基本信息
源码名称:c++ 图像去雾算法 示例源码
源码大小:0.86M
文件格式:.rar
开发语言:C/C++
更新时间:2017-12-23
友情提示:(无需注册或充值,赞助后即可获取资源下载链接)
嘿,亲!知识可是无价之宝呢,但咱这精心整理的资料也耗费了不少心血呀。小小地破费一下,绝对物超所值哦!如有下载和支付问题,请联系我们QQ(微信同号):813200300
本次赞助数额为: 2 元×
微信扫码支付:2 元
×
请留下您的邮箱,我们将在2小时内将文件发到您的邮箱
源码介绍
图像去雾算法,图像增强
图像去雾算法,图像增强
#include "hazeremovehandler.h" HazeRemoveHandler::HazeRemoveHandler() { kernel_size_ = 15; bright_percent_ = 0.1; A_[0] = A_[1] = A_[2] = 0; A_average =0; w_ = 0.95; r_ = 120; eps_ = 0.001*0.001; t0_ = 0.1; } // This Method is to read original image and convert it to float data type and been normilized to 0~1. // Input : input Mat type image. void HazeRemoveHandler::ReadOriImage(Mat img) { ori_image_ = Mat(img.rows,img.cols,CV_32FC3); img.convertTo(ori_image_,CV_32FC3); // Normilize to 0~1 ori_image_ = ori_image_/255; } void HazeRemoveHandler::ShowImage(int type) { switch (type) { case 0: // // imshow function notice // for different data types // 1. 8-bit unsigned char : show directly // 2. 16-bit unsigned or 32-bit integer : pixel value/256, [0,255*256] -> [0,255] // 3. 32-bit floating-point : pixel value*255, [0,1]->[0,255] // imshow("Original Image",ori_image_); break; case 1: imshow("Darkchannel Image",dark_channel_image_); break; case 2: imshow("Transmission Map",transmission_image_); break; case 3: imshow("Smoothed Transmission Map",transmission_smoothed_image_); break; case 4: imshow("Haze Free Image",haze_free_image_); break; default: break; } } // This Method is to get the dark channel of the original image // //J^{dark}(x)=min( min( J^c(y) ) ) // Output : // -1: means it has not load original image, should do ReadOriImage before. // 0: means computing dark channel succeed. int HazeRemoveHandler::DarkChannelPrior() { if( (ori_image_.cols==0) || (ori_image_.rows==0)) { return -1; } // Compute min value for every pixel during different three channels(R,G,B) Mat dark_tmp = Mat::zeros(ori_image_.rows,ori_image_.cols,CV_32FC1); int rows = dark_tmp.rows; int cols = dark_tmp.cols; for(int i = 0; i < rows; i ) for(int j = 0; j < cols; j ) { dark_tmp.at<float>(i,j) = min( min(ori_image_.at<Vec3f>(i,j)[0],ori_image_.at<Vec3f>(i,j)[1]), min(ori_image_.at<Vec3f>(i,j)[0],ori_image_.at<Vec3f>(i,j)[2]) ); } // Minfilter implemented by erode function erode(dark_tmp,dark_channel_image_,Mat::ones(kernel_size_,kernel_size_,CV_32FC1)); return 0; } // This Method is to estimate the transmission map of the original image // t(x) = 1- min( min( I^c(y)/A^c ) ) // Output : // -1: means it has not load original image or estimate the Atmospheric Light // , should do that before. // 0: means estimate the transmission map succeed. int HazeRemoveHandler::EstimateTransmission() { A_average = (A_[0] A_[1] A_[2])/3; if(A_average>0.86) A_average = 0.86; if( (ori_image_.cols==0) || (ori_image_.rows==0) || (A_average ==0)) { return -1; } Mat dark_tmp = Mat::zeros(ori_image_.rows,ori_image_.cols,CV_32FC1); int rows = dark_tmp.rows; int cols = dark_tmp.cols; for(int i = 0; i < rows; i ) for(int j = 0; j < cols; j ) { dark_tmp.at<float>(i,j) = min( // min(ori_image_.at<Vec3f>(i,j)[0]/A_average,ori_image_.at<Vec3f>(i,j)[1]/A_average), // min(ori_image_.at<Vec3f>(i,j)[0]/A_average,ori_image_.at<Vec3f>(i,j)[2]/A_average) min(ori_image_.at<Vec3f>(i,j)[0],ori_image_.at<Vec3f>(i,j)[1]), min(ori_image_.at<Vec3f>(i,j)[0],ori_image_.at<Vec3f>(i,j)[2]) ); } Mat dark_hazeimage; // Minfilter implemented by erode function erode(dark_tmp,dark_hazeimage,Mat::ones(kernel_size_,kernel_size_,CV_32FC1)); transmission_image_ = Mat(ori_image_.rows,ori_image_.cols,CV_32FC1); for(int i = 0; i < rows; i ) for(int j = 0; j < cols; j ) { transmission_image_.at<float>(i,j) = 1-w_*dark_hazeimage.at<float>(i,j); } return 0; } // This Method is to estimating the Atmospheric Light of the scene // Output : // -1: means it has not compute dark channel image, should do DarkChannelPrior before. // 0: means computing estimate succeed. int HazeRemoveHandler::EstimateAtmoLight() { if((dark_channel_image_.cols ==0 )||(dark_channel_image_.rows==0)) { return -1; } int bripix_num; bripix_num = (int)dark_channel_image_.cols*dark_channel_image_.rows*bright_percent_/100; // Change rows*cols data -> 1*(rows*cols) data Mat dark_reshape = dark_channel_image_.reshape(0,1).clone(); Mat sorted; sortIdx(dark_reshape,sorted,CV_SORT_EVERY_ROW CV_SORT_DESCENDING); vector<int> bripixel_idx; for(int i = 0 ; i < bripix_num; i ) { int pixel_rows; int pixel_cols; if(sorted.at<int>(0,i)%dark_channel_image_.cols==0) { pixel_rows = (sorted.at<int>(0,i) 1)/dark_channel_image_.cols-1; pixel_cols = (sorted.at<int>(0,i) 1)%dark_channel_image_.cols dark_channel_image_.cols; } else { pixel_rows = (sorted.at<int>(0,i) 1)/dark_channel_image_.cols; pixel_cols = (sorted.at<int>(0,i) 1)%dark_channel_image_.cols-1; } A_[0] = ori_image_.at<Vec3f>(pixel_rows,pixel_cols)[0]; A_[1] = ori_image_.at<Vec3f>(pixel_rows,pixel_cols)[1]; A_[2] = ori_image_.at<Vec3f>(pixel_rows,pixel_cols)[2]; } A_[0] /= bripix_num; A_[1] /= bripix_num; A_[2] /= bripix_num; return 0; } // This Method uses guided image filter to smooth the transimission map(keep edge and remove noise) // Output : // -1: means it has not compute transimission map, should do that before. // 0: means smooth succeed. int HazeRemoveHandler::SmoothTransmissionMap() { if((transmission_image_.cols ==0) || (transmission_image_.rows==0)) { return -1; } transmission_smoothed_image_ = guidedFilter(ori_image_,transmission_image_,r_,eps_); return 0; } int HazeRemoveHandler::HazeFree() { if((transmission_smoothed_image_.cols ==0) || (transmission_smoothed_image_.rows==0)) { return -1; } haze_free_image_ = Mat(ori_image_.rows,ori_image_.cols,CV_32FC3); int rows = haze_free_image_.rows; int cols = haze_free_image_.cols; for(int i = 0; i < rows; i ) for(int j = 0; j < cols; j ) { float t_max; if(transmission_smoothed_image_.at<float>(i,j) > t0_) t_max = transmission_smoothed_image_.at<float>(i,j); else t_max = t0_; haze_free_image_.at<Vec3f>(i,j)[0] = (ori_image_.at<Vec3f>(i,j)[0]-A_average)/t_max A_average; haze_free_image_.at<Vec3f>(i,j)[1] = (ori_image_.at<Vec3f>(i,j)[1]-A_average)/t_max A_average; haze_free_image_.at<Vec3f>(i,j)[2] = (ori_image_.at<Vec3f>(i,j)[2]-A_average)/t_max A_average; } return 0; }