矩陣掩模操作是指根據一個掩碼矩陣(也稱為核心kernel)重新計算影象中每個畫素的值。掩碼可以控制改變相鄰畫素和當前畫素對新畫素值的影響，從而產生新的影象畫素。

  本節通過影象銳化來比較影象指標運算與掩模操作的執行結果，觀察執行時間的差異。

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

using namespace std;
using namespace
 
 cv;

void sharpen(const Mat& myImage, Mat& Result);

int main(int argc, char* grav[])
{
    char* filename = "../data/fruits.jpg";

    Mat src, dst0, dst1;
    src = imread(filename, IMREAD_COLOR);
    if (src.empty())
    {               
        // cerr(無緩衝標準錯誤):沒有緩衝，傳送給它的內容立即被輸出
        cerr
 
 << "Can't open image" << endl;     
        return -1;
    }

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

    imshow("Input", src);                                       // 顯示原影象

    double t = (double)getTickCount();                          // 計算hand-coded方法的時間
 

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

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

    t = (double)getTickCount();                                 // 計算矩陣掩模方法的時間
    filter2D(src, dst1, src.depth(), kernel);                   // 濾波filter2D函式
    t = ((double)getTickCount() - t) / getTickFrequency();
    cout << "Built-in filter2D time passed in seconds:" << t << endl;
    imshow("Output", dst1);

    waitKey(0);
    return 0;   
}

void sharpen(const Mat& myImage, Mat& Result)
{
    CV_Assert(myImage.depth() == CV_8U);                        // 只允許uchar型影象

    const int nChannels = myImage.channels();
    Result.create(myImage.size(), myImage.type());

    for (int j = 1; j < myImage.rows - 1; ++j)                  // 遍歷影象
    {
        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)
        {
            // saturate_cast原理: if(data<0)  data = 0;   if (data>255)   data = 255 

            *output++ = saturate_cast<uchar>(5 * current[i]     // 防止資料溢位
                - current[i - nChannels] - current[i + nChannels] - previous[i] - next[i]);         
        }
    }

    Result.row(0).setTo(Scalar(0));                             // 影象四邊未處理畫素設為0
    Result.row(Result.rows - 1).setTo(Scalar(0));
    Result.col(0).setTo(Scalar(0));
    Result.col(Result.cols - 1).setTo(Scalar(0));
}

執行結果

Hand written function time passed in seconds:0.0174564
Built-in filter2D time passed in seconds:0.00683035