基于图像矩阵的mask（kernel）卷积操作

矩阵上的卷积操作非常简单。根据mask矩阵（也称为内核）重新计算图像中的每个像素值。该mask保存将调整相邻像素（和当前像素）对新像素值有多大影响的值。从数学的角度来看，我们用加权平均值与我们指定的值进行比较。

测试用例

考虑一个图像对比度增强方法的问题。基本上我们要为图像的每个像素应用以下公式：

mask

Code

#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>

using namespace std;
using namespace cv;

static 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_path -- default ../data/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.bmp";

    Mat src, dst0, dst1;

    if (argc >= 3 && !strcmp("G", argv[2]))
        src = imread(filename, IMREAD_GRAYSCALE);
    else
        src = imread(filename, IMREAD_COLOR);

    if (src.empty())
    {
        cerr << "Can't open image [" << filename << "]" << endl;
        return -1;
    }

    namedWindow("Input", WINDOW_AUTOSIZE);
    namedWindow("Output", WINDOW_AUTOSIZE);
    imshow("Input", src);

    double t = (double)getTickCount();

    Sharpen(src, dst0);

    t = ((double)getTickCount() - t) / getTickFrequency();
    cout << "Hand written function time passed in seconds: " << t << endl;
    imshow("Output", dst0);
    waitKey();

    Mat kernel = (Mat_<char>(3, 3) << 0, -1, 0,
        -1, 5, -1,
        0, -1, 0);

    t = (double)getTickCount();
    filter2D(src, dst1, src.depth(), kernel);
    t = ((double)getTickCount() - t) / getTickFrequency();

    cout << "Built-in filter2D time passed in seconds:     " << t << endl;
    imshow("Output", dst1);
    waitKey();
    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));
}

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