opencv学习-imgprocess-canny边缘提取

canny算法步骤：

1采用高斯核滤去噪声；

2求图像的梯度方向和梯度，可用sobel算子；

3非最大抑制应用：即没有被认为是边缘的像数就去除，留下候选边缘；

4判断边缘，设定了两个阈值，判断标准如下：

大于大阈值就认为是边缘，小于小阈值认为不是边缘，介于两者之间则需再次判断，如果该像数点周围连接了大于大阈值的边缘的就认为是边缘。

大小阈值之比建议为2或3:1

代码如下;

#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <stdlib.h>
#include <stdio.h>
using namespace cv;
/// Global variables
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";
/**
* @function CannyThreshold
* @brief Trackbar callback - Canny thresholds input with a ratio 1:3
*/
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 );
/// Using Canny’s output as a mask, we display our result
dst = Scalar::all(0);
src.copyTo( dst, detected_edges);
imshow( window_name, dst );
}
/** @function main */
int main( int argc, char** argv )
{
/// Load an image
src = imread( argv[1] );
if( !src.data )
{ return -1; }
/// Create a matrix of the same type and size as src (for dst)
dst.create( src.size(), src.type() );
/// Convert the image to grayscale
cvtColor( src, src_gray, CV_BGR2GRAY );
/// Create a window
namedWindow( window_name, CV_WINDOW_AUTOSIZE );
/// Create a Trackbar for user to enter threshold
createTrackbar( "Min Threshold:", window_name, &lowThreshold, max_lowThreshold, CannyThreshold );
/// Show the image
CannyThreshold(0, 0);
/// Wait until user exit program by pressing a key
waitKey(0);
return 0;
}