图像去色算法matlab到C++/opencv的移植
是对（Real-time Contrast Preserving Decolorization，作者 Cewu Lu Li Xu Jiaya Jia）论文配套的matlab代码一直到C++；并测试与其他算法的效果及时间对比
平台：qt5.12+MSVC2015（vs2015的编译器）+opencv4.2；
相关项目：http://cadik.posvete.cz/color_to_gray_overview/
测试图片为1200*768为28ms；512*512为21ms；因时间仓促对移植的代码没有优化，未来有时间可以优化代码，以减少运行时间；后边我会把核心代码粘贴上，希望感兴趣的一块交流学习。
测试：1、opencv自带的opencv自带的rgb2gray（左下方的灰度图时，时间a）；2、opencv中的decolor方法（右上方的灰度图，时间b）http://www.cse.cuhk.edu.hk/~leojia/projects/color2gray/。3、移植的灰度图（右下方的灰度图，时间c）；
测试结果：1、对于openv自带的rgb2gray（最常用的）速度最快但某些时候失真严重。因为它直接以Gray = R*0.299 + G*0.587 + B*0.114公式进行线性叠加导致灰度图像某些时候失真，如左下方的灰度图；
2、opencv中的decolor方法是源于 香港中文大学 计算机科学与工程系 的论文 Contrast Preserving Decolorization（OpenCV 3.0以上版本）；效果好但速度最慢对于1200*768需要400ms（右上方的灰度）；
3、本文移植到opencv下灰度图转换，也是源于香港中文大学论文Real-time Contrast Preserving Decolorization
（chrome-extension://ibllepbpahcoppkjjllbabhnigcbffpi/http://www.cse.cuhk.edu.hk/leojia/papers/siga12t_color2gray.pdf）；原论文提供matlab代码；在下边会给出；他的测试结果（右下方的灰度图）时间c也能能够满足实时需求；
测试demo链接 https://download.csdn.net/download/niuyuanye/12108794
matlab 源码
function img = rtcprgb2gray(im)
%% Proprocessing
%im=imread('flower.jpg');
[n,m,ch] = size(im);
sigma = 0.05;
%W = wei();
W = [ 0 0 1.0000
0 0.1000 0.9000
0 0.2000 0.8000
0 0.3000 0.7000
0 0.4000 0.6000
0 0.5000 0.5000
0 0.6000 0.4000
0 0.7000 0.3000
0 0.8000 0.2000
0 0.9000 0.1000
0 1.0000 0
0.1000 0 0.9000
0.1000 0.1000 0.8000
0.1000 0.2000 0.7000
0.1000 0.3000 0.6000
0.1000 0.4000 0.5000
0.1000 0.5000 0.4000
0.1000 0.6000 0.3000
0.1000 0.7000 0.2000
0.1000 0.8000 0.1000
0.1000 0.9000 0
0.2000 0 0.8000
0.2000 0.1000 0.7000
0.2000 0.2000 0.6000
0.2000 0.3000 0.5000
0.2000 0.4000 0.4000
0.2000 0.5000 0.3000
0.2000 0.6000 0.2000
0.2000 0.7000 0.1000
0.2000 0.8000 0
0.3000 0 0.7000
0.3000 0.1000 0.6000
0.3000 0.2000 0.5000
0.3000 0.3000 0.4000
0.3000 0.4000 0.3000
0.3000 0.5000 0.2000
0.3000 0.6000 0.1000
0.3000 0.7000 0.0000
0.4000 0 0.6000
0.4000 0.1000 0.5000
0.4000 0.2000 0.4000
0.4000 0.3000 0.3000
0.4000 0.4000 0.2000
0.4000 0.5000 0.1000
0.4000 0.6000 0.0000
0.5000 0 0.5000
0.5000 0.1000 0.4000
0.5000 0.2000 0.3000
0.5000 0.3000 0.2000
0.5000 0.4000 0.1000
0.5000 0.5000 0
0.6000 0 0.4000
0.6000 0.1000 0.3000
0.6000 0.2000 0.2000
0.6000 0.3000 0.1000
0.6000 0.4000 0.0000
0.7000 0 0.3000
0.7000 0.1000 0.2000
0.7000 0.2000 0.1000
0.7000 0.3000 0.0000
0.8000 0 0.2000
0.8000 0.1000 0.1000
0.8000 0.2000 0.0000
0.9000 0 0.1000
0.9000 0.1000 0.0000
1.0000 0 0];
%% Global and Local Contrast Computing
ims = imresize(im, round(64/sqrt(n*m)*[n,m]),'nearest');
ims=im2double(ims)
R = ims(:,:,1);G = ims(:,:,2);B = ims(:,:,3);
imV = [R(:),G(:),B(:)];
%defaultStream = RandStream.getDefaultStream;
%savedState = defaultStream.State;
t1 = randperm(size(imV,1));
Pg = [imV - imV(t1,:)];
ims = imresize(ims, 0.5 ,'nearest');
Rx = ims(:,1:end-1,1) - ims(:,2:end,1);
Gx = ims(:,1:end-1,2) - ims(:,2:end,2);
Bx = ims(:,1:end-1,3) - ims(:,2:end,3);
Ry = ims(1:end-1,:,1) - ims(2:end,:,1);
Gy = ims(1:end-1,:,2) - ims(2:end,:,2);
By = ims(1:end-1,:,3) - ims(2:end,:,3);
Pl = [[Rx(:),Gx(:),Bx(:)];[Ry(:),Gy(:),By(:)]];
P = [Pg;Pl ];
det = sqrt(sum(P.^2,2))/1.41;
% P( (det < 0.05),:) = [];
% det( (det < 0.05)) = [];
detM = repmat(det,[1,size(W,1)]);
L = P*W';
%% Energy optimization
%bbbb=exp(- ( detM ).^2/sigma.^2);
U = log(exp(- (L + detM ).^2/sigma.^2) + exp(- (L- detM).^2/sigma.^2));
Es = mean(U);
%% Output
[NULLval,bw] = max(Es);
img = imlincomb(W(bw,1),im(:,:,1) , W(bw,2),im(:,:,2) , W(bw,3),im(:,:,3));
%imshow(img);
endc++ opencv 移植源码
cv::Mat MainWindow::myRgb2Gray(cv::Mat image)
{
cv::Mat IMAGE=image.clone();
cv::Mat img_src=image.clone();
double star=(double)cv::getTickCount();
double scale=64/qSqrt(img_src.rows*img_src.cols);
int h=round(scale*img_src.rows);
int w=round(scale*img_src.cols);
cv::Mat ims;
cv::resize(img_src, img_src,cv::Size(w,h),cv::INTER_LINEAR);
cv::cvtColor(img_src,img_src,cv::COLOR_BGR2RGB);
img_src.convertTo(img_src,CV_64FC3);
cv::normalize(img_src, ims, 0, 1, cv::NORM_MINMAX);
std::vector vResultImage(3);
std::vector vSrcImage;
cv::split(ims, vSrcImage);
for(int i=0;i<3;i++)
{
// Mat dst = data.reshape(0, 1); //序列成行向量
// Mat dst = data.reshape(0, 1).t(); //序列成列向量
vResultImage[i]=vSrcImage[i].reshape(0,1).t();
}
cv::Mat img_re;
cv::merge(vResultImage,img_re);
cv::Mat img_Pg=cv::Mat::eye(img_re.rows,3,CV_64FC1);
cv::Mat deM_Pg=cv::Mat::eye(img_re.rows,1,CV_64FC1);
numbersList.clear();
randData(ims.rows*ims.cols);
for(int h=0;h(h,0)[c]-img_re.at(numbersList[h],0)[c];
img_Pg.at(h,c)=tmp;
tmp_dem+=tmp*tmp;
}
deM_Pg.at(h,0)=qSqrt(tmp_dem)/1.41;
//qDebug()<<("tmp:")<<(QString::number(h))<<(QString::number(tmp));
}
cv::resize(ims, ims,cv::Size(round(0.5*ims.cols), round(0.5*ims.rows)),cv::INTER_LINEAR);
int num_xy=(ims.rows-1)*(ims.cols-1);
cv::Mat ims_xy=cv::Mat::zeros(num_xy*2,3,CV_64FC1);
cv::Mat deM_xy=cv::Mat::zeros(num_xy*2,1,CV_64FC1);
//ims_x
for(int h=0;h(h,w)[c]-ims.at(h,w+1)[c];
ims_xy.at(hh+w,c)=tmp;
tmp_x+=tmp*tmp;
tmp=ims.at(h,w)[c]-ims.at(h+1,w)[c];
ims_xy.at(num_xy-1+hh+w,c)=tmp;
tmp_y+=tmp*tmp;
}
deM_xy.at(hh+w,0)=qSqrt(tmp_x)/1.41;
deM_xy.at(num_xy-1+hh+w,0)=qSqrt(tmp_y)/1.41;
}
}
cv::Mat ims_P;
cv::Mat ims_det;
//图像竖向连接
cv::vconcat(img_Pg, ims_xy,ims_P);
ims_xy.release();
cv::vconcat(deM_Pg, deM_xy,ims_det);
deM_xy.release();
cv::Mat ims_detM=ims_det.clone();
\
cv::Mat ims_L;
ims_L=ims_P*W;
cv::Mat ims_U=cv::Mat::eye(ims_L.rows,ims_L.cols,CV_64FC1);
for(int h=0;h(h,0)+ims_L.at(h,w);
sub=ims_detM.at(h,0)-ims_L.at(h,w);
double sub_tmp=sub*sub/(0.05*0.05);
double add_tmp=add*add/(0.05*0.05);
tmp=qLn(qExp(-sub_tmp)+qExp(-add_tmp));
ims_U.at(h,w)=tmp;
}
}
QList MeanList;
for(int w=0;w(h,w);
}
tmp=tmp/ims_U.rows;
MeanList.append(tmp);
}
int idx=0;
double tmp=MeanList[0];
for(int i=0;itmp)
{
tmp=MeanList[i];
idx=i;
}
}
//qDebug()<<(QString::number(idx)+":")<<(QString::number(tmp));
cv::Mat gray=cv::Mat::eye(IMAGE.rows,IMAGE.cols,CV_8UC1);
for(int h=0;h(h,w)[0]*W.at(2,idx);
double g=IMAGE.at(h,w)[1]*W.at(1,idx);
double b=IMAGE.at(h,w)[2]*W.at(0,idx);
gray.at(h,w)=(int)(r+g+b);
}
}
return gray;
}