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Commit 9c0e1525 authored by Alexander Alekhin's avatar Alexander Alekhin
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stereo: restore public descriptor.hpp

parents 4d57438c 20576d85
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modules/stereo/CMakeLists.txt
View file @ 9c0e1525
set(the_description "Stereo Correspondence")
ocv_define_module(stereo opencv_imgproc opencv_features2d opencv_core opencv_calib3d)
ocv_define_module(stereo opencv_core opencv_imgproc opencv_calib3d)
modules/stereo/include/opencv2/stereo.hpp
View file @ 9c0e1525
......@@ -45,10 +45,7 @@
#define __OPENCV_STEREO_HPP__
#include "opencv2/core.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/core/affine.hpp"
#include "opencv2/stereo/descriptor.hpp"
#include "opencv2/stereo/matching.hpp"
/**
@defgroup stereo Stereo Correspondance Algorithms
......@@ -61,8 +58,6 @@ namespace cv
{
//! @addtogroup stereo
//! @{
// void correctMatches( InputArray F, InputArray points1, InputArray points2,
// OutputArray newPoints1, OutputArray newPoints2 );
/** @brief Filters off small noise blobs (speckles) in the disparity map
@param img The input 16-bit signed disparity image
@param newVal The disparity value used to paint-off the speckles
......
modules/stereo/include/opencv2/stereo/descriptor.hpp
View file @ 9c0e1525
//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.
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
/*****************************************************************************************************************\
* The interface contains the main descriptors that will be implemented in the descriptor class *
\*****************************************************************************************************************/
#ifndef _OPENCV_STEREO_DESCRIPTOR_HPP_
#define _OPENCV_STEREO_DESCRIPTOR_HPP_
#include <stdint.h>
#ifndef _OPENCV_DESCRIPTOR_HPP_
#define _OPENCV_DESCRIPTOR_HPP_
#ifdef __cplusplus
namespace cv { namespace stereo {
namespace cv
{
namespace stereo
{
//types of supported kernels
enum {
// FIXIT deprecate and remove CV_ prefix
/// types of supported kernels
enum {
CV_DENSE_CENSUS, CV_SPARSE_CENSUS,
CV_CS_CENSUS, CV_MODIFIED_CS_CENSUS, CV_MODIFIED_CENSUS_TRANSFORM,
CV_MEAN_VARIATION, CV_STAR_KERNEL
};
//!Mean Variation is a robust kernel that compares a pixel
//!not just with the center but also with the mean of the window
template<int num_images>
struct MVKernel
{
uint8_t *image[num_images];
int *integralImage[num_images];
int stop;
MVKernel(){}
MVKernel(uint8_t **images, int **integral)
{
for(int i = 0; i < num_images; i++)
{
image[i] = images[i];
integralImage[i] = integral[i];
}
stop = num_images;
}
void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int c[num_images]) const
{
CV_UNUSED(w2);
for (int i = 0; i < stop; i++)
{
if (image[i][rrWidth + jj] > image[i][rWidth + j])
{
c[i] = c[i] + 1;
}
c[i] = c[i] << 1;
if (integralImage[i][rrWidth + jj] > image[i][rWidth + j])
{
c[i] = c[i] + 1;
}
c[i] = c[i] << 1;
}
}
};
//!Compares pixels from a patch giving high weights to pixels in which
//!the intensity is higher. The other pixels receive a lower weight
template <int num_images>
struct MCTKernel
{
uint8_t *image[num_images];
int t,imageStop;
MCTKernel(){}
MCTKernel(uint8_t ** images, int threshold)
{
for(int i = 0; i < num_images; i++)
{
image[i] = images[i];
}
imageStop = num_images;
t = threshold;
}
void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int c[num_images]) const
{
CV_UNUSED(w2);
for(int i = 0; i < imageStop; i++)
{
if (image[i][rrWidth + jj] > image[i][rWidth + j] - t)
{
c[i] = c[i] << 1;
c[i] = c[i] + 1;
c[i] = c[i] << 1;
c[i] = c[i] + 1;
}
else if (image[i][rWidth + j] - t < image[i][rrWidth + jj] && image[i][rWidth + j] + t >= image[i][rrWidth + jj])
{
c[i] = c[i] << 2;
c[i] = c[i] + 1;
}
else
{
c[i] <<= 2;
}
}
}
};
//!A madified cs census that compares a pixel with the imediat neightbour starting
//!from the center
template<int num_images>
struct ModifiedCsCensus
{
uint8_t *image[num_images];
int n2;
int imageStop;
ModifiedCsCensus(){}
ModifiedCsCensus(uint8_t **images, int ker)
{
for(int i = 0; i < num_images; i++)
image[i] = images[i];
imageStop = num_images;
n2 = ker;
}
void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int c[num_images]) const
{
CV_UNUSED(j);
CV_UNUSED(rWidth);
for(int i = 0; i < imageStop; i++)
{
if (image[i][(rrWidth + jj)] > image[i][(w2 + (jj + n2))])
{
c[i] = c[i] + 1;
}
c[i] = c[i] * 2;
}
}
};
//!A kernel in which a pixel is compared with the center of the window
template<int num_images>
struct CensusKernel
{
uint8_t *image[num_images];
int imageStop;
CensusKernel(){}
CensusKernel(uint8_t **images)
{
for(int i = 0; i < num_images; i++)
image[i] = images[i];
imageStop = num_images;
}
void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int c[num_images]) const
{
CV_UNUSED(w2);
for(int i = 0; i < imageStop; i++)
{
////compare a pixel with the center from the kernel
if (image[i][rrWidth + jj] > image[i][rWidth + j])
{
c[i] += 1;
}
c[i] <<= 1;
}
}
};
//template clas which efficiently combines the descriptors
template <int step_start, int step_end, int step_inc,int nr_img, typename Kernel>
class CombinedDescriptor:public ParallelLoopBody
{
private:
int width, height,n2;
int stride_;
int *dst[nr_img];
Kernel kernel_;
int n2_stop;
public:
CombinedDescriptor(int w, int h,int stride, int k2, int **distance, Kernel kernel,int k2Stop)
{
width = w;
height = h;
n2 = k2;
stride_ = stride;
for(int i = 0; i < nr_img; i++)
dst[i] = distance[i];
kernel_ = kernel;
n2_stop = k2Stop;
}
void operator()(const cv::Range &r) const CV_OVERRIDE {
for (int i = r.start; i <= r.end ; i++)
{
int rWidth = i * stride_;
for (int j = n2 + 2; j <= width - n2 - 2; j++)
{
int c[nr_img];
memset(c, 0, sizeof(c[0]) * nr_img);
for(int step = step_start; step <= step_end; step += step_inc)
{
for (int ii = - n2; ii <= + n2_stop; ii += step)
{
int rrWidth = (ii + i) * stride_;
int rrWidthC = (ii + i + n2) * stride_;
for (int jj = j - n2; jj <= j + n2; jj += step)
{
if (ii != i || jj != j)
{
kernel_(rrWidth,rrWidthC, rWidth, jj, j,c);
}
}
}
}
for(int l = 0; l < nr_img; l++)
dst[l][rWidth + j] = c[l];
}
}
}
};
//!calculate the mean of every windowSizexWindwoSize block from the integral Image
//!this is a preprocessing for MV kernel
class MeanKernelIntegralImage : public ParallelLoopBody
{
private:
int *img;
int windowSize,width;
float scalling;
int *c;
public:
MeanKernelIntegralImage(const cv::Mat &image, int window,float scale, int *cost):
img((int *)image.data),windowSize(window) ,width(image.cols) ,scalling(scale) , c(cost){};
void operator()(const cv::Range &r) const CV_OVERRIDE {
for (int i = r.start; i <= r.end; i++)
{
int iw = i * width;
for (int j = windowSize + 1; j <= width - windowSize - 1; j++)
{
c[iw + j] = (int)((img[(i + windowSize - 1) * width + j + windowSize - 1] + img[(i - windowSize - 1) * width + j - windowSize - 1]
- img[(i + windowSize) * width + j - windowSize] - img[(i - windowSize) * width + j + windowSize]) * scalling);
}
}
}
};
//!implementation for the star kernel descriptor
template<int num_images>
class StarKernelCensus:public ParallelLoopBody
{
private:
uint8_t *image[num_images];
int *dst[num_images];
int n2, width, height, im_num,stride_;
public:
StarKernelCensus(const cv::Mat *img, int k2, int **distance)
{
for(int i = 0; i < num_images; i++)
{
image[i] = img[i].data;
dst[i] = distance[i];
}
n2 = k2;
width = img[0].cols;
height = img[0].rows;
im_num = num_images;
stride_ = (int)img[0].step;
}
void operator()(const cv::Range &r) const CV_OVERRIDE {
for (int i = r.start; i <= r.end ; i++)
{
int rWidth = i * stride_;
for (int j = n2; j <= width - n2; j++)
{
for(int d = 0 ; d < im_num; d++)
{
int c = 0;
for(int step = 4; step > 0; step--)
{
for (int ii = i - step; ii <= i + step; ii += step)
{
int rrWidth = ii * stride_;
for (int jj = j - step; jj <= j + step; jj += step)
{
if (image[d][rrWidth + jj] > image[d][rWidth + j])
{
c = c + 1;
}
c = c * 2;
}
}
}
for (int ii = -1; ii <= +1; ii++)
{
int rrWidth = (ii + i) * stride_;
if (i == -1)
{
if (ii + i != i)
{
if (image[d][rrWidth + j] > image[d][rWidth + j])
{
c = c + 1;
}
c = c * 2;
}
}
else if (i == 0)
{
for (int j2 = -1; j2 <= 1; j2 += 2)
{
if (ii + i != i)
{
if (image[d][rrWidth + j + j2] > image[d][rWidth + j])
{
c = c + 1;
}
c = c * 2;
}
}
}
else
{
if (ii + i != i)
{
if (image[d][rrWidth + j] > image[d][rWidth + j])
{
c = c + 1;
}
c = c * 2;
}
}
}
dst[d][rWidth + j] = c;
}
}
}
}
};
//!paralel implementation of the center symetric census
template <int num_images>
class SymetricCensus:public ParallelLoopBody
{
private:
uint8_t *image[num_images];
int *dst[num_images];
int n2, width, height, im_num,stride_;
public:
SymetricCensus(const cv::Mat *img, int k2, int **distance)
{
for(int i = 0; i < num_images; i++)
{
image[i] = img[i].data;
dst[i] = distance[i];
}
n2 = k2;
width = img[0].cols;
height = img[0].rows;
im_num = num_images;
stride_ = (int)img[0].step;
}
void operator()(const cv::Range &r) const CV_OVERRIDE {
for (int i = r.start; i <= r.end ; i++)
{
int distV = i*stride_;
for (int j = n2; j <= width - n2; j++)
{
for(int d = 0; d < im_num; d++)
{
int c = 0;
//the classic center symetric census which compares the curent pixel with its symetric not its center.
for (int ii = -n2; ii <= 0; ii++)
{
int rrWidth = (ii + i) * stride_;
for (int jj = -n2; jj <= +n2; jj++)
{
if (image[d][(rrWidth + (jj + j))] > image[d][((ii * (-1) + i) * width + (-1 * jj) + j)])
{
c = c + 1;
}
c = c * 2;
if(ii == 0 && jj < 0)
{
if (image[d][(i * width + (jj + j))] > image[d][(i * width + (-1 * jj) + j)])
{
c = c + 1;
}
c = c * 2;
}
}
}
dst[d][(distV + j)] = c;
}
}
}
}
};
/**
Two variations of census applied on input images
Implementation of a census transform which is taking into account just the some pixels from the census kernel thus allowing for larger block sizes
**/
//void applyCensusOnImages(const cv::Mat &im1,const cv::Mat &im2, int kernelSize, cv::Mat &dist, cv::Mat &dist2, const int type);
CV_EXPORTS void censusTransform(const cv::Mat &image1, const cv::Mat &image2, int kernelSize, cv::Mat &dist1, cv::Mat &dist2, const int type);
//single image census transform
CV_EXPORTS void censusTransform(const cv::Mat &image1, int kernelSize, cv::Mat &dist1, const int type);
/**
STANDARD_MCT - Modified census which is memorizing for each pixel 2 bits and includes a tolerance to the pixel comparison
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
**/
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));
//single version of modified census transform descriptor
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));
/**The classical center symetric census
A modified version of cs census which is comparing a pixel with its correspondent after the center
**/
CV_EXPORTS void symetricCensusTransform(const cv::Mat &img1, const cv::Mat &img2, int kernelSize, cv::Mat &dist1, cv::Mat &dist2, const int type);
//single version of census transform
CV_EXPORTS void symetricCensusTransform(const cv::Mat &img1, int kernelSize, cv::Mat &dist1, const int type);
//in a 9x9 kernel only certain positions are choosen
CV_EXPORTS void starCensusTransform(const cv::Mat &img1, const cv::Mat &img2, int kernelSize, cv::Mat &dist1,cv::Mat &dist2);
//single image version of star kernel
CV_EXPORTS void starCensusTransform(const cv::Mat &img1, int kernelSize, cv::Mat &dist);
//integral image computation used in the Mean Variation Census Transform
void imageMeanKernelSize(const cv::Mat &img, int windowSize, cv::Mat &c);
}
}
#endif
};
/**
Two variations of census applied on input images
Implementation of a census transform which is taking into account just the some pixels from the census kernel thus allowing for larger block sizes
**/
CV_EXPORTS void censusTransform(const Mat &image1, const Mat &image2, int kernelSize, Mat &dist1, Mat &dist2, const int type);
/// single image census transform
CV_EXPORTS void censusTransform(const Mat &image1, int kernelSize, Mat &dist1, const int type);
/**
STANDARD_MCT - Modified census which is memorizing for each pixel 2 bits and includes a tolerance to the pixel comparison
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
**/
CV_EXPORTS void modifiedCensusTransform(const Mat &img1, const Mat &img2, int kernelSize, Mat &dist1, Mat &dist2, const int type, int t = 0, const Mat &integralImage1 = Mat(), const Mat &integralImage2 = Mat());
///single version of modified census transform descriptor
CV_EXPORTS void modifiedCensusTransform(const Mat &img1, int kernelSize, Mat &dist, const int type, int t = 0, const Mat &integralImage = Mat());
/**The classical center symetric census
A modified version of cs census which is comparing a pixel with its correspondent after the center
**/
CV_EXPORTS void symetricCensusTransform(const Mat &img1, const Mat &img2, int kernelSize, Mat &dist1, Mat &dist2, const int type);
///single version of census transform
CV_EXPORTS void symetricCensusTransform(const Mat &img1, int kernelSize, Mat &dist1, const int type);
///in a 9x9 kernel only certain positions are choosen
CV_EXPORTS void starCensusTransform(const Mat &img1, const Mat &img2, int kernelSize, Mat &dist1, Mat &dist2);
///single image version of star kernel
CV_EXPORTS void starCensusTransform(const Mat &img1, int kernelSize, Mat &dist);
}} // namespace
#endif
/*End of file*/
modules/stereo/perf/perf_precomp.hpp
View file @ 9c0e1525
......@@ -6,9 +6,6 @@
#include "opencv2/ts.hpp"
#include "opencv2/stereo.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/core/utility.hpp"
#include "opencv2/calib3d.hpp"
namespace opencv_test {
using namespace cv::stereo;
......
modules/stereo/src/descriptor.cpp
View file @ 9c0e1525
......@@ -125,7 +125,7 @@ namespace cv
//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_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);
......@@ -145,14 +145,25 @@ namespace cv
else if(type == CV_MEAN_VARIATION)
{
//MV
int *integral[2];
integral[0] = (int *)IntegralImage1.data;
integral[1] = (int *)IntegralImage2.data;
CV_Assert(!integralImage1.empty());
CV_Assert(!integralImage1.isContinuous());
CV_CheckTypeEQ(integralImage1.type(), CV_32SC1, "");
CV_CheckGE(integralImage1.cols, img1.cols, "");
CV_CheckGE(integralImage1.rows, img1.rows, "");
CV_Assert(!integralImage2.empty());
CV_Assert(!integralImage2.isContinuous());
CV_CheckTypeEQ(integralImage2.type(), CV_32SC1, "");
CV_CheckGE(integralImage2.cols, img2.cols, "");
CV_CheckGE(integralImage2.rows, img2.rows, "");
int *integral[2] = {
(int *)integralImage1.data,
(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_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);
......@@ -172,7 +183,12 @@ namespace cv
else if(type == CV_MEAN_VARIATION)
{
//MV
int *integral[] = { (int *)IntegralImage.data};
CV_Assert(!integralImage.empty());
CV_Assert(!integralImage.isContinuous());
CV_CheckTypeEQ(integralImage.type(), CV_32SC1, "");
CV_CheckGE(integralImage.cols, img1.cols, "");
CV_CheckGE(integralImage.rows, img1.rows, "");
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));
}
......
modules/stereo/src/descriptor.hpp 0 → 100644
View file @ 9c0e1525
//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 interface contains the main descriptors that will be implemented in the descriptor class *
\*****************************************************************************************************************/
#include <stdint.h>
#ifndef _OPENCV_DESCRIPTOR_HPP_
#define _OPENCV_DESCRIPTOR_HPP_
#ifdef __cplusplus
namespace cv
{
namespace stereo
{
//!Mean Variation is a robust kernel that compares a pixel
//!not just with the center but also with the mean of the window
template<int num_images>
struct MVKernel
{
uint8_t *image[num_images];
int *integralImage[num_images];
int stop;
MVKernel(){}
MVKernel(uint8_t **images, int **integral)
{
for(int i = 0; i < num_images; i++)
{
image[i] = images[i];
integralImage[i] = integral[i];
}
stop = num_images;
}
void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int c[num_images]) const
{
CV_UNUSED(w2);
for (int i = 0; i < stop; i++)
{
if (image[i][rrWidth + jj] > image[i][rWidth + j])
{
c[i] = c[i] + 1;
}
c[i] = c[i] << 1;
if (integralImage[i][rrWidth + jj] > image[i][rWidth + j])
{
c[i] = c[i] + 1;
}
c[i] = c[i] << 1;
}
}
};
//!Compares pixels from a patch giving high weights to pixels in which
//!the intensity is higher. The other pixels receive a lower weight
template <int num_images>
struct MCTKernel
{
uint8_t *image[num_images];
int t,imageStop;
MCTKernel(){}
MCTKernel(uint8_t ** images, int threshold)
{
for(int i = 0; i < num_images; i++)
{
image[i] = images[i];
}
imageStop = num_images;
t = threshold;
}
void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int c[num_images]) const
{
CV_UNUSED(w2);
for(int i = 0; i < imageStop; i++)
{
if (image[i][rrWidth + jj] > image[i][rWidth + j] - t)
{
c[i] = c[i] << 1;
c[i] = c[i] + 1;
c[i] = c[i] << 1;
c[i] = c[i] + 1;
}
else if (image[i][rWidth + j] - t < image[i][rrWidth + jj] && image[i][rWidth + j] + t >= image[i][rrWidth + jj])
{
c[i] = c[i] << 2;
c[i] = c[i] + 1;
}
else
{
c[i] <<= 2;
}
}
}
};
//!A madified cs census that compares a pixel with the imediat neightbour starting
//!from the center
template<int num_images>
struct ModifiedCsCensus
{
uint8_t *image[num_images];
int n2;
int imageStop;
ModifiedCsCensus(){}
ModifiedCsCensus(uint8_t **images, int ker)
{
for(int i = 0; i < num_images; i++)
image[i] = images[i];
imageStop = num_images;
n2 = ker;
}
void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int c[num_images]) const
{
CV_UNUSED(j);
CV_UNUSED(rWidth);
for(int i = 0; i < imageStop; i++)
{
if (image[i][(rrWidth + jj)] > image[i][(w2 + (jj + n2))])
{
c[i] = c[i] + 1;
}
c[i] = c[i] * 2;
}
}
};
//!A kernel in which a pixel is compared with the center of the window
template<int num_images>
struct CensusKernel
{
uint8_t *image[num_images];
int imageStop;
CensusKernel(){}
CensusKernel(uint8_t **images)
{
for(int i = 0; i < num_images; i++)
image[i] = images[i];
imageStop = num_images;
}
void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int c[num_images]) const
{
CV_UNUSED(w2);
for(int i = 0; i < imageStop; i++)
{
////compare a pixel with the center from the kernel
if (image[i][rrWidth + jj] > image[i][rWidth + j])
{
c[i] += 1;
}
c[i] <<= 1;
}
}
};
//template clas which efficiently combines the descriptors
template <int step_start, int step_end, int step_inc,int nr_img, typename Kernel>
class CombinedDescriptor:public ParallelLoopBody
{
private:
int width, height,n2;
int stride_;
int *dst[nr_img];
Kernel kernel_;
int n2_stop;
public:
CombinedDescriptor(int w, int h,int stride, int k2, int **distance, Kernel kernel,int k2Stop)
{
width = w;
height = h;
n2 = k2;
stride_ = stride;
for(int i = 0; i < nr_img; i++)
dst[i] = distance[i];
kernel_ = kernel;
n2_stop = k2Stop;
}
void operator()(const cv::Range &r) const CV_OVERRIDE {
for (int i = r.start; i <= r.end ; i++)
{
int rWidth = i * stride_;
for (int j = n2 + 2; j <= width - n2 - 2; j++)
{
int c[nr_img];
memset(c, 0, sizeof(c[0]) * nr_img);
for(int step = step_start; step <= step_end; step += step_inc)
{
for (int ii = - n2; ii <= + n2_stop; ii += step)
{
int rrWidth = (ii + i) * stride_;
int rrWidthC = (ii + i + n2) * stride_;
for (int jj = j - n2; jj <= j + n2; jj += step)
{
if (ii != i || jj != j)
{
kernel_(rrWidth,rrWidthC, rWidth, jj, j,c);
}
}
}
}
for(int l = 0; l < nr_img; l++)
dst[l][rWidth + j] = c[l];
}
}
}
};
//!calculate the mean of every windowSizexWindwoSize block from the integral Image
//!this is a preprocessing for MV kernel
class MeanKernelIntegralImage : public ParallelLoopBody
{
private:
int *img;
int windowSize,width;
float scalling;
int *c;
public:
MeanKernelIntegralImage(const cv::Mat &image, int window,float scale, int *cost):
img((int *)image.data),windowSize(window) ,width(image.cols) ,scalling(scale) , c(cost){};
void operator()(const cv::Range &r) const CV_OVERRIDE {
for (int i = r.start; i <= r.end; i++)
{
int iw = i * width;
for (int j = windowSize + 1; j <= width - windowSize - 1; j++)
{
c[iw + j] = (int)((img[(i + windowSize - 1) * width + j + windowSize - 1] + img[(i - windowSize - 1) * width + j - windowSize - 1]
- img[(i + windowSize) * width + j - windowSize] - img[(i - windowSize) * width + j + windowSize]) * scalling);
}
}
}
};
//!implementation for the star kernel descriptor
template<int num_images>
class StarKernelCensus:public ParallelLoopBody
{
private:
uint8_t *image[num_images];
int *dst[num_images];
int n2, width, height, im_num,stride_;
public:
StarKernelCensus(const cv::Mat *img, int k2, int **distance)
{
for(int i = 0; i < num_images; i++)
{
image[i] = img[i].data;
dst[i] = distance[i];
}
n2 = k2;
width = img[0].cols;
height = img[0].rows;
im_num = num_images;
stride_ = (int)img[0].step;
}
void operator()(const cv::Range &r) const CV_OVERRIDE {
for (int i = r.start; i <= r.end ; i++)
{
int rWidth = i * stride_;
for (int j = n2; j <= width - n2; j++)
{
for(int d = 0 ; d < im_num; d++)
{
int c = 0;
for(int step = 4; step > 0; step--)
{
for (int ii = i - step; ii <= i + step; ii += step)
{
int rrWidth = ii * stride_;
for (int jj = j - step; jj <= j + step; jj += step)
{
if (image[d][rrWidth + jj] > image[d][rWidth + j])
{
c = c + 1;
}
c = c * 2;
}
}
}
for (int ii = -1; ii <= +1; ii++)
{
int rrWidth = (ii + i) * stride_;
if (i == -1)
{
if (ii + i != i)
{
if (image[d][rrWidth + j] > image[d][rWidth + j])
{
c = c + 1;
}
c = c * 2;
}
}
else if (i == 0)
{
for (int j2 = -1; j2 <= 1; j2 += 2)
{
if (ii + i != i)
{
if (image[d][rrWidth + j + j2] > image[d][rWidth + j])
{
c = c + 1;
}
c = c * 2;
}
}
}
else
{
if (ii + i != i)
{
if (image[d][rrWidth + j] > image[d][rWidth + j])
{
c = c + 1;
}
c = c * 2;
}
}
}
dst[d][rWidth + j] = c;
}
}
}
}
};
//!paralel implementation of the center symetric census
template <int num_images>
class SymetricCensus:public ParallelLoopBody
{
private:
uint8_t *image[num_images];
int *dst[num_images];
int n2, width, height, im_num,stride_;
public:
SymetricCensus(const cv::Mat *img, int k2, int **distance)
{
for(int i = 0; i < num_images; i++)
{
image[i] = img[i].data;
dst[i] = distance[i];
}
n2 = k2;
width = img[0].cols;
height = img[0].rows;
im_num = num_images;
stride_ = (int)img[0].step;
}
void operator()(const cv::Range &r) const CV_OVERRIDE {
for (int i = r.start; i <= r.end ; i++)
{
int distV = i*stride_;
for (int j = n2; j <= width - n2; j++)
{
for(int d = 0; d < im_num; d++)
{
int c = 0;
//the classic center symetric census which compares the curent pixel with its symetric not its center.
for (int ii = -n2; ii <= 0; ii++)
{
int rrWidth = (ii + i) * stride_;
for (int jj = -n2; jj <= +n2; jj++)
{
if (image[d][(rrWidth + (jj + j))] > image[d][((ii * (-1) + i) * width + (-1 * jj) + j)])
{
c = c + 1;
}
c = c * 2;
if(ii == 0 && jj < 0)
{
if (image[d][(i * width + (jj + j))] > image[d][(i * width + (-1 * jj) + j)])
{
c = c + 1;
}
c = c * 2;
}
}
}
dst[d][(distV + j)] = c;
}
}
}
}
};
//integral image computation used in the Mean Variation Census Transform
void imageMeanKernelSize(const cv::Mat &img, int windowSize, cv::Mat &c);
}
}
#endif
#endif
/*End of file*/
modules/stereo/src/precomp.hpp
View file @ 9c0e1525
......@@ -42,16 +42,13 @@
#ifndef __OPENCV_STEREO_PRECOMP_H__
#define __OPENCV_STEREO_PRECOMP_H__
#include "opencv2/stereo.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/core.hpp"
#include "opencv2/core/utility.hpp"
#include "opencv2/core/private.hpp"
#include "opencv2/core/cvdef.h"
#include "opencv2/imgproc.hpp"
#include "opencv2/calib3d.hpp"
#include <algorithm>
#include <cmath>
#include "opencv2/stereo.hpp"
#include "descriptor.hpp"
#include "matching.hpp"
#endif
modules/stereo/test/test_precomp.hpp
View file @ 9c0e1525
......@@ -6,8 +6,6 @@
#include "opencv2/ts.hpp"
#include "opencv2/stereo.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/calib3d.hpp"
namespace opencv_test {
using namespace cv::stereo;
......
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