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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)
//
//Copyright (C) 2000-2015, Intel Corporation, all rights reserved.
//Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
//Copyright (C) 2009-2015, NVIDIA Corporation, all rights reserved.
//Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
//Copyright (C) 2015, OpenCV Foundation, all rights reserved.
//Copyright (C) 2015, Itseez Inc., all rights reserved.
//Third party copyrights are property of their respective owners.
//
//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.
/*****************************************************************************************************************\
* The file contains the implemented descriptors *
\******************************************************************************************************************/
#include "precomp.hpp"
namespace cv
{
namespace stereo
{
//function that performs the census transform on two images.
//Two variants of census are offered a sparse version whcih takes every second pixel as well as dense version
CV_EXPORTS void censusTransform(const Mat &image1, const Mat &image2, int kernelSize, Mat &dist1, Mat &dist2, const int type)
{
CV_Assert(image1.size() == image2.size());
CV_Assert(kernelSize % 2 != 0);
CV_Assert(image1.type() == CV_8UC1 && image2.type() == CV_8UC1);
CV_Assert(type != CV_DENSE_CENSUS || type != CV_SPARSE_CENSUS);
CV_Assert(kernelSize <= ((type == 0) ? 5 : 11));
int n2 = (kernelSize) / 2;
uint8_t *images[] = {image1.data, image2.data};
int *costs[] = {(int *)dist1.data,(int *)dist2.data};
int stride = (int)image1.step;
if(type == CV_DENSE_CENSUS)
{
parallel_for_( Range(n2, image1.rows - n2),
CombinedDescriptor<1,1,1,2,CensusKernel<2> >(image1.cols, image1.rows,stride,n2,costs,CensusKernel<2>(images),n2));
}
else if(type == CV_SPARSE_CENSUS)
{
parallel_for_( Range(n2, image1.rows - n2),
CombinedDescriptor<2,2,1,2,CensusKernel<2> >(image1.cols, image1.rows, stride,n2,costs,CensusKernel<2>(images),n2));
}
}
//function that performs census on one image
CV_EXPORTS void censusTransform(const Mat &image1, int kernelSize, Mat &dist1, const int type)
{
CV_Assert(image1.size() == dist1.size());
CV_Assert(kernelSize % 2 != 0);
CV_Assert(image1.type() == CV_8UC1);
CV_Assert(type != CV_DENSE_CENSUS || type != CV_SPARSE_CENSUS);
CV_Assert(kernelSize <= ((type == 0) ? 5 : 11));
int n2 = (kernelSize) / 2;
uint8_t *images[] = {image1.data};
int *costs[] = {(int *)dist1.data};
int stride = (int)image1.step;
if(type == CV_DENSE_CENSUS)
{
parallel_for_( Range(n2, image1.rows - n2),
CombinedDescriptor<1,1,1,1,CensusKernel<1> >(image1.cols, image1.rows,stride,n2,costs,CensusKernel<1>(images),n2));
}
else if(type == CV_SPARSE_CENSUS)
{
parallel_for_( Range(n2, image1.rows - n2),
CombinedDescriptor<2,2,1,1,CensusKernel<1> >(image1.cols, image1.rows,stride,n2,costs,CensusKernel<1>(images),n2));
}
}
//in a 9x9 kernel only certain positions are choosen for comparison
CV_EXPORTS void starCensusTransform(const Mat &img1, const Mat &img2, int kernelSize, Mat &dist1, Mat &dist2)
{
CV_Assert(img1.size() == img2.size());
CV_Assert(kernelSize % 2 != 0);
CV_Assert(img1.type() == CV_8UC1 && img2.type() == CV_8UC1);
CV_Assert(kernelSize >= 7);
int n2 = (kernelSize) >> 1;
Mat images[] = {img1, img2};
int *date[] = { (int *)dist1.data, (int *)dist2.data};
parallel_for_(Range(n2, img1.rows - n2), StarKernelCensus<2>(images, n2,date));
}
//single version of star census
CV_EXPORTS void starCensusTransform(const Mat &img1, int kernelSize, Mat &dist)
{
CV_Assert(img1.size() == dist.size());
CV_Assert(kernelSize % 2 != 0);
CV_Assert(img1.type() == CV_8UC1);
CV_Assert(kernelSize >= 7);
int n2 = (kernelSize) >> 1;
Mat images[] = {img1};
int *date[] = { (int *)dist.data};
parallel_for_(Range(n2, img1.rows - n2), StarKernelCensus<1>(images, n2,date));
}
//Modified census transforms
//the first one deals with small illumination changes
//the sencond modified census transform is invariant to noise; i.e.
//if the current pixel with whom we are dooing the comparison is a noise, this descriptor will provide a better result by comparing with the mean of the window
//otherwise if the pixel is not noise the information is strengthend
CV_EXPORTS void modifiedCensusTransform(const Mat &img1, const Mat &img2, int kernelSize, Mat &dist1,Mat &dist2, const int type, int t, const Mat &IntegralImage1, const Mat &IntegralImage2 )
{
CV_Assert(img1.size() == img2.size());
CV_Assert(kernelSize % 2 != 0);
CV_Assert(img1.type() == CV_8UC1 && img2.type() == CV_8UC1);
CV_Assert(type != CV_MODIFIED_CENSUS_TRANSFORM || type != CV_MEAN_VARIATION);
CV_Assert(kernelSize <= 9);
int n2 = (kernelSize - 1) >> 1;
uint8_t *images[] = {img1.data, img2.data};
int *date[] = { (int *)dist1.data, (int *)dist2.data};
int stride = (int)img1.cols;
if(type == CV_MODIFIED_CENSUS_TRANSFORM)
{
//MCT
parallel_for_( Range(n2, img1.rows - n2),
CombinedDescriptor<2,4,2, 2,MCTKernel<2> >(img1.cols, img1.rows,stride,n2,date,MCTKernel<2>(images,t),n2));
}
else if(type == CV_MEAN_VARIATION)
{
//MV
int *integral[2];
integral[0] = (int *)IntegralImage1.data;
integral[1] = (int *)IntegralImage2.data;
parallel_for_( Range(n2, img1.rows - n2),
CombinedDescriptor<2,3,2,2, MVKernel<2> >(img1.cols, img1.rows,stride,n2,date,MVKernel<2>(images,integral),n2));
}
}
CV_EXPORTS void modifiedCensusTransform(const Mat &img1, int kernelSize, Mat &dist, const int type, int t , Mat const &IntegralImage)
{
CV_Assert(img1.size() == dist.size());
CV_Assert(kernelSize % 2 != 0);
CV_Assert(img1.type() == CV_8UC1);
CV_Assert(type != CV_MODIFIED_CENSUS_TRANSFORM || type != CV_MEAN_VARIATION);
CV_Assert(kernelSize <= 9);
int n2 = (kernelSize - 1) >> 1;
uint8_t *images[] = {img1.data};
int *date[] = { (int *)dist.data};
int stride = (int)img1.step;
if(type == CV_MODIFIED_CENSUS_TRANSFORM)
{
//MCT
parallel_for_(Range(n2, img1.rows - n2),
CombinedDescriptor<2,4,2, 1,MCTKernel<1> >(img1.cols, img1.rows,stride,n2,date,MCTKernel<1>(images,t),n2));
}
else if(type == CV_MEAN_VARIATION)
{
//MV
int *integral[] = { (int *)IntegralImage.data};
parallel_for_(Range(n2, img1.rows - n2),
CombinedDescriptor<2,3,2,1, MVKernel<1> >(img1.cols, img1.rows,stride,n2,date,MVKernel<1>(images,integral),n2));
}
}
//different versions of simetric census
//These variants since they do not compare with the center they are invariant to noise
CV_EXPORTS void symetricCensusTransform(const Mat &img1, const Mat &img2, int kernelSize, Mat &dist1, Mat &dist2, const int type)
{
CV_Assert(img1.size() == img2.size());
CV_Assert(kernelSize % 2 != 0);
CV_Assert(img1.type() == CV_8UC1 && img2.type() == CV_8UC1);
CV_Assert(type != CV_CS_CENSUS || type != CV_MODIFIED_CS_CENSUS);
CV_Assert(kernelSize <= 7);
int n2 = kernelSize >> 1;
uint8_t *images[] = {img1.data, img2.data};
Mat imag[] = {img1, img2};
int *date[] = { (int *)dist1.data, (int *)dist2.data};
int stride = (int)img1.step;
if(type == CV_CS_CENSUS)
{
parallel_for_(Range(n2, img1.rows - n2), SymetricCensus<2>(imag, n2,date));
}
else if(type == CV_MODIFIED_CS_CENSUS)
{
parallel_for_(Range(n2, img1.rows - n2),
CombinedDescriptor<1,1,1,2,ModifiedCsCensus<2> >(img1.cols, img1.rows,stride,n2,date,ModifiedCsCensus<2>(images,n2),1));
}
}
CV_EXPORTS void symetricCensusTransform(const Mat &img1, int kernelSize, Mat &dist1, const int type)
{
CV_Assert(img1.size() == dist1.size());
CV_Assert(kernelSize % 2 != 0);
CV_Assert(img1.type() == CV_8UC1);
CV_Assert(type != CV_MODIFIED_CS_CENSUS || type != CV_CS_CENSUS);
CV_Assert(kernelSize <= 7);
int n2 = kernelSize >> 1;
uint8_t *images[] = {img1.data};
Mat imag[] = {img1};
int *date[] = { (int *)dist1.data};
int stride = (int)img1.step;
if(type == CV_CS_CENSUS)
{
parallel_for_( Range(n2, img1.rows - n2), SymetricCensus<1>(imag, n2,date));
}
else if(type == CV_MODIFIED_CS_CENSUS)
{
parallel_for_( Range(n2, img1.rows - n2),
CombinedDescriptor<1,1,1,1,ModifiedCsCensus<1> >(img1.cols, img1.rows,stride,n2,date,ModifiedCsCensus<1>(images,n2),1));
}
}
//integral image computation used in the Mean Variation Census Transform
void imageMeanKernelSize(const Mat &image, int windowSize, Mat &cost)
{
CV_Assert(!image.empty());
CV_Assert(!cost.empty());
CV_Assert(windowSize % 2 != 0);
int win = windowSize / 2;
float scalling = ((float) 1) / (windowSize * windowSize);
int height = image.rows;
cost.setTo(0);
int *c = (int *)cost.data;
parallel_for_(Range(win + 1, height - win - 1),MeanKernelIntegralImage(image,win,scalling,c));
}
}
}