\\opencv\\lena.png", 1 );   ///將映像轉換為灰階圖   cvtColor( src, src_gray, COLOR_RGB2GRAY );   ///進行Canny邊緣檢測   Canny( src_gray, edges, 50, 200, 3 );   ///建立閾值滑動條   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);   ///開始   Standard_Hough(0, 0);   Probabilistic_Hough(0, 0);   waitKey(0);   return 0;}/** * 標準霍夫變換 */void Standard_Hough( int, void* ){  vector<Vec2f> s_lines;  cvtColor( edges, standard_hough, CV_GRAY2BGR );  /// 標準霍夫變換  HoughLines( edges, s_lines, 1, CV_PI/180, min_threshold + s_trackbar, 0, 0 );  /// 顯示  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 );}/** * @機率霍夫變換 */void Probabilistic_Hough( int, void* ){  vector<Vec4i> p_lines;  cvtColor( edges, probabilistic_hough, CV_GRAY2BGR );  /// 機率霍夫變換  HoughLinesP( edges, p_lines, 1, CV_PI/180, min_threshold + p_trackbar, 30, 10 );  ///顯示  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 );}

運行結果如下:
HoughLines函數的功能使用標準霍夫變換在一張二值映像中檢測直線。函數原型:C++: void HoughLines(InputArray image, OutputArray lines, double rho, double theta, int threshold, double srn=0, double stn=0 )image表示輸入的二值映像,lines為檢測到的線向量，向量每個值用 極座標表示。rho為像素的距離解析度。theta為像素的角度解析度，threshold為累加器閾值
HoughLinesP函數的功能使用機率霍夫變換在一張二值映像中檢測直線。
函數原型為:C++: void HoughLinesP(InputArray image, OutputArray lines, double rho, double theta, int threshold, double minLineLength=0, doublemaxLineGap=0 )參數說明參照HoughLines。

OpenCV2.4.10之samples_cpp_tutorial-code_learn-----ImgTrans(Hough變換)