/usr/include/opencv2/stereo/descriptor.hpp is in libopencv-contrib-dev 3.2.0+dfsg-4build2.
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/*****************************************************************************************************************\
* 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
{
//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
{
(void)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
{
(void)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
{
(void)j;
(void)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
{
(void)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 {
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,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{
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 {
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 {
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
#endif
/*End of file*/
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