OpenCV2马拉松第14圈——边缘检测(Sobel,prewitt,roberts)

1. 是离散差分算子.
2. 结合了高斯滤波.

C++: void Sobel(InputArray src, OutputArray dst, int ddepth, int dx, int dy, int ksize=3, double scale=1, double delta=0, intborderType=BORDER_DEFAULT )

src – 输入. dst – 输出 ddepth – output image depth; the following combinations of src.depth() and ddepth are supported: src.depth() = CV_8U, ddepth = -1/CV_16S/CV_32F/CV_64F src.depth() = CV_16U/CV_16S, ddepth = -1/CV_32F/CV_64F src.depth() = CV_32F, ddepth = -1/CV_32F/CV_64F src.depth() = CV_64F, ddepth = -1/CV_64F when ddepth=-1, the destination image will have the same depth as the source; in the case of 8-bit input images it will result in truncated derivatives.这里要特别注意了，我们的depth不能为－1，因为我们的输入是uchar8类型的，而算出来的值可能>255也可能 <0 ,都会被截断，CV_16S是推荐的 xorder – order of the derivative x. yorder – order of the derivative y. ksize – sobel核大小，必须为1, 3, 5, or 7. scale – 扩大系数 delta – 附加系数 borderType – 边界类型

计算的时候，利用了可分离的滤波进行加速(Ksize=1的时候，用了1*3和 3*1的算子，无法加速)

当Ksize = 3,Sobel采用的算子会不准确，因此还有特殊的值ksize = CV_SCHARR(-1) 相当于使用  Scharr filter 比 Sobel算子能获得更准确的结果. Scharr 算子如下

C++: void Scharr(InputArray src, OutputArray dst, int ddepth, int dx, int dy, double scale=1, double delta=0, int borderType=BORDER_DEFAULT )

src – input image. dst – output image of the same size and the same number of channels as src. ddepth – output image depth (see Sobel() for the list of supported combination of src.depth() and ddepth). dx – order of the derivative x. dy – order of the derivative y. scale – optional scale factor for the computed derivative values; by default, no scaling is applied (see getDerivKernels() for details). delta – optional delta value that is added to the results prior to storing them in dst. borderType – pixel extrapolation method (see borderInterpolate() for details).

The function computes the first x- or y- spatial image derivative using the Scharr operator. The call

is equivalent to

#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <stdlib.h>
#include <stdio.h>

using namespace cv;

int main( int, char** argv )
{

  Mat src, src_gray;
  Mat grad;
  const char* window_name = "Sobel Demo - Simple Edge Detector";
  
  //因为以Sobel方式求完导数后会有负值，还有会大于255的值而你建的Sobel的图像是 CV_8U，也就是8位无符号数，所以Sobel建立的图像位数不够，要16位有符号的，也就是 CV_16S
  int ddepth = CV_16S;
  src = imread( argv[1] );
  if( !src.data )
    { return -1; }

  GaussianBlur( src, src, Size(3,3), 0, 0, BORDER_DEFAULT );
  cvtColor( src, src_gray, CV_RGB2GRAY );

  namedWindow( window_name, CV_WINDOW_AUTOSIZE );

  // Generate grad_x and grad_y
  Mat grad_x, grad_y;
  Mat abs_grad_x, abs_grad_y;

  // Gradient X
  //Scharr( src_gray, grad_x, ddepth, 1, 0);
  Sobel( src_gray, grad_x, ddepth, 1, 0, 3);
  convertScaleAbs( grad_x, abs_grad_x );

  // Gradient Y
  //Scharr( src_gray, grad_y, ddepth, 0, 1);
  Sobel( src_gray, grad_y, ddepth, 0, 1, 3);
  convertScaleAbs( grad_y, abs_grad_y );

  // Total Gradient (approximate)
  addWeighted( abs_grad_x, 0.5, abs_grad_y, 0.5, 0, grad );

  imshow( window_name, grad );

  waitKey(0);

  return 0;
}

#include "opencv2/highgui/highgui.hpp"  
#include "opencv2/imgproc/imgproc.hpp"  
using namespace cv;   

int main( int, char** argv )  
{  
	Mat src,gray,Kernelx,Kernely;
	 
    src = imread( argv[1] );  
    cvtColor( src, gray, CV_RGB2GRAY );
    namedWindow("srcImage", 1);  
    namedWindow("dstImage", 1);  
  
    Kernelx = (Mat_<double>(3,3) << 1, 1, 1, 0, 0, 0, -1, -1, -1);  
    Kernely = (Mat_<double>(3,3) << -1, 0, 1, -1, 0, 1, -1, 0, 1); 
    
    Mat grad_x, grad_y;
  	Mat abs_grad_x, abs_grad_y, grad;
  
    filter2D(gray, grad_x, CV_16S , Kernelx, Point(-1,-1));  
    filter2D(gray, grad_y, CV_16S , Kernely, Point(-1,-1));
    convertScaleAbs( grad_x, abs_grad_x );
    convertScaleAbs( grad_y, abs_grad_y );
    
    addWeighted( abs_grad_x, 0.5, abs_grad_y, 0.5, 0, grad ); 
    imshow("dstImage", grad);  
 
    waitKey();  
    return 0;  
}