gpu_mat_ref_image.h

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// ----------------------------------------------------------------------------
// -                        CloudViewer: www.cloudViewer.org                  -
// ----------------------------------------------------------------------------
// Copyright (c) 2018-2024 www.cloudViewer.org
// SPDX-License-Identifier: MIT
// ----------------------------------------------------------------------------
 
#pragma once
 
#include <memory>
 
#include "mvs/cuda_texture.h"
#include "mvs/gpu_mat.h"
 
namespace colmap {
namespace mvs {
 
class GpuMatRefImage {
public:
    GpuMatRefImage(const size_t width, const size_t height);
 
    // Filter image using sum convolution kernel to compute local sum of
    // intensities. The filtered images can then be used for repeated, efficient
    // NCC computation.
    void Filter(const uint8_t* image_data,
                const size_t window_radius,
                const size_t window_step,
                const float sigma_spatial,
                const float sigma_color);
 
    // Image intensities.
    std::unique_ptr<GpuMat<uint8_t>> image;
 
    // Local sum of image intensities.
    std::unique_ptr<GpuMat<float>> sum_image;
 
    // Local sum of squared image intensities.
    std::unique_ptr<GpuMat<float>> squared_sum_image;
 
private:
    const static size_t kBlockDimX = 16;
    const static size_t kBlockDimY = 12;
 
    const size_t width_;
    const size_t height_;
};
 
struct BilateralWeightComputer {
    __device__ BilateralWeightComputer(const float sigma_spatial,
                                       const float sigma_color)
        : spatial_normalization_(1.0f / (2.0f * sigma_spatial * sigma_spatial)),
          color_normalization_(1.0f / (2.0f * sigma_color * sigma_color)) {}
 
    __device__ inline float Compute(const float row_diff,
                                    const float col_diff,
                                    const float color1,
                                    const float color2) const {
        const float spatial_dist_squared =
                row_diff * row_diff + col_diff * col_diff;
        const float color_dist = color1 - color2;
        return exp(-spatial_dist_squared * spatial_normalization_ -
                   color_dist * color_dist * color_normalization_);
    }
 
private:
    const float spatial_normalization_;
    const float color_normalization_;
};
 
}  // namespace mvs
}  // namespace colmap