OpenCV学习笔记-\doc\tutorials\core\mat_mask_operations

最后我们再来看看opencv\samples\cpp\tutorial_code\core\mat_mask_operations中给出的示例代码，我在原代码的基础上作了一些小小的修改并加了注释，用lena.jpg做测试结果如下，可见kernel法确实快了很多。

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

using namespace std;
using namespace cv;

//程序运行时首先输出帮助信息
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_name -- default lena.jpg] [G -- grayscale] "        << endl << endl;
}

//对比度增强函数原型
void Sharpen(const Mat& myImage,Mat& Result);

int main( int argc, char* argv[])
{
    help(argv[0]);//输出帮助信息
    //如果输入参数大于等于两个的话，就从输入参数里面读取图形文件名，否则采用"lena.jpg"来示例
    const char* filename = argc >=2 ? argv[1] : "lena.jpg";

    Mat I, J, K;

    //如果还有一个附加参数G的话，就用灰度值来处理
    if (argc >= 3 && !strcmp("G", argv[2]))
        I = imread( filename, CV_LOAD_IMAGE_GRAYSCALE);
    else
        I = imread( filename, CV_LOAD_IMAGE_COLOR);

    //创建输入输出显示窗口
    namedWindow("Input", CV_WINDOW_AUTOSIZE);
    namedWindow("Output1", CV_WINDOW_AUTOSIZE);
    namedWindow("Output2",CV_WINDOW_AUTOSIZE);

    //显示输入图像
    imshow("Input", I);
    
    //处理时间计时开始
    double t = (double)getTickCount();

    //处理开始
    Sharpen(I, J);

    //处理时间计时结束
    t = ((double)getTickCount() - t)/getTickFrequency();
    
    //输出耗时
    cout << "Hand written function times passed in seconds: " << t << endl;

    //显示输出窗口
    imshow("Output1", J);
    

    //采用filter2D函数来处理，首先定义kernel
    Mat kern = (Mat_<char>(3,3) <<  0, -1,  0,
                                   -1,  5, -1,
                                    0, -1,  0);
    t = (double)getTickCount();//计时开始
    filter2D(I, K, I.depth(), kern );//处理
    //计时结束
    t = ((double)getTickCount() - t)/getTickFrequency();
    //输出处理耗时
    cout << "Built-in filter2D time passed in seconds:      " << t << endl;

    imshow("Output2", K);

    cvWaitKey(0);
    return 0;
}

//逐像素处理函数
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));
}