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/*
* Copyright 2011-2017 Blender Foundation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
CCL_NAMESPACE_BEGIN
/* First step of the shadow prefiltering, performs the shadow division and stores all data
* in a nice and easy rectangular array that can be passed to the NLM filter.
*
* Calculates:
* unfiltered: Contains the two half images of the shadow feature pass
* sampleVariance: The sample-based variance calculated in the kernel. Note: This calculation is biased in general, and especially here since the variance of the ratio can only be approximated.
* sampleVarianceV: Variance of the sample variance estimation, quite noisy (since it's essentially the buffer variance of the two variance halves)
* bufferVariance: The buffer-based variance of the shadow feature. Unbiased, but quite noisy.
*/
ccl_device void kernel_filter_divide_shadow(int sample,
TilesInfo *tiles,
int x, int y,
float *unfilteredA,
float *unfilteredB,
float *sampleVariance,
float *sampleVarianceV,
float *bufferVariance,
int4 rect,
int buffer_pass_stride,
int buffer_denoising_offset,
bool use_gradients)
{
int xtile = (x < tiles->x[1])? 0: ((x < tiles->x[2])? 1: 2);
int ytile = (y < tiles->y[1])? 0: ((y < tiles->y[2])? 1: 2);
int tile = ytile*3+xtile;
float *center_buffer = ((float*) tiles->buffers[tile]) + (tiles->offsets[tile] + y*tiles->strides[tile] + x)*buffer_pass_stride;
if(use_gradients && tile == 4) {
center_buffer[0] = center_buffer[1] = center_buffer[2] = center_buffer[3] = 0.0f;
}
center_buffer += buffer_denoising_offset;
int buffer_w = align_up(rect.z - rect.x, 4);
int idx = (y-rect.y)*buffer_w + (x - rect.x);
unfilteredA[idx] = center_buffer[15] / max(center_buffer[14], 1e-7f);
unfilteredB[idx] = center_buffer[18] / max(center_buffer[17], 1e-7f);
float varFac = 1.0f / (sample * (sample-1));
sampleVariance[idx] = (center_buffer[16] + center_buffer[19]) * varFac;
sampleVarianceV[idx] = 0.5f * (center_buffer[16] - center_buffer[19]) * (center_buffer[16] - center_buffer[19]) * varFac * varFac;
bufferVariance[idx] = 0.5f * (unfilteredA[idx] - unfilteredB[idx]) * (unfilteredA[idx] - unfilteredB[idx]);
}
/* Load a regular feature from the render buffers into the denoise buffer.
* Parameters:
* - sample: The sample amount in the buffer, used to normalize the buffer.
* - m_offset, v_offset: Render Buffer Pass offsets of mean and variance of the feature.
* - x, y: Current pixel
* - mean, variance: Target denoise buffers.
* - rect: The prefilter area (lower pixels inclusive, upper pixels exclusive).
*/
ccl_device void kernel_filter_get_feature(int sample,
TilesInfo *tiles,
int m_offset, int v_offset,
int x, int y,
float *mean, float *variance,
int4 rect, int buffer_pass_stride,
int buffer_denoising_offset, bool use_cross_denoising)
{
int xtile = (x < tiles->x[1])? 0: ((x < tiles->x[2])? 1: 2);
int ytile = (y < tiles->y[1])? 0: ((y < tiles->y[2])? 1: 2);
int tile = ytile*3+xtile;
float *center_buffer = ((float*) tiles->buffers[tile]) + (tiles->offsets[tile] + y*tiles->strides[tile] + x)*buffer_pass_stride + buffer_denoising_offset;
int buffer_w = align_up(rect.z - rect.x, 4);
int idx = (y-rect.y)*buffer_w + (x - rect.x);
/* TODO: This is an ugly hack! */
if(m_offset >= 20 && m_offset <= 22 && use_cross_denoising) {
int odd_sample = sample/2;
mean[idx] = (center_buffer[m_offset] - center_buffer[m_offset+6]) / odd_sample;
variance[idx] = center_buffer[v_offset] / (odd_sample * (sample-1));
}
else if(m_offset >= 26) {
int even_sample = (sample+1)/2;
mean[idx] = center_buffer[m_offset] / even_sample;
variance[idx] = center_buffer[v_offset-6] / (even_sample * (sample-1));
}
else {
mean[idx] = center_buffer[m_offset] / sample;
variance[idx] = center_buffer[v_offset] / (sample * (sample-1));
}
}
/* Combine A/B buffers.
* Calculates the combined mean and the buffer variance. */
ccl_device void kernel_filter_combine_halves(int x, int y,
float *mean, float *variance,
float *a, float *b,
int4 rect, int r)
{
int buffer_w = align_up(rect.z - rect.x, 4);
int idx = (y-rect.y)*buffer_w + (x - rect.x);
if(mean) mean[idx] = 0.5f * (a[idx]+b[idx]);
if(variance) {
if(r == 0) variance[idx] = 0.25f * (a[idx]-b[idx])*(a[idx]-b[idx]);
else {
variance[idx] = 0.0f;
float values[25];
int numValues = 0;
for(int py = max(y-r, rect.y); py < min(y+r+1, rect.w); py++) {
for(int px = max(x-r, rect.x); px < min(x+r+1, rect.z); px++) {
int pidx = (py-rect.y)*buffer_w + (px-rect.x);
values[numValues++] = 0.25f * (a[pidx]-b[pidx])*(a[pidx]-b[pidx]);
}
}
/* Insertion-sort the variances (fast enough for 25 elements). */
for(int i = 1; i < numValues; i++) {
float v = values[i];
int j;
for(j = i-1; j >= 0 && values[j] > v; j--)
values[j+1] = values[j];
values[j+1] = v;
}
variance[idx] = values[(7*numValues)/8];
}
}
}
CCL_NAMESPACE_END
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