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Optimized contrast enhancement for real-time image and video dehazin.cpp
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Optimized contrast enhancement for real-time image and video dehazin.cpp
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#include "opencv2/opencv.hpp"
#include "iostream"
#include "algorithm"
#include "vector"
using namespace std;
using namespace cv;
//计算大气光值
vector <int> m_anAirlight;
void AirlightEstimation(cv::Mat src)
{
int nMinDistance = 65536;
int nDistance;
int nMaxIndex;
double dpScore[3];
float afScore[4] = {0};
float nMaxScore = 0;
int cols = src.cols;
int rows = src.rows;
//4 sub-block
Mat R = Mat(rows / 2, cols / 2, CV_8UC1);
Mat G = Mat(rows / 2, cols / 2, CV_8UC1);
Mat B = Mat(rows / 2, cols / 2, CV_8UC1);
Rect temp1(0, 0, cols / 2, rows / 2);
Mat UpperLeft = src(temp1);
Rect temp2(cols / 2, 0, cols / 2, rows / 2);
Mat UpperRight = src(temp2);
Rect temp3(0, rows / 2, cols / 2, rows / 2);
Mat LowerLeft = src(temp3);
Rect temp4(cols / 2, rows / 2, cols / 2, rows / 2);
Mat LowerRight = src(temp4);
if(rows * cols > 200){
vector <Mat> channels;
//upper left sub-block
split(UpperLeft, channels);
B = channels[0];
G = channels[1];
R = channels[2];
Mat tmp_m, tmp_std;
meanStdDev(R, tmp_m, tmp_std);
dpScore[0] = tmp_m.at<double>(0,0) - tmp_std.at<double>(0,0);
meanStdDev(G, tmp_m, tmp_std);
dpScore[1] = tmp_m.at<double>(0,0) - tmp_std.at<double>(0,0);
meanStdDev(B, tmp_m, tmp_std);
dpScore[2] = tmp_m.at<double>(0,0) - tmp_std.at<double>(0,0);
afScore[0] = (float)(dpScore[0] + dpScore[1] + dpScore[2]);
nMaxScore = afScore[0];
nMaxIndex = 0;
//upper right sub-block
split(UpperRight, channels);
B = channels[0];
G = channels[1];
R = channels[2];
meanStdDev(R, tmp_m, tmp_std);
dpScore[0] = tmp_m.at<double>(0,0) - tmp_std.at<double>(0,0);
meanStdDev(G, tmp_m, tmp_std);
dpScore[1] = tmp_m.at<double>(0,0) - tmp_std.at<double>(0,0);
meanStdDev(B, tmp_m, tmp_std);
dpScore[2] = tmp_m.at<double>(0,0) - tmp_std.at<double>(0,0);
afScore[1] = (float)(dpScore[0] + dpScore[1] + dpScore[2]);
if(afScore[1] > nMaxScore){
nMaxScore = afScore[1];
nMaxIndex = 1;
}
//lower left sub-block
split(LowerLeft, channels);
B = channels[0];
G = channels[1];
R = channels[2];
meanStdDev(R, tmp_m, tmp_std);
dpScore[0] = tmp_m.at<double>(0,0) - tmp_std.at<double>(0,0);
meanStdDev(G, tmp_m, tmp_std);
dpScore[1] = tmp_m.at<double>(0,0) - tmp_std.at<double>(0,0);
meanStdDev(B, tmp_m, tmp_std);
dpScore[2] = tmp_m.at<double>(0,0) - tmp_std.at<double>(0,0);
afScore[2] = (float)(dpScore[0] + dpScore[1] + dpScore[2]);
if(afScore[2] > nMaxScore){
nMaxScore = afScore[2];
nMaxIndex = 2;
}
//lower right sub-block
split(LowerRight, channels);
B = channels[0];
G = channels[1];
R = channels[2];
meanStdDev(R, tmp_m, tmp_std);
dpScore[0] = tmp_m.at<double>(0,0) - tmp_std.at<double>(0,0);
meanStdDev(G, tmp_m, tmp_std);
dpScore[1] = tmp_m.at<double>(0,0) - tmp_std.at<double>(0,0);
meanStdDev(B, tmp_m, tmp_std);
dpScore[2] = tmp_m.at<double>(0,0) - tmp_std.at<double>(0,0);
afScore[3] = (float)(dpScore[0] + dpScore[1] + dpScore[2]);
if(afScore[3] > nMaxScore){
nMaxScore = afScore[3];
nMaxIndex = 3;
}
//select the sub-block, which has maximum score
switch (nMaxIndex){
case 0:
AirlightEstimation(UpperLeft); break;
case 1:
AirlightEstimation(UpperRight); break;
case 2:
AirlightEstimation(LowerLeft); break;
case 3:
AirlightEstimation(LowerRight); break;
}
}else{
//在子快中寻找最亮的点作为A
printf("%d %d\n", src.rows, src.cols);
for(int i = 0; i < rows; i++){
for(int j = 0; j < cols; j++){
nDistance = int(sqrt(float(255 - src.at<Vec3b>(i, j)[0]) * float(255 - src.at<Vec3b>(i, j)[0])) +
sqrt(float(255 - src.at<Vec3b>(i, j)[1]) * float(255 - src.at<Vec3b>(i, j)[1])) +
sqrt(float(255 - src.at<Vec3b>(i, j)[2]) * float(255 - src.at<Vec3b>(i, j)[2])));
if(nMinDistance > nDistance){
m_anAirlight.clear();
nMinDistance = nDistance;
m_anAirlight.push_back(src.at<Vec3b>(i, j)[0]);
m_anAirlight.push_back(src.at<Vec3b>(i, j)[1]);
m_anAirlight.push_back(src.at<Vec3b>(i, j)[2]);
}
}
}
printf("success\n");
}
}
//计算透射率
float NFTrsEstimationColor(cv::Mat src, float lamda=5.0){
int rows = src.rows;
int cols = src.cols;
int nOutR, nOutG, nOutB, nSquaredOut, nSumofOuts, nSumofSquaredOuts;
float fTrans, fOptTrs;
int nTrans, nSumofLoss;
float fCost, fMinCost, fMean;
int nNumberofPixels, nLossCount;
fTrans = 0.4f;
nTrans = 427;
nNumberofPixels = rows * cols * 3;
for(int cnt = 0; cnt < 5; cnt++){
nSumofLoss = 0;
nLossCount = 0;
nSumofSquaredOuts = 0;
nSumofOuts = 0;
for(int i = 0; i < rows; i++){
for(int j = 0; j < cols; j++){
nOutB = ((src.at<Vec3b>(i, j)[0] - m_anAirlight[0]) * nTrans + 128 * m_anAirlight[0]) >> 7; //(I-A)/t+A-->((I-A)*k*128+A*128)/128
nOutG = ((src.at<Vec3b>(i, j)[1] - m_anAirlight[1]) * nTrans + 128 * m_anAirlight[1]) >> 7;
nOutR = ((src.at<Vec3b>(i, j)[2] - m_anAirlight[2]) * nTrans + 128 * m_anAirlight[2]) >> 7;
if(nOutR>255){
nSumofLoss += (nOutR-255)*(nOutR-255);
nLossCount++;
}else if(nOutR<0){
nSumofLoss += nOutR*nOutR;
nLossCount++;
}
if(nOutG>255){
nSumofLoss += (nOutG-255)*(nOutG-255);
nLossCount++;
}else if(nOutG<0){
nSumofLoss += nOutG*nOutG;
nLossCount++;
}
if(nOutB>255){
nSumofLoss += (nOutB-255)*(nOutB-255);
nLossCount++;
}else if(nOutB<0){
nSumofLoss += nOutB*nOutB;
nLossCount++;
}
nSumofSquaredOuts += nOutB*nOutB + nOutR*nOutR + nOutG*nOutG;
nSumofOuts += nOutB + nOutG + nOutR;
}
}
fMean = (float)(nSumofOuts)/(float)(nNumberofPixels);
fCost = lamda * (float)nSumofLoss / (float)(nNumberofPixels) - ((float)nSumofSquaredOuts/(float)nNumberofPixels-fMean*fMean);
if(cnt == 0 || fMinCost > fCost){
fMinCost = fCost;
fOptTrs = fTrans;
}
fTrans += 0.1f;
nTrans = (int)(1.0f/fTrans*128.0f);
}
return fOptTrs;
}
//float NFTrsEstimationColor(cv::Mat src){
// float t = 0.0;
// int rows = src.rows;
// int cols = src.cols;
// float mi = 65536.0;
// float mx = 0.0;
// for(int i = 0; i < rows; i++){
// for(int j = 0; j < cols; j++){
// for(int k = 0; k < 3; k++){
// mi = std::min(mi, ((float)src.at<Vec3b>(i, j)[k] - (float)m_anAirlight[k]) / ((float)(-m_anAirlight[k])));
// mx = std::max(mx, ((float)src.at<Vec3b>(i, j)[k] - (float)m_anAirlight[k]) / float(255.0-(float)m_anAirlight[k]));
// }
// }
// }
// printf("%.5f %.5f\n", mi, mx);
// t = max(mi, mx);
// return t;
//}
int main(){
Mat src = cv::imread("./org-canon.png");
m_anAirlight.clear();
AirlightEstimation(src);
printf("%d %d %d\n", m_anAirlight[0], m_anAirlight[1], m_anAirlight[2]);
int rows = src.rows;
int cols = src.cols;
Mat dst(rows, cols, CV_8UC3);
int m_nTVlockSize = 41;
for(int nY = 0; nY+m_nTVlockSize < rows; nY+=m_nTVlockSize){
for(int nX = 0; nX+m_nTVlockSize < cols; nX+=m_nTVlockSize){
Rect temp(nX, nY, m_nTVlockSize, m_nTVlockSize);
Mat now = src(temp);
float t = NFTrsEstimationColor(now);
//printf("%.3f\n", t);
//float t = 0.;
for(int i = 0; i < m_nTVlockSize; i++){
for(int j = 0; j < m_nTVlockSize; j++){
for(int k = 0; k < 3; k++){
dst.at<Vec3b>(nY+i, nX+j)[k] = int((double)(src.at<Vec3b>(nY+i, nX+j)[k] - m_anAirlight[k]) / t) + m_anAirlight[k];
if(dst.at<Vec3b>(nY+i, nX+j)[k] > 255) dst.at<Vec3b>(nY+i, nX+j)[k] = 255;
else if(dst.at<Vec3b>(nY+i, nX+j)[k] < 0) dst.at<Vec3b>(nY+i, nX+j)[k] = 0;
}
}
}
}
}
cv::imshow("origin", src);
cv::imshow("result", dst);
waitKey(0);
return 0;
}