canny边缘检测

canny边缘检测

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author@jason_ql
http://blog.csdn.net/lql0716

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1、canny边缘检测代码

• python代码（对图片进行边缘检测）

import cv2import numpy as npimg = cv2.imread('D:/testSource/myImg/031.jpg',0)imgs = cv2.Canny(img,100,200)cv2.imshow('canny', imgs)cv2.waitKey(0)cv2.destroyAllWindows()

• C++代码（对图片进行边缘检测）

#include <opencv2/imgproc/imgproc.hpp>#include <opencv2/highgui/highgui.hpp>#include <stdlib.h>#include <stdio.h>#include <opencv2/opencv.hpp>using namespace cv;using namespace std;/// 全局变量Mat src, src_gray;Mat dst, detected_edges;int edgeThresh = 1;int lowThreshold;int const max_lowThreshold = 100;int ratio = 3;int kernel_size = 3;char* window_name = "Edge Map";void CannyThreshold(int, void*){    /// Reduce noise with a kernel 3x3    blur( src_gray, detected_edges, Size(3,3) );    /// Canny detector    Canny( detected_edges, detected_edges, lowThreshold, lowThreshold*ratio, kernel_size );    dst = Scalar::all(0);    src.copyTo( dst, detected_edges);    imshow( window_name, dst );}int main( ){  src = imread( "/home/jason/jason2/photo/2.jpg" );  if( !src.data )    { return -1; }  dst.create( src.size(), src.type() );  cvtColor( src, src_gray, CV_BGR2GRAY );  namedWindow( window_name, CV_WINDOW_AUTOSIZE );  createTrackbar( "Min Threshold:", window_name, &lowThreshold, max_lowThreshold, CannyThreshold );  CannyThreshold(0, 0);  waitKey(0);  return 0;}

• C++代码（读取摄像头进行边缘检测）

#include <opencv2/imgproc/imgproc.hpp>#include <opencv2/highgui/highgui.hpp>#include <stdlib.h>#include <stdio.h>#include <opencv2/opencv.hpp>using namespace cv;using namespace std;/// 全局变量Mat src, src_gray;Mat dst, detected_edges;int edgeThresh = 1;int lowThreshold;int const max_lowThreshold = 100;int ratio = 3;int kernel_size = 3;char* window_name = "Edge Map";void CannyThreshold(int, void*){    /// Reduce noise with a kernel 3x3    blur( src_gray, detected_edges, Size(3,3) );    /// Canny detector    Canny( detected_edges, detected_edges, lowThreshold, lowThreshold*ratio, kernel_size );    dst = Scalar::all(0);    src.copyTo( dst, detected_edges);    imshow( window_name, dst );}int main( ){    cv::VideoCapture cap(0);    if(!cap.isOpened()){        return -1;    }    cv::namedWindow(window_name,cv::WINDOW_AUTOSIZE);    while(1){        char key = cv::waitKey(1);        if(key == 'q'){            cv::destroyWindow("Video");            break;        }        cap>>src;//      src = imread( "/home/jason/jason2/photo/2.jpg" );      if( !src.data ){ return -1; }      dst.create( src.size(), src.type() );      cvtColor( src, src_gray, CV_BGR2GRAY );      createTrackbar( "Min Threshold:", window_name, &lowThreshold, max_lowThreshold, CannyThreshold );      CannyThreshold(0, 0);    //  cout<< "sqrt:" << sqrt(25) <<endl;  //it means 5    //  cout<< "pow:" << pow(5,2) <<endl;  //it means 5*5      waitKey(0);    }  cap.release();  return 0;}

2、canny的C++数据结构

C++

void Canny(InputArray image, OutputArray edges, double threshold1, double threshold2, int apertureSize=3, bool L2gradient=false )

3、canny的python函数

python

cv2.Canny(image, threshold1, threshold2[, edges[, apertureSize[, L2gradient]]]) → edges

4、canny调用的相关参数

• Parameters:
  
  • image – single-channel 8-bit input image.图像
  • edges – output edge map; it has the same size and type as image .原图像canny之后的二值化图像
  • threshold1 – first threshold for the hysteresis procedure.
  • threshold2 – second threshold for the hysteresis procedure.
  • apertureSize – aperture size for the Sobel() operator.
  • L2gradient – a flag, indicating whether a more accurate L2 norm =(dI/dx)2+(dI/dy)2−−−−−−−−−−−−−−−−√ should be used to calculate the image gradient magnitude ( L2gradient=true ), or whether the default L1 norm =|dI/dx|+|dI/dy| is enough ( L2gradient=false ).

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