矩阵掩模操作

int main(int argc, char** argv)
{
    Mat src, dst, dst1;
    src = imread("111111.jpg", CV_LOAD_IMAGE_COLOR);
    //两种检测图片是否存在的方式
    /*if(src.empty())
    {
    printf("could not load image...\n");
    return -1;
    }*/

    if (!src.data)
    {
        printf("could not load image...\n");
        return -1;
    }
    namedWindow("input iamge", CV_WINDOW_AUTOSIZE);
    imshow("input image", src);

    //返回系统从这里经过的毫秒数
    double t1 = getTickCount();
    //由于是3通道数，所以每个像素点要连续占用3个数组数值分别存储RGB3个通道
    int cols = (src.cols - 1) * src.channels();
    int offsetx = src.channels();
    int rows = src.rows;

    dst = Mat::zeros(src.size(), src.type());
    for (int row = 1; row < (rows - 1); row++)
    {
        //获取行坐标的指针，ptr函数就是获取的对应值的指针
        const uchar* previous = src.ptr<uchar>(row - 1);
        const uchar* current = src.ptr<uchar>(row);
        const uchar* next = src.ptr<uchar>(row + 1);
        uchar* output = dst.ptr<uchar>(row);
        for (int col = offsetx; col < cols; col++)
        {
            //进行逐点加权和的操作
            //saturate_cast对后面的值进行判定，>255的值赋值为255，<0的值赋值为0，在0~255之间的值数值不变
            output[col] = saturate_cast<uchar>(5 * current[col] - (current[col - offsetx] + current[col + offsetx] + previous[col] + next[col]));
        }
    }

    namedWindow("contrast image demo", CV_WINDOW_AUTOSIZE);
    imshow("contrast image demo", dst);
    //获取像素操作的消耗时间
    double timeconsume1 = (getTickCount() - t1) / getTickFrequency();
    printf("tiome consume1 %.2f\n", timeconsume1);

    double t2 = getTickCount();
    //定义3*3的卷积掩模矩阵
    Mat kernel = (Mat_<char>(3, 3) << 0, -1, 0,
                                     -1, 5, -1,
                                      0, -1, 0);
    //利用API函数借口直接进行卷积操作
    filter2D(src, dst1, src.depth(), kernel);
    double timeconsume = (getTickCount() - t2) / getTickFrequency();
    printf("time consume %.2f\n", timeconsume);

    namedWindow("contrast image demo1", CV_WINDOW_AUTOSIZE);
    imshow("contrast image demo1", dst1);
    //像素操作与API操作效果一致

    waitKey(0);
    return 0;
}

原图像：

逐像素操作结果：

API操作结果：