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/// 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;
__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;
tempBoundingBox = read_imagef(boundingBox, SAMPLER_NEAREST, coords).s0;
if (tempBoundingBox > 0.0f) {
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;
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 {
int2 imageCoordinates = realCoordinate - offsetInput;
color = read_imagef(inputImage, SAMPLER_NEAREST, imageCoordinates);
}
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);
}
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