opencv-基于c++实现的opencv图像处理算法之图像滤波算法方框滤波.zip

#include <iostream> #include <opencv2/core.hpp> #include <opencv2/highgui.hpp> #include <opencv2/imgproc.hpp> ///////////////////////////////////////// //求积分图-优化方法 //由上方negral(i-1,j)加上当前行的和即可 //对于W*H图像：2*(W-1)*(H-1)次加减法 //比常规方法快1.5倍左右 ///////////////////////////////////////// void Fast_integral(cv::Mat& src, cv::Mat& dst){ int nr = src.rows; int nc = src.cols; int sum_r = 0; dst = cv::Mat::zeros(nr + 1, nc + 1, CV_64F); for (int i = 1; i < dst.rows; ++i){ for (int j = 1, sum_r = 0; j < dst.cols; ++j){ //行累加，因为积分图相当于在原图上方加一行，左边加一列，所以积分图的(1,1)对应原图(0,0),(i,j)对应(i-1,j-1) sum_r = src.at<uchar>(i - 1, j - 1) + sum_r; //行累加 dst.at<double>(i, j) = dst.at<double>(i - 1, j) + sum_r; } } } ////////////////////////////////// //盒子滤波-均值滤波是其特殊情况 ///////////////////////////////// void BoxFilter(cv::Mat& src, cv::Mat& dst, cv::Size wsize, bool normalize){ //图像边界扩充 if (wsize.height % 2 == 0 || wsize.width % 2 == 0){ fprintf(stderr, "Please enter odd size!"); exit(-1); } int hh = (wsize.height - 1) / 2; int hw = (wsize.width - 1) / 2; cv::Mat Newsrc; cv::copyMakeBorder(src, Newsrc, hh, hh, hw, hw, cv::BORDER_CONSTANT);//以边缘为轴，对称 dst = cv::Mat::zeros(src.size(), src.type()); //计算积分图 cv::Mat inte; Fast_integral(Newsrc, inte); //BoxFilter double mean = 0; for (int i = hh + 1; i < src.rows + hh + 1; ++i){ //积分图图像比原图（边界扩充后的）多一行和一列 for (int j = hw + 1; j < src.cols + hw + 1; ++j){ double top_left = inte.at<double>(i - hh - 1, j - hw - 1); double top_right = inte.at<double>(i - hh - 1, j + hw); double buttom_left = inte.at<double>(i + hh, j - hw - 1); double buttom_right = inte.at<double>(i + hh, j + hw); if (normalize == true) mean = (buttom_right - top_right - buttom_left + top_left) / wsize.area(); else mean = buttom_right - top_right - buttom_left + top_left; //一定要进行判断和数据类型转换 if (mean < 0) mean = 0; else if (mean>255) mean = 255; dst.at<uchar>(i - hh - 1, j - hw - 1) = static_cast<uchar>(mean); } } } int main(){ cv::Mat src = cv::imread("I:\\Learning-and-Practice\\2019Change\\Image process algorithm\\Img\\lena.jpg"); if (src.empty()){ return -1; } if (src.channels()>1) cvtColor(src, src, CV_RGB2GRAY); //自编BoxFilter测试 cv::Mat dst1; double t2 = (double)cv::getTickCount(); //测时间 BoxFilter(src, dst1, cv::Size(9, 9), true);//盒子滤波 t2 = (double)cv::getTickCount() - t2; double time2 = (t2 *1000.) / ((double)cv::getTickFrequency()); std::cout << "FASTmy_process=" << time2 << " ms. " << std::endl << std::endl; int ss = dst1.at<uchar>(10, 0); std::cout << ss << std::endl<<std::endl; //opencv自带BoxFilter测试 cv::Mat dst2; double t1 = (double)cv::getTickCount(); //测时间 cv::boxFilter(src, dst2, -1, cv::Size(9, 9), cv::Point(-1, -1), true, cv::BORDER_CONSTANT);//盒子滤波 t1 = (double)cv::getTickCount() - t1; double time1 = (t1 *1000.) / ((double)cv::getTickFrequency()); std::cout << "FASTmy_process=" << time1 << " ms. " << std::endl << std::endl; ss = dst2.at<uchar>(10, 0); std::cout <<ss << std::endl; cv::namedWindow("src"); cv::imshow("src", src); cv::namedWindow("dst1"); cv::imshow("dst1", dst1); cv::namedWindow("dst2"); cv::imshow("dst2", dst2); cv::waitKey(0); }