Differential D2592 Diff 8686 intern/cycles/kernel/filter/filter_prefilter.h

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intern/cycles/kernel/filter/filter_prefilter.h

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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;

				ccl_global float ccl_readonly_ptr buffer = (ccl_global float*) tiles->buffers[tile];
				int offset = tiles->offsets[tile];
				int stride = tiles->strides[tile];
				ccl_global float ccl_readonly_ptr center_buffer = buffer + (ystride + x + offset)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);
				unfilteredA[idx] = center_buffer[15] / max(center_buffer[14], 1e-7f);
				unfilteredB[idx] = center_buffer[18] / max(center_buffer[17], 1e-7f);

				float varA, varB;
				int odd_sample = (sample+1)/2;
				int even_sample = sample/2;
				if(use_split_variance) {
				varA = max(0.0f, (center_buffer[16] - unfilteredA[idx]unfilteredA[idx]odd_sample) / (odd_sample - 1));
				varB = max(0.0f, (center_buffer[19] - unfilteredB[idx]unfilteredB[idx]even_sample) / (even_sample - 1));
				}
				else {
				varA = center_buffer[16] / (odd_sample - 1);
				varB = center_buffer[19] / (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] + ytiles->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));
				}
				}

				/* 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