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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 *
+* *
+\******************************************************************************************************************/
+#include <stdint.h>
+
+#ifndef _OPENCV_MATCHING_HPP_
+#define _OPENCV_MATCHING_HPP_
+#ifdef __cplusplus
+
+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 {
+ 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 {
+ 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 {
+ 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{
+ 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{
+ 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 speckleY;
+ Mat speckleX;
+ Mat puss;
+ //int *specklePointX;
+ //int *specklePointY;
+ //long long *pus;
+ int previous_size;
+ //!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.rows == hammingDistanceCost.rows);
+ 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);
+ int *pus = (int *)puss.data;
+ int *specklePointX = (int *)speckleX.data;
+ int *specklePointY = (int *)speckleY.data;
+ memset(pus, 0, previous_size * sizeof(pus[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;
+ 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)
+ {
+ 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++;
+ 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 >= 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
+#endif
+/*End of file*/
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