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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.
*/
#include "device/device_denoising.h"
#include "kernel/filter/filter_defines.h"
CCL_NAMESPACE_BEGIN
DenoisingTask::DenoisingTask(Device *device, const DeviceTask &task)
: tile_info_mem(device, "denoising tile info mem", MEM_READ_WRITE),
profiler(NULL),
storage(device),
buffer(device),
device(device)
{
radius = task.denoising_radius;
nlm_k_2 = powf(2.0f, lerp(-5.0f, 3.0f, task.denoising_strength));
if(task.denoising_relative_pca) {
pca_threshold = -powf(10.0f, lerp(-8.0f, 0.0f, task.denoising_feature_strength));
}
else {
pca_threshold = powf(10.0f, lerp(-5.0f, 3.0f, task.denoising_feature_strength));
}
render_buffer.pass_stride = task.pass_stride;
render_buffer.offset = task.pass_denoising_data;
target_buffer.pass_stride = task.pass_stride;
target_buffer.denoising_clean_offset = task.pass_denoising_clean;
functions.map_neighbor_tiles = function_bind(task.map_neighbor_tiles, _1, device);
functions.unmap_neighbor_tiles = function_bind(task.unmap_neighbor_tiles, _1, device);
}
DenoisingTask::~DenoisingTask()
{
storage.XtWX.free();
storage.XtWY.free();
storage.transform.free();
storage.rank.free();
buffer.mem.free();
buffer.temporary_mem.free();
tile_info_mem.free();
}
void DenoisingTask::set_render_buffer(RenderTile *rtiles)
{
tile_info = (TileInfo*) tile_info_mem.alloc(sizeof(TileInfo)/sizeof(int));
for(int i = 0; i < 9; i++) {
tile_info->offsets[i] = rtiles[i].offset;
tile_info->strides[i] = rtiles[i].stride;
tile_info->buffers[i] = rtiles[i].buffer;
}
tile_info->x[0] = rtiles[3].x;
tile_info->x[1] = rtiles[4].x;
tile_info->x[2] = rtiles[5].x;
tile_info->x[3] = rtiles[5].x + rtiles[5].w;
tile_info->y[0] = rtiles[1].y;
tile_info->y[1] = rtiles[4].y;
tile_info->y[2] = rtiles[7].y;
tile_info->y[3] = rtiles[7].y + rtiles[7].h;
target_buffer.offset = rtiles[9].offset;
target_buffer.stride = rtiles[9].stride;
target_buffer.ptr = rtiles[9].buffer;
tile_info_mem.copy_to_device();
}
void DenoisingTask::setup_denoising_buffer()
{
/* Expand filter_area by radius pixels and clamp the result to the extent of the neighboring tiles */
rect = rect_from_shape(filter_area.x, filter_area.y, filter_area.z, filter_area.w);
rect = rect_expand(rect, radius);
rect = rect_clip(rect, make_int4(tile_info->x[0], tile_info->y[0], tile_info->x[3], tile_info->y[3]));
buffer.passes = 14;
buffer.width = rect.z - rect.x;
buffer.stride = align_up(buffer.width, 4);
buffer.h = rect.w - rect.y;
int alignment_floats = divide_up(device->mem_sub_ptr_alignment(), sizeof(float));
buffer.pass_stride = align_up(buffer.stride * buffer.h, alignment_floats);
/* Pad the total size by four floats since the SIMD kernels might go a bit over the end. */
int mem_size = align_up(buffer.pass_stride * buffer.passes + 4, alignment_floats);
buffer.mem.alloc_to_device(mem_size, false);
/* CPUs process shifts sequentially while GPUs process them in parallel. */
int num_layers;
if(buffer.gpu_temporary_mem) {
/* Shadowing prefiltering uses a radius of 6, so allocate at least that much. */
int max_radius = max(radius, 6);
int num_shifts = (2*max_radius + 1) * (2*max_radius + 1);
num_layers = 2*num_shifts + 1;
}
else {
num_layers = 3;
}
/* Allocate two layers per shift as well as one for the weight accumulation. */
buffer.temporary_mem.alloc_to_device(num_layers * buffer.pass_stride);
}
void DenoisingTask::prefilter_shadowing()
{
device_ptr null_ptr = (device_ptr) 0;
device_sub_ptr unfiltered_a (buffer.mem, 0, buffer.pass_stride);
device_sub_ptr unfiltered_b (buffer.mem, 1*buffer.pass_stride, buffer.pass_stride);
device_sub_ptr sample_var (buffer.mem, 2*buffer.pass_stride, buffer.pass_stride);
device_sub_ptr sample_var_var (buffer.mem, 3*buffer.pass_stride, buffer.pass_stride);
device_sub_ptr buffer_var (buffer.mem, 5*buffer.pass_stride, buffer.pass_stride);
device_sub_ptr filtered_var (buffer.mem, 6*buffer.pass_stride, buffer.pass_stride);
/* Get the A/B unfiltered passes, the combined sample variance, the estimated variance of the sample variance and the buffer variance. */
functions.divide_shadow(*unfiltered_a, *unfiltered_b, *sample_var, *sample_var_var, *buffer_var);
/* Smooth the (generally pretty noisy) buffer variance using the spatial information from the sample variance. */
nlm_state.set_parameters(6, 3, 4.0f, 1.0f);
functions.non_local_means(*buffer_var, *sample_var, *sample_var_var, *filtered_var);
/* Reuse memory, the previous data isn't needed anymore. */
device_ptr filtered_a = *buffer_var,
filtered_b = *sample_var;
/* Use the smoothed variance to filter the two shadow half images using each other for weight calculation. */
nlm_state.set_parameters(5, 3, 1.0f, 0.25f);
functions.non_local_means(*unfiltered_a, *unfiltered_b, *filtered_var, filtered_a);
functions.non_local_means(*unfiltered_b, *unfiltered_a, *filtered_var, filtered_b);
device_ptr residual_var = *sample_var_var;
/* Estimate the residual variance between the two filtered halves. */
functions.combine_halves(filtered_a, filtered_b, null_ptr, residual_var, 2, rect);
device_ptr final_a = *unfiltered_a,
final_b = *unfiltered_b;
/* Use the residual variance for a second filter pass. */
nlm_state.set_parameters(4, 2, 1.0f, 0.5f);
functions.non_local_means(filtered_a, filtered_b, residual_var, final_a);
functions.non_local_means(filtered_b, filtered_a, residual_var, final_b);
/* Combine the two double-filtered halves to a final shadow feature. */
device_sub_ptr shadow_pass(buffer.mem, 4*buffer.pass_stride, buffer.pass_stride);
functions.combine_halves(final_a, final_b, *shadow_pass, null_ptr, 0, rect);
}
void DenoisingTask::prefilter_features()
{
device_sub_ptr unfiltered (buffer.mem, 8*buffer.pass_stride, buffer.pass_stride);
device_sub_ptr variance (buffer.mem, 9*buffer.pass_stride, buffer.pass_stride);
int mean_from[] = { 0, 1, 2, 12, 6, 7, 8 };
int variance_from[] = { 3, 4, 5, 13, 9, 10, 11};
int pass_to[] = { 1, 2, 3, 0, 5, 6, 7};
for(int pass = 0; pass < 7; pass++) {
device_sub_ptr feature_pass(buffer.mem, pass_to[pass]*buffer.pass_stride, buffer.pass_stride);
/* Get the unfiltered pass and its variance from the RenderBuffers. */
functions.get_feature(mean_from[pass], variance_from[pass], *unfiltered, *variance);
/* Smooth the pass and store the result in the denoising buffers. */
nlm_state.set_parameters(2, 2, 1.0f, 0.25f);
functions.non_local_means(*unfiltered, *unfiltered, *variance, *feature_pass);
}
}
void DenoisingTask::prefilter_color()
{
int mean_from[] = {20, 21, 22};
int variance_from[] = {23, 24, 25};
int mean_to[] = { 8, 9, 10};
int variance_to[] = {11, 12, 13};
int num_color_passes = 3;
device_only_memory<float> temporary_color(device, "denoising temporary color");
temporary_color.alloc_to_device(3*buffer.pass_stride, false);
for(int pass = 0; pass < num_color_passes; pass++) {
device_sub_ptr color_pass(temporary_color, pass*buffer.pass_stride, buffer.pass_stride);
device_sub_ptr color_var_pass(buffer.mem, variance_to[pass]*buffer.pass_stride, buffer.pass_stride);
functions.get_feature(mean_from[pass], variance_from[pass], *color_pass, *color_var_pass);
}
device_sub_ptr depth_pass (buffer.mem, 0, buffer.pass_stride);
device_sub_ptr color_var_pass(buffer.mem, variance_to[0]*buffer.pass_stride, 3*buffer.pass_stride);
device_sub_ptr output_pass (buffer.mem, mean_to[0]*buffer.pass_stride, 3*buffer.pass_stride);
functions.detect_outliers(temporary_color.device_pointer, *color_var_pass, *depth_pass, *output_pass);
}
void DenoisingTask::construct_transform()
{
storage.w = filter_area.z;
storage.h = filter_area.w;
storage.transform.alloc_to_device(storage.w*storage.h*TRANSFORM_SIZE, false);
storage.rank.alloc_to_device(storage.w*storage.h, false);
functions.construct_transform();
}
void DenoisingTask::reconstruct()
{
storage.XtWX.alloc_to_device(storage.w*storage.h*XTWX_SIZE, false);
storage.XtWY.alloc_to_device(storage.w*storage.h*XTWY_SIZE, false);
reconstruction_state.filter_window = rect_from_shape(filter_area.x-rect.x, filter_area.y-rect.y, storage.w, storage.h);
int tile_coordinate_offset = filter_area.y*target_buffer.stride + filter_area.x;
reconstruction_state.buffer_params = make_int4(target_buffer.offset + tile_coordinate_offset,
target_buffer.stride,
target_buffer.pass_stride,
target_buffer.denoising_clean_offset);
reconstruction_state.source_w = rect.z-rect.x;
reconstruction_state.source_h = rect.w-rect.y;
device_sub_ptr color_ptr (buffer.mem, 8*buffer.pass_stride, 3*buffer.pass_stride);
device_sub_ptr color_var_ptr(buffer.mem, 11*buffer.pass_stride, 3*buffer.pass_stride);
functions.reconstruct(*color_ptr, *color_var_ptr, target_buffer.ptr);
}
void DenoisingTask::run_denoising(RenderTile *tile)
{
RenderTile rtiles[10];
rtiles[4] = *tile;
functions.map_neighbor_tiles(rtiles);
set_render_buffer(rtiles);
setup_denoising_buffer();
prefilter_shadowing();
prefilter_features();
prefilter_color();
construct_transform();
reconstruct();
functions.unmap_neighbor_tiles(rtiles);
}
CCL_NAMESPACE_END
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