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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,
ccl_global TilesInfo *tiles,
int x, int y,
ccl_global float *unfilteredA,
ccl_global float *unfilteredB,
ccl_global float *sampleVariance,
ccl_global float *sampleVarianceV,
ccl_global float *bufferVariance,
int4 rect,
int buffer_pass_stride,
int buffer_denoising_offset,
bool use_split_variance)
{
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;
int offset = tiles->offsets[tile];
int stride = tiles->strides[tile];
const ccl_global float *ccl_restrict center_buffer = (ccl_global float*) tiles->buffers[tile];
center_buffer += (y*stride + x + offset)*buffer_pass_stride;
center_buffer += buffer_denoising_offset + 14;
int buffer_w = align_up(rect.z - rect.x, 4);
int idx = (y-rect.y)*buffer_w + (x - rect.x);
unfilteredA[idx] = center_buffer[1] / max(center_buffer[0], 1e-7f);
unfilteredB[idx] = center_buffer[4] / max(center_buffer[3], 1e-7f);
float varA = center_buffer[2];
float varB = center_buffer[5];
int odd_sample = (sample+1)/2;
int even_sample = sample/2;
if(use_split_variance) {
varA = max(0.0f, varA - unfilteredA[idx]*unfilteredA[idx]*odd_sample);
varB = max(0.0f, varB - unfilteredB[idx]*unfilteredB[idx]*even_sample);
}
varA /= (odd_sample - 1);
varB /= (even_sample - 1);
sampleVariance[idx] = 0.5f*(varA + varB) / sample;
sampleVarianceV[idx] = 0.5f * (varA - varB) * (varA - varB) / (sample*sample);
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,
ccl_global TilesInfo *tiles,
int m_offset, int v_offset,
int x, int y,
ccl_global float *mean,
ccl_global float *variance,
int4 rect, int buffer_pass_stride,
int buffer_denoising_offset,
bool use_split_variance)
{
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;
ccl_global float *center_buffer = ((ccl_global 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);
mean[idx] = center_buffer[m_offset] / sample;
if(use_split_variance) {
variance[idx] = max(0.0f, (center_buffer[v_offset] - mean[idx]*mean[idx]*sample) / (sample * (sample-1)));
}
else {
variance[idx] = center_buffer[v_offset] / (sample * (sample-1));
}
}
ccl_device void kernel_filter_detect_outliers(int x, int y,
ccl_global float *image,
ccl_global float *variance,
ccl_global float *depth,
ccl_global float *out,
int4 rect,
int pass_stride)
{
int buffer_w = align_up(rect.z - rect.x, 4);
int n = 0;
float values[25];
for(int y1 = max(y-2, rect.y); y1 < min(y+3, rect.w); y1++) {
for(int x1 = max(x-2, rect.x); x1 < min(x+3, rect.z); x1++) {
int idx = (y1-rect.y)*buffer_w + (x1-rect.x);
float L = average(make_float3(image[idx], image[idx+pass_stride], image[idx+2*pass_stride]));
/* Find the position of L. */
int i;
for(i = 0; i < n; i++) {
if(values[i] > L) break;
}
/* Make space for L by shifting all following values to the right. */
for(int j = n; j > i; j--) {
values[j] = values[j-1];
}
/* Insert L. */
values[i] = L;
n++;
}
}
int idx = (y-rect.y)*buffer_w + (x-rect.x);
float L = average(make_float3(image[idx], image[idx+pass_stride], image[idx+2*pass_stride]));
float ref = 2.0f*values[(int)(n*0.75f)];
float fac = 1.0f;
if(L > ref) {
/* The pixel appears to be an outlier.
* However, it may just be a legitimate highlight. Therefore, it is checked how likely it is that the pixel
* should actually be at the reference value:
* If the reference is within the 3-sigma interval, the pixel is assumed to be a statistical outlier.
* Otherwise, it is very unlikely that the pixel should be darker, which indicates a legitimate highlight.
*/
float stddev = sqrtf(average(make_float3(variance[idx], variance[idx+pass_stride], variance[idx+2*pass_stride])));
if(L - 3*stddev < ref) {
/* The pixel is an outlier, so negate the depth value to mark it as one.
* Also, scale its brightness down to the outlier threshold to avoid trouble with the NLM weights. */
depth[idx] = -depth[idx];
fac = ref/L;
variance[idx ] *= fac*fac;
variance[idx + pass_stride] *= fac*fac;
variance[idx+2*pass_stride] *= fac*fac;
}
}
out[idx ] = fac*image[idx];
out[idx + pass_stride] = fac*image[idx + pass_stride];
out[idx+2*pass_stride] = fac*image[idx+2*pass_stride];
}
/* Combine A/B buffers.
* Calculates the combined mean and the buffer variance. */
ccl_device void kernel_filter_combine_halves(int x, int y,
ccl_global float *mean,
ccl_global float *variance,
ccl_global float *a,
ccl_global 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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