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Diffstat (limited to 'intern/cycles/kernel/filter/filter_prefilter.h')
| -rw-r--r-- | intern/cycles/kernel/filter/filter_prefilter.h | 303 |
1 files changed, 0 insertions, 303 deletions
diff --git a/intern/cycles/kernel/filter/filter_prefilter.h b/intern/cycles/kernel/filter/filter_prefilter.h deleted file mode 100644 index 97cecba190e..00000000000 --- a/intern/cycles/kernel/filter/filter_prefilter.h +++ /dev/null @@ -1,303 +0,0 @@ -/* - * 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: - * \param unfiltered: Contains the two half images of the shadow feature pass - * \param 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. - * \param sampleVarianceV: Variance of the sample variance estimation, quite noisy - * (since it's essentially the buffer variance of the two variance halves) - * \param bufferVariance: The buffer-based variance of the shadow feature. - * Unbiased, but quite noisy. - */ -ccl_device void kernel_filter_divide_shadow(int sample, - CCL_FILTER_TILE_INFO, - 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 < tile_info->x[1]) ? 0 : ((x < tile_info->x[2]) ? 1 : 2); - int ytile = (y < tile_info->y[1]) ? 0 : ((y < tile_info->y[2]) ? 1 : 2); - int tile = ytile * 3 + xtile; - - int offset = tile_info->offsets[tile]; - int stride = tile_info->strides[tile]; - const ccl_global float *ccl_restrict center_buffer = (ccl_global float *)ccl_get_tile_buffer( - 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_FILTER_TILE_INFO, - int m_offset, - int v_offset, - int x, - int y, - ccl_global float *mean, - ccl_global float *variance, - float scale, - int4 rect, - int buffer_pass_stride, - int buffer_denoising_offset) -{ - int xtile = (x < tile_info->x[1]) ? 0 : ((x < tile_info->x[2]) ? 1 : 2); - int ytile = (y < tile_info->y[1]) ? 0 : ((y < tile_info->y[2]) ? 1 : 2); - int tile = ytile * 3 + xtile; - ccl_global float *center_buffer = ((ccl_global float *)ccl_get_tile_buffer(tile)) + - (tile_info->offsets[tile] + y * tile_info->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); - - float val = scale * center_buffer[m_offset]; - mean[idx] = val; - - if (v_offset >= 0) { - 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] - val * val * 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_write_feature(int sample, - int x, - int y, - int4 buffer_params, - ccl_global float *from, - ccl_global float *buffer, - int out_offset, - int4 rect) -{ - ccl_global float *combined_buffer = buffer + (y * buffer_params.y + x + buffer_params.x) * - buffer_params.z; - - int buffer_w = align_up(rect.z - rect.x, 4); - int idx = (y - rect.y) * buffer_w + (x - rect.x); - - combined_buffer[out_offset] = from[idx]; -} - -#define GET_COLOR(image) \ - make_float3(image[idx], image[idx + pass_stride], image[idx + 2 * pass_stride]) -#define SET_COLOR(image, color) \ - image[idx] = color.x; \ - image[idx + pass_stride] = color.y; \ - image[idx + 2 * pass_stride] = color.z - -ccl_device void kernel_filter_detect_outliers(int x, - int y, - ccl_global float *in, - ccl_global float *variance_out, - ccl_global float *depth, - ccl_global float *image_out, - int4 rect, - int pass_stride) -{ - int buffer_w = align_up(rect.z - rect.x, 4); - - ccl_global float *image_in = in; - ccl_global float *variance_in = in + 3 * pass_stride; - - int n = 0; - float values[25]; - float pixel_variance, max_variance = 0.0f; - 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); - float3 color = GET_COLOR(image_in); - color = max(color, make_float3(0.0f, 0.0f, 0.0f)); - float L = average(color); - - /* 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++; - - float3 pixel_var = GET_COLOR(variance_in); - float var = average(pixel_var); - if ((x1 == x) && (y1 == y)) { - pixel_variance = (pixel_var.x < 0.0f || pixel_var.y < 0.0f || pixel_var.z < 0.0f) ? -1.0f : - var; - } - else { - max_variance = max(max_variance, var); - } - } - } - - max_variance += 1e-4f; - - int idx = (y - rect.y) * buffer_w + (x - rect.x); - - float3 color = GET_COLOR(image_in); - float3 variance = GET_COLOR(variance_in); - color = max(color, make_float3(0.0f, 0.0f, 0.0f)); - variance = max(variance, make_float3(0.0f, 0.0f, 0.0f)); - - float L = average(color); - - float ref = 2.0f * values[(int)(n * 0.75f)]; - - /* Slightly offset values to avoid false positives in (almost) black areas. */ - max_variance += 1e-5f; - ref -= 1e-5f; - - 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. - */ - - if (pixel_variance < 0.0f || pixel_variance > 9.0f * max_variance) { - depth[idx] = -depth[idx]; - color *= ref / L; - variance = make_float3(max_variance, max_variance, max_variance); - } - else { - float stddev = sqrtf(pixel_variance); - 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]; - float fac = ref / L; - color *= fac; - variance *= sqr(fac); - } - } - } - - /* Apply log(1+x) transform to compress highlights and avoid halos in the denoised results. - * Variance is transformed accordingly - the derivative of the transform is 1/(1+x), so we - * scale by the square of that (since we have variance instead of standard deviation). */ - color = color_highlight_compress(color, &variance); - - SET_COLOR(image_out, color); - SET_COLOR(variance_out, variance); -} - -#undef GET_COLOR -#undef SET_COLOR - -/* 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 |