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Commit 707beb3f authored by Muresan Mircea Paul's avatar Muresan Mircea Paul
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modified matching class such that parameters are added at runtime 

Removed the matching cpp file as it is no longer usefull

removed warnings

header for some used functions

fixed the popcnt issue

changes according to comments
parent df859520
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Showing with 303 additions and 391 deletions
modules/stereo/include/opencv2/stereo.hpp
View file @ 707beb3f
......@@ -147,12 +147,6 @@ namespace cv
virtual int getSmallerBlockSize() const = 0;
virtual void setSmallerBlockSize(int blockSize) = 0;
virtual Rect getROI1() const = 0;
virtual void setROI1(Rect roi1) = 0;
virtual Rect getROI2() const = 0;
virtual void setROI2(Rect roi2) = 0;
virtual int getScalleFactor() const = 0 ;
virtual void setScalleFactor(int factor) = 0;
......@@ -233,9 +227,9 @@ namespace cv
zero. In the current implementation, this parameter must be divisible by 16.
@param blockSize Matched block size. It must be an odd number \>=1 . Normally, it should be
somewhere in the 3..11 range.
@param P1 The first parameter controlling the disparity smoothness. See below.
@param P2 The second parameter controlling the disparity smoothness. The larger the values are,
the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1
@param P1 The first parameter controlling the disparity smoothness.This parameter is used for the case of slanted surfaces (not fronto parallel).
@param P2 The second parameter controlling the disparity smoothness.This parameter is used for "solving" the depth discontinuities problem.
The larger the values are, the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1
between neighbor pixels. P2 is the penalty on the disparity change by more than 1 between neighbor
pixels. The algorithm requires P2 \> P1 . See stereo_match.cpp sample where some reasonably good
P1 and P2 values are shown (like 8\*number_of_image_channels\*SADWindowSize\*SADWindowSize and
......@@ -263,7 +257,7 @@ namespace cv
to a custom value.
*/
CV_EXPORTS static Ptr<cv::stereo::StereoBinarySGBM> create(int minDisparity, int numDisparities, int blockSize,
int P1 = 0, int P2 = 0, int disp12MaxDiff = 0,
int P1 = 100, int P2 = 1000, int disp12MaxDiff = 0,
int preFilterCap = 0, int uniquenessRatio = 0,
int speckleWindowSize = 0, int speckleRange = 0,
int mode = StereoBinarySGBM::MODE_SGBM);
......
modules/stereo/src/matching.cpp deleted 100644 → 0
View file @ df859520
//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)
{
//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();
}
//!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);
this->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,(int *)cost.data,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);
cost.setTo(0);
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);
this->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)
{
mapFinal.setTo(0);
//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 &originalImage, Mat &filteredImage)
{
CV_Assert(originalImage.rows == filteredImage.rows);
CV_Assert(originalImage.cols == filteredImage.cols);
parallel_for_(Range(1,originalImage.rows - 2), Median1x9(originalImage,filteredImage));
}
//!9x1 median filter
void Matching::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(originalImage,filteredImage));
}
}
}
modules/stereo/src/matching.hpp
View file @ 707beb3f
......@@ -58,10 +58,6 @@ namespace cv
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
......@@ -70,23 +66,93 @@ namespace cv
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);
static int minim(int *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 (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(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;
}
//!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();
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, *c;
int v,kernelSize, width, height,_stride;
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), _stride((int)leftImage.step1()), MASK(65535), hammLut(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++)
{
......@@ -323,24 +389,177 @@ namespace cv
}
};
protected:
//arrays used in the region removal
int *specklePointX;
int *specklePointY;
long long *pus;
//!method for setting the maximum disparity
void setMaxDisparity(int val);
void setMaxDisparity(int val)
{
CV_Assert(val > 10);
this->maxDisparity = val;
}
//!method for getting the disparity
int getMaxDisparity();
//!method for setting the scalling factor
void setScallingFactor(int val);
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();
int getScallingFactor()
{
return scallingFactor;
}
//!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);
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);
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,(int *)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.rows == hammingDistanceCost.rows);
CV_Assert(hammingDistanceCost.type() == CV_32SC4);
CV_Assert(cost.type() == CV_32SC4);
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 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));
}
//!remove small regions that have an area smaller than t, we fill the region with the average of the good pixels around it
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);
memset(pus, 0, sizeof(pus));
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++;
}
}
}
}
}
}
//!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
......@@ -348,17 +567,54 @@ namespace cv
*map - is the disparity map that will result
*th - is the LR threshold
*/
void dispartyMapFormation(const Mat &costVolume, Mat &map, int th);
void smallRegionRemoval(const Mat &input, int t, Mat &out);
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:
static void Median1x9Filter(const Mat &inputImage, Mat &outputImage);
static void Median9x1Filter(const Mat &inputImage, Mat &outputImage);
//!a median filter of 1x9 and 9x1
//!1x9 median filter
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(originalImage,filteredImage));
}
//!9x1 median filter
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(originalImage,filteredImage));
}
//!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
Matching(int maxDisp, int scallingFactor = 4,int confidenceCheck = 6);
Matching(void);
~Matching(void);
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();
}
};
}
}
......
modules/stereo/src/stereo_binary_bm.cpp
View file @ 707beb3f
......@@ -69,7 +69,6 @@ namespace cv
textureThreshold = 10;
uniquenessRatio = 15;
speckleRange = speckleWindowSize = 0;
roi1 = roi2 = Rect(0, 0, 0, 0);
disp12MaxDiff = -1;
dispType = CV_16S;
usePrefilter = false;
......@@ -89,7 +88,6 @@ namespace cv
int uniquenessRatio;
int speckleRange;
int speckleWindowSize;
Rect roi1, roi2;
int disp12MaxDiff;
int dispType;
int scalling;
......@@ -268,7 +266,7 @@ namespace cv
StereoBinaryBMParams* state;
};
class StereoBinaryBMImpl : public StereoBinaryBM,public Matching
class StereoBinaryBMImpl : public StereoBinaryBM, public Matching
{
public:
StereoBinaryBMImpl()
......@@ -279,6 +277,7 @@ namespace cv
StereoBinaryBMImpl(int _numDisparities, int _kernelSize) : Matching(_numDisparities)
{
params = StereoBinaryBMParams(_numDisparities, _kernelSize);
previous_size = 0;
}
void compute(InputArray leftarr, InputArray rightarr, OutputArray disparr)
......@@ -340,6 +339,14 @@ namespace cv
int width = left0.cols;
int height = left0.rows;
if(previous_size != width * height)
{
previous_size = width * height;
specklePointX = new int[width * height];
specklePointY = new int[width * height];
pus = new long long[width * height];
}
int wsz = params.kernelSize;
int bufSize0 = (int)((ndisp + 2)*sizeof(int));
bufSize0 += (int)((height + wsz + 2)*ndisp*sizeof(int));
......@@ -462,12 +469,6 @@ namespace cv
int getSmallerBlockSize() const { return 0; }
void setSmallerBlockSize(int) {}
Rect getROI1() const { return params.roi1; }
void setROI1(Rect roi1) { params.roi1 = roi1; }
Rect getROI2() const { return params.roi2; }
void setROI2(Rect roi2) { params.roi2 = roi2; }
void write(FileStorage& fs) const
{
fs << "name" << name_
......@@ -499,7 +500,6 @@ namespace cv
params.preFilterCap = (int)fn["preFilterCap"];
params.textureThreshold = (int)fn["textureThreshold"];
params.uniquenessRatio = (int)fn["uniquenessRatio"];
params.roi1 = params.roi2 = Rect();
}
StereoBinaryBMParams params;
......@@ -511,7 +511,7 @@ namespace cv
Mat partialSumsLR;
Mat agregatedHammingLRCost;
Mat Integral[2];
int previous_size;
static const char* name_;
};
......
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