tests/validation/reference/HOGDescriptor.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2017-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to
  * deal in the Software without restriction, including without limitation the
  * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
  * sell copies of the Software, and to permit persons to whom the Software is
  * furnished to do so, subject to the following conditions:
  *
  * The above copyright notice and this permission notice shall be included in all
  * copies or substantial portions of the Software.
  *
  * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
 #include "HOGDescriptor.h"

 #include "Derivative.h"
 #include "Magnitude.h"
 #include "Phase.h"

 namespace arm_compute
 {
 namespace test
 {
 namespace validation
 {
 namespace reference
 {
 namespace
 {
 template <typename T>
 void hog_orientation_compute(const SimpleTensor<T> &mag, const SimpleTensor<T> &phase, std::vector<T> &bins, const HOGInfo &hog_info)
 {
     const Size2D &cell_size = hog_info.cell_size();
     const size_t  num_bins  = hog_info.num_bins();

     float phase_scale = (PhaseType::SIGNED == hog_info.phase_type() ? num_bins / 360.0f : num_bins / 180.0f);
     phase_scale *= (PhaseType::SIGNED == hog_info.phase_type() ? 360.0f / 255.0f : 1.0f);

     int row_idx = 0;
     for(size_t yc = 0; yc < cell_size.height; ++yc)
     {
         for(size_t xc = 0; xc < cell_size.width; xc++)
         {
             const float mag_value   = mag[(row_idx + xc)];
             const float phase_value = phase[(row_idx + xc)] * phase_scale + 0.5f;
             const float w1          = phase_value - floor(phase_value);

             // The quantised phase is the histogram index [0, num_bins - 1]
             // Check limit of histogram index. If hidx == num_bins, hidx = 0
             const auto hidx = static_cast<unsigned int>(phase_value) % num_bins;

             // Weighted vote between 2 bins
             bins[hidx] += mag_value * (1.0f - w1);
             bins[(hidx + 1) % num_bins] += mag_value * w1;
         }

         row_idx += cell_size.width;
     }
 }

 template <typename T>
 void hog_block_normalization_compute(SimpleTensor<T> &block, SimpleTensor<T> &desc, const HOGInfo &hog_info, uint32_t block_idx)
 {
     const int         num_bins_per_block = desc.num_channels();
     const HOGNormType norm_type          = hog_info.normalization_type();
     const Coordinates id                 = index2coord(desc.shape(), block_idx);

     float sum = 0.0f;

     // Calculate sum
     for(int i = 0; i < num_bins_per_block; ++i)
     {
         const float val = block[i];
         sum += (norm_type == HOGNormType::L1_NORM) ? std::fabs(val) : val * val;
     }

     // Calculate normalization scale
     float scale = 1.0f / (std::sqrt(sum) + num_bins_per_block * 0.1f);

     if(norm_type == HOGNormType::L2HYS_NORM)
     {
         // Reset sum
         sum = 0.0f;
         for(int i = 0; i < num_bins_per_block; ++i)
         {
             float val = block[i] * scale;

             // Clip scaled input_value if over l2_hyst_threshold
             val = fmin(val, hog_info.l2_hyst_threshold());
             sum += val * val;
             block[i] = val;
         }

         // We use the same constants of OpenCV
         scale = 1.0f / (std::sqrt(sum) + 1e-3f);
     }

     for(int i = 0; i < num_bins_per_block; ++i)
     {
         block[i] *= scale;
         reinterpret_cast<float *>(desc(id))[i] = block[i];
     }
 }
 } // namespace

 template <typename T, typename U, typename V>
 void hog_orientation_binning(const SimpleTensor<T> &mag, const SimpleTensor<U> &phase, SimpleTensor<V> &hog_space, const HOGInfo &hog_info)
 {
     const Size2D &cell_size = hog_info.cell_size();

     const size_t num_bins     = hog_info.num_bins();
     const size_t shape_width  = hog_space.shape().x() * hog_info.cell_size().width;
     const size_t shape_height = hog_space.shape().y() * hog_info.cell_size().height;

     TensorShape cell_shape(cell_size.width, cell_size.height);

     SimpleTensor<V> mag_cell(cell_shape, DataType::F32);
     SimpleTensor<V> phase_cell(cell_shape, DataType::F32);

     int cell_idx = 0;
     int y_offset = 0;

     // Traverse shape
     for(auto sy = cell_size.height; sy <= shape_height; sy += cell_size.height)
     {
         int x_offset = 0;
         for(auto sx = cell_size.width; sx <= shape_width; sx += cell_size.width)
         {
             int row_idx  = 0;
             int elem_idx = 0;

             // Traverse cell
             for(auto y = 0u; y < cell_size.height; ++y)
             {
                 for(auto x = 0u; x < cell_size.width; ++x)
                 {
                     int shape_idx        = x + row_idx + x_offset + y_offset;
                     mag_cell[elem_idx]   = mag[shape_idx];
                     phase_cell[elem_idx] = phase[shape_idx];
                     elem_idx++;
                 }

                 row_idx += shape_width;
             }

             // Partition magnitude values into bins based on phase values
             std::vector<V> bins(num_bins);
             hog_orientation_compute(mag_cell, phase_cell, bins, hog_info);

             for(size_t i = 0; i < num_bins; ++i)
             {
                 hog_space[cell_idx * num_bins + i] = bins[i];
             }

             x_offset += cell_size.width;
             cell_idx++;
         }

         y_offset += (cell_size.height * shape_width);
     }
 }

 template <typename T>
 void hog_block_normalization(SimpleTensor<T> &desc, const SimpleTensor<T> &hog_space, const HOGInfo &hog_info)
 {
     const Size2D  cells_per_block        = hog_info.num_cells_per_block();
     const Size2D  cells_per_block_stride = hog_info.num_cells_per_block_stride();
     const Size2D &block_size             = hog_info.block_size();
     const Size2D &block_stride           = hog_info.block_stride();
     const size_t  num_bins               = hog_info.num_bins();

     const size_t shape_width          = hog_space.shape().x() * hog_info.cell_size().width;
     const size_t shape_height         = hog_space.shape().y() * hog_info.cell_size().height;
     const size_t num_bins_per_block_x = cells_per_block.width * num_bins;

     // Tensor representing single block
     SimpleTensor<T> block(TensorShape{ 1u, 1u }, DataType::F32, cells_per_block.area() * num_bins);

     uint32_t block_idx      = 0;
     int      block_y_offset = 0;

     // Traverse shape
     for(auto sy = block_size.height; sy <= shape_height; sy += block_stride.height)
     {
         int block_x_offset = 0;
         for(auto sx = block_size.width; sx <= shape_width; sx += block_stride.width)
         {
             int cell_y_offset = 0;
             int elem_idx      = 0;

             // Traverse block
             for(auto y = 0u; y < cells_per_block.height; ++y)
             {
                 for(auto x = 0u; x < num_bins_per_block_x; ++x)
                 {
                     int idx         = x + cell_y_offset + block_x_offset + block_y_offset;
                     block[elem_idx] = hog_space[idx];
                     elem_idx++;
                 }

                 cell_y_offset += hog_space.shape().x() * num_bins;
             }

             // Normalize block and write to descriptor
             hog_block_normalization_compute(block, desc, hog_info, block_idx);

             block_x_offset += cells_per_block_stride.width * num_bins;
             block_idx++;
         }

         block_y_offset += cells_per_block_stride.height * num_bins * hog_space.shape().x();
     }
 }

 template <typename T, typename U>
 SimpleTensor<T> hog_descriptor(const SimpleTensor<U> &src, BorderMode border_mode, U constant_border_value, const HOGInfo &hog_info)
 {
     SimpleTensor<int16_t> grad_x;
     SimpleTensor<int16_t> grad_y;

     // Create tensor info for HOG descriptor
     TensorInfo      desc_info(hog_info, src.shape().x(), src.shape().y());
     SimpleTensor<T> desc(desc_info.tensor_shape(), DataType::F32, desc_info.num_channels());

     // Create HOG space tensor (num_cells_x, num_cells_y)
     TensorShape hog_space_shape(src.shape().x() / hog_info.cell_size().width,
                                 src.shape().y() / hog_info.cell_size().height);

     // For each cell a histogram with a num_bins is created
     TensorInfo      info_hog_space(hog_space_shape, hog_info.num_bins(), DataType::F32);
     SimpleTensor<T> hog_space(info_hog_space.tensor_shape(), DataType::F32, info_hog_space.num_channels());

     // Calculate derivative
     std::tie(grad_x, grad_y) = derivative<int16_t>(src, border_mode, constant_border_value, GradientDimension::GRAD_XY);

     // For each cell create histogram based on magnitude and phase
     hog_orientation_binning(magnitude(grad_x, grad_y, MagnitudeType::L2NORM),
                             phase(grad_x, grad_y, hog_info.phase_type()),
                             hog_space,
                             hog_info);

     // Normalize histograms based on block size
     hog_block_normalization(desc, hog_space, hog_info);

     return desc;
 }

 template void hog_orientation_binning(const SimpleTensor<int16_t> &mag, const SimpleTensor<uint8_t> &phase, SimpleTensor<float> &hog_space, const HOGInfo &hog_info);
 template void hog_block_normalization(SimpleTensor<float> &desc, const SimpleTensor<float> &hog_space, const HOGInfo &hog_info);
 template SimpleTensor<float> hog_descriptor(const SimpleTensor<uint8_t> &src, BorderMode border_mode, uint8_t constant_border_value, const HOGInfo &hog_info);
 } // namespace reference
 } // namespace validation
 } // namespace test
 } // namespace arm_compute
	/*
	* Copyright (c) 2017-2019 ARM Limited.
	*
	* SPDX-License-Identifier: MIT
	*
	* Permission is hereby granted, free of charge, to any person obtaining a copy
	* of this software and associated documentation files (the "Software"), to
	* deal in the Software without restriction, including without limitation the
	* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
	* sell copies of the Software, and to permit persons to whom the Software is
	* furnished to do so, subject to the following conditions:
	*
	* The above copyright notice and this permission notice shall be included in all
	* copies or substantial portions of the Software.
	*
	* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
	* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
	* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
	* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
	* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
	* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
	* SOFTWARE.
	*/
	#include "HOGDescriptor.h"

	#include "Derivative.h"
	#include "Magnitude.h"
	#include "Phase.h"

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	namespace reference
	{
	namespace
	{
	template <typename T>
	void hog_orientation_compute(const SimpleTensor<T> &mag, const SimpleTensor<T> &phase, std::vector<T> &bins, const HOGInfo &hog_info)
	{
	const Size2D &cell_size = hog_info.cell_size();
	const size_t num_bins = hog_info.num_bins();

	float phase_scale = (PhaseType::SIGNED == hog_info.phase_type() ? num_bins / 360.0f : num_bins / 180.0f);
	phase_scale *= (PhaseType::SIGNED == hog_info.phase_type() ? 360.0f / 255.0f : 1.0f);

	int row_idx = 0;
	for(size_t yc = 0; yc < cell_size.height; ++yc)
	{
	for(size_t xc = 0; xc < cell_size.width; xc++)
	{
	const float mag_value = mag[(row_idx + xc)];
	const float phase_value = phase[(row_idx + xc)] * phase_scale + 0.5f;
	const float w1 = phase_value - floor(phase_value);

	// The quantised phase is the histogram index [0, num_bins - 1]
	// Check limit of histogram index. If hidx == num_bins, hidx = 0
	const auto hidx = static_cast<unsigned int>(phase_value) % num_bins;

	// Weighted vote between 2 bins
	bins[hidx] += mag_value * (1.0f - w1);
	bins[(hidx + 1) % num_bins] += mag_value * w1;
	}

	row_idx += cell_size.width;
	}
	}

	template <typename T>
	void hog_block_normalization_compute(SimpleTensor<T> &block, SimpleTensor<T> &desc, const HOGInfo &hog_info, uint32_t block_idx)
	{
	const int num_bins_per_block = desc.num_channels();
	const HOGNormType norm_type = hog_info.normalization_type();
	const Coordinates id = index2coord(desc.shape(), block_idx);

	float sum = 0.0f;

	// Calculate sum
	for(int i = 0; i < num_bins_per_block; ++i)
	{
	const float val = block[i];
	sum += (norm_type == HOGNormType::L1_NORM) ? std::fabs(val) : val * val;
	}

	// Calculate normalization scale
	float scale = 1.0f / (std::sqrt(sum) + num_bins_per_block * 0.1f);

	if(norm_type == HOGNormType::L2HYS_NORM)
	{
	// Reset sum
	sum = 0.0f;
	for(int i = 0; i < num_bins_per_block; ++i)
	{
	float val = block[i] * scale;

	// Clip scaled input_value if over l2_hyst_threshold
	val = fmin(val, hog_info.l2_hyst_threshold());
	sum += val * val;
	block[i] = val;
	}

	// We use the same constants of OpenCV
	scale = 1.0f / (std::sqrt(sum) + 1e-3f);
	}

	for(int i = 0; i < num_bins_per_block; ++i)
	{
	block[i] *= scale;
	reinterpret_cast<float *>(desc(id))[i] = block[i];
	}
	}
	} // namespace

	template <typename T, typename U, typename V>
	void hog_orientation_binning(const SimpleTensor<T> &mag, const SimpleTensor<U> &phase, SimpleTensor<V> &hog_space, const HOGInfo &hog_info)
	{
	const Size2D &cell_size = hog_info.cell_size();

	const size_t num_bins = hog_info.num_bins();
	const size_t shape_width = hog_space.shape().x() * hog_info.cell_size().width;
	const size_t shape_height = hog_space.shape().y() * hog_info.cell_size().height;

	TensorShape cell_shape(cell_size.width, cell_size.height);

	SimpleTensor<V> mag_cell(cell_shape, DataType::F32);
	SimpleTensor<V> phase_cell(cell_shape, DataType::F32);

	int cell_idx = 0;
	int y_offset = 0;

	// Traverse shape
	for(auto sy = cell_size.height; sy <= shape_height; sy += cell_size.height)
	{
	int x_offset = 0;
	for(auto sx = cell_size.width; sx <= shape_width; sx += cell_size.width)
	{
	int row_idx = 0;
	int elem_idx = 0;

	// Traverse cell
	for(auto y = 0u; y < cell_size.height; ++y)
	{
	for(auto x = 0u; x < cell_size.width; ++x)
	{
	int shape_idx = x + row_idx + x_offset + y_offset;
	mag_cell[elem_idx] = mag[shape_idx];
	phase_cell[elem_idx] = phase[shape_idx];
	elem_idx++;
	}

	row_idx += shape_width;
	}

	// Partition magnitude values into bins based on phase values
	std::vector<V> bins(num_bins);
	hog_orientation_compute(mag_cell, phase_cell, bins, hog_info);

	for(size_t i = 0; i < num_bins; ++i)
	{
	hog_space[cell_idx * num_bins + i] = bins[i];
	}

	x_offset += cell_size.width;
	cell_idx++;
	}

	y_offset += (cell_size.height * shape_width);
	}
	}

	template <typename T>
	void hog_block_normalization(SimpleTensor<T> &desc, const SimpleTensor<T> &hog_space, const HOGInfo &hog_info)
	{
	const Size2D cells_per_block = hog_info.num_cells_per_block();
	const Size2D cells_per_block_stride = hog_info.num_cells_per_block_stride();
	const Size2D &block_size = hog_info.block_size();
	const Size2D &block_stride = hog_info.block_stride();
	const size_t num_bins = hog_info.num_bins();

	const size_t shape_width = hog_space.shape().x() * hog_info.cell_size().width;
	const size_t shape_height = hog_space.shape().y() * hog_info.cell_size().height;
	const size_t num_bins_per_block_x = cells_per_block.width * num_bins;

	// Tensor representing single block
	SimpleTensor<T> block(TensorShape{ 1u, 1u }, DataType::F32, cells_per_block.area() * num_bins);

	uint32_t block_idx = 0;
	int block_y_offset = 0;

	// Traverse shape
	for(auto sy = block_size.height; sy <= shape_height; sy += block_stride.height)
	{
	int block_x_offset = 0;
	for(auto sx = block_size.width; sx <= shape_width; sx += block_stride.width)
	{
	int cell_y_offset = 0;
	int elem_idx = 0;

	// Traverse block
	for(auto y = 0u; y < cells_per_block.height; ++y)
	{
	for(auto x = 0u; x < num_bins_per_block_x; ++x)
	{
	int idx = x + cell_y_offset + block_x_offset + block_y_offset;
	block[elem_idx] = hog_space[idx];
	elem_idx++;
	}

	cell_y_offset += hog_space.shape().x() * num_bins;
	}

	// Normalize block and write to descriptor
	hog_block_normalization_compute(block, desc, hog_info, block_idx);

	block_x_offset += cells_per_block_stride.width * num_bins;
	block_idx++;
	}

	block_y_offset += cells_per_block_stride.height * num_bins * hog_space.shape().x();
	}
	}

	template <typename T, typename U>
	SimpleTensor<T> hog_descriptor(const SimpleTensor<U> &src, BorderMode border_mode, U constant_border_value, const HOGInfo &hog_info)
	{
	SimpleTensor<int16_t> grad_x;
	SimpleTensor<int16_t> grad_y;

	// Create tensor info for HOG descriptor
	TensorInfo desc_info(hog_info, src.shape().x(), src.shape().y());
	SimpleTensor<T> desc(desc_info.tensor_shape(), DataType::F32, desc_info.num_channels());

	// Create HOG space tensor (num_cells_x, num_cells_y)
	TensorShape hog_space_shape(src.shape().x() / hog_info.cell_size().width,
	src.shape().y() / hog_info.cell_size().height);

	// For each cell a histogram with a num_bins is created
	TensorInfo info_hog_space(hog_space_shape, hog_info.num_bins(), DataType::F32);
	SimpleTensor<T> hog_space(info_hog_space.tensor_shape(), DataType::F32, info_hog_space.num_channels());

	// Calculate derivative
	std::tie(grad_x, grad_y) = derivative<int16_t>(src, border_mode, constant_border_value, GradientDimension::GRAD_XY);

	// For each cell create histogram based on magnitude and phase
	hog_orientation_binning(magnitude(grad_x, grad_y, MagnitudeType::L2NORM),
	phase(grad_x, grad_y, hog_info.phase_type()),
	hog_space,
	hog_info);

	// Normalize histograms based on block size
	hog_block_normalization(desc, hog_space, hog_info);

	return desc;
	}

	template void hog_orientation_binning(const SimpleTensor<int16_t> &mag, const SimpleTensor<uint8_t> &phase, SimpleTensor<float> &hog_space, const HOGInfo &hog_info);
	template void hog_block_normalization(SimpleTensor<float> &desc, const SimpleTensor<float> &hog_space, const HOGInfo &hog_info);
	template SimpleTensor<float> hog_descriptor(const SimpleTensor<uint8_t> &src, BorderMode border_mode, uint8_t constant_border_value, const HOGInfo &hog_info);
	} // namespace reference
	} // namespace validation
	} // namespace test
	} // namespace arm_compute