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darknet_2_5_python/include/opencv2/stereo/matching.hpp

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//By downloading, copying, installing or using the software you agree to this license.
//If you do not agree to this license, do not download, install,
//copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
// (3-clause BSD License)
//
//Copyright (C) 2000-2015, Intel Corporation, all rights reserved.
//Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
//Copyright (C) 2009-2015, NVIDIA Corporation, all rights reserved.
//Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
//Copyright (C) 2015, OpenCV Foundation, all rights reserved.
//Copyright (C) 2015, Itseez Inc., all rights reserved.
//Third party copyrights are property of their respective owners.
//
//Redistribution and use in source and binary forms, with or without modification,
//are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * Neither the names of the copyright holders nor the names of the contributors
// may be used to endorse or promote products derived from this software
// without specific prior written permission.
//
//This software is provided by the copyright holders and contributors "as is" and
//any express or implied warranties, including, but not limited to, the implied
//warranties of merchantability and fitness for a particular purpose are disclaimed.
//In no event shall copyright holders or contributors be liable for any direct,
//indirect, incidental, special, exemplary, or consequential damages
//(including, but not limited to, procurement of substitute goods or services;
//loss of use, data, or profits; or business interruption) however caused
//and on any theory of liability, whether in contract, strict liability,
//or tort (including negligence or otherwise) arising in any way out of
//the use of this software, even if advised of the possibility of such damage.
/*****************************************************************************************************************\
* The interface contains the main methods for computing the matching between the left and right images *
* *
\******************************************************************************************************************/
#ifndef _OPENCV_MATCHING_HPP_
#define _OPENCV_MATCHING_HPP_
#include <stdint.h>
#include "opencv2/core.hpp"
namespace cv
{
namespace stereo
{
class Matching
{
private:
//!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 used for getting the minimum disparity from the cost volume"
static int minim(short *c, int iwpj, int widthDisp,const double confidence, 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(mini != 0)
{
if (mini3 / mini <= confidence)
return index;
}
return -1;
}
//!Interpolate in order to obtain better results
//!function for refining the disparity at sub pixel using simetric v
static double symetricVInterpolation(short *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;
}
//!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()
{
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;
}
}
//!the class used in computing the hamming distance
class hammingDistance : public ParallelLoopBody
{
private:
int *left, *right;
short *c;
int v,kernelSize, width;
int MASK;
int *hammLut;
public :
hammingDistance(const Mat &leftImage, const Mat &rightImage, short *cost, int maxDisp, int kerSize, int *hammingLUT):
left((int *)leftImage.data), right((int *)rightImage.data), c(cost), v(maxDisp),kernelSize(kerSize),width(leftImage.cols), MASK(65535), hammLut(hammingLUT){}
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 = 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_POPCNT
if (checkHardwareSupport(CV_CPU_POPCNT))
{
c[(iwj)* (v + 1) + d] = (short)_mm_popcnt_u32(xorul);
}
else
#endif
{
c[(iwj)* (v + 1) + d] = (short)(hammLut[xorul & MASK] + hammLut[(xorul >> 16) & MASK]);
}
}
}
}
}
};
//!cost aggregation
class agregateCost:public ParallelLoopBody
{
private:
int win;
short *c, *parSum;
int maxDisp,width, height;
public:
agregateCost(const Mat &partialSums, int windowSize, int maxDispa, Mat &cost)
{
win = windowSize / 2;
c = (short *)cost.data;
maxDisp = maxDispa;
width = cost.cols / ( maxDisp + 1) - 1;
height = cost.rows - 1;
parSum = (short *)partialSums.data;
}
void operator()(const cv::Range &r) const CV_OVERRIDE {
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;
short *c;
public:
makeMap(const Mat &costVolume, int threshold, int maxDisp, double confidence,int scale, Mat &mapFinal)
{
c = (short *)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 CV_OVERRIDE {
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
template <typename T>
class Median1x9:public ParallelLoopBody
{
private:
T *original;
T *filtered;
int height, width;
public:
Median1x9(const Mat &originalImage, Mat &filteredImage)
{
original = (T *)originalImage.data;
filtered = (T *)filteredImage.data;
height = originalImage.rows;
width = originalImage.cols;
}
void operator()(const cv::Range &r) const CV_OVERRIDE {
for (int m = r.start; m <= r.end; m++)
{
for (int n = 4; n < width - 4; ++n)
{
int k = 0;
T window[9];
for (int i = n - 4; i <= n + 4; ++i)
window[k++] = original[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 T temp = window[j];
window[j] = window[min];
window[min] = temp;
}
filtered[m * width + n] = window[4];
}
}
}
};
//!median 9x1 paralelized filter
template <typename T>
class Median9x1:public ParallelLoopBody
{
private:
T *original;
T *filtered;
int height, width;
public:
Median9x1(const Mat &originalImage, Mat &filteredImage)
{
original = (T *)originalImage.data;
filtered = (T *)filteredImage.data;
height = originalImage.rows;
width = originalImage.cols;
}
void operator()(const Range &r) const CV_OVERRIDE {
for (int n = r.start; n <= r.end; ++n)
{
for (int m = 4; m < height - 4; ++m)
{
int k = 0;
T window[9];
for (int i = m - 4; i <= m + 4; ++i)
window[k++] = original[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 T temp = window[j];
window[j] = window[min];
window[min] = temp;
}
filtered[m * width + n] = window[4];
}
}
}
};
protected:
//arrays used in the region removal
Mat_<int> speckleY;
Mat_<int> speckleX;
Mat_<int> puss;
//int *specklePointX;
//int *specklePointY;
//long long *pus;
//!method for setting the maximum disparity
void setMaxDisparity(int val)
{
CV_Assert(val > 10);
this->maxDisparity = val;
}
//!method for getting the disparity
int getMaxDisparity()
{
return this->maxDisparity;
}
//! a number by which the disparity will be multiplied for better display
void setScallingFactor(int val)
{
CV_Assert(val > 0);
this->scallingFactor = val;
}
//!method for getting the scalling factor
int getScallingFactor()
{
return scallingFactor;
}
//!setter for the confidence check
void setConfidence(double val)
{
CV_Assert(val >= 1);
this->confidenceCheck = val;
}
//getter for confidence check
double getConfidence()
{
return confidenceCheck;
}
//! 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 hammingDistanceBlockMatching(const Mat &leftImage, const Mat &rightImage, Mat &cost, const int kernelSize= 9)
{
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);
short *c = (short *)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,(short *)cost.data,maxDisparity,kernelSize / 2,hamLut));
}
//preprocessing the cost volume in order to get it ready for aggregation
void costGathering(const Mat &hammingDistanceCost, Mat &cost)
{
CV_Assert(hammingDistanceCost.type() == CV_16S);
CV_Assert(cost.type() == CV_16S);
int maxDisp = maxDisparity;
int width = cost.cols / ( maxDisp + 1) - 1;
int height = cost.rows - 1;
short *c = (short *)cost.data;
short *ham = (short *)hammingDistanceCost.data;
memset(c, 0, sizeof(c[0]) * (width + 1) * (height + 1) * (maxDisp + 1));
for (int i = 1; i <= height; 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];
}
}
}
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 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;
short *c = (short *)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));
}
//!remove small regions that have an area smaller than t, we fill the region with the average of the good pixels around it
template <typename T>
void smallRegionRemoval(const Mat &currentMap, int t, Mat &out)
{
CV_Assert(currentMap.cols == out.cols);
CV_Assert(currentMap.rows == out.rows);
CV_Assert(t >= 0);
CV_Assert(!puss.empty());
int *specklePointX = (int *)speckleX.data;
int *specklePointY = (int *)speckleY.data;
puss.setTo(Scalar::all(0));
T *map = (T *)currentMap.data;
T *outputMap = (T *)out.data;
int height = currentMap.rows;
int width = currentMap.cols;
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)
{
T nr = 1;
T avg = 0;
speckle_size = dr;
specklePointX[dr] = i;
specklePointY[dr] = j;
puss(i, 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 &&
puss(ii + di[d], jj + dj[d]) == 0)
{
T 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++;
puss(ii + di[d], 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 >= 1 && 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)
{
T fillValue = (T)(avg / nr);
while (speckle_size < st)
{
int ii = specklePointX[speckle_size];
int jj = specklePointY[speckle_size];
outputMap[ii * width + jj] = fillValue;
speckle_size++;
}
}
}
}
}
}
//!Method responsible for generating the disparity map
//!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 &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));
}
public:
//!a median filter of 1x9 and 9x1
//!1x9 median filter
template<typename T>
void Median1x9Filter(const Mat &originalImage, Mat &filteredImage)
{
CV_Assert(originalImage.rows == filteredImage.rows);
CV_Assert(originalImage.cols == filteredImage.cols);
parallel_for_(Range(1,originalImage.rows - 2), Median1x9<T>(originalImage,filteredImage));
}
//!9x1 median filter
template<typename T>
void Median9x1Filter(const Mat &originalImage, Mat &filteredImage)
{
CV_Assert(originalImage.cols == filteredImage.cols);
CV_Assert(originalImage.cols == filteredImage.cols);
parallel_for_(Range(1,originalImage.cols - 2), Median9x1<T>(originalImage,filteredImage));
}
//!constructor for the matching class
//!maxDisp - represents the maximum disparity
Matching(void)
{
hammingLut();
}
~Matching(void)
{
}
//constructor for the matching class
//maxDisp - represents the maximum disparity
//confidence - represents the confidence check
Matching(int maxDisp, int scalling = 4, int confidence = 6)
{
//set the maximum disparity
setMaxDisparity(maxDisp);
//set scalling factor
setScallingFactor(scalling);
//set the value for the confidence
setConfidence(confidence);
//generate the hamming lut in case SSE is not available
hammingLut();
}
};
}
}
#endif
/*End of file*/