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提交 d052d863 编写于 作者: V Vadim Pisarevsky
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Merge pull request #3315 from abak:seamless-refactor

上级 dce629d0 5442de7d
隐藏空白更改
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Showing with 595 addition and 566 deletion
modules/photo/include/opencv2/photo.hpp
浏览文件 @ d052d863
......@@ -315,7 +315,7 @@ CV_EXPORTS_W void illuminationChange(InputArray src, InputArray mask, OutputArra
float alpha = 0.2f, float beta = 0.4f);
CV_EXPORTS_W void textureFlattening(InputArray src, InputArray mask, OutputArray dst,
double low_threshold = 30, double high_threshold = 45,
float low_threshold = 30, float high_threshold = 45,
int kernel_size = 3);
CV_EXPORTS_W void edgePreservingFilter(InputArray src, OutputArray dst, int flags = 1,
......
modules/photo/src/seamless_cloning.cpp
浏览文件 @ d052d863
......@@ -41,7 +41,6 @@
#include "precomp.hpp"
#include "opencv2/photo.hpp"
#include <stdlib.h>
#include "seamless_cloning.hpp"
......@@ -50,9 +49,9 @@ using namespace cv;
void cv::seamlessClone(InputArray _src, InputArray _dst, InputArray _mask, Point p, OutputArray _blend, int flags)
{
Mat src = _src.getMat();
Mat dest = _dst.getMat();
Mat mask = _mask.getMat();
const Mat src = _src.getMat();
const Mat dest = _dst.getMat();
const Mat mask = _mask.getMat();
_blend.create(dest.size(), CV_8UC3);
Mat blend = _blend.getMat();
......@@ -87,6 +86,8 @@ void cv::seamlessClone(InputArray _src, InputArray _dst, InputArray _mask, Point
int lenx = maxx - minx;
int leny = maxy - miny;
Mat patch = Mat::zeros(Size(leny, lenx), CV_8UC3);
int minxd = p.y - lenx/2;
int maxxd = p.y + lenx/2;
int minyd = p.x - leny/2;
......@@ -102,12 +103,14 @@ void cv::seamlessClone(InputArray _src, InputArray _dst, InputArray _mask, Point
gray(roi_s).copyTo(destinationROI);
src(roi_s).copyTo(sourceROI,gray(roi_s));
src(roi_s).copyTo(patch, gray(roi_s));
destinationROI = cd_mask(roi_d);
cs_mask(roi_s).copyTo(destinationROI);
Cloning obj;
obj.normal_clone(dest,cd_mask,dst_mask,blend,flags);
obj.normalClone(dest,cd_mask,dst_mask,blend,flags);
}
......@@ -134,7 +137,7 @@ void cv::colorChange(InputArray _src, InputArray _mask, OutputArray _dst, float
src.copyTo(cs_mask,gray);
Cloning obj;
obj.local_color_change(src,cs_mask,gray,blend,red,green,blue);
obj.localColorChange(src,cs_mask,gray,blend,red,green,blue);
}
void cv::illuminationChange(InputArray _src, InputArray _mask, OutputArray _dst, float a, float b)
......@@ -159,12 +162,12 @@ void cv::illuminationChange(InputArray _src, InputArray _mask, OutputArray _dst,
src.copyTo(cs_mask,gray);
Cloning obj;
obj.illum_change(src,cs_mask,gray,blend,alpha,beta);
obj.illuminationChange(src,cs_mask,gray,blend,alpha,beta);
}
void cv::textureFlattening(InputArray _src, InputArray _mask, OutputArray _dst,
double low_threshold, double high_threshold, int kernel_size)
float low_threshold, float high_threshold, int kernel_size)
{
Mat src = _src.getMat();
......@@ -184,5 +187,5 @@ void cv::textureFlattening(InputArray _src, InputArray _mask, OutputArray _dst,
src.copyTo(cs_mask,gray);
Cloning obj;
obj.texture_flatten(src,cs_mask,gray,low_threshold,high_threshold,kernel_size,blend);
obj.textureFlatten(src,cs_mask,gray,low_threshold,high_threshold,kernel_size,blend);
}
modules/photo/src/seamless_cloning.hpp
浏览文件 @ d052d863
......@@ -39,546 +39,52 @@
//
//M*/
#ifndef CV_SEAMLESS_CLONING_HPP___
#define CV_SEAMLESS_CLONING_HPP___
#include "precomp.hpp"
#include "opencv2/photo.hpp"
#include <iostream>
#include <stdlib.h>
#include <complex>
#include "math.h"
using namespace std;
using namespace cv;
class Cloning
{
public:
vector <Mat> rgb_channel, rgbx_channel, rgby_channel, output;
Mat grx, gry, sgx, sgy, srx32, sry32, grx32, gry32, smask, smask1;
void init_var(Mat &I, Mat &wmask);
void initialization(Mat &I, Mat &mask, Mat &wmask);
void scalar_product(Mat mat, float r, float g, float b);
void array_product(Mat mat1, Mat mat2, Mat mat3);
void poisson(Mat &I, Mat &gx, Mat &gy, Mat &sx, Mat &sy);
void evaluate(Mat &I, Mat &wmask, Mat &cloned);
void getGradientx(const Mat &img, Mat &gx);
void getGradienty(const Mat &img, Mat &gy);
void lapx(const Mat &img, Mat &gxx);
void lapy(const Mat &img, Mat &gyy);
void dst(double *mod_diff, double *sineTransform,int h,int w);
void idst(double *mod_diff, double *sineTransform,int h,int w);
void transpose(double *mat, double *mat_t,int h,int w);
void solve(const Mat &img, double *mod_diff, Mat &result);
void poisson_solver(const Mat &img, Mat &gxx , Mat &gyy, Mat &result);
void normal_clone(Mat &I, Mat &mask, Mat &wmask, Mat &cloned, int num);
void local_color_change(Mat &I, Mat &mask, Mat &wmask, Mat &cloned, float red_mul, float green_mul, float blue_mul);
void illum_change(Mat &I, Mat &mask, Mat &wmask, Mat &cloned, float alpha, float beta);
void texture_flatten(Mat &I, Mat &mask, Mat &wmask, double low_threshold, double high_threhold, int kernel_size, Mat &cloned);
};
void Cloning::getGradientx( 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;
filter2D(img, gx, CV_32F, kernel);
}
void Cloning::getGradienty( 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;
filter2D(img, gy, CV_32F, kernel);
}
void Cloning::lapx( const Mat &img, Mat &gxx)
{
Mat kernel = Mat::zeros(1, 3, CV_8S);
kernel.at<char>(0,0) = -1;
kernel.at<char>(0,1) = 1;
filter2D(img, gxx, CV_32F, kernel);
}
void Cloning::lapy( const Mat &img, Mat &gyy)
{
Mat kernel = Mat::zeros(3, 1, CV_8S);
kernel.at<char>(0,0) = -1;
kernel.at<char>(1,0) = 1;
filter2D(img, gyy, CV_32F, kernel);
}
void Cloning::dst(double *mod_diff, double *sineTransform,int h,int w)
{
unsigned long int idx;
Mat temp = Mat(2*h+2,1,CV_32F);
Mat res = Mat(h,1,CV_32F);
Mat planes[] = {Mat_<float>(temp), Mat::zeros(temp.size(), CV_32F)};
Mat result;
int p=0;
for(int i=0;i<w;i++)
{
temp.at<float>(0,0) = 0.0;
for(int j=0,r=1;j<h;j++,r++)
{
idx = j*w+i;
temp.at<float>(r,0) = (float) mod_diff[idx];
}
temp.at<float>(h+1,0)=0.0;
for(int j=h-1, r=h+2;j>=0;j--,r++)
{
idx = j*w+i;
temp.at<float>(r,0) = (float) (-1.0 * mod_diff[idx]);
}
merge(planes, 2, result);
dft(result,result,0,0);
Mat planes1[] = {Mat::zeros(result.size(), CV_32F), Mat::zeros(result.size(), CV_32F)};
split(result, planes1);
std::complex<double> two_i = std::sqrt(std::complex<double>(-1));
double factor = -2*imag(two_i);
for(int c=1,z=0;c<h+1;c++,z++)
{
res.at<float>(z,0) = (float) (planes1[1].at<float>(c,0)/factor);
}
for(int q=0,z=0;q<h;q++,z++)
{
idx = q*w+p;
sineTransform[idx] = res.at<float>(z,0);
}
p++;
}
}
void Cloning::idst(double *mod_diff, double *sineTransform,int h,int w)
{
int nn = h+1;
unsigned long int idx;
dst(mod_diff,sineTransform,h,w);
for(int i= 0;i<h;i++)
for(int j=0;j<w;j++)
{
idx = i*w + j;
sineTransform[idx] = (double) (2*sineTransform[idx])/nn;
}
}
void Cloning::transpose(double *mat, double *mat_t,int h,int w)
{
Mat tmp = Mat(h,w,CV_32FC1);
unsigned long int idx;
for(int i = 0 ; i < h;i++)
{
for(int j = 0 ; j < w; j++)
{
idx = i*(w) + j;
tmp.at<float>(i,j) = (float) mat[idx];
}
}
Mat tmp_t = tmp.t();
for(int i = 0;i < tmp_t.size().height; i++)
for(int j=0;j<tmp_t.size().width;j++)
{
idx = i*tmp_t.size().width + j;
mat_t[idx] = tmp_t.at<float>(i,j);
}
}
#include <vector>
void Cloning::solve(const Mat &img, double *mod_diff, Mat &result)
namespace cv
{
int w = img.size().width;
int h = img.size().height;
unsigned long int idx,idx1;
double *sineTransform = new double[(h-2)*(w-2)];
double *sineTransform_t = new double[(h-2)*(w-2)];
double *denom = new double[(h-2)*(w-2)];
double *invsineTransform = new double[(h-2)*(w-2)];
double *invsineTransform_t = new double[(h-2)*(w-2)];
double *img_d = new double[(h)*(w)];
dst(mod_diff,sineTransform,h-2,w-2);
transpose(sineTransform,sineTransform_t,h-2,w-2);
dst(sineTransform_t,sineTransform,w-2,h-2);
transpose(sineTransform,sineTransform_t,w-2,h-2);
int cy = 1;
for(int i = 0 ; i < w-2;i++,cy++)
{
for(int j = 0,cx = 1; j < h-2; j++,cx++)
{
idx = j*(w-2) + i;
denom[idx] = (float) 2*cos(CV_PI*cy/( (double) (w-1))) - 2 + 2*cos(CV_PI*cx/((double) (h-1))) - 2;
}
}
for(idx = 0 ; idx < (unsigned)(w-2)*(h-2) ;idx++)
class Cloning
{
sineTransform_t[idx] = sineTransform_t[idx]/denom[idx];
}
idst(sineTransform_t,invsineTransform,h-2,w-2);
transpose(invsineTransform,invsineTransform_t,h-2,w-2);
idst(invsineTransform_t,invsineTransform,w-2,h-2);
transpose(invsineTransform,invsineTransform_t,w-2,h-2);
for(int i = 0 ; i < h;i++)
{
for(int j = 0 ; j < w; j++)
{
idx = i*w + j;
img_d[idx] = (double)img.at<uchar>(i,j);
}
}
for(int i = 1 ; i < h-1;i++)
{
for(int j = 1 ; j < w-1; j++)
{
idx = i*w + j;
img_d[idx] = 0.0;
}
}
for(int i = 1,id1=0 ; i < h-1;i++,id1++)
{
for(int j = 1,id2=0 ; j < w-1; j++,id2++)
{
idx = i*w + j;
idx1= id1*(w-2) + id2;
img_d[idx] = invsineTransform_t[idx1];
}
}
for(int i = 0 ; i < h;i++)
{
for(int j = 0 ; j < w; j++)
{
idx = i*w + j;
if(img_d[idx] < 0.0)
result.at<uchar>(i,j) = 0;
else if(img_d[idx] > 255.0)
result.at<uchar>(i,j) = 255;
else
result.at<uchar>(i,j) = (uchar) img_d[idx];
}
}
delete [] sineTransform;
delete [] sineTransform_t;
delete [] denom;
delete [] invsineTransform;
delete [] invsineTransform_t;
delete [] img_d;
}
void Cloning::poisson_solver(const Mat &img, Mat &gxx , Mat &gyy, Mat &result)
{
int w = img.size().width;
int h = img.size().height;
unsigned long int idx;
Mat lap = Mat(img.size(),CV_32FC1);
lap = gxx + gyy;
Mat bound = img.clone();
rectangle(bound, Point(1, 1), Point(img.cols-2, img.rows-2), Scalar::all(0), -1);
double *boundary_point = new double[h*w];
for(int i =1;i<h-1;i++)
for(int j=1;j<w-1;j++)
{
idx=i*w + j;
boundary_point[idx] = -4*(int)bound.at<uchar>(i,j) + (int)bound.at<uchar>(i,(j+1)) + (int)bound.at<uchar>(i,(j-1))
+ (int)bound.at<uchar>(i-1,j) + (int)bound.at<uchar>(i+1,j);
}
Mat diff = Mat(h,w,CV_32FC1);
for(int i =0;i<h;i++)
{
for(int j=0;j<w;j++)
{
idx = i*w+j;
diff.at<float>(i,j) = (float) (lap.at<float>(i,j) - boundary_point[idx]);
}
}
double *mod_diff = new double[(h-2)*(w-2)];
for(int i = 0 ; i < h-2;i++)
{
for(int j = 0 ; j < w-2; j++)
{
idx = i*(w-2) + j;
mod_diff[idx] = diff.at<float>(i+1,j+1);
}
}
///////////////////////////////////////////////////// Find DST /////////////////////////////////////////////////////
solve(img,mod_diff,result);
delete [] mod_diff;
delete [] boundary_point;
}
void Cloning::init_var(Mat &I, Mat &wmask)
{
grx = Mat(I.size(),CV_32FC3);
gry = Mat(I.size(),CV_32FC3);
sgx = Mat(I.size(),CV_32FC3);
sgy = Mat(I.size(),CV_32FC3);
split(I,rgb_channel);
smask = Mat(wmask.size(),CV_32FC1);
srx32 = Mat(I.size(),CV_32FC3);
sry32 = Mat(I.size(),CV_32FC3);
smask1 = Mat(wmask.size(),CV_32FC1);
grx32 = Mat(I.size(),CV_32FC3);
gry32 = Mat(I.size(),CV_32FC3);
}
void Cloning::initialization(Mat &I, Mat &mask, Mat &wmask)
{
init_var(I,wmask);
getGradientx(I,grx);
getGradienty(I,gry);
getGradientx(mask,sgx);
getGradienty(mask,sgy);
Mat Kernel(Size(3, 3), CV_8UC1);
Kernel.setTo(Scalar(1));
erode(wmask, wmask, Kernel, Point(-1,-1), 3);
wmask.convertTo(smask,CV_32FC1,1.0/255.0);
I.convertTo(srx32,CV_32FC3,1.0/255.0);
I.convertTo(sry32,CV_32FC3,1.0/255.0);
}
void Cloning::scalar_product(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::array_product(Mat mat1, Mat mat2, Mat mat3)
{
vector <Mat> channels_temp1;
vector <Mat> channels_temp2;
split(mat1,channels_temp1);
split(mat2,channels_temp2);
multiply(channels_temp2[2],mat3,channels_temp1[2]);
multiply(channels_temp2[1],mat3,channels_temp1[1]);
multiply(channels_temp2[0],mat3,channels_temp1[0]);
merge(channels_temp1,mat1);
}
void Cloning::poisson(Mat &I, Mat &gx, Mat &gy, Mat &sx, Mat &sy)
{
Mat fx = Mat(I.size(),CV_32FC3);
Mat fy = Mat(I.size(),CV_32FC3);
fx = gx + sx;
fy = gy + sy;
Mat gxx = Mat(I.size(),CV_32FC3);
Mat gyy = Mat(I.size(),CV_32FC3);
lapx(fx,gxx);
lapy(fy,gyy);
split(gxx,rgbx_channel);
split(gyy,rgby_channel);
split(I,output);
poisson_solver(rgb_channel[2],rgbx_channel[2], rgby_channel[2],output[2]);
poisson_solver(rgb_channel[1],rgbx_channel[1], rgby_channel[1],output[1]);
poisson_solver(rgb_channel[0],rgbx_channel[0], rgby_channel[0],output[0]);
}
void Cloning::evaluate(Mat &I, Mat &wmask, Mat &cloned)
{
bitwise_not(wmask,wmask);
wmask.convertTo(smask1,CV_32FC1,1.0/255.0);
I.convertTo(grx32,CV_32FC3,1.0/255.0);
I.convertTo(gry32,CV_32FC3,1.0/255.0);
array_product(grx32,grx,smask1);
array_product(gry32,gry,smask1);
poisson(I,grx32,gry32,srx32,sry32);
merge(output,cloned);
}
void Cloning::normal_clone(Mat &I, Mat &mask, Mat &wmask, Mat &cloned, int num)
{
int w = I.size().width;
int h = I.size().height;
int channel = I.channels();
initialization(I,mask,wmask);
if(num == 1)
{
array_product(srx32,sgx,smask);
array_product(sry32,sgy,smask);
}
else if(num == 2)
{
for(int i=0;i < h; i++)
{
for(int j=0; j < w; j++)
{
for(int c=0;c<channel;++c)
{
if(abs(sgx.at<float>(i,j*channel+c) - sgy.at<float>(i,j*channel+c)) >
abs(grx.at<float>(i,j*channel+c) - gry.at<float>(i,j*channel+c)))
{
srx32.at<float>(i,j*channel+c) = sgx.at<float>(i,j*channel+c)
* smask.at<float>(i,j);
sry32.at<float>(i,j*channel+c) = sgy.at<float>(i,j*channel+c)
* smask.at<float>(i,j);
}
else
{
srx32.at<float>(i,j*channel+c) = grx.at<float>(i,j*channel+c)
* smask.at<float>(i,j);
sry32.at<float>(i,j*channel+c) = gry.at<float>(i,j*channel+c)
* smask.at<float>(i,j);
}
}
}
}
}
else if(num == 3)
{
Mat gray = Mat(mask.size(),CV_8UC1);
Mat gray8 = Mat(mask.size(),CV_8UC3);
cvtColor(mask, gray, COLOR_BGR2GRAY );
vector <Mat> temp;
split(I,temp);
gray.copyTo(temp[2]);
gray.copyTo(temp[1]);
gray.copyTo(temp[0]);
merge(temp,gray8);
getGradientx(gray8,sgx);
getGradienty(gray8,sgy);
array_product(srx32,sgx,smask);
array_product(sry32,sgy,smask);
}
evaluate(I,wmask,cloned);
}
void Cloning::local_color_change(Mat &I, Mat &mask, Mat &wmask, Mat &cloned, float red_mul=1.0,
float green_mul=1.0, float blue_mul=1.0)
{
initialization(I,mask,wmask);
array_product(srx32,sgx,smask);
array_product(sry32,sgy,smask);
scalar_product(srx32,red_mul,green_mul,blue_mul);
scalar_product(sry32,red_mul,green_mul,blue_mul);
evaluate(I,wmask,cloned);
}
void Cloning::illum_change(Mat &I, Mat &mask, Mat &wmask, Mat &cloned, float alpha, float beta)
{
initialization(I,mask,wmask);
array_product(srx32,sgx,smask);
array_product(sry32,sgy,smask);
Mat mag = Mat(I.size(),CV_32FC3);
magnitude(srx32,sry32,mag);
Mat multX, multY, multx_temp, multy_temp;
multiply(srx32,pow(alpha,beta),multX);
pow(mag,-1*beta, multx_temp);
multiply(multX,multx_temp,srx32);
patchNaNs(srx32);
multiply(sry32,pow(alpha,beta),multY);
pow(mag,-1*beta, multy_temp);
multiply(multY,multy_temp,sry32);
patchNaNs(sry32);
Mat zeroMask = (srx32 != 0);
srx32.copyTo(srx32, zeroMask);
sry32.copyTo(sry32, zeroMask);
evaluate(I,wmask,cloned);
}
void Cloning::texture_flatten(Mat &I, Mat &mask, Mat &wmask, double low_threshold,
double high_threshold, int kernel_size, Mat &cloned)
{
initialization(I,mask,wmask);
Mat out = Mat(mask.size(),CV_8UC1);
Canny(mask,out,low_threshold,high_threshold,kernel_size);
Mat zeros(sgx.size(), CV_32FC3);
zeros.setTo(0);
Mat zerosMask = (out != 255);
zeros.copyTo(sgx, zerosMask);
zeros.copyTo(sgy, zerosMask);
array_product(srx32,sgx,smask);
array_product(sry32,sgy,smask);
evaluate(I,wmask,cloned);
}
public:
void normalClone(const cv::Mat& destination, const cv::Mat &mask, const cv::Mat &wmask, cv::Mat &cloned, int flag);
void illuminationChange(cv::Mat &I, cv::Mat &mask, cv::Mat &wmask, cv::Mat &cloned, float alpha, float beta);
void localColorChange(cv::Mat &I, cv::Mat &mask, cv::Mat &wmask, cv::Mat &cloned, float red_mul, float green_mul, float blue_mul);
void textureFlatten(cv::Mat &I, cv::Mat &mask, cv::Mat &wmask, float low_threshold, float high_threhold, int kernel_size, cv::Mat &cloned);
protected:
void initVariables(const cv::Mat &destination, const cv::Mat &binaryMask);
void computeDerivatives(const cv::Mat &destination, const cv::Mat &patch, const cv::Mat &binaryMask);
void scalarProduct(cv::Mat mat, float r, float g, float b);
void poisson(const cv::Mat &destination);
void evaluate(const cv::Mat &I, const cv::Mat &wmask, const cv::Mat &cloned);
void dst(const Mat& src, Mat& dest, bool invert = false);
void idst(const Mat& src, Mat& dest);
void solve(const Mat &img, Mat& mod_diff, Mat &result);
void poissonSolver(const cv::Mat &img, cv::Mat &gxx , cv::Mat &gyy, cv::Mat &result);
void arrayProduct(const cv::Mat& lhs, const cv::Mat& rhs, cv::Mat& result) const;
void computeGradientX(const cv::Mat &img, cv::Mat &gx);
void computeGradientY(const cv::Mat &img, cv::Mat &gy);
void computeLaplacianX(const cv::Mat &img, cv::Mat &gxx);
void computeLaplacianY(const cv::Mat &img, cv::Mat &gyy);
private:
std::vector <cv::Mat> rgbx_channel, rgby_channel, output;
cv::Mat destinationGradientX, destinationGradientY;
cv::Mat patchGradientX, patchGradientY;
cv::Mat binaryMaskFloat, binaryMaskFloatInverted;
std::vector<float> filter_X, filter_Y;
};
}
#endif
\ No newline at end of file
modules/photo/src/seamless_cloning_impl.cpp 0 → 100644
浏览文件 @ d052d863
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// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#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)
{
Mat tmp[3];
for(int chan = 0 ; chan < 3 ; ++chan)
{
filter2D(img, tmp[chan], CV_32F, kernel);
}
merge(tmp, 3, gx);
}
}
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)
{
Mat tmp[3];
for(int chan = 0 ; chan < 3 ; ++chan)
{
filter2D(img, tmp[chan], CV_32F, kernel);
}
merge(tmp, 3, gy);
}
}
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();
dest = Mat::zeros(src.size(), CV_32F);
temp(Rect( 0, 1, src.cols, src.rows)).copyTo(dest);
}
void Cloning::idst(const Mat& src, Mat& dest)
{
dst(src, dest, true);
}
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);
}
}
idst(res, mod_diff);
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 = Mat(img.size(),CV_32FC1);
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);
for(int i = 0 ; i < w-2 ; ++i)
filter_X[i] = 2.0f * std::cos(static_cast<float>(CV_PI) * (i + 1) / (w - 1));
const int h = destination.rows;
filter_Y.resize(h - 2);
for(int j = 0 ; j < h - 2 ; ++j)
filter_Y[j] = 2.0f * std::cos(static_cast<float>(CV_PI) * (j + 1) / (h - 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 = Mat(destination.size(),CV_32FC3);
Mat laplacianY = Mat(destination.size(),CV_32FC3);
laplacianX = destinationGradientX + patchGradientX;
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 = Mat(patch.size(),CV_8UC1);
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)
{
computeDerivatives(I,mask,wmask);
arrayProduct(patchGradientX,binaryMaskFloat, patchGradientX);
arrayProduct(patchGradientY,binaryMaskFloat, patchGradientY);
Mat mag = Mat(I.size(),CV_32FC3);
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 = Mat(mask.size(),CV_8UC1);
Canny(mask,out,low_threshold,high_threshold,kernel_size);
Mat zeros(patchGradientX.size(), CV_32FC3);
zeros.setTo(0);
Mat zerosMask = (out != 255);
zeros.copyTo(patchGradientX, zerosMask);
zeros.copyTo(patchGradientY, zerosMask);
arrayProduct(patchGradientX,binaryMaskFloat, patchGradientX);
arrayProduct(patchGradientY,binaryMaskFloat, patchGradientY);
evaluate(I,wmask,cloned);
}
modules/photo/test/test_cloning.cpp
浏览文件 @ d052d863
......@@ -39,6 +39,15 @@
//
//M*/
#define OUTPUT_SAVING 0
#if OUTPUT_SAVING
#define SAVE(x) std::vector<int> params;\
params.push_back(16);\
params.push_back(0);\
imwrite(folder + "output.png", x ,params);
#else
#define SAVE(x)
#endif
#include "test_precomp.hpp"
#include "opencv2/photo.hpp"
......@@ -47,6 +56,7 @@
using namespace cv;
using namespace std;
static const double numerical_precision = 1000.;
TEST(Photo_SeamlessClone_normal, regression)
{
......@@ -69,8 +79,13 @@ TEST(Photo_SeamlessClone_normal, regression)
p.y = destination.size().height/2;
seamlessClone(source, destination, mask, p, result, 1);
imwrite(folder + "cloned.png", result);
Mat reference = imread(folder + "reference.png");
SAVE(result);
double error = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(error, numerical_precision);
}
TEST(Photo_SeamlessClone_mixed, regression)
......@@ -94,7 +109,11 @@ TEST(Photo_SeamlessClone_mixed, regression)
p.y = destination.size().height/2;
seamlessClone(source, destination, mask, p, result, 2);
imwrite(folder + "cloned.png", result);
SAVE(result);
Mat reference = imread(folder + "reference.png");
double error = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(error, numerical_precision);
}
......@@ -119,7 +138,11 @@ TEST(Photo_SeamlessClone_featureExchange, regression)
p.y = destination.size().height/2;
seamlessClone(source, destination, mask, p, result, 3);
imwrite(folder + "cloned.png", result);
SAVE(result);
Mat reference = imread(folder + "reference.png");
double error = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(error, numerical_precision);
}
......@@ -138,7 +161,11 @@ TEST(Photo_SeamlessClone_colorChange, regression)
Mat result;
colorChange(source, mask, result, 1.5, .5, .5);
imwrite(folder + "cloned.png", result);
SAVE(result);
Mat reference = imread(folder + "reference.png");
double error = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(error, numerical_precision);
}
......@@ -157,7 +184,11 @@ TEST(Photo_SeamlessClone_illuminationChange, regression)
Mat result;
illuminationChange(source, mask, result, 0.2f, 0.4f);
imwrite(folder + "cloned.png", result);
SAVE(result);
Mat reference = imread(folder + "reference.png");
double error = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(error, numerical_precision);
}
......@@ -176,6 +207,10 @@ TEST(Photo_SeamlessClone_textureFlattening, regression)
Mat result;
textureFlattening(source, mask, result, 30, 45, 3);
imwrite(folder + "cloned.png", result);
SAVE(result);
Mat reference = imread(folder + "reference.png");
double error = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(error, numerical_precision);
}
modules/photo/test/test_decolor.cpp
浏览文件 @ d052d863
......@@ -47,6 +47,7 @@
using namespace cv;
using namespace std;
static const double numerical_precision = 10.;
TEST(Photo_Decolor, regression)
{
......@@ -61,7 +62,11 @@ TEST(Photo_Decolor, regression)
Mat grayscale, color_boost;
decolor(original, grayscale, color_boost);
imwrite(folder + "grayscale.png",grayscale);
imwrite(folder + "color_boost.png",color_boost);
Mat reference_grayscale = imread(folder + "grayscale_reference.png", 0 /* == grayscale image*/);
double error_grayscale = cvtest::norm(reference_grayscale, grayscale, NORM_L1);
EXPECT_LE(error_grayscale, numerical_precision);
Mat reference_boost = imread(folder + "boost_reference.png");
double error_boost = cvtest::norm(reference_boost, color_boost, NORM_L1);
EXPECT_LE(error_boost, numerical_precision);
}
modules/photo/test/test_npr.cpp
浏览文件 @ d052d863
......@@ -47,6 +47,7 @@
using namespace cv;
using namespace std;
static const double numerical_precision = 100.;
TEST(Photo_NPR_EdgePreserveSmoothing_RecursiveFilter, regression)
{
......@@ -60,8 +61,9 @@ TEST(Photo_NPR_EdgePreserveSmoothing_RecursiveFilter, regression)
Mat result;
edgePreservingFilter(source,result,1);
imwrite(folder + "smoothened_RF.png", result);
Mat reference = imread(folder + "smoothened_RF_reference.png");
double error = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(error, numerical_precision);
}
TEST(Photo_NPR_EdgePreserveSmoothing_NormConvFilter, regression)
......@@ -76,7 +78,9 @@ TEST(Photo_NPR_EdgePreserveSmoothing_NormConvFilter, regression)
Mat result;
edgePreservingFilter(source,result,2);
imwrite(folder + "smoothened_NCF.png", result);
Mat reference = imread(folder + "smoothened_NCF_reference.png");
double error = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(error, numerical_precision);
}
......@@ -92,8 +96,9 @@ TEST(Photo_NPR_DetailEnhance, regression)
Mat result;
detailEnhance(source,result);
imwrite(folder + "detail_enhanced.png", result);
Mat reference = imread(folder + "detail_enhanced_reference.png");
double error = cvtest::norm(reference, result, NORM_L1);
EXPECT_LE(error, numerical_precision);
}
TEST(Photo_NPR_PencilSketch, regression)
......@@ -105,12 +110,16 @@ TEST(Photo_NPR_PencilSketch, regression)
ASSERT_FALSE(source.empty()) << "Could not load input image " << original_path;
Mat result,result1;
pencilSketch(source,result,result1, 10, 0.1f, 0.03f);
Mat pencil_result, color_pencil_result;
pencilSketch(source,pencil_result, color_pencil_result, 10, 0.1f, 0.03f);
imwrite(folder + "pencil_sketch.png", result);
imwrite(folder + "color_pencil_sketch.png", result1);
Mat pencil_reference = imread(folder + "pencil_sketch_reference.png", 0 /* == grayscale*/);
double pencil_error = norm(pencil_reference, pencil_result, NORM_L1);
EXPECT_LE(pencil_error, numerical_precision);
Mat color_pencil_reference = imread(folder + "color_pencil_sketch_reference.png");
double color_pencil_error = cvtest::norm(color_pencil_reference, color_pencil_result, NORM_L1);
EXPECT_LE(color_pencil_error, numerical_precision);
}
TEST(Photo_NPR_Stylization, regression)
......@@ -125,6 +134,8 @@ TEST(Photo_NPR_Stylization, regression)
Mat result;
stylization(source,result);
imwrite(folder + "stylized.png", result);
Mat stylized_reference = imread(folder + "stylized_reference.png");
double stylized_error = cvtest::norm(stylized_reference, result, NORM_L1);
EXPECT_LE(stylized_error, numerical_precision);
}
samples/cpp/cloning_gui.cpp
浏览文件 @ d052d863
......@@ -65,7 +65,7 @@ float alpha,beta;
float red, green, blue;
double low_t, high_t;
float low_t, high_t;
void source(int, int, int, int, void*);
void destination(int, int, int, int, void*);
......
samples/cpp/tutorial_code/photo/seamless_cloning/cloning_gui.cpp
浏览文件 @ d052d863
......@@ -64,7 +64,7 @@ float alpha,beta;
float red, green, blue;
double low_t, high_t;
float low_t, high_t;
void source(int, int, int, int, void*);
void destination(int, int, int, int, void*);
......
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