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Commit 54d41251 authored by Muresan Mircea Paul's avatar Muresan Mircea Paul
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aded singe kernel matchings 

fixed warnings and error

replaced colls with strides

fixed warnings

replaced colls with strides everywhere

the number of images is estabished at run time

fixed braket warnings

replaced all enums with a single enum

removed whitespaces

fixed last issues

the matching class
this class contains the most important functions used in stereo correspondence
(StereoBM and StereoSGBM) call methods from these classes

fixed shadowing warning problems

included intrin.h header for popcnt instruction

replaced __popcnt with _mm_popcnt_u32() equivalent

fixed some warnings

fixed index warning
parent 7d13acdb
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modules/stereo/src/descriptor.cpp
View file @ 54d41251
......@@ -43,89 +43,199 @@
/*****************************************************************************************************************\
* The file contains the implemented descriptors *
\******************************************************************************************************************/
#include "precomp.hpp"
#include "descriptor.hpp"
using namespace cv;
using namespace stereo;
void cv::stereo::applyCensusOnImage(const cv::Mat &img, int kernelSize, cv::Mat &dist, const int type)
{
CV_Assert(img.type() == CV_8UC1);
CV_Assert(kernelSize <= 5);
CV_Assert(type < 2 && type >= 0);
int n2 = (kernelSize - 1) / 2;
parallel_for_(cv::Range(n2, img.rows - n2), singleImageCensus(img.data, img.cols, img.rows, n2, (int *)dist.data, type));
}
void cv::stereo::applyCensusOnImages(const cv::Mat &im1,const cv::Mat &im2, int kernelSize, cv::Mat &dist, cv::Mat &dist2, const int type)
namespace cv
{
CV_Assert(im1.size() == im2.size());
CV_Assert(im1.type() == CV_8UC1 && im2.type() == CV_8UC1);
CV_Assert(type < 2 && type >= 0);
CV_Assert(kernelSize <= (type == 0 ? 5 : 10));
int n2 = (kernelSize - 1) / 2;
if(type == Dense_Census)
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
void censusTransform(const Mat &image1, const Mat &image2, int kernelSize, Mat &dist1, Mat &dist2, const int type)
{
parallel_for_(cv::Range(n2, im1.rows - n2),
CombinedDescriptor<1,1,1,CensusKernel>(im1.cols, im1.rows,n2,(int *)dist.data,(int *)dist2.data,CensusKernel(im1.data, im2.data),n2));
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 == Sparse_Census)
else if(type == CV_SPARSE_CENSUS)
{
parallel_for_(cv::Range(n2, im1.rows - n2),
CombinedDescriptor<2,2,1,CensusKernel>(im1.cols, im1.rows,n2,(int *)dist.data,(int *)dist2.data,CensusKernel(im1.data, im2.data),n2));
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));
}
}
void cv::stereo::applyMCTOnImages(const cv::Mat &img1, const cv::Mat &img2, int kernelSize, int t, cv::Mat &dist, cv::Mat &dist2, const int type)
{
}
//function that performs census on one image
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
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(type < 2 && type >= 0);
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
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
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;
if(type == StandardMct)
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)
{
parallel_for_(cv::Range(n2, img1.rows - n2),
CombinedDescriptor<2,3,2,MCTKernel>(img1.cols, img1.rows,n2,(int *)dist.data,(int *)dist2.data,MCTKernel(img1.data, img2.data,t),n2));
//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
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));
}
}
void cv::stereo::applySimetricCensus(const cv::Mat &img1, const cv::Mat &img2, int kernelSize, cv::Mat &dist, cv::Mat &dist2, const int type)
{
}
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<2,3,2, 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
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 < 2 && type >= 0);
CV_Assert(type != CV_CS_CENSUS || type != CV_MODIFIED_CS_CENSUS);
CV_Assert(kernelSize <= 7);
int n2 = (kernelSize - 1) >> 1;
if(type == ClassicCenterSymetricCensus)
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_(cv::Range(n2, img1.rows - n2), parallelSymetricCensus(img1.data, img2.data, img1.cols, img2.rows, n2, (int *)dist.data, (int *)dist2.data, type));
parallel_for_(Range(n2, img1.rows - n2), SymetricCensus<2>(imag, n2,date));
}
else if(type == ModifiedCenterSymetricCensus)
else if(type == CV_MODIFIED_CS_CENSUS)
{
parallel_for_(cv::Range(n2, img1.rows - n2),
CombinedDescriptor<1,1,1,ModifiedCsCensus>(img1.cols, img1.rows,n2,(int *)dist.data,(int *)dist2.data,ModifiedCsCensus(img1.data, img2.data,n2),1));
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));
}
}
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.size > 0);
CV_Assert(cost.size > 0);
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));
}
}
}
void cv::stereo::applyBrifeDescriptor(const cv::Mat &image1, const cv::Mat &image2, int kernelSize, cv::Mat &dist, cv::Mat &dist2)
{
//TO DO
//marked the variables in order to avoid warnings
(void)image1;
(void)image2;
(void)dist;
(void)dist2;
(void)kernelSize;
}
void cv::stereo::applyRTDescriptor(const cv::Mat &image1, const cv::Mat &image2, int kernelSize, cv::Mat &dist, cv::Mat &dist2)
{
//TO DO
//marked the variables in order to avoid warnings
(void)image1;
(void)image2;
(void)dist;
(void)dist2;
(void)kernelSize;
}
modules/stereo/src/descriptor.hpp
View file @ 54d41251
......@@ -42,7 +42,7 @@
/*****************************************************************************************************************\
* The interface contains the main descriptors that will be implemented in the descriptor class *
\******************************************************************************************************************/
\*****************************************************************************************************************/
#include "precomp.hpp"
#include <stdint.h>
......@@ -54,194 +54,300 @@ namespace cv
{
namespace stereo
{
enum { Dense_Census, Sparse_Census, StarCensus};
enum {ClassicCenterSymetricCensus, ModifiedCenterSymetricCensus};
enum {StandardMct,MeanVariation};
enum {SSE, NonSSE};
//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
};
//!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 *image1;
uint8_t *image2;
uint8_t *integralLeft;
uint8_t *integralRight;
MVKernel(uint8_t *img, uint8_t *img2, uint8_t *integralL, uint8_t *integralR): image1(img),image2(img2),integralLeft(integralL), integralRight(integralR){}
void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int &c, int &c2) const
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];
}
};
//!kernel that takes the pixels from certain positions from a patch
//!offers verry good results
struct StarKernel
stop = num_images;
}
void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int c[num_images]) const
{
uint8_t *image1;
uint8_t *image2;
StarKernel(uint8_t *img, uint8_t *img2): image1(img),image2(img2){}
void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int &c, int &c2) 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 *image1;
uint8_t *image2;
int t;
MCTKernel(uint8_t * img,uint8_t *img2, int threshold) : image1(img),image2(img2), t(threshold) {}
void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int &c, int &c2) const
uint8_t *image[num_images];
int t,imageStop;
MCTKernel(){}
MCTKernel(uint8_t ** images, int threshold)
{
if (image1[rrWidth + jj] > image1[rWidth + j] - t)
for(int i = 0; i < num_images; i++)
{
c <<= 2;
c |= 0x3;
image[i] = images[i];
}
else if (image1[rWidth + j] - t < image1[rrWidth + jj] && image1[rWidth + j] + t >= image1[rrWidth + jj])
{
c <<= 2;
c = c + 1;
imageStop = num_images;
t = threshold;
}
else
void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int c[num_images]) const
{
c <<= 2;
}
if (image2[rrWidth + jj] > image2[rWidth + j] - t)
(void)w2;
for(int i = 0; i < imageStop; i++)
{
c2 <<= 2;
c2 |= 0x3;
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 (image2[rWidth + j] - t < image2[rrWidth + jj] && image2[rWidth + j] + t >= image2[rrWidth + jj])
else if (image[i][rWidth + j] - t < image[i][rrWidth + jj] && image[i][rWidth + j] + t >= image[i][rrWidth + jj])
{
c2 <<= 2;
c2 = c2 + 1;
c[i] = c[i] << 2;
c[i] = c[i] + 1;
}
else
{
c2 <<= 2;
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 *image1;
uint8_t *image2;
uint8_t *image[num_images];
int n2;
ModifiedCsCensus(uint8_t *im1, uint8_t *im2, int ker):image1(im1),image2(im2),n2(ker){}
void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int &c, int &c2) const
{
if (image1[(rrWidth + jj)] > image1[(w2 + (jj + n2))])
int imageStop;
ModifiedCsCensus(){}
ModifiedCsCensus(uint8_t **images, int ker)
{
c = c + 1;
for(int i = 0; i < num_images; i++)
image[i] = images[i];
imageStop = num_images;
n2 = ker;
}
c = c * 2;
if (image2[(rrWidth + jj)] > image2[(w2 + (jj + n2))])
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++)
{
c2 = c2 + 1;
if (image[i][(rrWidth + jj)] > image[i][(w2 + (jj + n2))])
{
c[i] = c[i] + 1;
}
c[i] = c[i] * 2;
}
c2 = c2 * 2;
}
};
//!A kernel in which a pixel is compared with the center of the window
template<int num_images>
struct CensusKernel
{
uint8_t *image1;
uint8_t *image2;
CensusKernel(uint8_t *im1, uint8_t *im2):image1(im1),image2(im2){}
void operator()(int rrWidth,int w2, int rWidth, int jj, int j, int &c, int &c2) const
{
//compare a pixel with the center from the kernel
if (image1[rrWidth + jj] > image1[rWidth + j])
uint8_t *image[num_images];
int imageStop;
CensusKernel(){}
CensusKernel(uint8_t **images)
{
c = c + 1;
for(int i = 0; i < num_images; i++)
image[i] = images[i];
imageStop = num_images;
}
c = c * 2;
//compare pixel with center for image 2
if (image2[rrWidth + jj] > image2[rWidth + j])
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++)
{
c2 = c2 + 1;
////compare a pixel with the center from the kernel
if (image[i][rrWidth + jj] > image[i][rWidth + j])
{
c[i] += 1;
}
c[i] <<= 1;
}
c2 = c2 * 2;
}
};
//template clas which efficiently combines the descriptors
template <int step_start, int step_end, int step_inc, typename Kernel>
template <int step_start, int step_end, int step_inc,int nr_img, typename Kernel>
class CombinedDescriptor:public ParallelLoopBody
{
private:
uint8_t *image1, *image2;
int *dst1, *dst2;
int n2 , width, height;
int n2_stop;
int width, height,n2;
int stride_;
int *dst[nr_img];
Kernel kernel_;
int n2_stop;
public:
CombinedDescriptor(int w, int h, int k2, int * distance1, int * distance2, Kernel kernel,int k2Stop) :
width(w), height(h), n2(k2),dst1(distance1), dst2(distance2), kernel_(kernel), n2_stop(k2Stop){}
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 * width;
int rWidth = i * stride_;
for (int j = n2 + 2; j <= width - n2 - 2; j++)
{
int c = 0;
int c2 = 0;
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) * width;
int rrWidthC = (ii + i + n2) * width;
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,c2);
kernel_(rrWidth,rrWidthC, rWidth, jj, j,c);
}
}
}
}
dst1[rWidth + j] = c;
dst2[rWidth + j] = c2;
for(int l = 0; l < nr_img; l++)
dst[l][rWidth + j] = c[l];
}
}
}
};
//!class that implemented the census descriptor on single images
class singleImageCensus : public ParallelLoopBody
//!calculate the mean of every windowSizexWindwoSize block from the integral Image
//!this is a preprocessing for MV kernel
class MeanKernelIntegralImage : public ParallelLoopBody
{
private:
uint8_t *image;
int *dst;
int n2, width, height, type;
int *img;
int windowSize,width;
float scalling;
int *c;
public:
singleImageCensus(uint8_t * img1, int w, int h, int k2, int * distance1,const int t) :
image(img1), dst(distance1), n2(k2), width(w), height(h), type(t){}
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 * width;
int rWidth = i * stride_;
for (int j = n2; j <= width - n2; j++)
{
if (type == SSE)
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])
{
//to do
c = c + 1;
}
else
c = c * 2;
}
}
}
for (int ii = -1; ii <= +1; ii++)
{
int c = 0;
for (int ii = i - n2; ii <= i + n2; ii++)
int rrWidth = (ii + i) * stride_;
if (i == -1)
{
int rrWidth = ii * width;
for (int jj = j - n2; jj <= j + n2; jj++)
if (ii + i != i)
{
if (ii != i || jj != j)
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])
{
if (image[(rrWidth + jj)] > image[(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;
}
......@@ -249,89 +355,97 @@ namespace cv
}
}
}
dst[(rWidth + j)] = c;
dst[d][rWidth + j] = c;
}
}
}
}
};
//!paralel implementation of the center symetric census
class parallelSymetricCensus:public ParallelLoopBody
template <int num_images>
class SymetricCensus:public ParallelLoopBody
{
private:
uint8_t *image1, *image2;
int *dst1, *dst2;
int n2, width, height, type;
uint8_t *image[num_images];
int *dst[num_images];
int n2, width, height, im_num,stride_;
public:
parallelSymetricCensus(uint8_t * img1, uint8_t * img2, int w, int h, int k2, int * distance1, int * distance2,const int t) :
image1(img1), image2(img2), dst1(distance1), dst2(distance2), n2(k2), width(w), height(h), type(t){}
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)* width;
int distV = i*stride_;
for (int j = n2; j <= width - n2; j++)
{
for(int d = 0; d < im_num; d++)
{
int c = 0;
int c2 = 0;
//the classic center symetric census which compares the curent pixel with its symetric not its center.
for (int ii = -n2; ii < 0; ii++)
for (int ii = -n2; ii <= 0; ii++)
{
int rrWidth = (ii + i) * width;
int rrWidth = (ii + i) * stride_;
for (int jj = -n2; jj <= +n2; jj++)
{
if (image1[(rrWidth + (jj + j))] > image1[((ii * (-1) + i) * width + (-1 * jj) + j)])
if (image[d][(rrWidth + (jj + j))] > image[d][((ii * (-1) + i) * width + (-1 * jj) + j)])
{
c = c + 1;
}
c = c * 2;
if (image2[(rrWidth + (jj + j))] > image2[((ii * (-1) + i) * width + (-1 * jj) + j)])
if(ii == 0 && jj < 0)
{
c2 = c2 + 1;
}
c2 = c2 * 2;
}
}
for (int jj = -n2; jj < 0; jj++)
{
if (image1[(i * width + (jj + j))] > image1[(i * width + (-1 * jj) + j)])
if (image[d][(i * width + (jj + j))] > image[d][(i * width + (-1 * jj) + j)])
{
c = c + 1;
}
c = c * 2;
if (image2[(i * width + (jj + j))] > image2[(i * width + (-1 * jj) + j)])
{
c2 = c2 + 1;
}
c2 = c2 * 2;
}//a modified version of cs census which compares each pixel with its correspondent from
//the same distance from the center
dst1[(distV + j)] = c;
dst2[(distV + j)] = c2;
}
}
dst[d][(distV + j)] = c;
}
}
}
}
};
//!Implementation for computing the Census transform on the given image
void applyCensusOnImage(const cv::Mat &img, int kernelSize, cv::Mat &dist, const int type);
/**
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);
//void applyCensusOnImages(const cv::Mat &im1,const cv::Mat &im2, int kernelSize, cv::Mat &dist, cv::Mat &dist2, const int type);
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
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
**/
void applyMCTOnImages(const cv::Mat &img1, const cv::Mat &img2, int kernelSize, int t, cv::Mat &dist, cv::Mat &dist2, const int type);
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
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
**/
void applySimetricCensus(const cv::Mat &img1, const cv::Mat &img2, int kernelSize, cv::Mat &dist, cv::Mat &dist2, const int type);
//!brief binary descriptor used in stereo correspondence
void applyBrifeDescriptor(const cv::Mat &image1, const cv::Mat &image2, int kernelSize, cv::Mat &dist, cv::Mat &dist2);
//The classical Rank Transform
void applyRTDescriptor(const cv::Mat &image1, const cv::Mat &image2, int kernelSize, cv::Mat &dist, cv::Mat &dist2);
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
void symetricCensusTransform(const cv::Mat &img1, int kernelSize, cv::Mat &dist1, const int type);
//in a 9x9 kernel only certain positions are choosen
void starCensusTransform(const cv::Mat &img1, const cv::Mat &img2, int kernelSize, cv::Mat &dist1,cv::Mat &dist2);
//single image version of star kernel
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
......
modules/stereo/src/matching.cpp 0 → 100644
View file @ 54d41251
//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.
#include "precomp.hpp"
#include "matching.hpp"
namespace cv
{
namespace stereo
{
//null constructor
Matching::Matching(void)
{
}
Matching::~Matching(void)
{
}
//constructor for the matching class
//maxDisp - represents the maximum disparity
Matching::Matching(int maxDisp, int scalling, int confidence)
{
CV_Assert(maxDisp > 10);
CV_Assert(scalling != 0);
CV_Assert(confidence >= 1);
this->scallingFactor = scalling;
//set the maximum disparity
this->maxDisparity = maxDisp;
//set the value for the confidence
this->confidenceCheck = confidence;
//generate the hamming lut in case SSE is not available
hammingLut();
}
//!method for setting the maximum disparity
void Matching::setMaxDisparity(int val)
{
CV_Assert(val > 10);
this->maxDisparity = val;
}
//!method for getting the disparity
int Matching::getMaxDisparity()
{
return this->maxDisparity;
}
void Matching::setScallingFactor(int val)
{
CV_Assert(val > 0);
scallingFactor = val;
}
//!method for getting the scalling factor
int Matching::getScallingFactor()
{
return scallingFactor;
}
//! Hamming distance computation method
//! leftImage and rightImage are the two transformed images
//! the cost is the resulted cost volume and kernel Size is the size of the matching window
void Matching::hammingDistanceBlockMatching(const Mat &leftImage, const Mat &rightImage, Mat &cost, const int kernelSize)
{
CV_Assert(leftImage.cols == rightImage.cols);
CV_Assert(leftImage.rows == rightImage.rows);
CV_Assert(kernelSize % 2 != 0);
CV_Assert(cost.rows == leftImage.rows);
CV_Assert(cost.cols / (maxDisparity + 1) == leftImage.cols);
// cost.setTo(0);
int *c = (int *)cost.data;
memset(c, 0, sizeof(c[0]) * leftImage.cols * leftImage.rows * (maxDisparity + 1));
parallel_for_(cv::Range(kernelSize / 2,leftImage.rows - kernelSize / 2), hammingDistance(leftImage,rightImage,c,maxDisparity,kernelSize / 2,hamLut));
}
//preprocessing the cost volume in order to get it ready for aggregation
void Matching::costGathering(const Mat &hammingDistanceCost, Mat &cost)
{
CV_Assert(hammingDistanceCost.rows == hammingDistanceCost.rows);
CV_Assert(hammingDistanceCost.type() == CV_32SC4);
CV_Assert(cost.type() == CV_32SC4);
//cost.setTo(0);
int maxDisp = maxDisparity;
int width = cost.cols / ( maxDisp + 1) - 1;
int height = cost.rows - 1;
int *c = (int *)cost.data;
memset(c, 0, sizeof(c[0]) * (width + 1) * (height + 1) * (maxDisp + 1));
parallel_for_(cv::Range(1,height), costGatheringHorizontal(hammingDistanceCost,maxDisparity,cost));
for (int i = 1; i <= height; i++)
{
for (int j = 1; j <= width; j++)
{
int iwj = (i * width + j) * (maxDisp + 1);
int iwjmu = ((i - 1) * width + j) * (maxDisp + 1);
for (int d = 0; d <= maxDisp; d++)
{
c[iwj + d] += c[iwjmu + d];
}
}
}
}
//!The aggregation on the cost volume
void Matching::blockAgregation(const Mat &partialSums, int windowSize, Mat &cost)
{
CV_Assert(windowSize % 2 != 0);
CV_Assert(partialSums.rows == cost.rows);
CV_Assert(partialSums.cols == cost.cols);
int win = windowSize / 2;
int *c = (int *)cost.data;
int maxDisp = maxDisparity;
int width = cost.cols / ( maxDisp + 1) - 1;
int height = cost.rows - 1;
memset(c, 0, sizeof(c[0]) * width * height * (maxDisp + 1));
parallel_for_(cv::Range(win + 1,height - win - 1), agregateCost(partialSums,windowSize,maxDisp,cost));
}
//!Finding the correct disparity from the cost volume, we also make a confidence check
int Matching ::minim(int *c, int iwpj, int widthDisp,const double confidenceCheck, const int search_region)
{
double mini, mini2, mini3;
mini = mini2 = mini3 = DBL_MAX;
int index = 0;
int iw = iwpj;
int widthDisp2;
widthDisp2 = widthDisp;
widthDisp -= 1;
for (int i = 0; i <= widthDisp; i++)
{
if (c[(iw + i * search_region) * widthDisp2 + i] < mini)
{
mini3 = mini2;
mini2 = mini;
mini = c[(iw + i * search_region) * widthDisp2 + i];
index = i;
}
else if (c[(iw + i * search_region) * widthDisp2 + i] < mini2)
{
mini3 = mini2;
mini2 = c[(iw + i * search_region) * widthDisp2 + i];
}
else if (c[(iw + i * search_region) * widthDisp2 + i] < mini3)
{
mini3 = c[(iw + i * search_region) * widthDisp2 + i];
}
}
if (mini3 / mini <= confidenceCheck)
return index;
return -1;
}
//!Interpolate in order to obtain better results
double Matching::symetricVInterpolation(int *c, int iwjp, int widthDisp, int winDisp,const int search_region)
{
if (winDisp == 0 || winDisp == widthDisp - 1)
return winDisp;
double m2m1, m3m1, m3, m2, m1;
m2 = c[(iwjp + (winDisp - 1) * search_region) * widthDisp + winDisp - 1];
m3 = c[(iwjp + (winDisp + 1) * search_region)* widthDisp + winDisp + 1];
m1 = c[(iwjp + winDisp * search_region) * widthDisp + winDisp];
m2m1 = m2 - m1;
m3m1 = m3 - m1;
if (m2m1 == 0 || m3m1 == 0) return winDisp;
double p;
p = 0;
if (m2 > m3)
{
p = (0.5 - 0.25 * ((m3m1 * m3m1) / (m2m1 * m2m1) + (m3m1 / m2m1)));
}
else
{
p = -1 * (0.5 - 0.25 * ((m2m1 * m2m1) / (m3m1 * m3m1) + (m2m1 / m3m1)));
}
if (p >= -0.5 && p <= 0.5)
p = winDisp + p;
return p;
}
//!Generate the hamming LUT
void Matching::hammingLut()
{
for (int i = 0; i <= 65536; i++)
{
int dist = 0;
int j = i;
//we number the bits from our number
while (j)
{
dist = dist + 1;
j = j & (j - 1);
}
hamLut[i] = dist;
}
}
//!remove small regions that have an area smaller than t, we fill the region with the average of the good pixels around it
void Matching::smallRegionRemoval(const Mat &currentMap, int t, Mat &out)
{
CV_Assert(currentMap.cols == out.cols);
CV_Assert(currentMap.rows == out.rows);
CV_Assert(t > 0);
memset(pus, 0, 2000000 * sizeof(pus[0]));
uint8_t *map = currentMap.data;
uint8_t *outputMap = out.data;
int height = currentMap.rows;
int width = currentMap.cols;
uint8_t k = 1;
int st, dr;
int di[] = { -1, -1, -1, 0, 1, 1, 1, 0 },
dj[] = { -1, 0, 1, 1, 1, 0, -1, -1 };
int speckle_size = 0;
st = 0;
dr = 0;
for (int i = 1; i < height - 1; i++)
{
int iw = i * width;
for (int j = 1; j < width - 1; j++)
{
if (map[iw + j] != 0)
{
outputMap[iw + j] = map[iw + j];
}
else if (map[iw + j] == 0)
{
int nr = 1;
int avg = 0;
speckle_size = dr;
specklePointX[dr] = i;
specklePointY[dr] = j;
pus[i * width + j] = 1;
dr++;
map[iw + j] = k;
while (st < dr)
{
int ii = specklePointX[st];
int jj = specklePointY[st];
//going on 8 directions
for (int d = 0; d < 8; d++)
{//if insisde
if (ii + di[d] >= 0 && ii + di[d] < height && jj + dj[d] >= 0 && jj + dj[d] < width &&
pus[(ii + di[d]) * width + jj + dj[d]] == 0)
{
int val = map[(ii + di[d]) * width + jj + dj[d]];
if (val == 0)
{
map[(ii + di[d]) * width + jj + dj[d]] = k;
specklePointX[dr] = (ii + di[d]);
specklePointY[dr] = (jj + dj[d]);
dr++;
pus[(ii + di[d]) * width + jj + dj[d]] = 1;
}//this means that my point is a good point to be used in computing the final filling value
else if (val > 2 && val < 250)
{
avg += val;
nr++;
}
}
}
st++;
}//if hole size is smaller than a specified threshold we fill the respective hole with the average of the good neighbours
if (st - speckle_size <= t)
{
uint8_t fillValue = (uint8_t)(avg / nr);
while (speckle_size < st)
{
int ii = specklePointX[speckle_size];
int jj = specklePointY[speckle_size];
outputMap[ii * width + jj] = fillValue;
speckle_size++;
}
}
}
}
}
}
//!setting the confidence to a certain value
void Matching ::setConfidence(double val)
{
CV_Assert(val >= 1);
confidenceCheck = val;
}
//getter for confidence check
double Matching ::getConfidence()
{
return confidenceCheck;
}
//!Method responsible for generating the disparity map
void Matching::dispartyMapFormation(const Mat &costVolume, Mat &mapFinal, int th)
{
uint8_t *map = mapFinal.data;
int disparity = maxDisparity;
int width = costVolume.cols / ( disparity + 1) - 1;
int height = costVolume.rows - 1;
memset(map, 0, sizeof(map[0]) * width * height);
parallel_for_(Range(0,height - 1), makeMap(costVolume,th,disparity,confidenceCheck,scallingFactor,mapFinal));
}
//!1x9 median filter
void Matching::Median1x9Filter(const Mat &originalMap, Mat &map)
{
CV_Assert(originalMap.rows == map.rows);
CV_Assert(originalMap.cols == map.cols);
parallel_for_(Range(1,originalMap.rows - 2), Median1x9(originalMap,map));
}
//!9x1 median filter
void Matching::Median9x1Filter(const Mat &originalMap, Mat &map)
{
CV_Assert(originalMap.cols == map.cols);
CV_Assert(originalMap.cols == map.cols);
parallel_for_(Range(1,originalMap.cols - 2), Median9x1(originalMap,map));
}
}
}
modules/stereo/src/matching.hpp 0 → 100644
View file @ 54d41251
//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 methods for computing the matching between the left and right images *
* *
\******************************************************************************************************************/
#include "precomp.hpp"
#include <stdint.h>
#ifndef _OPENCV_MATCHING_HPP_
#define _OPENCV_MATCHING_HPP_
#ifdef __cplusplus
namespace cv
{
namespace stereo
{
class Matching
{
private:
//arrays used in the region removal
int specklePointX[1000001];
int specklePointY[1000001];
long long pus[2000001];
//!The maximum disparity
int maxDisparity;
//!the factor by which we are multiplying the disparity
int scallingFactor;
//!the confidence to which a min disparity found is good or not
double confidenceCheck;
//!the LUT used in case SSE is not available
int hamLut[65537];
//!function for refining the disparity at sub pixel using simetric v
static double symetricVInterpolation(int *c, int iwjp, int widthDisp, int winDisp,const int search_region);
//!function used for getting the minimum disparity from the cost volume"
static int minim(int *c, int iwpj, int widthDisp,const double confidenceCheck, const int search_region);
//!a pre processing function that generates the Hamming LUT in case the algorithm will ever be used on platform where SSE is not available
void hammingLut();
//!the class used in computing the hamming distance
class hammingDistance : public ParallelLoopBody
{
private:
int *left, *right, *c;
int v,kernelSize, width, height;
int MASK;
int *hammLut;
public :
hammingDistance(const Mat &leftImage, const Mat &rightImage, int *cost, int maxDisp, int kerSize, int *hammingLUT):
left((int *)leftImage.data), right((int *)rightImage.data), c(cost), v(maxDisp),kernelSize(kerSize),width(leftImage.cols), height(leftImage.rows), MASK(65535), hammLut(hammingLUT){}
void operator()(const cv::Range &r) const {
for (int i = r.start; i <= r.end ; i++)
{
int iw = i * width;
for (int j = kernelSize; j < width - kernelSize; j++)
{
int j2;
int xorul;
int iwj;
iwj = iw + j;
for (int d = 0; d <= v; d++)
{
j2 = (0 > j - d) ? (0) : (j - d);
xorul = left[(iwj)] ^ right[(iw + j2)];
#if CV_SSE4_1
c[(iwj)* (v + 1) + d] = _mm_popcnt_u32(xorul);
#else
c[(iwj)* (v + 1) + d] = hammLut[xorul & MASK] + hammLut[xorul >> 16];
#endif
}
}
}
}
};
//!preprocessing used for agregation
class costGatheringHorizontal:public ParallelLoopBody
{
private:
int *c, *ham;
int width, maxDisp;
public:
costGatheringHorizontal(const Mat &hamimg,const int maxDispa, Mat &output)
{
ham = (int *)hamimg.data;
c = (int *)output.data;
maxDisp = maxDispa;
width = output.cols / ( maxDisp + 1) - 1;
}
void operator()(const cv::Range &r) const {
for (int i = r.start; i <= r.end; i++)
{
int iw = i * width;
int iwi = (i - 1) * width;
for (int j = 1; j <= width; j++)
{
int iwj = (iw + j) * (maxDisp + 1);
int iwjmu = (iw + j - 1) * (maxDisp + 1);
int iwijmu = (iwi + j - 1) * (maxDisp + 1);
for (int d = 0; d <= maxDisp; d++)
{
c[iwj + d] = ham[iwijmu + d] + c[iwjmu + d];
}
}
}
}
};
//!cost aggregation
class agregateCost:public ParallelLoopBody
{
private:
int win;
int *c, *parSum;
int maxDisp,width, height;
public:
agregateCost(const Mat &partialSums, int windowSize, int maxDispa, Mat &cost)
{
win = windowSize / 2;
c = (int *)cost.data;
maxDisp = maxDispa;
width = cost.cols / ( maxDisp + 1) - 1;
height = cost.rows - 1;
parSum = (int *)partialSums.data;
}
void operator()(const cv::Range &r) const {
for (int i = r.start; i <= r.end; i++)
{
int iwi = (i - 1) * width;
for (int j = win + 1; j <= width - win - 1; j++)
{
int w1 = ((i + win + 1) * width + j + win) * (maxDisp + 1);
int w2 = ((i - win) * width + j - win - 1) * (maxDisp + 1);
int w3 = ((i + win + 1) * width + j - win - 1) * (maxDisp + 1);
int w4 = ((i - win) * width + j + win) * (maxDisp + 1);
int w = (iwi + j - 1) * (maxDisp + 1);
for (int d = 0; d <= maxDisp; d++)
{
c[w + d] = parSum[w1 + d] + parSum[w2 + d]
- parSum[w3 + d] - parSum[w4 + d];
}
}
}
}
};
//!class that is responsable for generating the disparity map
class makeMap:public ParallelLoopBody
{
private:
//enum used to notify wether we are searching on the vertical ie (lr) or diagonal (rl)
enum {CV_VERTICAL_SEARCH, CV_DIAGONAL_SEARCH};
int width,disparity,scallingFact,th;
double confCheck;
uint8_t *map;
int *c;
public:
makeMap(const Mat &costVolume, int threshold, int maxDisp, double confidence,int scale, Mat &mapFinal)
{
c = (int *)costVolume.data;
map = mapFinal.data;
disparity = maxDisp;
width = costVolume.cols / ( disparity + 1) - 1;
th = threshold;
scallingFact = scale;
confCheck = confidence;
}
void operator()(const cv::Range &r) const {
for (int i = r.start; i <= r.end ; i++)
{
int lr;
int v = -1;
double p1, p2;
int iw = i * width;
for (int j = 0; j < width; j++)
{
lr = Matching:: minim(c, iw + j, disparity + 1, confCheck,CV_VERTICAL_SEARCH);
if (lr != -1)
{
v = Matching::minim(c, iw + j - lr, disparity + 1, confCheck,CV_DIAGONAL_SEARCH);
if (v != -1)
{
p1 = Matching::symetricVInterpolation(c, iw + j - lr, disparity + 1, v,CV_DIAGONAL_SEARCH);
p2 = Matching::symetricVInterpolation(c, iw + j, disparity + 1, lr,CV_VERTICAL_SEARCH);
if (abs(p1 - p2) <= th)
map[iw + j] = (uint8_t)((p2)* scallingFact);
else
{
map[iw + j] = 0;
}
}
else
{
if (width - j <= disparity)
{
p2 = Matching::symetricVInterpolation(c, iw + j, disparity + 1, lr,CV_VERTICAL_SEARCH);
map[iw + j] = (uint8_t)(p2* scallingFact);
}
}
}
else
{
map[iw + j] = 0;
}
}
}
}
};
//!median 1x9 paralelized filter
class Median1x9:public ParallelLoopBody
{
private:
uint8_t *harta;
uint8_t *mapModified;
int height, width;
public:
Median1x9(const Mat &hartaOriginala, Mat &map)
{
harta = hartaOriginala.data;
mapModified = map.data;
height = hartaOriginala.rows;
width = hartaOriginala.cols;
}
void operator()(const cv::Range &r) const{
for (int m = r.start; m <= r.end; m++)
{
for (int n = 4; n < width - 4; ++n)
{
int k = 0;
uint8_t window[9];
for (int i = n - 4; i <= n + 4; ++i)
window[k++] = harta[m * width + i];
for (int j = 0; j < 5; ++j)
{
int min = j;
for (int l = j + 1; l < 9; ++l)
if (window[l] < window[min])
min = l;
const uint8_t temp = window[j];
window[j] = window[min];
window[min] = temp;
}
mapModified[m * width + n] = window[4];
}
}
}
};
//!median 9x1 paralelized filter
class Median9x1:public ParallelLoopBody
{
private:
uint8_t *harta;
uint8_t *mapModified;
int height, width;
public:
Median9x1(const Mat &hartaOriginala, Mat &map)
{
harta = hartaOriginala.data;
mapModified = map.data;
height = hartaOriginala.rows;
width = hartaOriginala.cols;
}
void operator()(const Range &r) const{
for (int n = r.start; n <= r.end; ++n)
{
for (int m = 4; m < height - 4; ++m)
{
int k = 0;
uint8_t window[9];
for (int i = m - 4; i <= m + 4; ++i)
window[k++] = harta[i * width + n];
for (int j = 0; j < 5; j++)
{
int min = j;
for (int l = j + 1; l < 9; ++l)
if (window[l] < window[min])
min = l;
const uint8_t temp = window[j];
window[j] = window[min];
window[min] = temp;
}
mapModified[m * width + n] = window[4];
}
}
}
};
public:
//!method for setting the maximum disparity
void setMaxDisparity(int val);
//!method for getting the disparity
int getMaxDisparity();
//!method for setting the scalling factor
void setScallingFactor(int val);
//!method for getting the scalling factor
int getScallingFactor();
//!setter for the confidence check
void setConfidence(double val);
//!getter for confidence check
double getConfidence();
//!method for computing the hamming difference
void hammingDistanceBlockMatching(const Mat &left, const Mat &right, Mat &c, const int kernelSize = 9);
//!precomputation done on the cost volume to efficiently compute the block matching
void costGathering(const Mat &hammingDistanceCost, Mat &c);
//the aggregation on the cost volume
void blockAgregation(const Mat &parSum, int windowSize, Mat &c);
//!function for generating disparity maps at sub pixel level
/* costVolume - represents the cost volume
* width, height - represent the width and height of the iage
*disparity - represents the maximum disparity
*map - is the disparity map that will result
*th - is the LR threshold
*/
void dispartyMapFormation(const Mat &costVolume, Mat &map, int th);
//!constructor for the matching class
//!maxDisp - represents the maximum disparity
//!a median filter that has proven to work a bit better especially when applied on disparity maps
static void Median1x9Filter(const Mat &hartaOriginala, Mat &map);
static void Median9x1Filter(const Mat &hartaOriginala, Mat &map);
void smallRegionRemoval(const Mat &harta, int t, Mat &out);
Matching(int maxDisp, int scallingFactor = 4,int confidenceCheck = 6);
Matching(void);
~Matching(void);
};
}
}
#endif
#endif
/*End of file*/
\ No newline at end of file
modules/stereo/src/stereo_binary_sgbm.cpp
View file @ 54d41251
......@@ -47,7 +47,6 @@ by Heiko Hirschmuller.
We match blocks rather than individual pixels, thus the algorithm is called
SGBM (Semi-global block matching)
*/
#include "precomp.hpp"
#include <limits.h>
......@@ -58,7 +57,6 @@ namespace cv
typedef uchar PixType;
typedef short CostType;
typedef short DispType;
enum { NR = 16, NR2 = NR/2 };
struct StereoBinarySGBMParams
{
......@@ -74,7 +72,6 @@ namespace cv
speckleRange = 0;
mode = StereoBinarySGBM::MODE_SGBM;
}
StereoBinarySGBMParams( int _minDisparity, int _numDisparities, int _SADWindowSize,
int _P1, int _P2, int _disp12MaxDiff, int _preFilterCap,
int _uniquenessRatio, int _speckleWindowSize, int _speckleRange,
......@@ -161,10 +158,8 @@ namespace cv
}
}
memset( cost, 0, width1*D*sizeof(cost[0]) );
buffer -= minX2;
cost -= minX1*D + minD; // simplify the cost indices inside the loop
#if CV_SSE2
volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE2);
#endif
......@@ -298,7 +293,6 @@ namespace cv
volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE2);
#endif
const int ALIGN = 16;
const int DISP_SHIFT = StereoMatcher::DISP_SHIFT;
const int DISP_SCALE = (1 << DISP_SHIFT);
......@@ -410,17 +404,14 @@ namespace cv
for( d = 0; d < D; d++ )
hsumAdd[d] = (CostType)(hsumAdd[d] + pixDiff[x + d]*scale);
}
if( y > 0 )
{
const CostType* hsumSub = hsumBuf + (std::max(y - SH2 - 1, 0) % hsumBufNRows)*costBufSize;
const CostType* Cprev = !fullDP || y == 0 ? C : C - costBufSize;
for( x = D; x < width1*D; x += D )
{
const CostType* pixAdd = pixDiff + std::min(x + SW2*D, (width1-1)*D);
const CostType* pixSub = pixDiff + std::max(x - (SW2+1)*D, 0);
#if CV_SSE2
if( useSIMD )
{
......@@ -563,28 +554,21 @@ namespace cv
#endif
{
int minL0 = MAX_COST, minL1 = MAX_COST, minL2 = MAX_COST, minL3 = MAX_COST;
for( d = 0; d < D; d++ )
{
int Cpd = Cp[d], L0, L1, L2, L3;
L0 = Cpd + std::min((int)Lr_p0[d], std::min(Lr_p0[d-1] + P1, std::min(Lr_p0[d+1] + P1, delta0))) - delta0;
L1 = Cpd + std::min((int)Lr_p1[d], std::min(Lr_p1[d-1] + P1, std::min(Lr_p1[d+1] + P1, delta1))) - delta1;
L2 = Cpd + std::min((int)Lr_p2[d], std::min(Lr_p2[d-1] + P1, std::min(Lr_p2[d+1] + P1, delta2))) - delta2;
L3 = Cpd + std::min((int)Lr_p3[d], std::min(Lr_p3[d-1] + P1, std::min(Lr_p3[d+1] + P1, delta3))) - delta3;
Lr_p[d] = (CostType)L0;
minL0 = std::min(minL0, L0);
Lr_p[d + D2] = (CostType)L1;
minL1 = std::min(minL1, L1);
Lr_p[d + D2*2] = (CostType)L2;
minL2 = std::min(minL2, L2);
Lr_p[d + D2*3] = (CostType)L3;
minL3 = std::min(minL3, L3);
Sp[d] = saturate_cast<CostType>(Sp[d] + L0 + L1 + L2 + L3);
}
minLr[0][xm] = (CostType)minL0;
......@@ -593,7 +577,6 @@ namespace cv
minLr[0][xm+3] = (CostType)minL3;
}
}
if( pass == npasses )
{
for( x = 0; x < width; x++ )
......@@ -610,21 +593,17 @@ namespace cv
if( npasses == 1 )
{
int xm = x*NR2, xd = xm*D2;
int minL0 = MAX_COST;
int delta0 = minLr[0][xm + NR2] + P2;
CostType* Lr_p0 = Lr[0] + xd + NRD2;
Lr_p0[-1] = Lr_p0[D] = MAX_COST;
CostType* Lr_p = Lr[0] + xd;
const CostType* Cp = C + x*D;
#if CV_SSE2
if( useSIMD )
{
__m128i _P1 = _mm_set1_epi16((short)P1);
__m128i _delta0 = _mm_set1_epi16((short)delta0);
__m128i _minL0 = _mm_set1_epi16((short)minL0);
__m128i _minS = _mm_set1_epi16(MAX_COST), _bestDisp = _mm_set1_epi16(-1);
__m128i _d8 = _mm_setr_epi16(0, 1, 2, 3, 4, 5, 6, 7), _8 = _mm_set1_epi16(8);
......@@ -645,7 +624,6 @@ namespace cv
_bestDisp = _mm_xor_si128(_bestDisp, _mm_and_si128(_mm_xor_si128(_bestDisp,_d8), mask));
_d8 = _mm_adds_epi16(_d8, _8);
}
short CV_DECL_ALIGNED(16) bestDispBuf[8];
_mm_store_si128((__m128i*)bestDispBuf, _bestDisp);
_minL0 = _mm_min_epi16(_minL0, _mm_srli_si128(_minL0, 8));
......@@ -764,8 +742,7 @@ namespace cv
disparr.create( left.size(), CV_16S );
Mat disp = disparr.getMat();
computeDisparityBinarySGBM( left, right, disp, params, buffer );
medianBlur(disp, disp, 3);
// medianBlur(disp, disp, 3);
if( params.speckleWindowSize > 0 )
filterSpeckles(disp, (params.minDisparity - 1)*StereoMatcher::DISP_SCALE, params.speckleWindowSize,
StereoMatcher::DISP_SCALE*params.speckleRange, buffer);
......@@ -818,7 +795,6 @@ namespace cv
<< "P2" << params.P2
<< "mode" << params.mode;
}
void read(const FileNode& fn)
{
FileNode n = fn["name"];
......@@ -835,14 +811,11 @@ namespace cv
params.P2 = (int)fn["P2"];
params.mode = (int)fn["mode"];
}
StereoBinarySGBMParams params;
Mat buffer;
static const char* name_;
};
const char* StereoBinarySGBMImpl::name_ = "StereoMatcher.SGBM";
Ptr<StereoBinarySGBM> StereoBinarySGBM::create(int minDisparity, int numDisparities, int SADWindowSize,
int P1, int P2, int disp12MaxDiff,
int preFilterCap, int uniquenessRatio,
......@@ -856,7 +829,6 @@ namespace cv
speckleWindowSize, speckleRange,
mode));
}
typedef cv::Point_<short> Point2s;
}
}
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