C++ DImage::allocate方法代码示例

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 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();
}