diff options
Diffstat (limited to 'intern/cycles/kernel/filter/filter_transform.h')
| -rw-r--r-- | intern/cycles/kernel/filter/filter_transform.h | 59 |
1 files changed, 30 insertions, 29 deletions
diff --git a/intern/cycles/kernel/filter/filter_transform.h b/intern/cycles/kernel/filter/filter_transform.h index a5f87c05ec0..94e27bb02fd 100644 --- a/intern/cycles/kernel/filter/filter_transform.h +++ b/intern/cycles/kernel/filter/filter_transform.h @@ -17,8 +17,10 @@ CCL_NAMESPACE_BEGIN ccl_device void kernel_filter_construct_transform(const float *ccl_restrict buffer, + CCL_FILTER_TILE_INFO, int x, int y, int4 rect, - int pass_stride, + int pass_stride, int frame_stride, + bool use_time, float *transform, int *rank, int radius, float pca_threshold) { @@ -26,59 +28,58 @@ ccl_device void kernel_filter_construct_transform(const float *ccl_restrict buff float features[DENOISE_FEATURES]; - /* Temporary storage, used in different steps of the algorithm. */ - float tempmatrix[DENOISE_FEATURES*DENOISE_FEATURES]; - float tempvector[2*DENOISE_FEATURES]; const float *ccl_restrict pixel_buffer; - int2 pixel; + int3 pixel; + + int num_features = use_time? 11 : 10; /* === Calculate denoising window. === */ int2 low = make_int2(max(rect.x, x - radius), max(rect.y, y - radius)); int2 high = make_int2(min(rect.z, x + radius + 1), min(rect.w, y + radius + 1)); - int num_pixels = (high.y - low.y) * (high.x - low.x); + int num_pixels = (high.y - low.y) * (high.x - low.x) * tile_info->num_frames; /* === Shift feature passes to have mean 0. === */ float feature_means[DENOISE_FEATURES]; - math_vector_zero(feature_means, DENOISE_FEATURES); + math_vector_zero(feature_means, num_features); FOR_PIXEL_WINDOW { - filter_get_features(pixel, pixel_buffer, features, NULL, pass_stride); - math_vector_add(feature_means, features, DENOISE_FEATURES); + filter_get_features(pixel, pixel_buffer, features, use_time, NULL, pass_stride); + math_vector_add(feature_means, features, num_features); } END_FOR_PIXEL_WINDOW - math_vector_scale(feature_means, 1.0f / num_pixels, DENOISE_FEATURES); + math_vector_scale(feature_means, 1.0f / num_pixels, num_features); /* === Scale the shifted feature passes to a range of [-1; 1], will be baked into the transform later. === */ - float *feature_scale = tempvector; - math_vector_zero(feature_scale, DENOISE_FEATURES); + float feature_scale[DENOISE_FEATURES]; + math_vector_zero(feature_scale, num_features); FOR_PIXEL_WINDOW { - filter_get_feature_scales(pixel, pixel_buffer, features, feature_means, pass_stride); - math_vector_max(feature_scale, features, DENOISE_FEATURES); + filter_get_feature_scales(pixel, pixel_buffer, features, use_time, feature_means, pass_stride); + math_vector_max(feature_scale, features, num_features); } END_FOR_PIXEL_WINDOW - filter_calculate_scale(feature_scale); + filter_calculate_scale(feature_scale, use_time); /* === Generate the feature transformation. === - * This transformation maps the DENOISE_FEATURES-dimentional feature space to a reduced feature (r-feature) space + * This transformation maps the num_features-dimentional feature space to a reduced feature (r-feature) space * which generally has fewer dimensions. This mainly helps to prevent overfitting. */ - float* feature_matrix = tempmatrix; - math_matrix_zero(feature_matrix, DENOISE_FEATURES); + float feature_matrix[DENOISE_FEATURES*DENOISE_FEATURES]; + math_matrix_zero(feature_matrix, num_features); FOR_PIXEL_WINDOW { - filter_get_features(pixel, pixel_buffer, features, feature_means, pass_stride); - math_vector_mul(features, feature_scale, DENOISE_FEATURES); - math_matrix_add_gramian(feature_matrix, DENOISE_FEATURES, features, 1.0f); + filter_get_features(pixel, pixel_buffer, features, use_time, feature_means, pass_stride); + math_vector_mul(features, feature_scale, num_features); + math_matrix_add_gramian(feature_matrix, num_features, features, 1.0f); } END_FOR_PIXEL_WINDOW - math_matrix_jacobi_eigendecomposition(feature_matrix, transform, DENOISE_FEATURES, 1); + math_matrix_jacobi_eigendecomposition(feature_matrix, transform, num_features, 1); *rank = 0; /* Prevent overfitting when a small window is used. */ - int max_rank = min(DENOISE_FEATURES, num_pixels/3); + int max_rank = min(num_features, num_pixels/3); if(pca_threshold < 0.0f) { float threshold_energy = 0.0f; - for(int i = 0; i < DENOISE_FEATURES; i++) { - threshold_energy += feature_matrix[i*DENOISE_FEATURES+i]; + for(int i = 0; i < num_features; i++) { + threshold_energy += feature_matrix[i*num_features+i]; } threshold_energy *= 1.0f - (-pca_threshold); @@ -86,13 +87,13 @@ ccl_device void kernel_filter_construct_transform(const float *ccl_restrict buff for(int i = 0; i < max_rank; i++, (*rank)++) { if(i >= 2 && reduced_energy >= threshold_energy) break; - float s = feature_matrix[i*DENOISE_FEATURES+i]; + float s = feature_matrix[i*num_features+i]; reduced_energy += s; } } else { for(int i = 0; i < max_rank; i++, (*rank)++) { - float s = feature_matrix[i*DENOISE_FEATURES+i]; + float s = feature_matrix[i*num_features+i]; if(i >= 2 && sqrtf(s) < pca_threshold) break; } @@ -100,9 +101,9 @@ ccl_device void kernel_filter_construct_transform(const float *ccl_restrict buff /* Bake the feature scaling into the transformation matrix. */ for(int i = 0; i < (*rank); i++) { - math_vector_mul(transform + i*DENOISE_FEATURES, feature_scale, DENOISE_FEATURES); + math_vector_mul(transform + i*num_features, feature_scale, num_features); } - math_matrix_transpose(transform, DENOISE_FEATURES, 1); + math_matrix_transpose(transform, num_features, 1); } CCL_NAMESPACE_END |