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)); }