C++ Image2DCPtr::Value方法代码示例

std::pair<TimeFrequencyData,TimeFrequencyMetaDataPtr> RSPReader::ReadSingleBeamlet(unsigned long timestepStart, unsigned long timestepEnd, unsigned beamletCount, unsigned beamletIndex)
{
	std::pair<TimeFrequencyData,TimeFrequencyMetaDataPtr> data = ReadAllBeamlets(timestepStart, timestepEnd, beamletCount);
	
	const unsigned width = timestepEnd - timestepStart;
	Image2DPtr realX = Image2D::CreateZeroImagePtr(width, 1);
	Image2DPtr imaginaryX = Image2D::CreateZeroImagePtr(width, 1);
	Image2DPtr realY = Image2D::CreateZeroImagePtr(width, 1);
	Image2DPtr imaginaryY = Image2D::CreateZeroImagePtr(width, 1);
	Mask2DPtr mask = Mask2D::CreateUnsetMaskPtr(width, 1);
	
	TimeFrequencyData allX = data.first.Make(Polarization::XX);
	TimeFrequencyData allY = data.first.Make(Polarization::YY);
	Image2DCPtr xr = allX.GetRealPart();
	Image2DCPtr xi = allX.GetImaginaryPart();
	Image2DCPtr yr = allY.GetRealPart();
	Image2DCPtr yi = allY.GetImaginaryPart();
	Mask2DCPtr maskWithBeamlets = data.first.GetSingleMask();
	
	for(unsigned x=0;x<width;++x)
	{
		realX->SetValue(x, 0, xr->Value(x, beamletIndex));
		imaginaryX->SetValue(x, 0, xi->Value(x, beamletIndex));
		realY->SetValue(x, 0, yr->Value(x, beamletIndex));
		imaginaryY->SetValue(x, 0, yi->Value(x, beamletIndex));
		mask->SetValue(x, 0, maskWithBeamlets->Value(x, beamletIndex));
	}
	data.first = TimeFrequencyData(Polarization::XX, realX, imaginaryX, Polarization::YY, realY, imaginaryY);
	data.first.SetGlobalMask(mask);
	BandInfo band = data.second->Band();
	band.channels[0] = data.second->Band().channels[beamletIndex];
	band.channels.resize(1);
	data.second->SetBand(band);
	return data;
}

void HighPassFilter::elementWiseDivide(const Image2DPtr &leftHand, const Image2DCPtr &rightHand)
{
	for(unsigned y=0;y<leftHand->Height();++y) {
		for(unsigned x=0;x<leftHand->Width();++x) {
			if(rightHand->Value(x, y) == 0.0)
				leftHand->SetValue(x, y, 0.0);
			else
				leftHand->SetValue(x, y, leftHand->Value(x, y) / rightHand->Value(x, y));
		}
	}
}

void ThresholdMitigater::VerticalSumThresholdLarge(Image2DCPtr input, Mask2DPtr mask, num_t threshold)
{
	Mask2DPtr maskCopy = Mask2D::CreateCopy(mask);
	const size_t width = mask->Width(), height = mask->Height();
	if(Length <= height)
	{
		for(size_t x=0;x<width;++x)
		{
			num_t sum = 0.0;
			size_t count = 0, yTop, yBottom;

			for(yBottom=0;yBottom<Length-1;++yBottom)
			{
				if(!mask->Value(x, yBottom))
				{
					sum += input->Value(x, yBottom);
					++count;
				}
			}

			yTop = 0;
			while(yBottom < height)
			{
				// add the sample at the bottom
				if(!mask->Value(x, yBottom))
				{
					sum += input->Value(x, yBottom);
					++count;
				}
				// Check
				if(count>0 && fabs(sum/count) > threshold)
				{
					for(size_t i=0;i<Length;++i)
						maskCopy->SetValue(x, yTop + i, true);
				}
				// subtract the sample at the top
				if(!mask->Value(x, yTop))
				{
					sum -= input->Value(x, yTop);
					--count;
				}
				++yTop;
				++yBottom;
			}
		}
	}
	mask->Swap(maskCopy);
}

void Compress::Write(std::ofstream &stream, Image2DCPtr image, Mask2DCPtr mask)
{
	const num_t
		max = ThresholdTools::MaxValue(image, mask),
		min = ThresholdTools::MinValue(image, mask),
		mid = (min + max) / 2.0;
	const num_t normalizeFactor = (num_t) ((2<<22) + ((2<<22)-1)) / (max - min);
	const uint32_t
		width = image->Width(),
		height = image->Height();
	const char mode = 0;

	stream.write(reinterpret_cast<const char*>(&max), sizeof(max));
	stream.write(reinterpret_cast<const char*>(&min), sizeof(min));
	stream.write(reinterpret_cast<const char*>(&width), sizeof(width));
	stream.write(reinterpret_cast<const char*>(&height), sizeof(height));
	stream.write(&mode, sizeof(mode));

	for(unsigned y=0;y<height;++y)
	{
		for(unsigned x=0;x<width;++x)
		{
			if(!mask->Value(x, y))
			{
				int32_t value = (int32_t) round((image->Value(x, y) - mid) * normalizeFactor);
				stream.write(reinterpret_cast<char*>(&value)+1, 3);
			}
		}
	}
}

void ThresholdMitigater::HorizontalSumThresholdLarge(Image2DCPtr input, Mask2DPtr mask, num_t threshold)
{
	Mask2DPtr maskCopy = Mask2D::CreateCopy(mask);
	const size_t width = mask->Width(), height = mask->Height();
	if(Length <= width)
	{
		for(size_t y=0;y<height;++y)
		{
			num_t sum = 0.0;
			size_t count = 0, xLeft, xRight;

			for(xRight=0;xRight<Length-1;++xRight)
			{
				if(!mask->Value(xRight, y))
				{
					sum += input->Value(xRight, y);
					count++;
				}
			}

			xLeft = 0;
			while(xRight < width)
			{
				// add the sample at the right
				if(!mask->Value(xRight, y))
				{
					sum += input->Value(xRight, y);
					++count;
				}
				// Check
				if(count>0 && fabs(sum/count) > threshold)
				{
					maskCopy->SetHorizontalValues(xLeft, y, true, Length);
				}
				// subtract the sample at the left
				if(!mask->Value(xLeft, y))
				{
					sum -= input->Value(xLeft, y);
					--count;
				}
				++xLeft;
				++xRight;
			}
		}
	}
	mask->Swap(maskCopy);
}

	num_t SpatialCompositionAction::sumAutoCorrelations(Image2DCPtr image) const
	{
		num_t sum = 0;
		for(size_t y=0;y<image->Height();++y)
		{
			sum += image->Value(y, y);
		}
		return sum;
	}

void HighPassFilter::setFlaggedValuesToZeroAndMakeWeights(const Image2DCPtr &inputImage, const Image2DPtr &outputImage, const Mask2DCPtr &inputMask, const Image2DPtr &weightsOutput)
{
	const size_t width = inputImage->Width();
	for(size_t y=0;y<inputImage->Height();++y)
	{
		for(size_t x=0;x<width;++x)
		{
			if(inputMask->Value(x, y) || !isfinite(inputImage->Value(x, y)))
			{
				outputImage->SetValue(x, y, 0.0);
				weightsOutput->SetValue(x, y, 0.0);
			} else {
				outputImage->SetValue(x, y, inputImage->Value(x, y));
				weightsOutput->SetValue(x, y, 1.0);
			}
		}
	}
}

void ThresholdMitigater::VerticalVarThreshold(Image2DCPtr input, Mask2DPtr mask, size_t length, num_t threshold)
{
	size_t height = input->Height()-length+1; 
	for(size_t y=0;y<height;++y) {
		for(size_t x=0;x<input->Width();++x) {
			bool flag = true;
			for(size_t i=0;i<length;++i) {
				if(input->Value(x, y+i) <= threshold && input->Value(x, y+i) >= -threshold) {
					flag = false;
					break;
				}
			}
			if(flag) {
				for(size_t i=0;i<length;++i)
					mask->SetValue(x, y + i, true);
			}
		}
	}
}

void ThresholdMitigater::HorizontalVarThreshold(Image2DCPtr input, Mask2DPtr mask, size_t length, num_t threshold)
{
	size_t width = input->Width()-length+1;
	for(size_t y=0;y<input->Height();++y) {
		for(size_t x=0;x<width;++x) {
			bool flag = true;
			for(size_t i=0;i<length;++i) {
				if(input->Value(x+i, y) < threshold && input->Value(x+i, y) > -threshold) {
					flag = false;
					break;
				}
			}
			if(flag) {
				for(size_t i=0;i<length;++i)
					mask->SetValue(x + i, y, true);
			}
		}
	}
}

	num_t SpatialCompositionAction::sumCrossCorrelations(Image2DCPtr image) const
	{
		num_t sum = 0;
		for(size_t y=0;y<image->Height();++y)
		{
			for(size_t x=0;x<y;++x)
				sum += image->Value(x, y);
		}
		return sum;
	}

	void ImagerAction::Perform(ArtifactSet &artifacts, ProgressListener &progress)
	{
		boost::mutex::scoped_lock lock(_imagerMutex);
		UVImager *imager = artifacts.Imager();
		if(imager == 0)
			throw BadUsageException("No imager available to create image.");
		TimeFrequencyData &data = artifacts.ContaminatedData();
		TimeFrequencyMetaDataCPtr metaData = artifacts.MetaData();
		if(data.PolarisationCount() > 1)
		{
			TimeFrequencyData *tmp = data.CreateTFData(StokesIPolarisation);
			data = *tmp;
			delete tmp;
		}
		
		bool btPlaneImager = true;
		if(btPlaneImager)
		{
			typedef double ImagerNumeric;
			BaselineTimePlaneImager<ImagerNumeric> btImager;
			BandInfo band = metaData->Band();
			Image2DCPtr
				inputReal = data.GetRealPart(),
				inputImag = data.GetImaginaryPart();
			Mask2DCPtr mask = data.GetSingleMask();
			size_t width = inputReal->Width();
			
			for(size_t t=0;t!=width;++t)
			{
				UVW uvw = metaData->UVW()[t];
				size_t channelCount = inputReal->Height();
				std::vector<std::complex<ImagerNumeric> > data(channelCount);
				for(size_t ch=0;ch!=channelCount;++ch) {
					if(mask->Value(t, ch))
						data[ch] = std::complex<ImagerNumeric>(0.0, 0.0);
					else
						data[ch] = std::complex<ImagerNumeric>(inputReal->Value(t, ch), inputImag->Value(t, ch));
				}
				
				btImager.Image(uvw.u, uvw.v, uvw.w, band.channels[0].frequencyHz, band.channels[1].frequencyHz-band.channels[0].frequencyHz, channelCount, &(data[0]), imager->FTReal());
			}
		} else {
			progress.OnStartTask(*this, 0, 1, "Imaging baseline");
			for(size_t y=0;y<data.ImageHeight();++y)
			{
				imager->Image(data, metaData, y);
				progress.OnProgress(*this, y, data.ImageHeight());
			}
			progress.OnEndTask(*this);
		}
	}

void ImagePlaneWindow::onMemorySubtractClicked()
{
	if(_memory != 0)
	{
		Image2DPtr subtracted(Image2D::MakePtr(*_memory));
		Image2DCPtr old = _heatMapPlot.Image();
		for(size_t y=0;y<subtracted->Height();++y)
		{
			for(size_t x=0;x<subtracted->Width();++x)
			{
				subtracted->SetValue(x, y, subtracted->Value(x, y) - old->Value(x, y));
			}
		}
		_heatMapPlot.SetImage(std::move(subtracted));
		_imageWidget.Update();
		printStats();
	}
}

void ImagePlaneWindow::onMemoryMultiplyClicked()
{
	if(_memory != 0)
	{
		Image2DPtr multiplied(Image2D::MakePtr(*_memory));
		Image2DCPtr old = _heatMapPlot.Image();
		for(size_t y=0;y<multiplied->Height();++y)
		{
			for(size_t x=0;x<multiplied->Width();++x)
			{
				multiplied->SetValue(x, y, multiplied->Value(x, y) * old->Value(x, y));
			}
		}
		_heatMapPlot.SetImage(std::move(multiplied));
		_imageWidget.Update();
		printStats();
	}
}

void UVImager::Image(const TimeFrequencyData &data, TimeFrequencyMetaDataCPtr metaData, unsigned frequencyIndex)
{
	if(_uvReal == 0)
		Empty();

	Image2DCPtr
		real = data.GetRealPart(),
		imaginary = data.GetImaginaryPart();
	Mask2DCPtr
		flags = data.GetSingleMask();

	for(unsigned i=0;i<data.ImageWidth();++i) {
		switch(_imageKind) {
			case Homogeneous:
			if(flags->Value(i, frequencyIndex)==0.0L) {
				num_t
					vr = real->Value(i, frequencyIndex),
					vi = imaginary->Value(i, frequencyIndex);
				if(std::isfinite(vr) && std::isfinite(vi))
				{
					num_t u,v;
					GetUVPosition(u, v, i, frequencyIndex, metaData);
					SetUVValue(u, v, vr, vi, 1.0);
					SetUVValue(-u, -v, vr, -vi, 1.0);
				}
			} 
			break;
			case Flagging:
			if((flags->Value(i, frequencyIndex)!=0.0L && !_invertFlagging) ||
					(flags->Value(i, frequencyIndex)==0.0L && _invertFlagging)) {
				num_t u,v;
				GetUVPosition(u, v, i, frequencyIndex, metaData);
				SetUVValue(u, v, 1, 0, 1.0);
				SetUVValue(-u, -v, 1, 0, 1.0);
			}
			break;
		}
	}
}

void Compress::WriteSubtractFrequencies(std::ofstream &stream, Image2DCPtr image, Mask2DCPtr mask)
{
	const num_t
		max = ThresholdTools::MaxValue(image, mask),
		min = ThresholdTools::MinValue(image, mask);
	const num_t normalizeFactor = (num_t) ((2<<22) + ((2<<22)-1)) / (max - min);
	//const num_t normalizeFactor = 256.0;
	const uint32_t
		width = image->Width(),
		height = image->Height();
	const char mode = 1;

	stream.write(reinterpret_cast<const char*>(&max), sizeof(max));
	stream.write(reinterpret_cast<const char*>(&min), sizeof(min));
	stream.write(reinterpret_cast<const char*>(&width), sizeof(width));
	stream.write(reinterpret_cast<const char*>(&height), sizeof(height));
	stream.write(&mode, sizeof(mode));

	std::vector<int32_t> basis(width);
	for(size_t x=0;x<width;++x)
	{
		SampleRowPtr row = SampleRow::CreateFromColumn(image, x);
		basis[x] = (int32_t) round(row->Median() * normalizeFactor);
	}
	stream.write(reinterpret_cast<char*>(&basis[0]), sizeof(basis));

	for(unsigned y=0;y<height;++y)
	{
		for(unsigned x=0;x<width;++x)
		{
			if(!mask->Value(x, y))
			{
				int32_t value = (int32_t) (round(image->Value(x, y) * normalizeFactor) - basis[x]);
				stream.write(reinterpret_cast<char*>(&value)+1, 3);
			}
		}
	}
}