kernel-tester/source/model/processor/strategies/Enhance.cpp

#include "model/processor/strategies/Enhance.h"
#include "model/processor/BIP.h"
#include <QPixmap>
#include <QImage>

Enhance::Enhance(const Context context,
		 const QString kernelPath,
		 const QObject *parent = Q_NULLPTR) :
	IProcessStrategy(context, kernelPath, "Enhance", parent),
	_kernelFunctor(KernelFunctor<Image2D, Image2D,
				   Buffer, Enhance_t>(_kernel))
{
	memset(&_kernelParameters, 0, sizeof (Enhance_t));
	_filter = Q_NULLPTR;
}

void Enhance::cpuProcess(ScenGenOutput_t parameters)
{
	if(parameters.enhance.isUpdated || parameters.enhanceAlgorithm.isUpdated)
	{
		_run = false;
		if(parameters.enhance.value == 0)
		{
			return;
		}

		float *hpf;
		int *backgroundRecovery;

		//sharpen
		if(parameters.enhanceAlgorithm.value == 0)
		{
			auto alpha = 0.25f * (parameters.enhance.value - 1);

			_kernelParameters.filterWidth = 3;
			_kernelParameters.filterHeight = 3;

			hpf = new float[9];
			backgroundRecovery = new int[9];

			auto factor = 4 / (alpha + 1);
			int index = 0;

			hpf[index++] = factor * (alpha / 4);
			hpf[index++] = factor * ((1 - alpha) / 4);
			hpf[index++] = factor * (alpha / 4);

			hpf[index++] = factor * ((1 - alpha) / 4);
			hpf[index++] = factor * -1;
			hpf[index++] = factor * ((1 - alpha) / 4);

			hpf[index++] = factor * (alpha / 4);
			hpf[index++] = factor * ((1 - alpha) / 4);
			hpf[index++] = factor * (alpha / 4);

			index = 0;

			memset(backgroundRecovery, 0, 9 * sizeof (int));
			backgroundRecovery[4] = 1;

			_run = true;
		}
		//log
		else if(parameters.enhanceAlgorithm.value == 1)
		{
			_kernelParameters.filterWidth = 5;
			_kernelParameters.filterHeight = 5;

			hpf = new float[25];
			backgroundRecovery = new int[25];

			auto sigma = 0.5f + 0.25f * (parameters.enhance.value - 1);
			float sumHg = 0;
			float h[25];

			for(int i = -2; i <= 2; i++)
			{
				for(int j = -2; j <= 2; j++)
				{
					float temp = expf(-(powf(i, 2) + powf(j, 2))
													 / 2 / powf(sigma, 2));
					h[(i + 2) * 5 + (j + 2)] = (powf(i, 2) + powf(j, 2) - 2 * powf(sigma, 2)) *
							temp;

					sumHg += temp;
				}
			}

			for(int i = 0; i < 25 ; i++)
			{
				hpf[i] = h[i] / powf(sigma, 4) / sumHg;
			}

			memset(backgroundRecovery, 0, 25 * sizeof (int));
			backgroundRecovery[12] = 1;

			_run = true;
		}

		if(_run)
		{
			auto filterSize = _kernelParameters.filterWidth * _kernelParameters.filterHeight;
            auto filter = new float[filterSize];
			for(int i = 0; i < filterSize ; i++)
			{
				filter[i] = backgroundRecovery[i] - hpf[i];
			}

			delete [] hpf;
			delete [] backgroundRecovery;

			if(_filter)
				delete  _filter;

			_filter = _openCLHelper.array2CLBuffer(_CLContext, filterSize * sizeof (float));
			BIP::getInstance()->CLQueue.enqueueWriteBuffer(*_filter, CL_TRUE,
														   0, filterSize * sizeof (float),
														   filter);
            delete[] filter;
		}
	}
}

void Enhance::finalize()
{
	if(_filter)
		delete  _filter;
}


Image* Enhance::processKernel(Image *frames, Buffer* scratchPad)
{
	if(_run)
	{
		auto format = frames->getImageInfo<CL_IMAGE_FORMAT>();
		auto width = frames->getImageInfo<CL_IMAGE_WIDTH>();
		auto height = frames->getImageInfo<CL_IMAGE_HEIGHT>();

		_kernelParameters.width = width;
		_kernelParameters.height = height;

		auto imageOutput = new Image2D(_CLContext,
								CL_MEM_READ_WRITE,
								ImageFormat(format.image_channel_order, format.image_channel_data_type),
								width,
								height);
		cl::EnqueueArgs eargs(BIP::getInstance()->CLQueue, cl::NDRange(width, height));
		_openCLHelper.runKernelFunctor<Image2D, Image2D, Buffer, Enhance_t>(_kernelFunctor,
																					   eargs,
																					   *static_cast<Image2D*>(frames),
																					   *imageOutput,
																						*_filter,
																					   _kernelParameters);


		delete frames;
		return imageOutput;
	}
	else
	{
		return frames;
	}
}