//--------------------------------------------------------------------------------------
// function to perfomr coarse to fine optical flow estimation
//--------------------------------------------------------------------------------------
void OpticalFlow::Coarse2FineFlow(DImage &vx, DImage &vy, DImage &warpI2,const DImage &Im1, const DImage &Im2, double alpha, double ratio, int minWidth,
int nOuterFPIterations, int nInnerFPIterations, int nCGIterations)
{
// first build the pyramid of the two images
GaussianPyramid GPyramid1;
GaussianPyramid GPyramid2;
if(IsDisplay)
cout<<"Constructing pyramid...";
GPyramid1.ConstructPyramid(Im1,ratio,minWidth);
GPyramid2.ConstructPyramid(Im2,ratio,minWidth);
if(IsDisplay)
cout<<"done!"<<endl;
// now iterate from the top level to the bottom
DImage Image1,Image2,WarpImage2;
for(int k=GPyramid1.nlevels()-1; k>=0; k--)
{
if(IsDisplay)
cout<<"Pyramid level "<<k;
int width=GPyramid1.Image(k).width();
int height=GPyramid1.Image(k).height();
im2feature(Image1,GPyramid1.Image(k));
im2feature(Image2,GPyramid2.Image(k));
if(k==GPyramid1.nlevels()-1) // if at the top level
{
vx.allocate(width,height);
vy.allocate(width,height);
//warpI2.copyData(Image2);
WarpImage2.copyData(Image2);
}
else
{
vx.imresize(width,height);
vx.Multiplywith(1/ratio);
vy.imresize(width,height);
vy.Multiplywith(1/ratio);
//warpFL(warpI2,GPyramid1.Image(k),GPyramid2.Image(k),vx,vy);
warpFL(WarpImage2,Image1,Image2,vx,vy);
}
//SmoothFlowPDE(GPyramid1.Image(k),GPyramid2.Image(k),warpI2,vx,vy,alpha,nOuterFPIterations,nInnerFPIterations,nCGIterations);
//SmoothFlowPDE(Image1,Image2,WarpImage2,vx,vy,alpha*pow((1/ratio),k),nOuterFPIterations,nInnerFPIterations,nCGIterations);
SmoothFlowPDE(Image1,Image2,WarpImage2,vx,vy,alpha,nOuterFPIterations,nInnerFPIterations,nCGIterations);
if(IsDisplay)
cout<<endl;
}
warpFL(warpI2,Im1,Im2,vx,vy);
}
void OpticalFlow::genInImageMask(DImage &mask, const DImage &flow,int interval)
{
int imWidth,imHeight;
imWidth=flow.width();
imHeight=flow.height();
if(mask.matchDimension(flow.width(),flow.height(),1)==false)
mask.allocate(imWidth,imHeight);
else
mask.reset();
const _FlowPrecision *pFlow;
_FlowPrecision *pMask;
pFlow = flow.data();;
pMask=mask.data();
double x,y;
for(int i=0;i<imHeight;i++)
for(int j=0;j<imWidth;j++)
{
int offset=i*imWidth+j;
y=i+pFlow[offset*2+1];
x=j+pFlow[offset*2];
if(x<interval || x>imWidth-1-interval || y<interval || y>imHeight-1-interval)
continue;
pMask[offset]=1;
}
}
//--------------------------------------------------------------------------------------------------------
// function to generate mask of the pixels that move inside the image boundary
//--------------------------------------------------------------------------------------------------------
void OpticalFlow::genInImageMask(DImage &mask, const DImage &vx, const DImage &vy,int interval)
{
int imWidth,imHeight;
imWidth=vx.width();
imHeight=vx.height();
if(mask.matchDimension(vx)==false)
mask.allocate(imWidth,imHeight);
const _FlowPrecision *pVx,*pVy;
_FlowPrecision *pMask;
pVx=vx.data();
pVy=vy.data();
mask.reset();
pMask=mask.data();
double x,y;
for(int i=0;i<imHeight;i++)
for(int j=0;j<imWidth;j++)
{
int offset=i*imWidth+j;
y=i+pVx[offset];
x=j+pVy[offset];
if(x<interval || x>imWidth-1-interval || y<interval || y>imHeight-1-interval)
continue;
pMask[offset]=1;
}
}
//---------------------------------------------------------------------------------------
// function to convert image to feature image
//---------------------------------------------------------------------------------------
void OpticalFlow::im2feature(DImage &imfeature, const DImage &im)
{
int width=im.width();
int height=im.height();
int nchannels=im.nchannels();
if(nchannels==1)
{
imfeature.allocate(im.width(),im.height(),3);
DImage imdx,imdy;
im.dx(imdx,true);
im.dy(imdy,true);
_FlowPrecision* data=imfeature.data();
for(int i=0;i<height;i++)
for(int j=0;j<width;j++)
{
int offset=i*width+j;
data[offset*3]=im.data()[offset];
data[offset*3+1]=imdx.data()[offset];
data[offset*3+2]=imdy.data()[offset];
}
}
else if(nchannels==3)
{
DImage grayImage;
im.desaturate(grayImage);
imfeature.allocate(im.width(),im.height(),5);
DImage imdx,imdy;
grayImage.dx(imdx,true);
grayImage.dy(imdy,true);
_FlowPrecision* data=imfeature.data();
for(int i=0;i<height;i++)
for(int j=0;j<width;j++)
{
int offset=i*width+j;
data[offset*5]=grayImage.data()[offset];
data[offset*5+1]=imdx.data()[offset];
data[offset*5+2]=imdy.data()[offset];
data[offset*5+3]=im.data()[offset*3+1]-im.data()[offset*3];
data[offset*5+4]=im.data()[offset*3+1]-im.data()[offset*3+2];
}
}
else
imfeature.copyData(im);
}
void OpticalFlow::Laplacian(DImage &output, const DImage &input, const DImage& weight)
{
if(output.matchDimension(input)==false)
output.allocate(input);
output.reset();
if(input.matchDimension(weight)==false)
{
cout<<"Error in image dimension matching OpticalFlow::Laplacian()!"<<endl;
return;
}
const _FlowPrecision *inputData=input.data(),*weightData=weight.data();
int width=input.width(),height=input.height();
DImage foo(width,height);
_FlowPrecision *fooData=foo.data(),*outputData=output.data();
// horizontal filtering
for(int i=0;i<height;i++)
for(int j=0;j<width-1;j++)
{
int offset=i*width+j;
fooData[offset]=(inputData[offset+1]-inputData[offset])*weightData[offset];
}
for(int i=0;i<height;i++)
for(int j=0;j<width;j++)
{
int offset=i*width+j;
if(j<width-1)
outputData[offset]-=fooData[offset];
if(j>0)
outputData[offset]+=fooData[offset-1];
}
foo.reset();
// vertical filtering
for(int i=0;i<height-1;i++)
for(int j=0;j<width;j++)
{
int offset=i*width+j;
fooData[offset]=(inputData[offset+width]-inputData[offset])*weightData[offset];
}
for(int i=0;i<height;i++)
for(int j=0;j<width;j++)
{
int offset=i*width+j;
if(i<height-1)
outputData[offset]-=fooData[offset];
if(i>0)
outputData[offset]+=fooData[offset-width];
}
}
bool OpticalFlow::LoadOpticalFlow(ifstream& myfile,DImage& flow)
{
Image<unsigned short int> foo;
if(foo.loadImage(myfile) == false)
return false;
if(!flow.matchDimension(foo))
flow.allocate(foo);
for(int i = 0;i<flow.npixels();i++)
{
flow.data()[i*2] = (double)foo.data()[i*2]/160-200;
flow.data()[i*2+1] = (double)foo.data()[i*2+1]/160-200;
}
return true;
}
//--------------------------------------------------------------------------------------
// function to perfomr coarse to fine optical flow estimation
//--------------------------------------------------------------------------------------
void OpticalFlow::Coarse2FineFlow(DImage &vx, DImage &vy, DImage &warpI2,const DImage &Im1, const DImage &Im2, double alpha, double ratio, int minWidth,
int nOuterFPIterations, int nInnerFPIterations, int nCGIterations)
{
// first build the pyramid of the two images
GaussianPyramid GPyramid1;
GaussianPyramid GPyramid2;
if(IsDisplay)
cout<<"Constructing pyramid...";
GPyramid1.ConstructPyramid(Im1,ratio,minWidth);
GPyramid2.ConstructPyramid(Im2,ratio,minWidth);
if(IsDisplay)
cout<<"done!"<<endl;
// now iterate from the top level to the bottom
DImage Image1,Image2,WarpImage2;
// initialize noise
// cout << GPyramid1.nlevels() << " pyramid levels\n";
for(int k=GPyramid1.nlevels()-1;k>=0;k--)
{
if(IsDisplay)
cout<<"Pyramid level "<<k+1;
int width=GPyramid1.Image(k).width();
int height=GPyramid1.Image(k).height();
im2feature(Image1,GPyramid1.Image(k));
im2feature(Image2,GPyramid2.Image(k));
// cout << "\t- level " << k+1 << " size " << width <<"x" << height <<endl;
if(k==GPyramid1.nlevels()-1) // if at the top level
{
vx.allocate(width,height);
vy.allocate(width,height);
WarpImage2.copyData(Image2);
}
else
{
vx.imresize(width,height);
vx.Multiplywith(1/ratio);
vy.imresize(width,height);
vy.Multiplywith(1/ratio);
if(interpolation == Bilinear)
warpFL(WarpImage2,Image1,Image2,vx,vy);
else
Image2.warpImageBicubicRef(Image1,WarpImage2,vx,vy);
}
SmoothFlowSOR(Image1,Image2,WarpImage2,vx,vy,alpha,nOuterFPIterations+k,nInnerFPIterations,nCGIterations+k*3);
//GMPara.display();
if(IsDisplay)
cout<<endl;
}
//warpFL(warpI2,Im1,Im2,vx,vy);
Im2.warpImageBicubicRef(Im1,warpI2,vx,vy);
warpI2.threshold();
}
void OpticalFlow::warpFL(DImage &warpIm2, const DImage &Im1, const DImage &Im2, const DImage &Flow)
{
if(warpIm2.matchDimension(Im2)==false)
warpIm2.allocate(Im2.width(),Im2.height(),Im2.nchannels());
ImageProcessing::warpImageFlow(warpIm2.data(),Im1.data(),Im2.data(),Flow.data(),Im2.width(),Im2.height(),Im2.nchannels());
}
//--------------------------------------------------------------------------------------
// function to perfomr coarse to fine optical flow estimation
//--------------------------------------------------------------------------------------
void OpticalFlow::Coarse2FineFlow(DImage &vx, DImage &vy, DImage &warpI2,const DImage &Im1, const DImage &Im2, double alpha, double ratio, int minWidth,
int nOuterFPIterations, int nInnerFPIterations, int nCGIterations)
{
// first build the pyramid of the two images
GaussianPyramid GPyramid1;
GaussianPyramid GPyramid2;
if(IsDisplay)
cout<<"Constructing pyramid...";
GPyramid1.ConstructPyramid(Im1,ratio,minWidth);
GPyramid2.ConstructPyramid(Im2,ratio,minWidth);
if(IsDisplay)
cout<<"done!"<<endl;
// now iterate from the top level to the bottom
DImage Image1,Image2,WarpImage2;
//GaussianMixture GMPara(Im1.nchannels()+2);
// initialize noise
switch(noiseModel){
case GMixture:
GMPara.reset(Im1.nchannels()+2);
break;
case Lap:
LapPara.allocate(Im1.nchannels()+2);
for(int i = 0;i<LapPara.dim();i++)
LapPara[i] = 0.02;
break;
}
for(int k=GPyramid1.nlevels()-1;k>=0;k--)
{
if(IsDisplay)
cout<<"Pyramid level "<<k;
int width=GPyramid1.Image(k).width();
int height=GPyramid1.Image(k).height();
im2feature(Image1,GPyramid1.Image(k));
im2feature(Image2,GPyramid2.Image(k));
if(k==GPyramid1.nlevels()-1) // if at the top level
{
vx.allocate(width,height);
vy.allocate(width,height);
//warpI2.copyData(Image2);
WarpImage2.copyData(Image2);
}
else
{
vx.imresize(width,height);
vx.Multiplywith(1/ratio);
vy.imresize(width,height);
vy.Multiplywith(1/ratio);
//warpFL(warpI2,GPyramid1.Image(k),GPyramid2.Image(k),vx,vy);
if(interpolation == Bilinear)
warpFL(WarpImage2,Image1,Image2,vx,vy);
else
Image2.warpImageBicubicRef(Image1,WarpImage2,vx,vy);
}
//SmoothFlowPDE(GPyramid1.Image(k),GPyramid2.Image(k),warpI2,vx,vy,alpha,nOuterFPIterations,nInnerFPIterations,nCGIterations);
//SmoothFlowPDE(Image1,Image2,WarpImage2,vx,vy,alpha*pow((1/ratio),k),nOuterFPIterations,nInnerFPIterations,nCGIterations,GMPara);
//SmoothFlowPDE(Image1,Image2,WarpImage2,vx,vy,alpha,nOuterFPIterations,nInnerFPIterations,nCGIterations);
SmoothFlowSOR(Image1,Image2,WarpImage2,vx,vy,alpha,nOuterFPIterations+k,nInnerFPIterations,nCGIterations+k*3);
//GMPara.display();
if(IsDisplay)
cout<<endl;
}
//warpFL(warpI2,Im1,Im2,vx,vy);
Im2.warpImageBicubicRef(Im1,warpI2,vx,vy);
warpI2.threshold();
}