// Match Red, Green and Blue histograms of ‘src’ to that of ‘dst’, according to both masks.
// Based on: http://www.morethantechnical.com/2010/01/28/recoloring-via-histogram-matching-with-opencv-w-code/
// Modified by Shervin Emami so it can also pass NULL for the masks if you want to process the whole image.
void histMatchRGB(cv::Mat& src, const cv::Mat* src_mask, const cv::Mat& dst, const cv::Mat* dst_mask)
{
double const HISTMATCH_EPSILON = 0.000001;

vector chns;
cv::split(src,chns);
vector chns1;
cv::split(dst,chns1);
cv::Mat src_hist = cv::Mat::zeros(1,256,CV_64FC1);
cv::Mat dst_hist = cv::Mat::zeros(1,256,CV_64FC1);
cv::Mat src_cdf = cv::Mat::zeros(1,256,CV_64FC1);
cv::Mat dst_cdf = cv::Mat::zeros(1,256,CV_64FC1);
cv::Mat Mv(1,256,CV_8UC1);
uchar* M = Mv.ptr();

for(int i=0;i<3;i++)
{
src_hist.setTo(0);
dst_hist.setTo(0);
src_cdf.setTo(0);
src_cdf.setTo(0);

double* _src_cdf = src_cdf.ptr();
double* _dst_cdf = dst_cdf.ptr();
double* _src_hist = src_hist.ptr();
double* _dst_hist = dst_hist.ptr();

do1ChnHist(chns[i], src_mask, _src_hist, _src_cdf);
do1ChnHist(chns1[i], dst_mask, _dst_hist, _dst_cdf);

uchar last = 0;

for(int j=0;j<src_cdf.cols;j++)
{
double F1j = _src_cdf[j];

for(uchar k = last; k (F1j – HISTMATCH_EPSILON))
{
M[j] = k;
last = k;
break;
}
}
}

cv::Mat lut(1,256,CV_8UC1,M);
cv::LUT(chns[i],lut,chns[i]);
}

cv::Mat res;
cv::merge(chns,res);

res.copyTo(src);
}

// Compute histogram and CDF for an image with mask
void do1ChnHist(const cv::Mat& _i, const cv::Mat* mask, double* h, double* cdf)
{
cv::Mat _t = _i.reshape(1,1);

// Get the histogram with or without the mask
if (mask)
{
cv::Mat _tm;
mask->copyTo(_tm);
_tm = _tm.reshape(1,1);
for(int p=0; p<_t.cols; p++)
{
CvPoint po;
po.x = 0;
po.y = p;
uchar m = _tm.at<>(po);

if(m > 0)
{ // Mask value
uchar c = _t.at(0,p); // Image value
h += 1.0; // Histogram
}
}
}
else
{
//uchar* Uptr = _t.ptr(0);
for(int p = 0; p < _t.cols; p++)
{
//Uptr[p] = saturate_cast((Uptr[p]-128)/2 + 128);
uchar c = _t.at(0, p);
//Vxy = saturate_cast((Vxy-128)/2 + 128);

//uchar c = _t(0,p); // Image value
h+= 1.0; // Histogram
}
}

//normalize hist to a max value of 1.0
cv::Mat _tmp(1,256,CV_64FC1,h);
double minVal,maxVal;
cv::minMaxLoc(_tmp, &minVal, &maxVal);
_tmp = _tmp / maxVal;

// Calculate the Cumulative Distribution Function
cdf[0] = h[0];
for(int j=1; j<256; j++)
{
cdf[j] = cdf[j-1]+h[j];
}

//normalize CDF to a max value of 1.0
_tmp.data = (uchar*)cdf; // Array of doubles, but gets a byte pointer.
cv::minMaxLoc(_tmp, &minVal, &maxVal);
_tmp = _tmp / maxVal;
}

void do1ChnHist(const cv::Mat& _i, const cv::Mat* mask, double* h, double* cdf);

// Match Red, Green and Blue histograms of ‘src’ to that of ‘dst’, according to both masks.
// Based on: http://www.morethantechnical.com/2010/01/28/recoloring-via-histogram-matching-with-opencv-w-code/
// Modified by Shervin Emami so it can also pass NULL for the masks if you want to process the whole image.
void histMatchRGB(cv::Mat& src, const cv::Mat* src_mask, const cv::Mat& dst, const cv::Mat* dst_mask)
{
double const HISTMATCH_EPSILON = 0.000001;

vector chns;
cv::split(src,chns);
vector chns1;
cv::split(dst,chns1);
cv::Mat src_hist = cv::Mat::zeros(1,256,CV_64FC1);
cv::Mat dst_hist = cv::Mat::zeros(1,256,CV_64FC1);
cv::Mat src_cdf = cv::Mat::zeros(1,256,CV_64FC1);
cv::Mat dst_cdf = cv::Mat::zeros(1,256,CV_64FC1);
cv::Mat Mv(1,256,CV_8UC1);
uchar* M = Mv.ptr();

for(int i=0;i<3;i++)
{
src_hist.setTo(0);
dst_hist.setTo(0);
src_cdf.setTo(0);
src_cdf.setTo(0);

double* _src_cdf = src_cdf.ptr();
double* _dst_cdf = dst_cdf.ptr();
double* _src_hist = src_hist.ptr();
double* _dst_hist = dst_hist.ptr();

do1ChnHist(chns[i], src_mask, _src_hist, _src_cdf);
do1ChnHist(chns1[i], dst_mask, _dst_hist, _dst_cdf);

uchar last = 0;

for(int j=0;j<src_cdf.cols;j++)
{
double F1j = _src_cdf[j];

for(uchar k = last; k (F1j – HISTMATCH_EPSILON))
{
M[j] = k;
last = k;
break;
}
}
}

cv::Mat lut(1,256,CV_8UC1,M);
cv::LUT(chns[i],lut,chns[i]);
}

cv::Mat res;
cv::merge(chns,res);

res.copyTo(src);
}

// Compute histogram and CDF for an image with mask
void do1ChnHist(const cv::Mat& _i, const cv::Mat* mask, double* h, double* cdf)
{
cv::Mat _t = _i.reshape(1,1);

// Get the histogram with or without the mask
if (mask)
{
cv::Mat _tm;
mask->copyTo(_tm);
_tm = _tm.reshape(1,1);
for(int p=0; p<_t.cols; p++)
{
CvPoint po;
po.x = 0;
po.y = p;
uchar m = _tm.at(po);

if(m > 0)
{ // Mask value
uchar c = _t.at(0,p); // Image value
h += 1.0; // Histogram
}
}
}
else
{
//uchar* Uptr = _t.ptr(0);
for(int p = 0; p < _t.cols; p++)
{
//Uptr[p] = saturate_cast((Uptr[p]-128)/2 + 128);
uchar c = _t.at(0, p);
//Vxy = saturate_cast((Vxy-128)/2 + 128);

//uchar c = _t(0,p); // Image value
h+= 1.0; // Histogram
}
}

//normalize hist to a max value of 1.0
cv::Mat _tmp(1,256,CV_64FC1,h);
double minVal,maxVal;
cv::minMaxLoc(_tmp, &minVal, &maxVal);
_tmp = _tmp / maxVal;

// Calculate the Cumulative Distribution Function
cdf[0] = h[0];
for(int j=1; j<256; j++)
{
cdf[j] = cdf[j-1]+h[j];
}

//normalize CDF to a max value of 1.0
_tmp.data = (uchar*)cdf; // Array of doubles, but gets a byte pointer.
cv::minMaxLoc(_tmp, &minVal, &maxVal);
_tmp = _tmp / maxVal;
}

Thanks for this nice code example. But like others had noted, it has some problems with most compilers, so here is a working version of the code (tested on GCC):

void do1ChnHist(const cv::Mat& _i, const cv::Mat* mask, double* h, double* cdf);

// Match Red, Green and Blue histograms of ‘src’ to that of ‘dst’, according to both masks.
// Based on: http://www.morethantechnical.com/2010/01/28/recoloring-via-histogram-matching-with-opencv-w-code/
// Modified by Shervin Emami so it can also pass NULL for the masks if you want to process the whole image.
void histMatchRGB(cv::Mat& src, const cv::Mat* src_mask, const cv::Mat& dst, const cv::Mat* dst_mask)
{
vector chns;
cv::split(src,chns);
vector chns1;
cv::split(dst,chns1);
cv::Mat src_hist = cv::Mat::zeros(1,256,CV_64FC1);
cv::Mat dst_hist = cv::Mat::zeros(1,256,CV_64FC1);
cv::Mat src_cdf = cv::Mat::zeros(1,256,CV_64FC1);
cv::Mat dst_cdf = cv::Mat::zeros(1,256,CV_64FC1);
cv::Mat Mv(1,256,CV_8UC1);
uchar* M = Mv.ptr();

for(int i=0;i<3;i++) {
src_hist.setTo(cvScalar(0));
dst_hist.setTo(cvScalar(0));
src_cdf.setTo(cvScalar(0));
src_cdf.setTo(cvScalar(0));

double* _src_cdf = src_cdf.ptr();
double* _dst_cdf = dst_cdf.ptr();
double* _src_hist = src_hist.ptr();
double* _dst_hist = dst_hist.ptr();

do1ChnHist(chns[i], src_mask, _src_hist, _src_cdf);
do1ChnHist(chns1[i], dst_mask, _dst_hist, _dst_cdf);

uchar last = 0;
double const HISTMATCH_EPSILON = 0.000001;

for(int j=0;j<src_cdf.cols;j++) {
double F1j = _src_cdf[j];

for(uchar k = last; k<dst_cdf.cols; k++) {
double F2k = _dst_cdf[k];
// Note: Two tests were combined into one for efficiency, by Shervin Emami, Apr 24th 2011.
//if (abs(F2k – F1j) F1j) {
if (F2k > F1j – HISTMATCH_EPSILON) {
M[j] = k;
last = k;
break;
}
}
}

cv::Mat lut(1,256,CV_8UC1,M);
cv::LUT(chns[i],lut,chns[i]);
}

cv::Mat res;
cv::merge(chns,res);

res.copyTo(src);
}

// Compute histogram and CDF for an image with mask
void do1ChnHist(const cv::Mat& _i, const cv::Mat* mask, double* h, double* cdf)
{
cv::Mat _t = _i.reshape(1,1);

// Get the histogram with or without the mask
if (mask) {
cv::Mat _tm;
mask->copyTo(_tm);
_tm = _tm.reshape(1,1);
for(int p=0; p<_t.cols; p++) {
uchar m = _tm.at(0,p);
if(m > 0) { // Mask value
uchar c = _t.at(0,p); // Image value
h += 1.0; // Histogram
}
}
}
else {
for(int p=0; p<_t.cols; p++) {
uchar c = _t.at(0,p); // Image value
h += 1.0; // Histogram
}
}

//normalize hist to a max value of 1.0
cv::Mat _tmp(1,256,CV_64FC1,h);
double minVal,maxVal;
cv::minMaxLoc(_tmp, &minVal, &maxVal);
_tmp = _tmp / maxVal;

// Calculate the Cumulative Distribution Function
cdf[0] = h[0];
for(int j=1; j<256; j++) {
cdf[j] = cdf[j-1]+h[j];
}

//normalize CDF to a max value of 1.0
_tmp.data = (uchar*)cdf; // Array of doubles, but gets a byte pointer.
cv::minMaxLoc(_tmp, &minVal, &maxVal);
_tmp = _tmp / maxVal;
}

I’m trying your code with the new OpenCV 2.2 C++ API and I have discovered some difficulties. First, I need to specify the data type of some template calls as in _tm.at(0, p). I changed it to _tm.at(0, p) and now it seems ok.

The second issue is with the calls of the do1ChnHist function. You are calling this function with variables of type cv::Mat as third and fourth parameter, but it is specified to take double* instead. Could you please explain to me how that is supposed to work?

I’m really only beginning with using C++ and OpenCV, so maybe it’s quite obvious how to fix this, but I wasn’t able to figure it out by now.

Thanks a lot in advance for your answer and of course for sharing your work with us!

Here we just need two images and there histograms. But I am unable to understand that what is the purpose of following images in the code above
Mat src_mask = cvLoadImage(“image008_mask.bmp”,0);
Mat dst_mask = cvLoadImage(“image003_mask.bmp”,0);
Can u plz elaborate it a lil bit more. I am new in computer vision and opencv. And kindly also explain dat wat do u mean by image mask in the code above

How i can solve it?

Much appreciated!