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Add ACE.cpp
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‎Correction algorithm/ACE.cpp

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#include "opencv2/opencv.hpp"
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#include "opencv2/imgproc/imgproc.hpp"
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#include "iostream"
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#include "algorithm"
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#include "vector"
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#include "stdio.h"
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#include "map"
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#include "unordered_map"
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using namespace std;
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using namespace cv;
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#define PI 3.14159265
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Mat work(Mat src){
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int row = src.rows;
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int col = src.cols;
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Mat dst(row, col, CV_8UC3);
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//RGB2HSI
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Mat H = Mat(row, col, CV_64FC1);
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Mat S = Mat(row, col, CV_64FC1);
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Mat I = Mat(row, col, CV_64FC1);
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int mp[256]={0};
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double mp2[256]={0.0};
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for(int i = 0; i < row; i++){
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for(int j = 0; j < col; j++){
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double h, s, newi, th;
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double B = (double)src.at<Vec3b>(i, j)[0] / 255.0;
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double G = (double)src.at<Vec3b>(i, j)[1] / 255.0;
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double R = (double)src.at<Vec3b>(i, j)[2] / 255.0;
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double mi, mx;
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if(R > G && R > B){
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mx = R;
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mi = min(G, B);
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}
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else{
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if(G > B){
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mx = G;
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mi = min(R, B);
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}else{
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mx = B;
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mi = min(R, G);
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}
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}
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newi = (R + G + B) / 3.0;
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if(newi < 0) newi = 0;
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else if(newi > 1) newi = 1.0;
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if(newi == 0 || mx == mi){
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s = 0;
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h = 0;
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}else{
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s = 1 - mi / newi;
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th = (R - G) * (R - G) + (R - B) * (G - B);
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th = sqrt(th)+1e-5;
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th = acos(((R-G+R-B)*0.5)/th);
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if(G >= B) h = th;
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else h = 2 * PI - th;
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}
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h = h / (2*PI);
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H.at<double>(i, j) = h;
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S.at<double>(i, j) = s;
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I.at<double>(i, j) = newi;
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mp[(int)((src.at<Vec3b>(i, j)[0] + src.at<Vec3b>(i, j)[1] + src.at<Vec3b>(i, j)[2]) / 3)]++;
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}
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}
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for(int i = 0; i < 256; i++){
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mp2[i] = (double)mp[i] / (double)(row * col);
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}
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double mI = 0;
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for(int i = 0; i < 256; i++){
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mI += (i / 255.0) * mp2[i];
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}
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printf("mI: %.5f\n", mI);
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double var = 0;
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double ThresHold = 0;
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for(int i = 0; i < 256; i++){
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double T = 1.0 * i / 256;
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double P1 = 0.0;
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double mT = 0.0;
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for(int j = 0; j <= i; j++){
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P1 += mp2[j];
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mT += (double)(j / 255.0) * mp2[j];
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}
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if(P1 == 0) continue;
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if(((mI*P1 - mT)*(mI*P1 - mT) / (P1*(1-P1))) > var){
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var = (mI*P1 - mT)*(mI*P1 - mT) / (P1*(1-P1));
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ThresHold = T;
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//printf("%d %.5f\n", i, ThresHold);
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}
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}
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printf("Thres: %.5f\n", ThresHold);
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int k = 50;
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int cnt = 0;
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for(int i = 0; i < row; i++){
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for(int j = 0; j < col; j++){
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if(I.at<double>(i, j) <= ThresHold){
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cnt++;
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}
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}
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}
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printf("cnt: %d\n", cnt);
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double A = (double)k * sqrt((double)cnt / (double)(row * col - cnt));
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printf("A: %.5f\n", A);
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for(int i = 0; i < row; i++){
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for(int j = 0; j < col; j++){
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double D, C;
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if(I.at<uchar>(i, j) <= ThresHold){
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D = A;
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C = 1.0 / log2(D+1);
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}else{
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D = (double)(ThresHold * A - ThresHold) / double((1 - ThresHold) * (I.at<double>(i, j))) - (double)(ThresHold * A - 1) / (1 - ThresHold);
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C = 1.0 / log2(D+1);
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}
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I.at<double>(i, j) = (C * log2(D * (double)I.at<double>(i, j) + 1));
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}
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}
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for(int i = 0; i < row; i++){
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for(int j = 0; j < col; j++){
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double preh = H.at<double>(i, j);
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double pres = S.at<double>(i, j);
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double prei = I.at<double>(i, j);
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// printf("H: %.5f S: %.5f I: %.5f\n", preh, pres, prei);
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double r = 0, g = 0, b = 0;
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if(prei == 0.0){
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r = 0;
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g = 0;
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b = 0;
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}
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else{
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if(pres == 0.0){
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r = g = b = prei;
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}
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double t1, t2, t3;
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t1 = (1.0 - pres) / 3.0;
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t2 = pres * cos(preh);
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if(preh >= 0 && preh < (PI * 2 / 3)){
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b = t1;
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t3 = cos(PI/3 - preh);
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r = (1 + t2 / t3) / 3;
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g = 1.0 - r - b;
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r = 3 * prei * r;
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g = 3 * g * prei;
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b = 3 * prei * b;
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}else if(preh >= (PI * 2 / 3) && preh < (PI * 4 / 3)){
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r = t1;
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t3 = cos(PI - preh);
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g = (1 + t2 / t3) / 3;
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b = 1 - r - g;
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r = 3 * prei * r;
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g = 3 * g * prei;
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b = 3 * prei * b;
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}else if(preh >= (PI * 4 / 3) && preh < (PI * 2)){
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g = t1;
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t3 = cos(PI * 5 / 3 - preh);
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b = (1 + t2 / t3) / 3;
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r = 1 - g - b;
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r = 3 * prei * r;
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g = 3 * g * prei;
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b = 3 * prei * b;
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}
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}
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//printf("%d %d %d\n", (int)(r*255), (int)(g*255), (int)(b*255));
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dst.at<Vec3b>(i, j)[0] = (int)(b*255);
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dst.at<Vec3b>(i, j)[1] = (int)(g*255);
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dst.at<Vec3b>(i, j)[2] = (int)(r*255);
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}
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}
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return dst;
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}
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//自适应对比度增强算法，C表示对高频的直接增益系数,n表示滤波半径，maxCG表示对CG做最大值限制
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Mat ACE(Mat src, int C = 3, int n = 3, float MaxCG = 7.5){
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int row = src.rows;
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int col = src.cols;
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Mat meanLocal; //图像局部均值
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Mat varLocal; //图像局部方差
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Mat meanGlobal; //全局均值
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Mat varGlobal; //全局标准差
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blur(src.clone(), meanLocal, Size(n, n));
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Mat highFreq = src - meanLocal;
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varLocal = highFreq.mul(highFreq);
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varLocal.convertTo(varLocal, CV_32F);
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for(int i = 0; i < row; i++){
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for(int j = 0; j < col; j++){
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varLocal.at<float>(i, j) = (float)sqrt(varLocal.at<float>(i, j));
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}
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}
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meanStdDev(src, meanGlobal, varGlobal);
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Mat gainArr = varGlobal / varLocal; //增益系数矩阵
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for(int i = 0; i < row; i++){
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for(int j = 0; j < col; j++){
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if(gainArr.at<float>(i, j) > MaxCG){
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gainArr.at<float>(i, j) = MaxCG;
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}
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}
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}
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printf("%d %d\n", row, col);
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gainArr.convertTo(gainArr, CV_8U);
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gainArr = gainArr.mul(highFreq);
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Mat dst1 = meanLocal + gainArr;
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Mat dst2 = meanLocal + C * highFreq;
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return dst1;
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}
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int main(){
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Mat src = imread("../test.png");
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vector <Mat> now;
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split(src, now);
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int C = 4;
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int n = 50;
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float MaxCG = 5;
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Mat dst1 = ACE(now[0], C, n, MaxCG);
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Mat dst2 = ACE(now[1], C, n, MaxCG);
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Mat dst3 = ACE(now[2], C, n, MaxCG);
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now.clear();
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Mat dst;
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now.push_back(dst1);
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now.push_back(dst2);
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now.push_back(dst3);
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cv::merge(now, dst);
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imshow("origin", src);
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imshow("result", dst);
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imwrite("../result.jpg", dst);
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waitKey(0);
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return 0;
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}

‎Correction algorithm/README.md

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- HistograEqualization.cpp 直方图均衡化C++实现。原理请看：https://blog.csdn.net/just_sort/article/details/85013803
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- ACE.cpp 对《Real-time adptive contrast enhancement for imaging sensors》论文的C++复现，也就是自适应局部对比度增强。原理请看：https://blog.csdn.net/just_sort/article/details/85208124

‎README.md

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## 3. Feature Extraction 复现了一些图像特征提取算法
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## 4. 大家有数字图像需要复现的论文可以在ISSUE提问，在本人能力范围内，我可以免费复现开源
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## 4. 有意思的论文可以在ISSUE发出来，在本人能力范围内，可以复现并开源
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- Retinex MSRCR.cpp 带色彩恢复的多尺度视网膜增强算法。 算法原理请看：http://www.cnblogs.com/Imageshop/archive/2013/04/17/3026881.html
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- ImageDataIncrease.cpp 常见的图片数据扩充。包括一些PS算法 具体为旋转，添加高斯，椒盐噪声，增加老照片效果，增加和降低图像饱和度，对原图缩放，亮度增强，对比度增强，磨皮美白，偏色矫正，同态滤波，过曝，灰度化，轮换通道，图像错切，运动模糊，钝化蒙版，PS滤镜算法之球面化 (凸出和凹陷效果)

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