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Merge pull request #239 from sbokov/disparityFiltering

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Interface and demo for disparity filtering, basic WLS implementation
pull/180/head
Maksim Shabunin 10 years ago
parent 6123e89122 a70af9f7d6
commit 844c30e8b2
6 changed files with 831 additions and 1 deletions
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  1. 2
      modules/ximgproc/CMakeLists.txt
  2. 92
      modules/ximgproc/include/opencv2/ximgproc/disparity_filter.hpp
  3. 11
      modules/ximgproc/include/opencv2/ximgproc/edge_filter.hpp
  4. 258
      modules/ximgproc/samples/disparity_filtering.cpp
  5. 185
      modules/ximgproc/src/disparity_filters.cpp
  6. 284
      modules/ximgproc/src/wls_filter.cpp

2
modules/ximgproc/CMakeLists.txt
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@ -1,5 +1,5 @@
set(the_description "Extended image processing module. It includes edge-aware filters and etc.")
set(OPENCV_MODULE_IS_PART_OF_WORLD OFF)
ocv_define_module(ximgproc opencv_imgproc opencv_core opencv_highgui WRAP python)
ocv_define_module(ximgproc opencv_imgproc opencv_core opencv_highgui opencv_calib3d WRAP python)
target_link_libraries(opencv_ximgproc)

92
modules/ximgproc/include/opencv2/ximgproc/disparity_filter.hpp
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/*
* By downloading, copying, installing or using the software you agree to this license.
* If you do not agree to this license, do not download, install,
* copy or use the software.
*
*
* License Agreement
* For Open Source Computer Vision Library
* (3 - clause BSD License)
*
* Redistribution and use in source and binary forms, with or without modification,
* are permitted provided that the following conditions are met :
*
* *Redistributions of source code must retain the above copyright notice,
* this list of conditions and the following disclaimer.
*
* * Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and / or other materials provided with the distribution.
*
* * Neither the names of the copyright holders nor the names of the contributors
* may 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 copyright holders 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.
*/
#ifndef __OPENCV_DISPARITYFILTER_HPP__
#define __OPENCV_DISPARITYFILTER_HPP__
#ifdef __cplusplus
#include <opencv2/core.hpp>
namespace cv {
namespace ximgproc {
class CV_EXPORTS_W DisparityFilter : public Algorithm
{
public:
CV_WRAP virtual void filter(InputArray disparity_map, InputArray left_view, OutputArray filtered_disparity_map,Rect ROI) = 0;
};
/////////////////////////////////////////////////////////////////////////////////////////////////
class CV_EXPORTS_W DisparityDTFilter : public DisparityFilter
{
public:
CV_WRAP virtual double getSigmaSpatial() = 0;
CV_WRAP virtual void setSigmaSpatial(double _sigmaSpatial) = 0;
CV_WRAP virtual double getSigmaColor() = 0;
CV_WRAP virtual void setSigmaColor(double _sigmaColor) = 0;
};
CV_EXPORTS_W
Ptr<DisparityDTFilter> createDisparityDTFilter();
class CV_EXPORTS_W DisparityGuidedFilter : public DisparityFilter
{
public:
CV_WRAP virtual double getEps() = 0;
CV_WRAP virtual void setEps(double _eps) = 0;
CV_WRAP virtual int getRadius() = 0;
CV_WRAP virtual void setRadius(int _radius) = 0;
};
CV_EXPORTS_W
Ptr<DisparityGuidedFilter> createDisparityGuidedFilter();
class CV_EXPORTS_W DisparityWLSFilter : public DisparityFilter
{
CV_WRAP virtual double getLambda() = 0;
CV_WRAP virtual void setLambda(double _lambda) = 0;
CV_WRAP virtual double getSigmaColor() = 0;
CV_WRAP virtual void setSigmaColor(double _sigma_color) = 0;
};
CV_EXPORTS_W
Ptr<DisparityWLSFilter> createDisparityWLSFilter();
}
}
#endif
#endif

11
modules/ximgproc/include/opencv2/ximgproc/edge_filter.hpp
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@ -318,6 +318,17 @@ void jointBilateralFilter(InputArray joint, InputArray src, OutputArray dst, int
//! @}
class CV_EXPORTS_W WeightedLeastSquaresFilter : public Algorithm
{
public:
CV_WRAP virtual void filter(InputArray src, OutputArray dst) = 0;
virtual ~WeightedLeastSquaresFilter();
};
CV_EXPORTS_W Ptr<WeightedLeastSquaresFilter> createWeightedLeastSquaresFilter(InputArray guide, double lambda, double sigma_color, int num_iter=3);
CV_EXPORTS_W void weightedLeastSquaresFilter(InputArray guide, InputArray src, OutputArray dst, double lambda, double sigma_color, int num_iter=3);
}
}
#endif

258
modules/ximgproc/samples/disparity_filtering.cpp
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#include "opencv2/calib3d.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/core/utility.hpp"
#include "opencv2/ximgproc/disparity_filter.hpp"
#include <stdio.h>
#include <string>
#include <vector>
#include <map>
#if defined(_WIN32)
#include <direct.h>
#else
#include <sys/stat.h>
#endif
using namespace cv;
using namespace cv::ximgproc;
using namespace std;
#define UNKNOWN_DISPARITY 16320
static void print_help()
{
printf("\nDemo for disparity filtering, evaluating speed and performance of different filters\n");
printf("\nUsage: disparity_filtering.exe <path_to_dataset_folder> <path_to_results_folder>\n");
}
struct dataset_entry
{
string name;
string dataset_folder;
string left_file,right_file,GT_file;
dataset_entry(string _dataset_folder): dataset_folder(_dataset_folder){}
void readEntry(Mat& dst_left,Mat& dst_right,Mat& dst_GT)
{
dst_left = imread(dataset_folder+"/"+left_file, IMREAD_COLOR);
dst_right = imread(dataset_folder+"/"+right_file, IMREAD_COLOR);
Mat raw_disp = imread(dataset_folder+"/"+GT_file, IMREAD_COLOR);
dst_GT = Mat(raw_disp.rows,raw_disp.cols,CV_16S);
for(int i=0;i<raw_disp.rows;i++)
for(int j=0;j<raw_disp.cols;j++)
{
Vec3b bgrPixel = raw_disp.at<Vec3b>(i, j);
dst_GT.at<short>(i,j) = 64*bgrPixel.val[2]+bgrPixel.val[1]/4; //16-multiplied disparity
}
}
};
struct config
{
Ptr<StereoMatcher> matcher_instance;
Ptr<DisparityFilter> filter_instance;
config(Ptr<StereoMatcher> _matcher_instance,Ptr<DisparityFilter> _filter_instance)
{
matcher_instance = _matcher_instance;
filter_instance = _filter_instance;
}
config() {}
};
void operator>>(const FileNode& node,dataset_entry& entry);
double computeMSE(Mat& GT, Mat& src, Rect ROI);
double computeBadPixelPercent(Mat& GT, Mat& src, Rect ROI, int thresh=24/*1.5 pixels*/);
void getDisparityVis(Mat& disparity_map,Mat& dst);
Rect computeROI(Size2i src_sz, Ptr<StereoMatcher> matcher_instance);
void setConfigsForTesting(map<string,config>& cfgs);
void CreateDir(string path);
int main(int argc, char** argv)
{
if(argc < 3)
{
print_help();
return 0;
}
string dataset_folder(argv[1]);
string res_folder(argv[2]);
map<string,config> configs_for_testing;
setConfigsForTesting(configs_for_testing);
CreateDir(res_folder);
for (map<string,config>::iterator cfg = configs_for_testing.begin(); cfg != configs_for_testing.end(); cfg++)
{
string vis_folder = res_folder+"/vis_"+cfg->first;
CreateDir(vis_folder);
string cfg_file_name = res_folder+"/"+cfg->first+"_res.csv";
FILE* cur_cfg_res_file = fopen(cfg_file_name.c_str(),"w");
fprintf(cur_cfg_res_file,"Name,MSE,MSE after postfiltering,Percent bad,Percent bad after postfiltering,Matcher Execution Time(s),Filter Execution Time(s)\n");
printf("Processing configuration: %s\n",cfg->first.c_str());
FileStorage fs(dataset_folder + "/_dataset.xml", FileStorage::READ);
FileNode n = fs["data_set"];
double MSE_pre,percent_pre,MSE_post,percent_post,matching_time,filtering_time;
double average_MSE_pre=0,average_percent_pre=0,average_MSE_post=0,
average_percent_post=0,average_matching_time=0,average_filtering_time=0;
int cnt = 0;
for (FileNodeIterator it = n.begin(); it != n.end(); it++)
{
dataset_entry entry(dataset_folder);
(*it)>>entry;
printf("%s ",entry.name.c_str());
Mat left,right,GT;
entry.readEntry(left,right,GT);
Mat raw_disp;
Mat left_gray; cvtColor(left, left_gray, COLOR_BGR2GRAY );
Mat right_gray; cvtColor(right, right_gray, COLOR_BGR2GRAY );
matching_time = (double)getTickCount();
cfg->second.matcher_instance->compute(left_gray,right_gray,raw_disp);
matching_time = ((double)getTickCount() - matching_time)/getTickFrequency();
Rect ROI = computeROI(left.size(),cfg->second.matcher_instance);
Mat filtered_disp;
filtering_time = (double)getTickCount();
cfg->second.filter_instance->filter(raw_disp,left,filtered_disp,ROI);
filtering_time = ((double)getTickCount() - filtering_time)/getTickFrequency();
MSE_pre = computeMSE(GT,raw_disp,ROI);
percent_pre = computeBadPixelPercent(GT,raw_disp,ROI);
MSE_post = computeMSE(GT,filtered_disp,ROI);
percent_post = computeBadPixelPercent(GT,filtered_disp,ROI);
fprintf(cur_cfg_res_file,"%s,%.1f,%.1f,%.1f,%.1f,%.3f,%.3f\n",entry.name.c_str(),MSE_pre,MSE_post,
percent_pre,percent_post,matching_time,filtering_time);
average_matching_time+=matching_time; average_filtering_time+=filtering_time;
average_MSE_pre+=MSE_pre; average_percent_pre+=percent_pre;
average_MSE_post+=MSE_post; average_percent_post+=percent_post;
cnt++;
// dump visualizations:
imwrite(vis_folder + "/" + entry.name + "_left.png",left);
Mat GT_vis,raw_disp_vis,filtered_disp_vis;
getDisparityVis(GT,GT_vis);
getDisparityVis(raw_disp,raw_disp_vis);
getDisparityVis(filtered_disp,filtered_disp_vis);
imwrite(vis_folder + "/" + entry.name + "_disparity_GT.png",GT_vis);
imwrite(vis_folder + "/" + entry.name + "_disparity_raw.png",raw_disp_vis);
imwrite(vis_folder + "/" + entry.name + "_disparity_filtered.png",filtered_disp_vis);
printf("- Done\n");
}
fprintf(cur_cfg_res_file,"%s,%.1f,%.1f,%.1f,%.1f,%.3f,%.3f\n","average",average_MSE_pre/cnt,
average_MSE_post/cnt,average_percent_pre/cnt,average_percent_post/cnt,
average_matching_time/cnt,average_filtering_time/cnt);
fclose(cur_cfg_res_file);
}
return 0;
}
void operator>>(const FileNode& node,dataset_entry& entry)
{
node["name"] >> entry.name;
node["left_file"] >> entry.left_file;
node["right_file"] >> entry.right_file;
node["GT_file"] >> entry.GT_file;
}
double computeMSE(Mat& GT, Mat& src, Rect ROI)
{
double res = 0;
Mat GT_ROI(GT,ROI);
Mat src_ROI(src,ROI);
int cnt=0;
for(int i=0;i<src_ROI.rows;i++)
for(int j=0;j<src_ROI.cols;j++)
{
if(GT_ROI.at<short>(i,j)!=UNKNOWN_DISPARITY)
{
res += (GT_ROI.at<short>(i,j) - src_ROI.at<short>(i,j))*(GT_ROI.at<short>(i,j) - src_ROI.at<short>(i,j));
cnt++;
}
}
res /= cnt*256;
return res;
}
double computeBadPixelPercent(Mat& GT, Mat& src, Rect ROI, int thresh)
{
int bad_pixel_num = 0;
Mat GT_ROI(GT,ROI);
Mat src_ROI(src,ROI);
int cnt=0;
for(int i=0;i<src_ROI.rows;i++)
for(int j=0;j<src_ROI.cols;j++)
{
if(GT_ROI.at<short>(i,j)!=UNKNOWN_DISPARITY)
{
if( abs(GT_ROI.at<short>(i,j) - src_ROI.at<short>(i,j))>=thresh )
bad_pixel_num++;
cnt++;
}
}
return (100.0*bad_pixel_num)/cnt;
}
void getDisparityVis(Mat& disparity_map,Mat& dst)
{
dst = Mat(disparity_map.rows,disparity_map.cols,CV_8UC3);
for(int i=0;i<dst.rows;i++)
for(int j=0;j<dst.cols;j++)
{
if(disparity_map.at<short>(i,j)==UNKNOWN_DISPARITY)
dst.at<Vec3b>(i,j) = Vec3b(0,0,0);
else
dst.at<Vec3b>(i,j) = Vec3b(saturate_cast<unsigned char>(disparity_map.at<short>(i,j)/8),
saturate_cast<unsigned char>(disparity_map.at<short>(i,j)/8),
saturate_cast<unsigned char>(disparity_map.at<short>(i,j)/8));
}
}
Rect computeROI(Size2i src_sz, Ptr<StereoMatcher> matcher_instance)
{
int min_disparity = matcher_instance->getMinDisparity();
int num_disparities = matcher_instance->getNumDisparities();
int block_size = matcher_instance->getBlockSize();
int bs2 = block_size/2;
int minD = min_disparity, maxD = min_disparity + num_disparities - 1;
int xmin = maxD + bs2;
int xmax = src_sz.width - minD - bs2;
int ymin = bs2;
int ymax = src_sz.height - bs2;
Rect r(xmin, ymin, xmax - xmin, ymax - ymin);
return r;
}
void setConfigsForTesting(map<string,config>& cfgs)
{
Ptr<StereoBM> stereobm_matcher = StereoBM::create(128,21);
stereobm_matcher->setTextureThreshold(0);
stereobm_matcher->setUniquenessRatio(0);
Ptr<DisparityFilter> wls_filter = createDisparityWLSFilter();
Ptr<DisparityFilter> dt_filter = createDisparityDTFilter();
Ptr<DisparityFilter> guided_filter = createDisparityGuidedFilter();
cfgs["stereobm_wls"] = config(stereobm_matcher,wls_filter);
cfgs["stereobm_dtf"] = config(stereobm_matcher,dt_filter);
cfgs["stereobm_gf"] = config(stereobm_matcher,guided_filter);
}
void CreateDir(string path)
{
#if defined(_WIN32)
_mkdir(path.c_str());
#else
mkdir(path.c_str(), 0777);
#endif
}

185
modules/ximgproc/src/disparity_filters.cpp
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/*
* By downloading, copying, installing or using the software you agree to this license.
* If you do not agree to this license, do not download, install,
* copy or use the software.
*
*
* License Agreement
* For Open Source Computer Vision Library
* (3 - clause BSD License)
*
* Redistribution and use in source and binary forms, with or without modification,
* are permitted provided that the following conditions are met :
*
* *Redistributions of source code must retain the above copyright notice,
* this list of conditions and the following disclaimer.
*
* * Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and / or other materials provided with the distribution.
*
* * Neither the names of the copyright holders nor the names of the contributors
* may 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 copyright holders 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.
*/
#include "precomp.hpp"
#include "opencv2/ximgproc/disparity_filter.hpp"
#include <math.h>
namespace cv {
namespace ximgproc {
void setROI(InputArray disparity_map, InputArray left_view, OutputArray filtered_disparity_map,
Mat& disp,Mat& src,Mat& dst,Rect ROI);
class DisparityDTFilterImpl : public DisparityDTFilter
{
protected:
double sigmaSpatial,sigmaColor;
void init(double _sigmaSpatial, double _sigmaColor)
{
sigmaColor = _sigmaColor;
sigmaSpatial = _sigmaSpatial;
}
public:
double getSigmaSpatial() {return sigmaSpatial;}
void setSigmaSpatial(double _sigmaSpatial) {sigmaSpatial = _sigmaSpatial;}
double getSigmaColor() {return sigmaColor;}
void setSigmaColor(double _sigmaColor) {sigmaColor = _sigmaColor;}
static Ptr<DisparityDTFilterImpl> create()
{
DisparityDTFilterImpl *dtf = new DisparityDTFilterImpl();
dtf->init(25.0,60.0);
return Ptr<DisparityDTFilterImpl>(dtf);
}
void filter(InputArray disparity_map, InputArray left_view, OutputArray filtered_disparity_map,Rect ROI)
{
Mat disp,src,dst;
setROI(disparity_map,left_view,filtered_disparity_map,disp,src,dst,ROI);
Mat disp_32F,dst_32F;
disp.convertTo(disp_32F,CV_32F);
dtFilter(src,disp_32F,dst_32F,sigmaSpatial,sigmaColor);
dst_32F.convertTo(dst,CV_16S);
}
};
CV_EXPORTS_W
Ptr<DisparityDTFilter> createDisparityDTFilter()
{
return Ptr<DisparityDTFilter>(DisparityDTFilterImpl::create());
}
//////////////////////////////////////////////////////////////////////////////////////////////////////
class DisparityGuidedFilterImpl : public DisparityGuidedFilter
{
protected:
int radius;
double eps;
void init(int _radius, double _eps)
{
radius = _radius;
eps = _eps;
}
public:
double getEps() {return eps;}
void setEps(double _eps) {eps = _eps;}
int getRadius() {return radius;}
void setRadius(int _radius) {radius = _radius;}
static Ptr<DisparityGuidedFilterImpl> create()
{
DisparityGuidedFilterImpl *gf = new DisparityGuidedFilterImpl();
gf->init(20,100.0);
return Ptr<DisparityGuidedFilterImpl>(gf);
}
void filter(InputArray disparity_map, InputArray left_view, OutputArray filtered_disparity_map,Rect ROI)
{
Mat disp,src,dst;
setROI(disparity_map,left_view,filtered_disparity_map,disp,src,dst,ROI);
Mat disp_32F,dst_32F;
disp.convertTo(disp_32F,CV_32F);
guidedFilter(src,disp_32F,dst_32F,radius,eps);
dst_32F.convertTo(dst,CV_16S);
}
};
CV_EXPORTS_W
Ptr<DisparityGuidedFilter> createDisparityGuidedFilter()
{
return Ptr<DisparityGuidedFilter>(DisparityGuidedFilterImpl::create());
}
//////////////////////////////////////////////////////////////////////////////////////////////////////
class DisparityWLSFilterImpl : public DisparityWLSFilter
{
protected:
double lambda,sigma_color;
void init(double _lambda, double _sigma_color)
{
lambda = _lambda;
sigma_color = _sigma_color;
}
public:
double getLambda() {return lambda;}
void setLambda(double _lambda) {lambda = _lambda;}
double getSigmaColor() {return sigma_color;}
void setSigmaColor(double _sigma_color) {sigma_color = _sigma_color;}
static Ptr<DisparityWLSFilterImpl> create()
{
DisparityWLSFilterImpl *wls = new DisparityWLSFilterImpl();
wls->init(500.0,2.0);
return Ptr<DisparityWLSFilterImpl>(wls);
}
void filter(InputArray disparity_map, InputArray left_view, OutputArray filtered_disparity_map,Rect ROI)
{
Mat disp,src,dst;
setROI(disparity_map,left_view,filtered_disparity_map,disp,src,dst,ROI);
weightedLeastSquaresFilter(src,disp,dst,lambda,sigma_color);
}
};
CV_EXPORTS_W
Ptr<DisparityWLSFilter> createDisparityWLSFilter()
{
return Ptr<DisparityWLSFilter>(DisparityWLSFilterImpl::create());
}
//////////////////////////////////////////////////////////////////////////////////////////////////////
void setROI(InputArray disparity_map, InputArray left_view, OutputArray filtered_disparity_map,
Mat& disp,Mat& src,Mat& dst,Rect ROI)
{
Mat disp_full_size = disparity_map.getMat();
Mat src_full_size = left_view.getMat();
CV_Assert( (disp_full_size.depth() == CV_16S) && (disp_full_size.channels() == 1) );
CV_Assert( (src_full_size.depth() == CV_8U) && (src_full_size.channels() <= 4) );
disp = Mat(disp_full_size,ROI);
src = Mat(src_full_size ,ROI);
filtered_disparity_map.create(disp_full_size.size(), disp_full_size.type());
Mat& dst_full_size = filtered_disparity_map.getMatRef();
dst = Mat(dst_full_size,ROI);
}
}
}

284
modules/ximgproc/src/wls_filter.cpp
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/*
* By downloading, copying, installing or using the software you agree to this license.
* If you do not agree to this license, do not download, install,
* copy or use the software.
*
*
* License Agreement
* For Open Source Computer Vision Library
* (3 - clause BSD License)
*
* Redistribution and use in source and binary forms, with or without modification,
* are permitted provided that the following conditions are met :
*
* *Redistributions of source code must retain the above copyright notice,
* this list of conditions and the following disclaimer.
*
* * Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and / or other materials provided with the distribution.
*
* * Neither the names of the copyright holders nor the names of the contributors
* may 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 copyright holders 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.
*/
#include "precomp.hpp"
namespace cv {
namespace ximgproc {
class WeightedLeastSquaresFilterImpl : public WeightedLeastSquaresFilter
{
public:
static Ptr<WeightedLeastSquaresFilterImpl> create(InputArray guide, double lambda, double sigma_color, int num_iter);
void filter(InputArray src, OutputArray dst);
~WeightedLeastSquaresFilterImpl();
protected:
int w,h;
double sigmaColor,lambda;
int num_iter;
double *weights_LUT;
double *Ahor, *Bhor, *Chor,
*Avert, *Bvert, *Cvert;
double *interD,*interE,*cur_res;
void init(InputArray guide,double _lambda,double _sigmaColor,int _num_iter);
void buildCoefMatrices(Mat& guide);
void horizontalPass(double* cur);
void verticalPass(double* cur);
};
void WeightedLeastSquaresFilterImpl::init(InputArray guide,double _lambda,double _sigmaColor,int _num_iter)
{
//currently support only 3 channel 8bit images as guides
CV_Assert( !guide.empty() && _lambda >= 0 && _sigmaColor >= 0 && _num_iter >=1 );
CV_Assert( guide.depth() == CV_8U && guide.channels() == 3 );
sigmaColor = _sigmaColor;
lambda = _lambda;
num_iter = _num_iter;
int num_levels = 3*256*256;
weights_LUT = new double[num_levels];
for(int i=0;i<num_levels;i++)
weights_LUT[i] = exp(-sqrt((double)i)/sigmaColor);
w = guide.cols();
h = guide.rows();
int sz = w*h;
Ahor = new double[sz];Bhor = new double[sz];Chor = new double[sz];
Avert = new double[sz];Bvert = new double[sz];Cvert = new double[sz];
interD = new double[sz];interE = new double[sz];cur_res = new double[sz];
Mat guideMat = guide.getMat();
buildCoefMatrices(guideMat);
}
Ptr<WeightedLeastSquaresFilterImpl> WeightedLeastSquaresFilterImpl::create(InputArray guide, double lambda, double sigma_color, int num_iter)
{
WeightedLeastSquaresFilterImpl *wls = new WeightedLeastSquaresFilterImpl();
wls->init(guide,lambda,sigma_color,num_iter);
return Ptr<WeightedLeastSquaresFilterImpl>(wls);
}
WeightedLeastSquaresFilter::~WeightedLeastSquaresFilter(){}
WeightedLeastSquaresFilterImpl::~WeightedLeastSquaresFilterImpl()
{
delete[] weights_LUT;
delete[] Ahor; delete[] Bhor; delete[] Chor;
delete[] Avert; delete[] Bvert; delete[] Cvert;
delete[] interD;delete[] interE;delete[] cur_res;
}
void WeightedLeastSquaresFilterImpl::buildCoefMatrices(Mat& guide)
{
double hor_weight;
const unsigned char *row,*row_prev,*row_next;
for(int i=0;i<h;i++)
{
//compute horizontal coefs:
row = guide.ptr(i);
Ahor[i*w] = 0;
hor_weight = weights_LUT[ (row[0]-row[3])*(row[0]-row[3])+
(row[1]-row[4])*(row[1]-row[4])+
(row[2]-row[5])*(row[2]-row[5]) ];
Chor[i*w] = -lambda*hor_weight;
Bhor[i*w] = 1 - Ahor[i*w] - Chor[i*w];
row+=3;
for(int j=1;j<w-1;j++)
{
Ahor[i*w+j] = -lambda*hor_weight;
hor_weight = weights_LUT[ (row[0]-row[3])*(row[0]-row[3])+
(row[1]-row[4])*(row[1]-row[4])+
(row[2]-row[5])*(row[2]-row[5]) ];
Chor[i*w+j] = -lambda*hor_weight;
Bhor[i*w+j] = 1 - Ahor[i*w+j] - Chor[i*w+j];
row+=3;
}
Ahor[i*w+w-1] = -lambda*hor_weight;
Chor[i*w+w-1] = 0;
Bhor[i*w+w-1] = 1 - Ahor[i*w+w-1] - Chor[i*w+w-1];
//compute vertical coefs:
row = guide.ptr(i);
if(i==0)
{
row_next = guide.ptr(i+1);
for(int j=0;j<w;j++)
{
Avert[i*w+j] = 0;
Cvert[i*w+j] = -lambda*weights_LUT[ (row[0]-row_next[0])*(row[0]-row_next[0])+
(row[1]-row_next[1])*(row[1]-row_next[1])+
(row[2]-row_next[2])*(row[2]-row_next[2]) ];
Bvert[i*w+j] = 1 - Avert[i*w+j] - Cvert[i*w+j];
row+=3;
row_next+=3;
}
}
else if(i==h-1)
{
row_prev = guide.ptr(i-1);
for(int j=0;j<w;j++)
{
Avert[i*w+j] = -lambda*weights_LUT[ (row[0]-row_prev[0])*(row[0]-row_prev[0])+
(row[1]-row_prev[1])*(row[1]-row_prev[1])+
(row[2]-row_prev[2])*(row[2]-row_prev[2]) ];
Cvert[i*w+j] = 0;
Bvert[i*w+j] = 1 - Avert[i*w+j] - Cvert[i*w+j];
row+=3;
row_prev+=3;
}
}
else
{
row_prev = guide.ptr(i-1);
row_next = guide.ptr(i+1);
for(int j=0;j<w;j++)
{
Avert[i*w+j] = -lambda*weights_LUT[ (row[0]-row_prev[0])*(row[0]-row_prev[0])+
(row[1]-row_prev[1])*(row[1]-row_prev[1])+
(row[2]-row_prev[2])*(row[2]-row_prev[2]) ];
Cvert[i*w+j] = -lambda*weights_LUT[ (row[0]-row_next[0])*(row[0]-row_next[0])+
(row[1]-row_next[1])*(row[1]-row_next[1])+
(row[2]-row_next[2])*(row[2]-row_next[2]) ];
Bvert[i*w+j] = 1 - Avert[i*w+j] - Cvert[i*w+j];
row+=3;
row_prev+=3;
row_next+=3;
}
}
}
}
void WeightedLeastSquaresFilterImpl::filter(InputArray src, OutputArray dst)
{
//temporarily support only one-channel CV_16S src type (for disparity map filtering)
CV_Assert(!src.empty() && (src.depth() == CV_16S) && src.channels()==1);
if (src.rows() != h || src.cols() != w)
{
CV_Error(Error::StsBadSize, "Size of filtering image must be equal to size of guide image");
return;
}
Mat srcMat = src.getMat();
Mat& dstMat = dst.getMatRef();
short* row;
for(int i=0;i<h;i++)
{
row = (short*)srcMat.ptr(i);
for(int j=0;j<w;j++)
{
cur_res[i*w+j] = (double)(*row);
row++;
}
}
for(int n=0;n<num_iter;n++)
{
horizontalPass(cur_res);
verticalPass(cur_res);
}
for(int i=0;i<h;i++)
{
row = (short*)dstMat.ptr(i);
for(int j=0;j<w;j++)
{
*row = saturate_cast<short>(cur_res[i*w+j]);
row++;
}
}
}
void WeightedLeastSquaresFilterImpl::horizontalPass(double* cur)
{
double denom;
for(int i=0;i<h;i++)
{
//forward pass:
interD[i*w] = Chor[i*w]/Bhor[i*w];
interE[i*w] = cur[i*w] /Bhor[i*w];
for(int j=1;j<w;j++)
{
denom = Bhor[i*w+j]-interD[i*w+j-1]*Ahor[i*w+j];
interD[i*w+j] = Chor[i*w+j]/denom;
interE[i*w+j] = (cur[i*w+j]-interE[i*w+j-1]*Ahor[i*w+j])/denom;
}
//backward pass:
cur[i*w+w-1] = interE[i*w+w-1];
for(int j=w-2;j>=0;j--)
cur[i*w+j] = interE[i*w+j]-interD[i*w+j]*cur[i*w+j+1];
}
}
void WeightedLeastSquaresFilterImpl::verticalPass(double* cur)
{
double denom;
//forward pass:
for(int j=0;j<w;j++)
{
interD[j] = Cvert[j]/Bvert[j];
interE[j] = cur[j]/Bvert[j];
}
for(int i=1;i<h;i++)
{
for(int j=0;j<w;j++)
{
denom = Bvert[i*w+j]-interD[(i-1)*w+j]*Avert[i*w+j];
interD[i*w+j] = Cvert[i*w+j]/denom;
interE[i*w+j] = (cur[i*w+j]-interE[(i-1)*w+j]*Avert[i*w+j])/denom;
}
}
//backward pass:
for(int j=0;j<w;j++)
cur[(h-1)*w+j] = interE[(h-1)*w+j];
for(int i=h-2;i>=0;i--)
for(int j=w-1;j>=0;j--)
cur[i*w+j] = interE[i*w+j]-interD[i*w+j]*cur[(i+1)*w+j];
}
////////////////////////////////////////////////////////////////////////////////////////////////
CV_EXPORTS_W
Ptr<WeightedLeastSquaresFilter> createWeightedLeastSquaresFilter(InputArray guide, double lambda, double sigma_color, int num_iter)
{
return Ptr<WeightedLeastSquaresFilter>(WeightedLeastSquaresFilterImpl::create(guide, lambda, sigma_color, num_iter));
}
CV_EXPORTS_W
void weightedLeastSquaresFilter(InputArray guide, InputArray src, OutputArray dst, double lambda, double sigma_color, int num_iter)
{
Ptr<WeightedLeastSquaresFilter> wls = createWeightedLeastSquaresFilter(guide, lambda, sigma_color, num_iter);
wls->filter(src, dst);
}
}
}
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