18.Canny边缘检测

　　Canny边缘检测算法以Canny的名字命名，其中Canny的目标是找到一个最优的边缘检测算法，其有三种衡量标准：

1. 低错误率：标识出尽可能多的实际边缘，同时尽可能的减少噪声产生的误报
2. 高定位性：标识出的边缘要与图像中的图像中的实际边缘尽可能接近
3. 最小响应：图像中的边缘只能标识一次，并且可能存在的图像噪声不应标识为边缘

//Canny
int g_nCannySize;
int g_nCannyThresHoldValue;
int g_nSobelValue;
Mat g_srcImage,g_dstImage,g_grayImage,g_edgeImage;
void on_ChangeThresHoldAndCannySize(int,void*)
{
	if(g_nSobelValue>2 && g_nSobelValue%2==1)
	{
		blur(g_grayImage,g_edgeImage,Size(g_nCannySize,g_nCannySize));
		Canny(g_edgeImage,g_edgeImage,1,g_nCannyThresHoldValue,3);
		g_dstImage=Scalar::all(0);
		g_srcImage.copyTo(g_dstImage,g_edgeImage);
		imshow("Canny图",g_dstImage);
	}
}
void Test_Canny()
{
	g_srcImage=imread("D:\\OpenCV Projects\\OpenCV_Test_Image\\7.jpg");
	g_dstImage.create(g_srcImage.size(),g_srcImage.type());
	cvtColor(g_srcImage,g_grayImage,COLOR_BGR2GRAY);//得到灰度图
	namedWindow("原图");
	namedWindow("Canny图");
	g_nCannySize=1;
	g_nCannyThresHoldValue=3;
	//g_nSobelValue=3;
	createTrackbar("size","Canny图",&g_nCannySize,30,on_ChangeThresHoldAndCannySize);
	//createTrackbar("sobel","Canny图",&g_nSobelValue,9,on_ChangeThresHoldAndCannySize);
	createTrackbar("value","Canny图",&g_nCannyThresHoldValue,80,on_ChangeThresHoldAndCannySize);
	on_ChangeThresHoldAndCannySize(g_nCannySize,0);
	on_ChangeThresHoldAndCannySize(g_nCannyThresHoldValue,0);
	//on_ChangeThresHoldAndCannySize(g_nSobelValue,0);
	imshow("原图",g_srcImage);
}