/*
 * Copyright 2011, Blender Foundation.
 *
 * This program is free software; you can redistribute it and/or
 * modify it under the terms of the GNU General Public License
 * as published by the Free Software Foundation; either version 2
 * of the License, or (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program; if not, write to the Free Software Foundation,
 * Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
 *
 * Contributor: 
 *		Jeroen Bakker 
 *		Monique Dewanchand
 */

/// This file contains all opencl kernels for node-operation implementations 

// Global SAMPLERS
const sampler_t SAMPLER_NEAREST       = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP_TO_EDGE | CLK_FILTER_NEAREST;
const sampler_t SAMPLER_NEAREST_CLAMP = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;

__constant const int2 zero = {0,0};

// KERNEL --- BOKEH BLUR ---
__kernel void bokehBlurKernel(__read_only image2d_t boundingBox, __read_only image2d_t inputImage, 
                              __read_only image2d_t bokehImage, __write_only image2d_t output, 
                              int2 offsetInput, int2 offsetOutput, int radius, int step, int2 dimension, int2 offset) 
{
	int2 coords = {get_global_id(0), get_global_id(1)};
	coords += offset;
	float tempBoundingBox;
	float4 color = {0.0f,0.0f,0.0f,0.0f};
	float4 multiplyer = {0.0f,0.0f,0.0f,0.0f};
	float4 bokeh;
	const float radius2 = radius*2.0f;
	const int2 realCoordinate = coords + offsetOutput;
	int2 imageCoordinates = realCoordinate - offsetInput;

	tempBoundingBox = read_imagef(boundingBox, SAMPLER_NEAREST, coords).s0;

	if (tempBoundingBox > 0.0f && radius > 0 ) {
		const int2 bokehImageDim = get_image_dim(bokehImage);
		const int2 bokehImageCenter = bokehImageDim/2;
		const int2 minXY = max(realCoordinate - radius, zero);
		const int2 maxXY = min(realCoordinate + radius, dimension);
		int nx, ny;
		
		float2 uv;
		int2 inputXy;
		
		if (radius < 2) {
			color = read_imagef(inputImage, SAMPLER_NEAREST, imageCoordinates);
			multiplyer = (float4)(1.0f, 1.0f, 1.0f, 1.0f);
		}
		
		for (ny = minXY.y, inputXy.y = ny - offsetInput.y ; ny < maxXY.y ; ny += step, inputXy.y += step) {
			uv.y = ((realCoordinate.y-ny)/radius2)*bokehImageDim.y+bokehImageCenter.y;
			
			for (nx = minXY.x, inputXy.x = nx - offsetInput.x; nx < maxXY.x ; nx += step, inputXy.x += step) {
				uv.x = ((realCoordinate.x-nx)/radius2)*bokehImageDim.x+bokehImageCenter.x;
				bokeh = read_imagef(bokehImage, SAMPLER_NEAREST, uv);
				color += bokeh * read_imagef(inputImage, SAMPLER_NEAREST, inputXy);
				multiplyer += bokeh;
			}
		}
		color /= multiplyer;
	}
	else {
		color = read_imagef(inputImage, SAMPLER_NEAREST, imageCoordinates);
	}
	
	write_imagef(output, coords, color);
}

//KERNEL --- DEFOCUS /VARIABLESIZEBOKEHBLUR ---
__kernel void defocusKernel(__read_only image2d_t inputImage, __read_only image2d_t bokehImage, 
					__read_only image2d_t inputSize,
					__write_only image2d_t output, int2 offsetInput, int2 offsetOutput, 
					int step, int maxBlurScalar, float threshold, float scalar, int2 dimension, int2 offset) 
{
	float4 color = {1.0f, 0.0f, 0.0f, 1.0f};
	int2 coords = {get_global_id(0), get_global_id(1)};
	coords += offset;
	const int2 realCoordinate = coords + offsetOutput;

	float4 readColor;
	float4 tempColor;
	float4 bokeh;
	float size;
	float4 multiplier_accum = {1.0f, 1.0f, 1.0f, 1.0f};
	float4 color_accum;
	
	int minx = max(realCoordinate.s0 - maxBlurScalar, 0);
	int miny = max(realCoordinate.s1 - maxBlurScalar, 0);
	int maxx = min(realCoordinate.s0 + maxBlurScalar, dimension.s0);
	int maxy = min(realCoordinate.s1 + maxBlurScalar, dimension.s1);
	
	{
		int2 inputCoordinate = realCoordinate - offsetInput;
		float size_center = read_imagef(inputSize, SAMPLER_NEAREST, inputCoordinate).s0 * scalar;
		color_accum = read_imagef(inputImage, SAMPLER_NEAREST, inputCoordinate);
		readColor = color_accum;

		if (size_center > threshold) {
			for (int ny = miny; ny < maxy; ny += step) {
				inputCoordinate.s1 = ny - offsetInput.s1;
				float dy = ny - realCoordinate.s1;
				for (int nx = minx; nx < maxx; nx += step) {
					float dx = nx - realCoordinate.s0;
					if (dx != 0 || dy != 0) {
						inputCoordinate.s0 = nx - offsetInput.s0;
						size = min(read_imagef(inputSize, SAMPLER_NEAREST, inputCoordinate).s0 * scalar, size_center);
						if (size > threshold) {
							if (size >= fabs(dx) && size >= fabs(dy)) {
								float2 uv = {256.0f + dx * 255.0f / size,
								             256.0f + dy * 255.0f / size};
								bokeh = read_imagef(bokehImage, SAMPLER_NEAREST, uv);
								tempColor = read_imagef(inputImage, SAMPLER_NEAREST, inputCoordinate);
								color_accum += bokeh * tempColor;
								multiplier_accum += bokeh;
							}
						}
					}
				}
			}
		}

		color = color_accum * (1.0f / multiplier_accum);
		
		/* blend in out values over the threshold, otherwise we get sharp, ugly transitions */
		if ((size_center > threshold) &&
		    (size_center < threshold * 2.0f))
		{
			/* factor from 0-1 */
			float fac = (size_center - threshold) / threshold;
			color = (readColor * (1.0f - fac)) +  (color * fac);
		}
		
		write_imagef(output, coords, color);
	}
}


// KERNEL --- DILATE ---
__kernel void dilateKernel(__read_only image2d_t inputImage,  __write_only image2d_t output,
                           int2 offsetInput, int2 offsetOutput, int scope, int distanceSquared, int2 dimension, 
                           int2 offset)
{
	int2 coords = {get_global_id(0), get_global_id(1)};
	coords += offset;
	const int2 realCoordinate = coords + offsetOutput;

	const int2 minXY = max(realCoordinate - scope, zero);
	const int2 maxXY = min(realCoordinate + scope, dimension);
	
	float value = 0.0f;
	int nx, ny;
	int2 inputXy;
	
	for (ny = minXY.y, inputXy.y = ny - offsetInput.y ; ny < maxXY.y ; ny ++, inputXy.y++) {
		const float deltaY = (realCoordinate.y - ny);
		for (nx = minXY.x, inputXy.x = nx - offsetInput.x; nx < maxXY.x ; nx ++, inputXy.x++) {
			const float deltaX = (realCoordinate.x - nx);
			const float measuredDistance = deltaX * deltaX + deltaY * deltaY;
			if (measuredDistance <= distanceSquared) {
				value = max(value, read_imagef(inputImage, SAMPLER_NEAREST, inputXy).s0);
			}
		}
	}

	float4 color = {value,0.0f,0.0f,0.0f};
	write_imagef(output, coords, color);
}

// KERNEL --- DILATE ---
__kernel void erodeKernel(__read_only image2d_t inputImage,  __write_only image2d_t output,
                           int2 offsetInput, int2 offsetOutput, int scope, int distanceSquared, int2 dimension, 
                           int2 offset)
{
	int2 coords = {get_global_id(0), get_global_id(1)};
	coords += offset;
	const int2 realCoordinate = coords + offsetOutput;

	const int2 minXY = max(realCoordinate - scope, zero);
	const int2 maxXY = min(realCoordinate + scope, dimension);
	
	float value = 1.0f;
	int nx, ny;
	int2 inputXy;
	
	for (ny = minXY.y, inputXy.y = ny - offsetInput.y ; ny < maxXY.y ; ny ++, inputXy.y++) {
		for (nx = minXY.x, inputXy.x = nx - offsetInput.x; nx < maxXY.x ; nx ++, inputXy.x++) {
			const float deltaX = (realCoordinate.x - nx);
			const float deltaY = (realCoordinate.y - ny);
			const float measuredDistance = deltaX * deltaX+deltaY * deltaY;
			if (measuredDistance <= distanceSquared) {
				value = min(value, read_imagef(inputImage, SAMPLER_NEAREST, inputXy).s0);
			}
		}
	}

	float4 color = {value,0.0f,0.0f,0.0f};
	write_imagef(output, coords, color);
}

// KERNEL --- DIRECTIONAL BLUR ---
__kernel void directionalBlurKernel(__read_only image2d_t inputImage,  __write_only image2d_t output,
                           int2 offsetOutput, int iterations, float scale, float rotation, float2 translate,
                           float2 center, int2 offset)
{
	int2 coords = {get_global_id(0), get_global_id(1)};
	coords += offset;
	const int2 realCoordinate = coords + offsetOutput;

	float4 col;
	float2 ltxy = translate;
	float lsc = scale;
	float lrot = rotation;
	
	col = read_imagef(inputImage, SAMPLER_NEAREST, realCoordinate);

	/* blur the image */
	for (int i = 0; i < iterations; ++i) {
		const float cs = cos(lrot), ss = sin(lrot);
		const float isc = 1.0f / (1.0f + lsc);

		const float v = isc * (realCoordinate.s1 - center.s1) + ltxy.s1;
		const float u = isc * (realCoordinate.s0 - center.s0) + ltxy.s0;
		float2 uv = {
			cs * u + ss * v + center.s0,
			cs * v - ss * u + center.s1
		};

		col += read_imagef(inputImage, SAMPLER_NEAREST_CLAMP, uv);

		/* double transformations */
		ltxy += translate;
		lrot += rotation;
		lsc += scale;
	}

	col *= (1.0f/(iterations+1));

	write_imagef(output, coords, col);
}

// KERNEL --- GAUSSIAN BLUR ---
__kernel void gaussianXBlurOperationKernel(__read_only image2d_t inputImage,
                                           int2 offsetInput,
                                           __write_only image2d_t output,
                                           int2 offsetOutput,
                                           int filter_size,
                                           int2 dimension,
                                           __global float *gausstab,
                                           int2 offset)
{
	float4 color = {0.0f, 0.0f, 0.0f, 0.0f};
	int2 coords = {get_global_id(0), get_global_id(1)};
	coords += offset;
	const int2 realCoordinate = coords + offsetOutput;
	int2 inputCoordinate = realCoordinate - offsetInput;
	float weight = 0.0f;

	int xmin = max(realCoordinate.x - filter_size,     0) - offsetInput.x;
	int xmax = min(realCoordinate.x + filter_size + 1, dimension.x) - offsetInput.x;

	for (int nx = xmin, i = max(filter_size - realCoordinate.x, 0); nx < xmax; ++nx, ++i) {
		float w = gausstab[i];
		inputCoordinate.x = nx;
		color += read_imagef(inputImage, SAMPLER_NEAREST, inputCoordinate) * w;
		weight += w;
	}

	color *= (1.0f / weight);

	write_imagef(output, coords, color);
}

__kernel void gaussianYBlurOperationKernel(__read_only image2d_t inputImage,
                                           int2 offsetInput,
                                           __write_only image2d_t output,
                                           int2 offsetOutput,
                                           int filter_size,
                                           int2 dimension,
                                           __global float *gausstab,
                                           int2 offset)
{
	float4 color = {0.0f, 0.0f, 0.0f, 0.0f};
	int2 coords = {get_global_id(0), get_global_id(1)};
	coords += offset;
	const int2 realCoordinate = coords + offsetOutput;
	int2 inputCoordinate = realCoordinate - offsetInput;
	float weight = 0.0f;

	int ymin = max(realCoordinate.y - filter_size,     0) - offsetInput.y;
	int ymax = min(realCoordinate.y + filter_size + 1, dimension.y) - offsetInput.y;

	for (int ny = ymin, i = max(filter_size - realCoordinate.y, 0); ny < ymax; ++ny, ++i) {
		float w = gausstab[i];
		inputCoordinate.y = ny;
		color += read_imagef(inputImage, SAMPLER_NEAREST, inputCoordinate) * w;
		weight += w;
	}

	color *= (1.0f / weight);

	write_imagef(output, coords, color);
}
