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HxOpticalFlowMultiScale.h File Reference

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#include "HxImageRep.h"

Go to the source code of this file.

Functions
HxImageRep L_HXIMAGEREP	HxOpticalFlowMultiScale (HxImageRep im1, HxImageRep im2)
	implement Lucas and Kanade's method for optical flow detection then go for Bergen - multiscale the output is a HxVec2DDouble image where the displacement x and y values are exported. More...

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Detailed Description

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Function Documentation

HxImageRep L_HXIMAGEREP HxOpticalFlowMultiScale (  HxImageRep    im1, HxImageRep    im2 )

implement Lucas and Kanade's method for optical flow detection then go for Bergen - multiscale the output is a HxVec2DDouble image where the displacement x and y values are exported. { //to compute the image derivatives //for x and y use the coefficients: 1/12(-1,8,0,-8,1) int pixelDimensionality=1; int dimensions=2; HxSizes sizes(5,1,1); //the data pointer for Hx should have 3 shorts per pixels? double data[]={ -1.0/12, 8.0/12, 0, -8.0/12, 1.0/12 }; HxImageRep kernel = HxMakeFromDoubleData(pixelDimensionality, dimensions, sizes, data); //create the pyramid images double sigma = 1.5; HxImageRep im1_1=HxGauss(im1,sigma,HxImageRep::ARITH_PREC); HxImageRep im2_1=HxGauss(im2,sigma,HxImageRep::ARITH_PREC); im1_1 = HxScale(im1_1, 0.5, 0.5, 1, NEAREST, 1); //NEAREST im2_1 = HxScale(im2_1, 0.5, 0.5, 1, NEAREST, 1); // sigma = 2.0; HxImageRep im1_2=HxGauss(im1_1,sigma,HxImageRep::ARITH_PREC); HxImageRep im2_2=HxGauss(im2_1,sigma,HxImageRep::ARITH_PREC); // im1_2 = HxScale(im1_2, 0.25, 0.25, 1, LINEAR, 1); // im2_2 = HxScale(im2_2, 0.25, 0.25, 1, LINEAR, 1); im1_2 = HxScale(im1_2, 0.5, 0.5, 1, NEAREST, 1); im2_2 = HxScale(im2_2, 0.5, 0.5, 1, NEAREST, 1); //you have to start with the smallest image (top of the pyramid) HxImageRep ix,iy,it; //derivatives on x, y and time HxImageRep ix_1,iy_1,it_1; //derivatives on x, y and time HxImageRep ix_2,iy_2,it_2; //derivatives on x, y and time ix_2 = im1_2.generalizedConvolutionK1d(1, kernel,"mul", "addAssign", HxImageRep::ARITH_PREC); iy_2 = im1_2.generalizedConvolutionK1d(2, kernel,"mul", "addAssign", HxImageRep::ARITH_PREC); it_2 = HxSub(im2_2,im1_2); HxImageRep res2 = HxOFOneScale(ix_2,iy_2,it_2); res2 = HxScale(res2, 2, 2, 1, LINEAR, 1); res2 = HxMulVal(res2,2); ix_1 = im1_1.generalizedConvolutionK1d(1, kernel,"mul", "addAssign", HxImageRep::ARITH_PREC); iy_1 = im1_1.generalizedConvolutionK1d(2, kernel,"mul", "addAssign", HxImageRep::ARITH_PREC); //estimate now the image at time t+1 by motion in smallar image it_1 = HxSub(im2_2,HxAdd(im1_1,res2)); HxImageRep res1 = HxOFOneScale(ix_1,iy_1,it_1); res1 = HxScale(res1, 2, 2, 1, LINEAR, 1); res1 = HxMulVal(res1,2); ix = im1.generalizedConvolutionK1d(1, kernel,"mul", "addAssign", HxImageRep::ARITH_PREC); iy = im1.generalizedConvolutionK1d(2, kernel,"mul", "addAssign", HxImageRep::ARITH_PREC); //estimate now the image at time t+1 by motion in smallar image it = HxSub(im2,HxAdd(im1,res1)); /* HxWriteFile(ix,"ix.tif"); HxWriteFile(iy,"iy.tif"); HxWriteFile(it,"it.tif"); */ HxImageRep res = HxOFOneScale(ix,iy,it); //export now the two components to vizualize with Hieu's program /* HxImageRep u = HxProjectRange(res,1); HxImageRep v = HxProjectRange(res,2); // u = HxAbs(u); // v = HxAbs(v); double umin = HxPixMax(u).HxScalarDoubleValue().x(); double vmin = HxPixMax(v).HxScalarDoubleValue().x(); u = HxAddVal(u,fabs(umin)); v = HxAddVal(v,fabs(vmin)); double umax = HxPixMax(HxAbs(u)).HxScalarDoubleValue().x(); double vmax = HxPixMax(HxAbs(v)).HxScalarDoubleValue().x(); HxWriteFile(HxImageAsByte(HxMulVal(u,255.0/umax)),"u1.tif"); HxWriteFile(HxImageAsByte(HxMulVal(v,255.0/vmax)),"v1.tif"); */ return res; }

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