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// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
//
// * Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of
// its contributors may be used to endorse or promote products derived
// from this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de)
#ifndef COLMAP_SRC_MVS_GPU_MAT_REF_IMAGE_H_
#define COLMAP_SRC_MVS_GPU_MAT_REF_IMAGE_H_
#include <memory>
#include "mvs/cuda_array_wrapper.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;
size_t width_;
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
#endif // COLMAP_SRC_MVS_GPU_MAT_REF_IMAGE_H_
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