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Diffstat (limited to 'intern/cycles/kernel/filter/filter_prefilter.h')
| -rw-r--r-- | intern/cycles/kernel/filter/filter_prefilter.h | 215 |
1 files changed, 215 insertions, 0 deletions
diff --git a/intern/cycles/kernel/filter/filter_prefilter.h b/intern/cycles/kernel/filter/filter_prefilter.h new file mode 100644 index 00000000000..eefcbfea230 --- /dev/null +++ b/intern/cycles/kernel/filter/filter_prefilter.h @@ -0,0 +1,215 @@ +/* + * 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) +{ + 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; + + /* Approximate variance as E[x^2] - 1/N * (E[x])^2, since online variance + * update does not work efficiently with atomics in the kernel. */ + varA = max(0.0f, varA - unfilteredA[idx]*unfilteredA[idx]*odd_sample); + varB = max(0.0f, varB - unfilteredB[idx]*unfilteredB[idx]*even_sample); + + varA /= max(odd_sample - 1, 1); + varB /= max(even_sample - 1, 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) +{ + 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(sample > 1) { + /* Approximate variance as E[x^2] - 1/N * (E[x])^2, since online variance + * update does not work efficiently with atomics in the kernel. */ + variance[idx] = max(0.0f, (center_buffer[v_offset] - mean[idx]*mean[idx]*sample) / (sample * (sample-1))); + } + else { + /* Can't compute variance with single sample, just set it very high. */ + variance[idx] = 1e10f; + } +} + +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 idx = (y-rect.y)*buffer_w + (x-rect.x); + float3 color = make_float3(image[idx], image[idx+pass_stride], image[idx+2*pass_stride]); + + float fac = 1.0f; + if(color.x < 0.0f || color.y < 0.0f || color.z < 0.0f) { + depth[idx] = -depth[idx]; + fac = 0.0f; + } + else { + float L = average(color); + 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++; + } + } + + float ref = 2.0f*values[(int)(n*0.75f)]; + 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 |