Go OpenCV convolution operation

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Mask operation filter2d function last Edit 2013/12/24 the so-called mask operation is filtering. First step: Build Mask:

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123	Mat kern = (Mat_<char>(3,3) <<  0, -1,  0,                                -1,  5, -1,                                0, -1,  0);</char>

Mat_ is a template that establishes a 3*3 matrix, the value of the matrix in -128~127.
Step two: Use Filter2d. Function Prototypes:

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void filter2D(InputArray src, //要进行滤波的图像              OutputArray dst,//滤波后的图像              int ddepth,     //原图像的深度  src.depth()              InputArray kernel, //第一步建立的Mask              Point anchor=Point(-1,-1),//Mask的中心点              double delta=0, //Optional value added to the filtered pixels before storing them in dst              int borderType=BORDER_DEFAULT               )

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1	filter2D(I, K, I.depth(), kern );

The following is the sample provided by OpenCV2.0:

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#include <opencv2 core="" core.hpp="">#include <opencv2 highgui="" highgui.hpp="">#include <opencv2 imgproc="" imgproc.hpp="">#include <iostream>using namespace std; using namespace cv;void help(char* progName){    cout << endl         <<  "This program shows how to filter images with mask: the write it yourself and the"        << "filter2d way. " << endl        <<  "Usage:"<< endl        << progName << " [image_name -- default lena.jpg] [G -- grayscale] "<< endl << endl;} void Sharpen(const Mat& myImage,Mat& Result);int main( int argc, char* argv[]){    help(argv[0]);    const char* filename = argc >=2 ? argv[1] : "lena.jpg";    Mat I, J, K;    if (argc >= 3 && !strcmp("G", argv[2]))        I = imread( filename, CV_LOAD_IMAGE_GRAYSCALE);    else        I = imread( filename, CV_LOAD_IMAGE_COLOR);    namedWindow("Input", CV_WINDOW_AUTOSIZE);    namedWindow("Output", CV_WINDOW_AUTOSIZE);    imshow("Input", I);    double t = (double)getTickCount();        Sharpen(I, J);          t = ((double)getTickCount() - t)/getTickFrequency();    cout << "Hand written function times passed in seconds: " << t << endl;    imshow("Output", J);    cvWaitKey(0);    Mat kern = (Mat_<char>(3,3) <<  0, -1,  0,                                    -1,  5, -1,                                    0, -1,  0);    t = (double)getTickCount();    filter2D(I, K, I.depth(), kern );     t = ((double)getTickCount() - t)/getTickFrequency();    cout << "Built-in filter2D time passed in seconds:      " << t << endl;    imshow("Output", K);    cvWaitKey(0);    return 0; }void Sharpen(const Mat& myImage,Mat& Result){    CV_Assert(myImage.depth() == CV_8U);  // accept only uchar images    const int nChannels = myImage.channels();    Result.create(myImage.size(),myImage.type());         for(int j = 1 ; j < myImage.rows-1; ++j)    {        const uchar* previous = myImage.ptr<uchar>(j - 1);        const uchar* current  = myImage.ptr<uchar>(j    );        const uchar* next     = myImage.ptr<uchar>(j + 1);        uchar* output = Result.ptr<uchar>(j);        for(int i= nChannels;i < nChannels*(myImage.cols-1); ++i)        {            *output++ = saturate_cast<uchar>(5*current[i]                          -current[i-nChannels] - current[i+nChannels] - previous[i] - next[i]);        }    }    Result.row(0).setTo(Scalar(0));    Result.row(Result.rows-1).setTo(Scalar(0));    Result.col(0).setTo(Scalar(0));    Result.col(Result.cols-1).setTo(Scalar(0));}</uchar></uchar></uchar></uchar></uchar></char></iostream></opencv2></o

Go OpenCV convolution operation