(转)Opencv卷积操作

转自:http://www.2cto.com/kf/201312/267308.html

Mask Operation filter2D函数 Last Edit 2013/12/24 所谓的Mask Operation就是滤波。 第一步:建立Mask:

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Mat kern = (Mat_<char>(3,3) <<  0, -10,
                               -15, -1,
                                0, -10);</char>


Mat_是一个模板,建立了一个3*3的矩阵,矩阵的值在-128~127. 
第二步:使用filter2D. 函数原型:

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


以下是OpenCV2.0提供的sample:

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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, -10,
                                   -15, -1,
                                    0, -10);
    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
原文地址:https://www.cnblogs.com/qiaozhoulin/p/4930354.html