Open Source Computer Vision Library https://opencv.org/
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/*M///////////////////////////////////////////////////////////////////////////////////////
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#include "seamless_cloning.hpp"
using namespace cv;
using namespace std;
void Cloning::computeGradientX( const Mat &img, Mat &gx)
{
Mat kernel = Mat::zeros(1, 3, CV_8S);
kernel.at<char>(0,2) = 1;
kernel.at<char>(0,1) = -1;
if(img.channels() == 3)
{
filter2D(img, gx, CV_32F, kernel);
}
else if (img.channels() == 1)
{
filter2D(img, gx, CV_32F, kernel);
cvtColor(gx, gx, COLOR_GRAY2BGR);
}
}
void Cloning::computeGradientY( const Mat &img, Mat &gy)
{
Mat kernel = Mat::zeros(3, 1, CV_8S);
kernel.at<char>(2,0) = 1;
kernel.at<char>(1,0) = -1;
if(img.channels() == 3)
{
filter2D(img, gy, CV_32F, kernel);
}
else if (img.channels() == 1)
{
filter2D(img, gy, CV_32F, kernel);
cvtColor(gy, gy, COLOR_GRAY2BGR);
}
}
void Cloning::computeLaplacianX( const Mat &img, Mat &laplacianX)
{
Mat kernel = Mat::zeros(1, 3, CV_8S);
kernel.at<char>(0,0) = -1;
kernel.at<char>(0,1) = 1;
filter2D(img, laplacianX, CV_32F, kernel);
}
void Cloning::computeLaplacianY( const Mat &img, Mat &laplacianY)
{
Mat kernel = Mat::zeros(3, 1, CV_8S);
kernel.at<char>(0,0) = -1;
kernel.at<char>(1,0) = 1;
filter2D(img, laplacianY, CV_32F, kernel);
}
void Cloning::dst(const Mat& src, Mat& dest, bool invert)
{
Mat temp = Mat::zeros(src.rows, 2 * src.cols + 2, CV_32F);
int flag = invert ? DFT_ROWS + DFT_SCALE + DFT_INVERSE: DFT_ROWS;
src.copyTo(temp(Rect(1,0, src.cols, src.rows)));
for(int j = 0 ; j < src.rows ; ++j)
{
float * tempLinePtr = temp.ptr<float>(j);
const float * srcLinePtr = src.ptr<float>(j);
for(int i = 0 ; i < src.cols ; ++i)
{
tempLinePtr[src.cols + 2 + i] = - srcLinePtr[src.cols - 1 - i];
}
}
Mat planes[] = {temp, Mat::zeros(temp.size(), CV_32F)};
Mat complex;
merge(planes, 2, complex);
dft(complex, complex, flag);
split(complex, planes);
temp = Mat::zeros(src.cols, 2 * src.rows + 2, CV_32F);
for(int j = 0 ; j < src.cols ; ++j)
{
float * tempLinePtr = temp.ptr<float>(j);
for(int i = 0 ; i < src.rows ; ++i)
{
float val = planes[1].ptr<float>(i)[j + 1];
tempLinePtr[i + 1] = val;
tempLinePtr[temp.cols - 1 - i] = - val;
}
}
Mat planes2[] = {temp, Mat::zeros(temp.size(), CV_32F)};
merge(planes2, 2, complex);
dft(complex, complex, flag);
split(complex, planes2);
temp = planes2[1].t();
temp(Rect( 0, 1, src.cols, src.rows)).copyTo(dest);
}
void Cloning::solve(const Mat &img, Mat& mod_diff, Mat &result)
{
const int w = img.cols;
const int h = img.rows;
Mat res;
dst(mod_diff, res);
for(int j = 0 ; j < h-2; j++)
{
float * resLinePtr = res.ptr<float>(j);
for(int i = 0 ; i < w-2; i++)
{
resLinePtr[i] /= (filter_X[i] + filter_Y[j] - 4);
}
}
dst(res, mod_diff, true);
unsigned char * resLinePtr = result.ptr<unsigned char>(0);
const unsigned char * imgLinePtr = img.ptr<unsigned char>(0);
const float * interpLinePtr = NULL;
//first col
for(int i = 0 ; i < w ; ++i)
result.ptr<unsigned char>(0)[i] = img.ptr<unsigned char>(0)[i];
for(int j = 1 ; j < h-1 ; ++j)
{
resLinePtr = result.ptr<unsigned char>(j);
imgLinePtr = img.ptr<unsigned char>(j);
interpLinePtr = mod_diff.ptr<float>(j-1);
//first row
resLinePtr[0] = imgLinePtr[0];
for(int i = 1 ; i < w-1 ; ++i)
{
//saturate cast is not used here, because it behaves differently from the previous implementation
//most notable, saturate_cast rounds before truncating, here it's the opposite.
float value = interpLinePtr[i-1];
if(value < 0.)
resLinePtr[i] = 0;
else if (value > 255.0)
resLinePtr[i] = 255;
else
resLinePtr[i] = static_cast<unsigned char>(value);
}
//last row
resLinePtr[w-1] = imgLinePtr[w-1];
}
//last col
resLinePtr = result.ptr<unsigned char>(h-1);
imgLinePtr = img.ptr<unsigned char>(h-1);
for(int i = 0 ; i < w ; ++i)
resLinePtr[i] = imgLinePtr[i];
}
void Cloning::poissonSolver(const Mat &img, Mat &laplacianX , Mat &laplacianY, Mat &result)
{
const int w = img.cols;
const int h = img.rows;
Mat lap = laplacianX + laplacianY;
Mat bound = img.clone();
rectangle(bound, Point(1, 1), Point(img.cols-2, img.rows-2), Scalar::all(0), -1);
Mat boundary_points;
Laplacian(bound, boundary_points, CV_32F);
boundary_points = lap - boundary_points;
Mat mod_diff = boundary_points(Rect(1, 1, w-2, h-2));
solve(img,mod_diff,result);
}
void Cloning::initVariables(const Mat &destination, const Mat &binaryMask)
{
destinationGradientX = Mat(destination.size(),CV_32FC3);
destinationGradientY = Mat(destination.size(),CV_32FC3);
patchGradientX = Mat(destination.size(),CV_32FC3);
patchGradientY = Mat(destination.size(),CV_32FC3);
binaryMaskFloat = Mat(binaryMask.size(),CV_32FC1);
binaryMaskFloatInverted = Mat(binaryMask.size(),CV_32FC1);
//init of the filters used in the dst
const int w = destination.cols;
filter_X.resize(w - 2);
double scale = CV_PI / (w - 1);
for(int i = 0 ; i < w-2 ; ++i)
filter_X[i] = 2.0f * (float)std::cos(scale * (i + 1));
const int h = destination.rows;
filter_Y.resize(h - 2);
scale = CV_PI / (h - 1);
for(int j = 0 ; j < h - 2 ; ++j)
filter_Y[j] = 2.0f * (float)std::cos(scale * (j + 1));
}
void Cloning::computeDerivatives(const Mat& destination, const Mat &patch, const Mat &binaryMask)
{
initVariables(destination, binaryMask);
computeGradientX(destination, destinationGradientX);
computeGradientY(destination, destinationGradientY);
computeGradientX(patch, patchGradientX);
computeGradientY(patch, patchGradientY);
Mat Kernel(Size(3, 3), CV_8UC1);
Kernel.setTo(Scalar(1));
erode(binaryMask, binaryMask, Kernel, Point(-1,-1), 3);
binaryMask.convertTo(binaryMaskFloat, CV_32FC1, 1.0/255.0);
}
void Cloning::scalarProduct(Mat mat, float r, float g, float b)
{
vector <Mat> channels;
split(mat,channels);
multiply(channels[2],r,channels[2]);
multiply(channels[1],g,channels[1]);
multiply(channels[0],b,channels[0]);
merge(channels,mat);
}
void Cloning::arrayProduct(const cv::Mat& lhs, const cv::Mat& rhs, cv::Mat& result) const
{
vector <Mat> lhs_channels;
vector <Mat> result_channels;
split(lhs,lhs_channels);
split(result,result_channels);
for(int chan = 0 ; chan < 3 ; ++chan)
multiply(lhs_channels[chan],rhs,result_channels[chan]);
merge(result_channels,result);
}
void Cloning::poisson(const Mat &destination)
{
Mat laplacianX = destinationGradientX + patchGradientX;
Mat laplacianY = destinationGradientY + patchGradientY;
computeLaplacianX(laplacianX,laplacianX);
computeLaplacianY(laplacianY,laplacianY);
split(laplacianX,rgbx_channel);
split(laplacianY,rgby_channel);
split(destination,output);
for(int chan = 0 ; chan < 3 ; ++chan)
{
poissonSolver(output[chan], rgbx_channel[chan], rgby_channel[chan], output[chan]);
}
}
void Cloning::evaluate(const Mat &I, const Mat &wmask, const Mat &cloned)
{
bitwise_not(wmask,wmask);
wmask.convertTo(binaryMaskFloatInverted,CV_32FC1,1.0/255.0);
arrayProduct(destinationGradientX, binaryMaskFloatInverted, destinationGradientX);
arrayProduct(destinationGradientY, binaryMaskFloatInverted, destinationGradientY);
poisson(I);
merge(output,cloned);
}
void Cloning::normalClone(const Mat &destination, const Mat &patch, const Mat &binaryMask, Mat &cloned, int flag)
{
const int w = destination.cols;
const int h = destination.rows;
const int channel = destination.channels();
const int n_elem_in_line = w * channel;
computeDerivatives(destination,patch,binaryMask);
switch(flag)
{
case NORMAL_CLONE:
arrayProduct(patchGradientX, binaryMaskFloat, patchGradientX);
arrayProduct(patchGradientY, binaryMaskFloat, patchGradientY);
break;
case MIXED_CLONE:
{
AutoBuffer<int> maskIndices(n_elem_in_line);
for (int i = 0; i < n_elem_in_line; ++i)
maskIndices[i] = i / channel;
for(int i=0;i < h; i++)
{
float * patchXLinePtr = patchGradientX.ptr<float>(i);
float * patchYLinePtr = patchGradientY.ptr<float>(i);
const float * destinationXLinePtr = destinationGradientX.ptr<float>(i);
const float * destinationYLinePtr = destinationGradientY.ptr<float>(i);
const float * binaryMaskLinePtr = binaryMaskFloat.ptr<float>(i);
for(int j=0; j < n_elem_in_line; j++)
{
int maskIndex = maskIndices[j];
if(abs(patchXLinePtr[j] - patchYLinePtr[j]) >
abs(destinationXLinePtr[j] - destinationYLinePtr[j]))
{
patchXLinePtr[j] *= binaryMaskLinePtr[maskIndex];
patchYLinePtr[j] *= binaryMaskLinePtr[maskIndex];
}
else
{
patchXLinePtr[j] = destinationXLinePtr[j]
* binaryMaskLinePtr[maskIndex];
patchYLinePtr[j] = destinationYLinePtr[j]
* binaryMaskLinePtr[maskIndex];
}
}
}
}
break;
case MONOCHROME_TRANSFER:
Mat gray;
cvtColor(patch, gray, COLOR_BGR2GRAY );
computeGradientX(gray,patchGradientX);
computeGradientY(gray,patchGradientY);
arrayProduct(patchGradientX, binaryMaskFloat, patchGradientX);
arrayProduct(patchGradientY, binaryMaskFloat, patchGradientY);
break;
}
evaluate(destination,binaryMask,cloned);
}
void Cloning::localColorChange(Mat &I, Mat &mask, Mat &wmask, Mat &cloned, float red_mul=1.0,
float green_mul=1.0, float blue_mul=1.0)
{
computeDerivatives(I,mask,wmask);
arrayProduct(patchGradientX,binaryMaskFloat, patchGradientX);
arrayProduct(patchGradientY,binaryMaskFloat, patchGradientY);
scalarProduct(patchGradientX,red_mul,green_mul,blue_mul);
scalarProduct(patchGradientY,red_mul,green_mul,blue_mul);
evaluate(I,wmask,cloned);
}
void Cloning::illuminationChange(Mat &I, Mat &mask, Mat &wmask, Mat &cloned, float alpha, float beta)
{
CV_INSTRUMENT_REGION();
computeDerivatives(I,mask,wmask);
arrayProduct(patchGradientX,binaryMaskFloat, patchGradientX);
arrayProduct(patchGradientY,binaryMaskFloat, patchGradientY);
Mat mag;
magnitude(patchGradientX,patchGradientY,mag);
Mat multX, multY, multx_temp, multy_temp;
multiply(patchGradientX,pow(alpha,beta),multX);
pow(mag,-1*beta, multx_temp);
multiply(multX,multx_temp, patchGradientX);
patchNaNs(patchGradientX);
multiply(patchGradientY,pow(alpha,beta),multY);
pow(mag,-1*beta, multy_temp);
multiply(multY,multy_temp,patchGradientY);
patchNaNs(patchGradientY);
Mat zeroMask = (patchGradientX != 0);
patchGradientX.copyTo(patchGradientX, zeroMask);
patchGradientY.copyTo(patchGradientY, zeroMask);
evaluate(I,wmask,cloned);
}
void Cloning::textureFlatten(Mat &I, Mat &mask, Mat &wmask, float low_threshold,
float high_threshold, int kernel_size, Mat &cloned)
{
computeDerivatives(I,mask,wmask);
Mat out;
Canny(mask,out,low_threshold,high_threshold,kernel_size);
Mat zeros = Mat::zeros(patchGradientX.size(), CV_32FC3);
Mat zerosMask = (out != 255);
zeros.copyTo(patchGradientX, zerosMask);
zeros.copyTo(patchGradientY, zerosMask);
arrayProduct(patchGradientX,binaryMaskFloat, patchGradientX);
arrayProduct(patchGradientY,binaryMaskFloat, patchGradientY);
evaluate(I,wmask,cloned);
}