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;
}
}
void OpticalFlow::estLaplacianNoise(const DImage& Im1,const DImage& Im2,Vector<double>& para)
{
int nChannels = Im1.nchannels();
if(para.dim()!=nChannels)
para.allocate(nChannels);
else
para.reset();
double temp;
Vector<double> total(nChannels);
for(int k = 0;k<nChannels;k++)
total[k] = 0;
for(int i =0;i<Im1.npixels();i++)
for(int k = 0;k<nChannels;k++)
{
int offset = i*nChannels+k;
temp= abs(Im1.data()[offset]-Im2.data()[offset]);
if(temp>0 && temp<1000000)
{
para[k] += temp;
total[k]++;
}
}
for(int k = 0;k<nChannels;k++)
{
if(total[k]==0)
{
cout<<"All the pixels are invalid in estimation Laplacian noise!!!"<<endl;
cout<<"Something severely wrong happened!!!"<<endl;
para[k] = 0.001;
}
else
para[k]/=total[k];
}
}
bool OpticalFlow::SaveOpticalFlow(const DImage& flow,ofstream& myfile)
{
Image<unsigned short int> foo;
foo.allocate(flow);
for(int i =0;i<flow.npixels();i++)
{
foo.data()[i*2] = (__min(__max(flow.data()[i*2],-200),200)+200)*160;
foo.data()[i*2+1] = (__min(__max(flow.data()[i*2+1],-200),200)+200)*160;
}
return foo.saveImage(myfile);
}
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 do sanity check: imdx*du+imdy*dy+imdt=0
//--------------------------------------------------------------------------------------------------------
void OpticalFlow::SanityCheck(const DImage &imdx, const DImage &imdy, const DImage &imdt, double du, double dv)
{
if(imdx.matchDimension(imdy)==false || imdx.matchDimension(imdt)==false)
{
cout<<"The dimensions of the derivatives don't match!"<<endl;
return;
}
const _FlowPrecision* pImDx,*pImDy,*pImDt;
pImDx=imdx.data();
pImDy=imdy.data();
pImDt=imdt.data();
double error=0;
for(int i=0;i<imdx.height();i++)
for(int j=0;j<imdx.width();j++)
for(int k=0;k<imdx.nchannels();k++)
{
int offset=(i*imdx.width()+j)*imdx.nchannels()+k;
double temp=pImDx[offset]*du+pImDy[offset]*dv+pImDt[offset];
error+=fabs(temp);
}
error/=imdx.nelements();
cout<<"The mean error of |dx*u+dy*v+dt| is "<<error<<endl;
}
//---------------------------------------------------------------------------------------
// 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);
}
cv::Mat ParImageToIplImage(DImage& img)
{
int width = img.width();
int height = img.height();
int nChannels = img.nchannels();
if(width <= 0 || height <= 0 || nChannels != 1)
return cv::Mat();
BaseType*& pData = img.data();
cv::Mat image = cv::Mat(height, width, CV_MAKETYPE(8, 1));
for(int i = 0;i < height;i++)
{
for(int j = 0;j < width;j++)
{
image.ptr<uchar>(i)[j] = pData[i*width + j] * 255;
}
}
return image;
}
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());
}
