//By downloading, copying, installing or using the software you agree to this license.
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//If you do not agree to this license, do not download, install,
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//copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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// (3-clause BSD License)
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//
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//Copyright (C) 2000-2015, Intel Corporation, all rights reserved.
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//Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
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//Copyright (C) 2009-2015, NVIDIA Corporation, all rights reserved.
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//Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
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//Copyright (C) 2015, OpenCV Foundation, all rights reserved.
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//Copyright (C) 2015, Itseez Inc., all rights reserved.
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//Third party copyrights are property of their respective owners.
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//
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//Redistribution and use in source and binary forms, with or without modification,
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//are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the names of the copyright holders nor the names of the contributors
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// may be used to endorse or promote products derived from this software
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// without specific prior written permission.
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//This software is provided by the copyright holders and contributors "as is" and
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//any express or implied warranties, including, but not limited to, the implied
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//warranties of merchantability and fitness for a particular purpose are disclaimed.
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//In no event shall copyright holders or contributors be liable for any direct,
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//indirect, incidental, special, exemplary, or consequential damages
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//loss of use, data, or profits; or business interruption) however caused
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//and on any theory of liability, whether in contract, strict liability,
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//the use of this software, even if advised of the possibility of such damage.
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/*****************************************************************************************************************\
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* The interface contains the main descriptors that will be implemented in the descriptor class *
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\*****************************************************************************************************************/
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#include <stdint.h>
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#ifndef _OPENCV_DESCRIPTOR_HPP_
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#define _OPENCV_DESCRIPTOR_HPP_
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#ifdef __cplusplus
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namespace cv
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{
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namespace stereo
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{
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//types of supported kernels
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enum {
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CV_DENSE_CENSUS, CV_SPARSE_CENSUS,
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CV_CS_CENSUS, CV_MODIFIED_CS_CENSUS, CV_MODIFIED_CENSUS_TRANSFORM,
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CV_MEAN_VARIATION, CV_STAR_KERNEL
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};
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//!Mean Variation is a robust kernel that compares a pixel
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//!not just with the center but also with the mean of the window
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template<int num_images>
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struct MVKernel
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{
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uint8_t *image[num_images];
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int *integralImage[num_images];
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int stop;
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MVKernel(){}
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MVKernel(uint8_t **images, int **integral)
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{
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for(int i = 0; i < num_images; i++)
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{
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image[i] = images[i];
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integralImage[i] = integral[i];
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}
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stop = num_images;
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}
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void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int c[num_images]) const
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{
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CV_UNUSED(w2);
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for (int i = 0; i < stop; i++)
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{
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if (image[i][rrWidth + jj] > image[i][rWidth + j])
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{
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c[i] = c[i] + 1;
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}
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c[i] = c[i] << 1;
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if (integralImage[i][rrWidth + jj] > image[i][rWidth + j])
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{
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c[i] = c[i] + 1;
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}
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c[i] = c[i] << 1;
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}
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}
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};
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//!Compares pixels from a patch giving high weights to pixels in which
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//!the intensity is higher. The other pixels receive a lower weight
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template <int num_images>
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struct MCTKernel
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{
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uint8_t *image[num_images];
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int t,imageStop;
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MCTKernel(){}
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MCTKernel(uint8_t ** images, int threshold)
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{
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for(int i = 0; i < num_images; i++)
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{
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image[i] = images[i];
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}
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imageStop = num_images;
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t = threshold;
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}
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void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int c[num_images]) const
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{
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CV_UNUSED(w2);
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for(int i = 0; i < imageStop; i++)
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{
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if (image[i][rrWidth + jj] > image[i][rWidth + j] - t)
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{
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c[i] = c[i] << 1;
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c[i] = c[i] + 1;
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c[i] = c[i] << 1;
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c[i] = c[i] + 1;
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}
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else if (image[i][rWidth + j] - t < image[i][rrWidth + jj] && image[i][rWidth + j] + t >= image[i][rrWidth + jj])
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{
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c[i] = c[i] << 2;
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c[i] = c[i] + 1;
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}
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else
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{
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c[i] <<= 2;
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}
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}
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}
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};
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//!A madified cs census that compares a pixel with the imediat neightbour starting
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//!from the center
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template<int num_images>
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struct ModifiedCsCensus
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{
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uint8_t *image[num_images];
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int n2;
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int imageStop;
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ModifiedCsCensus(){}
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ModifiedCsCensus(uint8_t **images, int ker)
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{
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for(int i = 0; i < num_images; i++)
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image[i] = images[i];
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imageStop = num_images;
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n2 = ker;
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}
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void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int c[num_images]) const
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{
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CV_UNUSED(j);
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CV_UNUSED(rWidth);
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for(int i = 0; i < imageStop; i++)
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{
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if (image[i][(rrWidth + jj)] > image[i][(w2 + (jj + n2))])
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{
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c[i] = c[i] + 1;
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}
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c[i] = c[i] * 2;
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}
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}
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};
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//!A kernel in which a pixel is compared with the center of the window
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template<int num_images>
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struct CensusKernel
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{
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uint8_t *image[num_images];
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int imageStop;
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CensusKernel(){}
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CensusKernel(uint8_t **images)
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{
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for(int i = 0; i < num_images; i++)
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image[i] = images[i];
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imageStop = num_images;
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}
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void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int c[num_images]) const
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{
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CV_UNUSED(w2);
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for(int i = 0; i < imageStop; i++)
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{
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////compare a pixel with the center from the kernel
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if (image[i][rrWidth + jj] > image[i][rWidth + j])
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{
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c[i] += 1;
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}
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c[i] <<= 1;
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}
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}
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};
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//template clas which efficiently combines the descriptors
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template <int step_start, int step_end, int step_inc,int nr_img, typename Kernel>
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class CombinedDescriptor:public ParallelLoopBody
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{
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private:
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int width, height,n2;
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int stride_;
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int *dst[nr_img];
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Kernel kernel_;
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int n2_stop;
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public:
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CombinedDescriptor(int w, int h,int stride, int k2, int **distance, Kernel kernel,int k2Stop)
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{
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width = w;
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height = h;
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n2 = k2;
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stride_ = stride;
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for(int i = 0; i < nr_img; i++)
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dst[i] = distance[i];
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kernel_ = kernel;
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n2_stop = k2Stop;
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}
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void operator()(const cv::Range &r) const CV_OVERRIDE {
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for (int i = r.start; i <= r.end ; i++)
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{
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int rWidth = i * stride_;
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for (int j = n2 + 2; j <= width - n2 - 2; j++)
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{
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int c[nr_img];
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memset(c,0,nr_img);
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for(int step = step_start; step <= step_end; step += step_inc)
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{
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for (int ii = - n2; ii <= + n2_stop; ii += step)
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{
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int rrWidth = (ii + i) * stride_;
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int rrWidthC = (ii + i + n2) * stride_;
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for (int jj = j - n2; jj <= j + n2; jj += step)
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{
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if (ii != i || jj != j)
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{
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kernel_(rrWidth,rrWidthC, rWidth, jj, j,c);
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}
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}
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}
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}
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for(int l = 0; l < nr_img; l++)
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dst[l][rWidth + j] = c[l];
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}
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}
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}
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};
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//!calculate the mean of every windowSizexWindwoSize block from the integral Image
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//!this is a preprocessing for MV kernel
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class MeanKernelIntegralImage : public ParallelLoopBody
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{
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private:
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int *img;
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int windowSize,width;
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float scalling;
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int *c;
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public:
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MeanKernelIntegralImage(const cv::Mat &image, int window,float scale, int *cost):
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img((int *)image.data),windowSize(window) ,width(image.cols) ,scalling(scale) , c(cost){};
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void operator()(const cv::Range &r) const CV_OVERRIDE {
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for (int i = r.start; i <= r.end; i++)
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{
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int iw = i * width;
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for (int j = windowSize + 1; j <= width - windowSize - 1; j++)
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{
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c[iw + j] = (int)((img[(i + windowSize - 1) * width + j + windowSize - 1] + img[(i - windowSize - 1) * width + j - windowSize - 1]
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- img[(i + windowSize) * width + j - windowSize] - img[(i - windowSize) * width + j + windowSize]) * scalling);
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}
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}
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}
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};
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//!implementation for the star kernel descriptor
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template<int num_images>
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class StarKernelCensus:public ParallelLoopBody
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{
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private:
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uint8_t *image[num_images];
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int *dst[num_images];
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int n2, width, height, im_num,stride_;
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public:
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StarKernelCensus(const cv::Mat *img, int k2, int **distance)
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{
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for(int i = 0; i < num_images; i++)
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{
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image[i] = img[i].data;
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dst[i] = distance[i];
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}
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n2 = k2;
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width = img[0].cols;
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height = img[0].rows;
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im_num = num_images;
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stride_ = (int)img[0].step;
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}
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void operator()(const cv::Range &r) const CV_OVERRIDE {
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for (int i = r.start; i <= r.end ; i++)
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{
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int rWidth = i * stride_;
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for (int j = n2; j <= width - n2; j++)
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{
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for(int d = 0 ; d < im_num; d++)
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{
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int c = 0;
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for(int step = 4; step > 0; step--)
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{
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for (int ii = i - step; ii <= i + step; ii += step)
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{
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int rrWidth = ii * stride_;
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for (int jj = j - step; jj <= j + step; jj += step)
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{
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if (image[d][rrWidth + jj] > image[d][rWidth + j])
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{
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c = c + 1;
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}
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c = c * 2;
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}
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}
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}
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for (int ii = -1; ii <= +1; ii++)
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{
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int rrWidth = (ii + i) * stride_;
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if (i == -1)
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{
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if (ii + i != i)
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{
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if (image[d][rrWidth + j] > image[d][rWidth + j])
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{
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c = c + 1;
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}
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c = c * 2;
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}
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}
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else if (i == 0)
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{
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for (int j2 = -1; j2 <= 1; j2 += 2)
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{
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if (ii + i != i)
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{
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if (image[d][rrWidth + j + j2] > image[d][rWidth + j])
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{
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c = c + 1;
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}
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c = c * 2;
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}
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}
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}
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else
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{
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if (ii + i != i)
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{
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if (image[d][rrWidth + j] > image[d][rWidth + j])
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{
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c = c + 1;
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}
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c = c * 2;
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}
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}
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}
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dst[d][rWidth + j] = c;
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}
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}
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}
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}
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};
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//!paralel implementation of the center symetric census
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template <int num_images>
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class SymetricCensus:public ParallelLoopBody
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{
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private:
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uint8_t *image[num_images];
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int *dst[num_images];
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int n2, width, height, im_num,stride_;
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public:
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SymetricCensus(const cv::Mat *img, int k2, int **distance)
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{
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for(int i = 0; i < num_images; i++)
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{
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image[i] = img[i].data;
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dst[i] = distance[i];
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}
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n2 = k2;
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width = img[0].cols;
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height = img[0].rows;
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im_num = num_images;
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stride_ = (int)img[0].step;
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}
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void operator()(const cv::Range &r) const CV_OVERRIDE {
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for (int i = r.start; i <= r.end ; i++)
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{
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int distV = i*stride_;
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for (int j = n2; j <= width - n2; j++)
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{
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for(int d = 0; d < im_num; d++)
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{
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int c = 0;
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//the classic center symetric census which compares the curent pixel with its symetric not its center.
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for (int ii = -n2; ii <= 0; ii++)
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{
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int rrWidth = (ii + i) * stride_;
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for (int jj = -n2; jj <= +n2; jj++)
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{
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if (image[d][(rrWidth + (jj + j))] > image[d][((ii * (-1) + i) * width + (-1 * jj) + j)])
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{
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c = c + 1;
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}
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c = c * 2;
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if(ii == 0 && jj < 0)
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{
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if (image[d][(i * width + (jj + j))] > image[d][(i * width + (-1 * jj) + j)])
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{
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c = c + 1;
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}
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c = c * 2;
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}
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}
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}
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dst[d][(distV + j)] = c;
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}
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}
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}
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}
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};
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/**
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Two variations of census applied on input images
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Implementation of a census transform which is taking into account just the some pixels from the census kernel thus allowing for larger block sizes
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**/
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//void applyCensusOnImages(const cv::Mat &im1,const cv::Mat &im2, int kernelSize, cv::Mat &dist, cv::Mat &dist2, const int type);
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CV_EXPORTS void censusTransform(const cv::Mat &image1, const cv::Mat &image2, int kernelSize, cv::Mat &dist1, cv::Mat &dist2, const int type);
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//single image census transform
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CV_EXPORTS void censusTransform(const cv::Mat &image1, int kernelSize, cv::Mat &dist1, const int type);
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/**
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STANDARD_MCT - Modified census which is memorizing for each pixel 2 bits and includes a tolerance to the pixel comparison
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MCT_MEAN_VARIATION - Implementation of a modified census transform which is also taking into account the variation to the mean of the window not just the center pixel
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**/
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CV_EXPORTS void modifiedCensusTransform(const cv::Mat &img1, const cv::Mat &img2, int kernelSize, cv::Mat &dist1,cv::Mat &dist2, const int type, int t = 0 , const cv::Mat &IntegralImage1 = cv::Mat::zeros(100,100,CV_8UC1), const cv::Mat &IntegralImage2 = cv::Mat::zeros(100,100,CV_8UC1));
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//single version of modified census transform descriptor
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CV_EXPORTS void modifiedCensusTransform(const cv::Mat &img1, int kernelSize, cv::Mat &dist, const int type, int t = 0 ,const cv::Mat &IntegralImage = cv::Mat::zeros(100,100,CV_8UC1));
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/**The classical center symetric census
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A modified version of cs census which is comparing a pixel with its correspondent after the center
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**/
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CV_EXPORTS void symetricCensusTransform(const cv::Mat &img1, const cv::Mat &img2, int kernelSize, cv::Mat &dist1, cv::Mat &dist2, const int type);
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//single version of census transform
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CV_EXPORTS void symetricCensusTransform(const cv::Mat &img1, int kernelSize, cv::Mat &dist1, const int type);
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//in a 9x9 kernel only certain positions are choosen
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CV_EXPORTS void starCensusTransform(const cv::Mat &img1, const cv::Mat &img2, int kernelSize, cv::Mat &dist1,cv::Mat &dist2);
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//single image version of star kernel
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CV_EXPORTS void starCensusTransform(const cv::Mat &img1, int kernelSize, cv::Mat &dist);
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//integral image computation used in the Mean Variation Census Transform
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void imageMeanKernelSize(const cv::Mat &img, int windowSize, cv::Mat &c);
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}
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}
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#endif
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#endif
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/*End of file*/
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