9cc038f580
Make size inference consistent with the viewport compositor (from a user's perspective). This patch uses constat folding to create a constant output out of constant inputs. This is consistent with the results of the realtime compositor. Nodes not included in this patch require further refactoring or discussion. They will be addressed in future patches. Pull Request: https://projects.blender.org/blender/blender/pulls/114755
157 lines
7.4 KiB
C++
157 lines
7.4 KiB
C++
/* SPDX-FileCopyrightText: 2023 Blender Authors
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*
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* SPDX-License-Identifier: GPL-2.0-or-later */
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#include "COM_KuwaharaAnisotropicStructureTensorOperation.h"
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#include "BLI_math_base.hh"
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#include "BLI_math_vector.h"
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#include "BLI_math_vector.hh"
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#include "BLI_math_vector_types.hh"
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namespace blender::compositor {
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KuwaharaAnisotropicStructureTensorOperation::KuwaharaAnisotropicStructureTensorOperation()
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{
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this->add_input_socket(DataType::Color);
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this->add_output_socket(DataType::Color);
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this->flags_.is_fullframe_operation = true;
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this->flags_.can_be_constant = true;
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}
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void KuwaharaAnisotropicStructureTensorOperation::init_execution()
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{
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image_reader_ = this->get_input_socket_reader(0);
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}
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void KuwaharaAnisotropicStructureTensorOperation::deinit_execution()
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{
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image_reader_ = nullptr;
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}
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/* Computes the structure tensor of the image using a Dirac delta window function as described in
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* section "3.2 Local Structure Estimation" of the paper:
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*
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* Kyprianidis, Jan Eric. "Image and video abstraction by multi-scale anisotropic Kuwahara
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* filtering." 2011.
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*
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* The structure tensor should then be smoothed using a Gaussian function to eliminate high
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* frequency details. */
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void KuwaharaAnisotropicStructureTensorOperation::execute_pixel_sampled(float output[4],
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float x_float,
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float y_float,
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PixelSampler /*sampler*/)
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{
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using math::max, math::min, math::dot;
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const int x = x_float;
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const int y = y_float;
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const int width = this->get_width();
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const int height = this->get_height();
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/* The weight kernels of the filter optimized for rotational symmetry described in section "3.2.1
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* Gradient Calculation". */
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const float corner_weight = 0.182f;
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const float center_weight = 1.0f - 2.0f * corner_weight;
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float4 input_color;
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float3 x_partial_derivative = float3(0.0f);
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image_reader_->read(input_color, max(0, x - 1), min(height - 1, y + 1), nullptr);
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x_partial_derivative += input_color.xyz() * -corner_weight;
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image_reader_->read(input_color, max(0, x - 1), y, nullptr);
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x_partial_derivative += input_color.xyz() * -center_weight;
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image_reader_->read(input_color, max(0, x - 1), max(0, y - 1), nullptr);
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x_partial_derivative += input_color.xyz() * -corner_weight;
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image_reader_->read(input_color, min(width, x + 1), min(height - 1, y + 1), nullptr);
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x_partial_derivative += input_color.xyz() * corner_weight;
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image_reader_->read(input_color, min(width, x + 1), y, nullptr);
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x_partial_derivative += input_color.xyz() * center_weight;
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image_reader_->read(input_color, min(width, x + 1), max(0, y - 1), nullptr);
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x_partial_derivative += input_color.xyz() * corner_weight;
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float3 y_partial_derivative = float3(0.0f);
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image_reader_->read(input_color, max(0, x - 1), min(height - 1, y + 1), nullptr);
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y_partial_derivative += input_color.xyz() * corner_weight;
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image_reader_->read(input_color, x, min(height - 1, y + 1), nullptr);
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y_partial_derivative += input_color.xyz() * center_weight;
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image_reader_->read(input_color, min(width, x + 1), min(height - 1, y + 1), nullptr);
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y_partial_derivative += input_color.xyz() * corner_weight;
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image_reader_->read(input_color, max(0, x - 1), max(0, y - 1), nullptr);
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y_partial_derivative += input_color.xyz() * -corner_weight;
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image_reader_->read(input_color, x, max(0, y - 1), nullptr);
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y_partial_derivative += input_color.xyz() * -center_weight;
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image_reader_->read(input_color, min(width, x + 1), max(0, y - 1), nullptr);
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y_partial_derivative += input_color.xyz() * -corner_weight;
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/* We encode the structure tensor in a float4 using a column major storage order. */
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float4 structure_tensor = float4(dot(x_partial_derivative, x_partial_derivative),
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dot(x_partial_derivative, y_partial_derivative),
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dot(x_partial_derivative, y_partial_derivative),
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dot(y_partial_derivative, y_partial_derivative));
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copy_v4_v4(output, structure_tensor);
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}
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/* Computes the structure tensor of the image using a Dirac delta window function as described in
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* section "3.2 Local Structure Estimation" of the paper:
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*
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* Kyprianidis, Jan Eric. "Image and video abstraction by multi-scale anisotropic Kuwahara
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* filtering." 2011.
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*
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* The structure tensor should then be smoothed using a Gaussian function to eliminate high
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* frequency details. */
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void KuwaharaAnisotropicStructureTensorOperation::update_memory_buffer_partial(
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MemoryBuffer *output, const rcti &area, Span<MemoryBuffer *> inputs)
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{
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using math::max, math::min, math::dot;
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MemoryBuffer *image = inputs[0];
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const int width = image->get_width();
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const int height = image->get_height();
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/* The weight kernels of the filter optimized for rotational symmetry described in section
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* "3.2.1 Gradient Calculation". */
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const float corner_weight = 0.182f;
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const float center_weight = 1.0f - 2.0f * corner_weight;
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for (BuffersIterator<float> it = output->iterate_with(inputs, area); !it.is_end(); ++it) {
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const int x = it.x;
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const int y = it.y;
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float3 input_color;
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float3 x_partial_derivative = float3(0.0f);
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input_color = float3(image->get_elem(max(0, x - 1), min(height - 1, y + 1)));
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x_partial_derivative += input_color * -corner_weight;
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input_color = float3(image->get_elem(max(0, x - 1), y));
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x_partial_derivative += input_color * -center_weight;
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input_color = float3(image->get_elem(max(0, x - 1), max(0, y - 1)));
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x_partial_derivative += input_color * -corner_weight;
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input_color = float3(image->get_elem(min(width - 1, x + 1), min(height - 1, y + 1)));
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x_partial_derivative += input_color * corner_weight;
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input_color = float3(image->get_elem(min(width - 1, x + 1), y));
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x_partial_derivative += input_color * center_weight;
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input_color = float3(image->get_elem(min(width - 1, x + 1), max(0, y - 1)));
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x_partial_derivative += input_color * corner_weight;
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float3 y_partial_derivative = float3(0.0f);
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input_color = float3(image->get_elem(max(0, x - 1), min(height - 1, y + 1)));
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y_partial_derivative += input_color * corner_weight;
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input_color = float3(image->get_elem(x, min(height - 1, y + 1)));
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y_partial_derivative += input_color * center_weight;
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input_color = float3(image->get_elem(min(width - 1, x + 1), min(height - 1, y + 1)));
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y_partial_derivative += input_color * corner_weight;
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input_color = float3(image->get_elem(max(0, x - 1), max(0, y - 1)));
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y_partial_derivative += input_color * -corner_weight;
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input_color = float3(image->get_elem(x, max(0, y - 1)));
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y_partial_derivative += input_color * -center_weight;
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input_color = float3(image->get_elem(min(width - 1, x + 1), max(0, y - 1)));
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y_partial_derivative += input_color * -corner_weight;
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/* We encode the structure tensor in a float4 using a column major storage order. */
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float4 structure_tensor = float4(dot(x_partial_derivative, x_partial_derivative),
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dot(x_partial_derivative, y_partial_derivative),
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dot(x_partial_derivative, y_partial_derivative),
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dot(y_partial_derivative, y_partial_derivative));
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copy_v4_v4(it.out, structure_tensor);
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}
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}
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} // namespace blender::compositor
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