my own gist test

1dPoc.cpp

// 1D POC ---------------------------------------------------------------------------------------
cv::Point2d phaseCorrelate1D(InputArray _src1, InputArray _src2, InputArray _window, int k=65535, int flag_pixel = false)
{
	Mat src1 = _src1.getMat();
	Mat src2 = _src2.getMat();
	Mat window = _window.getMat();

	CV_Assert( src1.type() == src2.type());
	CV_Assert( src1.type() == CV_32FC1 || src1.type() == CV_64FC1 );
	CV_Assert( src1.size == src2.size);

	if(!window.empty())
	{
		CV_Assert( src1.type() == window.type());
		CV_Assert( src1.size == window.size);
	}

	int M = src1.rows;
	int N = getOptimalDFTSize(src1.cols);

	Mat padded1, padded2, paddedWin;

	if(M != src1.rows || N != src1.cols)
	{
		copyMakeBorder(src1, padded1, 0, 0, 0, N - src1.cols, BORDER_CONSTANT, Scalar::all(0));
		copyMakeBorder(src2, padded2, 0, 0, 0, N - src2.cols, BORDER_CONSTANT, Scalar::all(0));

		if(!window.empty())
		{
			copyMakeBorder(window, paddedWin, 0, 0, 0, N - window.cols, BORDER_CONSTANT, Scalar::all(0));
		}
	}
	else
	{
		padded1 = src1;
		padded2 = src2;
		paddedWin = window;
	}

	// perform window multiplication if available
	if(!paddedWin.empty())
	{
		// apply window to both images before proceeding...
		multiply(paddedWin, padded1, padded1);
		multiply(paddedWin, padded2, padded2);
	}
	
	vector<Mat> C;
	C.resize(M);


	for(int i=0; i<M; i++){
		Mat FFT1, FFT2, P, Pm;
		
		Rect roi(0,i,N,1);
		Mat padded1_roi = padded1(roi);
		Mat padded2_roi = padded2(roi);

		// execute phase correlation equation
		// Reference: http://en.wikipedia.org/wiki/Phase_correlation
		dft(padded1_roi, FFT1, DFT_REAL_OUTPUT);
		dft(padded2_roi, FFT2, DFT_REAL_OUTPUT);

		mulSpectrums(FFT1, FFT2, P, 0, true);

		magSpectrums1D(P, Pm);
		divSpectrums1D(P, Pm, C[i], 0, false); // FF* / |FF*| (phase correlation equation completed here...)
		
		if(0<k&&k<C[i].cols){
			Rect lpf_roi(k,0,C[i].cols-k,1);
			Mat zero = C[i](lpf_roi);
			zero = Mat::zeros(zero.size(), zero.type());
		}
	
	
	Mat Rall;

		for(int i=0; i<M; i++){
			Rall = Rall + C[i]  / M;
		}
	
	else{
		vector<double> hann;
		hann.resize(M);
		double sum_hann = 0;
		for(int i=0; i<M; i++){
			hann[i] = 0.5 - 0.5*cos(2.0 * CV_PI * static_cast<double>(i) / M); // ww-1
			sum_hann += hann[i];
		}

		for(int i=0; i<M; i++){
			Rall = Rall + C[i] * hann[i] / sum_hann;
		}
	

	idft(Rall, Rall); // gives us the nice peak shift location...

	fftShift1D(Rall); // shift the energy to the center of the frame.

	// locate the highest peak
	Point peakLoc;
	minMaxLoc(Rall, NULL, NULL, NULL, &peakLoc); 

	Point2d t(0,0);
	if(!flag_pixel&&(peakLoc.x>=1)&&(peakLoc.x<Rall.cols-1)){
		t.x = (Rall.at<double>(0, peakLoc.x-1)-Rall.at<double>(0, peakLoc.x+1))/(2.0*Rall.at<double>(0,peakLoc.x-1)-4.0*Rall.at<double>(0,peakLoc.x)+2.0*Rall.at<double>(0,peakLoc.x+1));
	}
	
	// adjust shift relative to image center...
	Point2d center((double)padded1.cols / 2.0, (double)padded1.rows / 2.0);

	C.clear();

	t.x = t.x + peakLoc.x;
	return (center - t);
}

divSpectrum1d.cpp

// 1D div spectrum for dividing ----------------------------------------------------
static void divSpectrums1D( Mat &srcA, Mat &srcB, Mat &dst, int flags, bool conjB)
{
    int depth = srcA.depth(), cn = srcA.channels(), type = srcA.type();
    int rows = srcA.rows, cols = srcA.cols;
    int j, k;

    CV_Assert( type == srcB.type() && srcA.size() == srcB.size() );
    CV_Assert( type == CV_32FC1 || type == CV_32FC2 || type == CV_64FC1 || type == CV_64FC2 );

	dst.create( srcA.rows, srcA.cols, type );

    bool is_1d = (flags & DFT_ROWS) || (rows == 1 || (cols == 1 &&
             srcA.isContinuous() && srcB.isContinuous() && dst.isContinuous()));

    if( is_1d && !(flags & DFT_ROWS) )
        cols = cols + rows - 1, rows = 1;

    int ncols = cols*cn;
    int j0 = cn == 1;
    int j1 = ncols - (cols % 2 == 0 && cn == 1);

	const double* dataA = (const double*)srcA.data;
	const double* dataB = (const double*)srcB.data;
	double* dataC = (double*)dst.data;
	double eps = DBL_EPSILON; // prevent div0 problems

	size_t stepA = srcA.step/sizeof(dataA[0]);
	size_t stepB = srcB.step/sizeof(dataB[0]);
	size_t stepC = dst.step/sizeof(dataC[0]);

	for( ; rows--; dataA += stepA, dataB += stepB, dataC += stepC )
	{
		if( is_1d && cn == 1 )
		{
			dataC[0] = dataA[0] / (dataB[0] + eps);
			if( cols % 2 == 0 )
				dataC[j1] = dataA[j1] / (dataB[j1] + eps);
		}

		if( !conjB )
			for( j = j0; j < j1; j += 2 )
			{
				double denom = dataB[j]*dataB[j] + dataB[j+1]*dataB[j+1] + eps;
				double re = dataA[j]*dataB[j] + dataA[j+1]*dataB[j+1];
				double im = dataA[j+1]*dataB[j] - dataA[j]*dataB[j+1];
				dataC[j] = re / denom;
				dataC[j+1] = im / denom;
			}
		else
			for( j = j0; j < j1; j += 2 )
			{
				double denom = dataB[j]*dataB[j] + dataB[j+1]*dataB[j+1] + eps;
				double re = dataA[j]*dataB[j] - dataA[j+1]*dataB[j+1];
				double im = dataA[j+1]*dataB[j] + dataA[j]*dataB[j+1];
				dataC[j] = re / denom;
				dataC[j+1] = im / denom;
			}
	}
}

fftshift1d.cpp

// fftshift 1D ---------------------------------------------------------------------
static void fftShift1D(Mat &out)
{
    if(out.rows == 1 && out.cols == 1)
    {
        // trivially shifted.
        return;
    }

    std::vector<Mat> planes;
    split(out, planes);

    int xMid = out.cols >> 1;
    int yMid = out.rows >> 1;

    bool is_1d = xMid == 0 || yMid == 0;

	xMid = xMid + yMid;

	for(size_t i = 0; i < planes.size(); i++)
	{
		Mat tmp;
		Mat half0(planes[i], Rect(0, 0, xMid, 1));
		Mat half1(planes[i], Rect(xMid, 0, xMid, 1));

		half0.copyTo(tmp);
		half1.copyTo(half0);
		tmp.copyTo(half1);
	}

    merge(planes, out);
}

magSpectrum1d.cpp

// 1D spectrum ---------------------------------------------------------------------
static void magSpectrums1D( Mat &src, Mat &dst)
{
    int depth = src.depth(), cn = src.channels(), type = src.type();
    int rows = src.rows, cols = src.cols;
    int j, k;

    CV_Assert( type == CV_32FC1 || type == CV_32FC2 || type == CV_64FC1 || type == CV_64FC2 );

	dst.create( src.rows, src.cols,  CV_64FC1 );

    dst.setTo(0);//Mat elements are not equal to zero by default!

    bool is_1d = (rows == 1 || (cols == 1 && src.isContinuous() && dst.isContinuous()));

    if( is_1d )
        cols = cols + rows - 1, rows = 1;

    int ncols = cols*cn;
    int j0 = cn == 1;
    int j1 = ncols - (cols % 2 == 0 && cn == 1);

	const double* dataSrc = (const double*)src.data;
	double* dataDst = (double*)dst.data;

	size_t stepSrc = src.step/sizeof(dataSrc[0]);
	size_t stepDst = dst.step/sizeof(dataDst[0]);

	for( ; rows--; dataSrc += stepSrc, dataDst += stepDst )
	{
		if( is_1d && cn == 1 )
		{
			dataDst[0] = dataSrc[0]*dataSrc[0];
			if( cols % 2 == 0 )
				dataDst[j1] = dataSrc[j1]*dataSrc[j1];
		}

		for( j = j0; j < j1; j += 2 )
		{
			dataDst[j] = std::sqrt(dataSrc[j]*dataSrc[j] + dataSrc[j+1]*dataSrc[j+1]);
		}
	}
}

main.cpp

#include "opencv2/opencv.hpp"
#include <omp.h>
using namespace cv;

#if _DEBUG
#pragma comment(lib, "opencv_core248d.lib")
#pragma comment(lib, "opencv_highgui248d.lib")
#pragma comment(lib, "opencv_imgproc248d.lib")
#pragma comment(lib, "opencv_contrib248d.lib")
#else
#pragma comment(lib, "opencv_core248.lib")
#pragma comment(lib, "opencv_highgui248.lib")
#pragma comment(lib, "opencv_imgproc248.lib")
#pragma comment(lib, "opencv_contrib248.lib")
#endif

// インクルードパス、ライブラリパスをプロジェクトに登録してください。
// include path, library path 

// from OpenCV
static void magSpectrums1D( InputArray _src, OutputArray _dst);
static void divSpectrums1D( InputArray _srcA, InputArray _srcB, OutputArray _dst, int flags, bool conjB);
static void fftShift1D(InputOutputArray _out);

// main method ==============================================================================
int main(int argc, char* argv[])
{
	// Parameters
	Mat tempI = imread("tsukuba_r.png", 0);
	Mat tempJ = imread("tsukuba_l.png", 0);
	double mult_scale = 2;      // １階層ごとの画像サイズの縮尺
	int thresh = tempI.cols/3;  // 階層の深さ決定の目安
	int ww = getOptimalDFTSize(32); // 窓の幅 半値幅が有効幅
	int wh = 17; // 窓の高さ

	// Decision of the Image Hierarchical Level
	int lmax=0;
	for(;;){
		int value = (int)(pow(mult_scale,-(double)(lmax++))*tempI.cols);
		if(thresh>value) break;
	}

	vector<int> width, height;
	width.resize(lmax);
	height.resize(lmax);
	for(int i=0; i<lmax; i++){
		width[i] = (int)(pow(mult_scale,-(i))*tempI.cols);
		height[i] = tempI.rows;
	}

	Mat hann = Mat(wh,ww,CV_64F);
	for(int j=0; j<wh; j++){
		for(int i=0; i<ww; i++){
			hann.at<double>(j,i) = 0.5 - 0.5*cos(2.0 * CV_PI * static_cast<double>(i) / ww); // ww-1
		}
	}
	
	// Step1: Image Pyramid
	vector<Mat> I, J, LI, LJ;
	LI.resize(lmax);
	LJ.resize(lmax);
	I.resize(lmax);
	J.resize(lmax);

	for(int l=0; l<lmax; l++){
		resize(tempI, I[l], Size(width[l], height[l]), 0, 0, 1);
		resize(tempJ, J[l], Size(width[l], height[l]), 0, 0, 1);
		copyMakeBorder(I[l], LI[l], wh/2, wh/2, ww/2, ww/2, BORDER_CONSTANT, Scalar::all(0));
		copyMakeBorder(J[l], LJ[l], wh/2, wh/2, ww/2, ww/2, BORDER_CONSTANT, Scalar::all(0));
	}

	Mat disparity = Mat::zeros( Size(tempI.cols, tempI.rows), CV_64F);

	for(int v= 0; v<(int)I[0].rows; v++){

		for(int u=0; u<(int)I[0].cols; u++){
			int error = 0;

			vector<Point2d> P, Q, Qd;
			P.resize(lmax+1);
			Q.resize(lmax+1);
			Qd.resize(lmax+1);

			// Step2:
			P[lmax] = Point2d(pow(mult_scale,-lmax)*u,v);
			Q[lmax] = Point2d(pow(mult_scale,-lmax)*u,v);

			for(int l=lmax-1; l>=0; l--){
				// Step3:
				P[l] = Point2d(pow(mult_scale,-l)*u,v);
				Qd[l] = Point2d(mult_scale*Q[l+1].x,Q[l+1].y);

				// Step4:
				Rect i_roi((int)P[l].x,(int)P[l].y,ww,wh);
				Mat Iroi = LI[l](i_roi);

				if((Qd[l].x<0)||(LJ[l].cols<Qd[l].x+ww)){
					Q[l] = P[l];
					continue;
				}
				Rect j_roi = Rect((int)Qd[l].x,(int)Qd[l].y,ww,wh);
				Mat Jroi = LJ[l](j_roi);

				Mat I64f, J64f;
				Iroi.convertTo(I64f, CV_64F);
				Jroi.convertTo(J64f, CV_64F);

				Point2d shift = phaseCorrelate1D(I64f, J64f, hann, ww/2, true);

				if( P[l].x < (shift.x+Qd[l].x)){
					Q[l] = Qd[l]+Point2d(shift.x, 0);
				}
				else {
					Q[l] = Qd[l];
				}
			} // Step5
			{ // Step6
				Rect i_roi((int)P[0].x,(int)P[0].y,ww,wh);
				Mat Iroi = LI[0](i_roi);

				if((Q[0].x<0)||(LJ[0].cols<Q[0].x+ww)){
					error = 1;
					goto end;
				}
				Rect j_roi = Rect((int)Q[0].x,(int)Q[0].y,ww,wh);
				Mat Jroi = LJ[0](j_roi);

				Mat I64f, J64f;
				Iroi.convertTo(I64f, CV_64F);
				Jroi.convertTo(J64f, CV_64F);

				Point2d shift = phaseCorrelate1D(I64f, J64f, hann, ww/2, false);

				if( P[0].x < (shift.x+Q[0].x)){
					Q[0] = Q[0]+Point2d(shift.x, 0);
				}
					
#if _DEBUG
				Mat matchedImg = Mat::zeros(Size(I[0].cols*2,0), CV_8UC1);

				vconcat(I[0],J[0],matchedImg);
				cvtColor( matchedImg,  matchedImg, COLOR_GRAY2RGB);
				line(matchedImg,Point(P[0].x,P[0].y),Point(Q[0].x,matchedImg.rows/2+Q[0].y),Scalar(0,0,255));
				imshow("matched", matchedImg);

				waitKey(1);
#endif
			}
end:
			double d = Q[0].x-P[0].x;
			double view_band = 0.6;  // エラー閾値、表示調整用
			if(view_band*ww<d||error) d=0;
			disparity.at<double>(v, u) = d;
		}
	}

	// http://stackoverflow.com/questions/13840013/opencv-how-to-visualize-a-depth-image
	double min, max;
	cv::minMaxIdx(disparity, &min, &max);
	Mat adjMap;
	double scale = 255/(max-min);
	disparity.convertTo(adjMap, CV_8UC1, scale, -min*scale);

	cv::Mat falseColorMap;
	applyColorMap(adjMap, falseColorMap, cv::COLORMAP_JET);

	imshow("disparity", falseColorMap);
	imwrite("disparity.bmp", falseColorMap);
	waitKey();

	return 0;
}