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

 /*
  * Copyright (c) 2017-2018 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 "HOGMultiDetection.h"

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

 namespace arm_compute
 {
 namespace test
 {
 namespace validation
 {
 namespace reference
 {
 namespace
 {
 void validate_models(const std::vector<HOGInfo> &models)
 {
     ARM_COMPUTE_ERROR_ON(0 == models.size());

     for(size_t i = 1; i < models.size(); ++i)
     {
         ARM_COMPUTE_ERROR_ON_MSG(models[0].phase_type() != models[i].phase_type(),
                                  "All HOG parameters must have the same phase type");

         ARM_COMPUTE_ERROR_ON_MSG(models[0].normalization_type() != models[i].normalization_type(),
                                  "All HOG parameters must have the same normalization_type");

         ARM_COMPUTE_ERROR_ON_MSG((models[0].l2_hyst_threshold() != models[i].l2_hyst_threshold()) && (models[0].normalization_type() == arm_compute::HOGNormType::L2HYS_NORM),
                                  "All HOG parameters must have the same l2 hysteresis threshold if you use L2 hysteresis normalization type");
     }
 }
 } // namespace

 void detection_windows_non_maxima_suppression(std::vector<DetectionWindow> &multi_windows, float min_distance)
 {
     const size_t num_candidates = multi_windows.size();
     size_t       num_detections = 0;

     // Sort by idx_class first and by score second
     std::sort(multi_windows.begin(), multi_windows.end(), [](const DetectionWindow & lhs, const DetectionWindow & rhs)
     {
         if(lhs.idx_class < rhs.idx_class)
         {
             return true;
         }
         if(rhs.idx_class < lhs.idx_class)
         {
             return false;
         }

         // idx_classes are equal so compare by score
         if(lhs.score > rhs.score)
         {
             return true;
         }
         if(rhs.score > lhs.score)
         {
             return false;
         }

         return false;
     });

     const float min_distance_pow2 = min_distance * min_distance;

     // Euclidean distance
     for(size_t i = 0; i < num_candidates; ++i)
     {
         if(0.0f != multi_windows.at(i).score)
         {
             DetectionWindow cur;
             cur.x         = multi_windows.at(i).x;
             cur.y         = multi_windows.at(i).y;
             cur.width     = multi_windows.at(i).width;
             cur.height    = multi_windows.at(i).height;
             cur.idx_class = multi_windows.at(i).idx_class;
             cur.score     = multi_windows.at(i).score;

             // Store window
             multi_windows.at(num_detections) = cur;
             ++num_detections;

             const float xc = cur.x + cur.width * 0.5f;
             const float yc = cur.y + cur.height * 0.5f;

             for(size_t k = i + 1; k < (num_candidates) && (cur.idx_class == multi_windows.at(k).idx_class); ++k)
             {
                 const float xn = multi_windows.at(k).x + multi_windows.at(k).width * 0.5f;
                 const float yn = multi_windows.at(k).y + multi_windows.at(k).height * 0.5f;

                 const float dx = std::fabs(xn - xc);
                 const float dy = std::fabs(yn - yc);

                 if(dx < min_distance && dy < min_distance)
                 {
                     const float d = dx * dx + dy * dy;

                     if(d < min_distance_pow2)
                     {
                         // Invalidate detection window
                         multi_windows.at(k).score = 0.0f;
                     }
                 }
             }
         }
     }

     multi_windows.resize(num_detections);
 }

 template <typename T>
 std::vector<DetectionWindow> hog_multi_detection(const SimpleTensor<T> &src, BorderMode border_mode, T constant_border_value,
                                                  const std::vector<HOGInfo> &models, std::vector<std::vector<float>> descriptors,
                                                  unsigned int max_num_detection_windows, float threshold, bool non_maxima_suppression, float min_distance)
 {
     ARM_COMPUTE_ERROR_ON(descriptors.size() != models.size());
     validate_models(models);

     const size_t width      = src.shape().x();
     const size_t height     = src.shape().y();
     const size_t num_models = models.size();

     // Initialize previous values
     size_t prev_num_bins     = models[0].num_bins();
     Size2D prev_cell_size    = models[0].cell_size();
     Size2D prev_block_size   = models[0].block_size();
     Size2D prev_block_stride = models[0].block_stride();

     std::vector<size_t> input_orient_bin;
     std::vector<size_t> input_hog_detect;
     std::vector<std::pair<size_t, size_t>> input_block_norm;

     input_orient_bin.push_back(0);
     input_hog_detect.push_back(0);
     input_block_norm.emplace_back(0, 0);

     // Iterate through the number of models and check if orientation binning
     // and block normalization steps can be skipped
     for(size_t i = 1; i < num_models; ++i)
     {
         size_t cur_num_bins     = models[i].num_bins();
         Size2D cur_cell_size    = models[i].cell_size();
         Size2D cur_block_size   = models[i].block_size();
         Size2D cur_block_stride = models[i].block_stride();

         // Check if binning and normalization steps are required
         if((cur_num_bins != prev_num_bins) || (cur_cell_size.width != prev_cell_size.width) || (cur_cell_size.height != prev_cell_size.height))
         {
             prev_num_bins     = cur_num_bins;
             prev_cell_size    = cur_cell_size;
             prev_block_size   = cur_block_size;
             prev_block_stride = cur_block_stride;

             // Compute orientation binning and block normalization. Update input to process
             input_orient_bin.push_back(i);
             input_block_norm.emplace_back(i, input_orient_bin.size() - 1);
         }
         else if((cur_block_size.width != prev_block_size.width) || (cur_block_size.height != prev_block_size.height) || (cur_block_stride.width != prev_block_stride.width)
                 || (cur_block_stride.height != prev_block_stride.height))
         {
             prev_block_size   = cur_block_size;
             prev_block_stride = cur_block_stride;

             // Compute block normalization. Update input to process
             input_block_norm.emplace_back(i, input_orient_bin.size() - 1);
         }

         // Update input to process for hog detector
         input_hog_detect.push_back(input_block_norm.size() - 1);
     }

     size_t num_orient_bin = input_orient_bin.size();
     size_t num_block_norm = input_block_norm.size();
     size_t num_hog_detect = input_hog_detect.size();

     std::vector<SimpleTensor<float>> hog_spaces(num_orient_bin);
     std::vector<SimpleTensor<float>> hog_norm_spaces(num_block_norm);

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

     // Calculate magnitude and phase
     SimpleTensor<int16_t> _mag   = magnitude(grad_x, grad_y, MagnitudeType::L2NORM);
     SimpleTensor<uint8_t> _phase = phase(grad_x, grad_y, models[0].phase_type());

     // Calculate Tensors for the HOG space and orientation binning
     for(size_t i = 0; i < num_orient_bin; ++i)
     {
         const size_t idx_multi_hog = input_orient_bin[i];

         const size_t num_bins    = models[idx_multi_hog].num_bins();
         const size_t num_cells_x = width / models[idx_multi_hog].cell_size().width;
         const size_t num_cells_y = height / models[idx_multi_hog].cell_size().height;

         // TensorShape of hog space
         TensorShape hog_space_shape(num_cells_x, num_cells_y);

         // Initialise HOG space
         TensorInfo info_hog_space(hog_space_shape, num_bins, DataType::F32);
         hog_spaces.at(i) = SimpleTensor<float>(info_hog_space.tensor_shape(), DataType::F32, info_hog_space.num_channels());

         // For each cell create histogram based on magnitude and phase
         hog_orientation_binning(_mag, _phase, hog_spaces[i], models[idx_multi_hog]);
     }

     // Calculate Tensors for the normalized HOG space and block normalization
     for(size_t i = 0; i < num_block_norm; ++i)
     {
         const size_t idx_multi_hog  = input_block_norm[i].first;
         const size_t idx_orient_bin = input_block_norm[i].second;

         // Create tensor info for HOG descriptor
         TensorInfo tensor_info(models[idx_multi_hog], src.shape().x(), src.shape().y());
         hog_norm_spaces.at(i) = SimpleTensor<float>(tensor_info.tensor_shape(), DataType::F32, tensor_info.num_channels());

         // Normalize histograms based on block size
         hog_block_normalization(hog_norm_spaces[i], hog_spaces[idx_orient_bin], models[idx_multi_hog]);
     }

     std::vector<DetectionWindow> multi_windows;

     // Calculate Detection Windows for HOG detector
     for(size_t i = 0; i < num_hog_detect; ++i)
     {
         const size_t idx_block_norm = input_hog_detect[i];

         // NOTE: Detection window stride fixed to block stride
         const Size2D detection_window_stride = models[i].block_stride();

         std::vector<DetectionWindow> windows = hog_detector(hog_norm_spaces[idx_block_norm], descriptors[i],
                                                             max_num_detection_windows, models[i], detection_window_stride, threshold, i);

         multi_windows.insert(multi_windows.end(), windows.begin(), windows.end());
     }

     // Suppress Non-maxima detection windows
     if(non_maxima_suppression)
     {
         detection_windows_non_maxima_suppression(multi_windows, min_distance);
     }

     return multi_windows;
 }

 template std::vector<DetectionWindow> hog_multi_detection(const SimpleTensor<uint8_t> &src, BorderMode border_mode, uint8_t constant_border_value,
                                                           const std::vector<HOGInfo> &models, std::vector<std::vector<float>> descriptors,
                                                           unsigned int max_num_detection_windows, float threshold, bool non_maxima_suppression, float min_distance);
 } // namespace reference
 } // namespace validation
 } // namespace test
 } // namespace arm_compute
	/*
	* Copyright (c) 2017-2018 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 "HOGMultiDetection.h"

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

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	namespace reference
	{
	namespace
	{
	void validate_models(const std::vector<HOGInfo> &models)
	{
	ARM_COMPUTE_ERROR_ON(0 == models.size());

	for(size_t i = 1; i < models.size(); ++i)
	{
	ARM_COMPUTE_ERROR_ON_MSG(models[0].phase_type() != models[i].phase_type(),
	"All HOG parameters must have the same phase type");

	ARM_COMPUTE_ERROR_ON_MSG(models[0].normalization_type() != models[i].normalization_type(),
	"All HOG parameters must have the same normalization_type");

	ARM_COMPUTE_ERROR_ON_MSG((models[0].l2_hyst_threshold() != models[i].l2_hyst_threshold()) && (models[0].normalization_type() == arm_compute::HOGNormType::L2HYS_NORM),
	"All HOG parameters must have the same l2 hysteresis threshold if you use L2 hysteresis normalization type");
	}
	}
	} // namespace

	void detection_windows_non_maxima_suppression(std::vector<DetectionWindow> &multi_windows, float min_distance)
	{
	const size_t num_candidates = multi_windows.size();
	size_t num_detections = 0;

	// Sort by idx_class first and by score second
	std::sort(multi_windows.begin(), multi_windows.end(), [](const DetectionWindow & lhs, const DetectionWindow & rhs)
	{
	if(lhs.idx_class < rhs.idx_class)
	{
	return true;
	}
	if(rhs.idx_class < lhs.idx_class)
	{
	return false;
	}

	// idx_classes are equal so compare by score
	if(lhs.score > rhs.score)
	{
	return true;
	}
	if(rhs.score > lhs.score)
	{
	return false;
	}

	return false;
	});

	const float min_distance_pow2 = min_distance * min_distance;

	// Euclidean distance
	for(size_t i = 0; i < num_candidates; ++i)
	{
	if(0.0f != multi_windows.at(i).score)
	{
	DetectionWindow cur;
	cur.x = multi_windows.at(i).x;
	cur.y = multi_windows.at(i).y;
	cur.width = multi_windows.at(i).width;
	cur.height = multi_windows.at(i).height;
	cur.idx_class = multi_windows.at(i).idx_class;
	cur.score = multi_windows.at(i).score;

	// Store window
	multi_windows.at(num_detections) = cur;
	++num_detections;

	const float xc = cur.x + cur.width * 0.5f;
	const float yc = cur.y + cur.height * 0.5f;

	for(size_t k = i + 1; k < (num_candidates) && (cur.idx_class == multi_windows.at(k).idx_class); ++k)
	{
	const float xn = multi_windows.at(k).x + multi_windows.at(k).width * 0.5f;
	const float yn = multi_windows.at(k).y + multi_windows.at(k).height * 0.5f;

	const float dx = std::fabs(xn - xc);
	const float dy = std::fabs(yn - yc);

	if(dx < min_distance && dy < min_distance)
	{
	const float d = dx * dx + dy * dy;

	if(d < min_distance_pow2)
	{
	// Invalidate detection window
	multi_windows.at(k).score = 0.0f;
	}
	}
	}
	}
	}

	multi_windows.resize(num_detections);
	}

	template <typename T>
	std::vector<DetectionWindow> hog_multi_detection(const SimpleTensor<T> &src, BorderMode border_mode, T constant_border_value,
	const std::vector<HOGInfo> &models, std::vector<std::vector<float>> descriptors,
	unsigned int max_num_detection_windows, float threshold, bool non_maxima_suppression, float min_distance)
	{
	ARM_COMPUTE_ERROR_ON(descriptors.size() != models.size());
	validate_models(models);

	const size_t width = src.shape().x();
	const size_t height = src.shape().y();
	const size_t num_models = models.size();

	// Initialize previous values
	size_t prev_num_bins = models[0].num_bins();
	Size2D prev_cell_size = models[0].cell_size();
	Size2D prev_block_size = models[0].block_size();
	Size2D prev_block_stride = models[0].block_stride();

	std::vector<size_t> input_orient_bin;
	std::vector<size_t> input_hog_detect;
	std::vector<std::pair<size_t, size_t>> input_block_norm;

	input_orient_bin.push_back(0);
	input_hog_detect.push_back(0);
	input_block_norm.emplace_back(0, 0);

	// Iterate through the number of models and check if orientation binning
	// and block normalization steps can be skipped
	for(size_t i = 1; i < num_models; ++i)
	{
	size_t cur_num_bins = models[i].num_bins();
	Size2D cur_cell_size = models[i].cell_size();
	Size2D cur_block_size = models[i].block_size();
	Size2D cur_block_stride = models[i].block_stride();

	// Check if binning and normalization steps are required
	if((cur_num_bins != prev_num_bins) \|\| (cur_cell_size.width != prev_cell_size.width) \|\| (cur_cell_size.height != prev_cell_size.height))
	{
	prev_num_bins = cur_num_bins;
	prev_cell_size = cur_cell_size;
	prev_block_size = cur_block_size;
	prev_block_stride = cur_block_stride;

	// Compute orientation binning and block normalization. Update input to process
	input_orient_bin.push_back(i);
	input_block_norm.emplace_back(i, input_orient_bin.size() - 1);
	}
	else if((cur_block_size.width != prev_block_size.width) \|\| (cur_block_size.height != prev_block_size.height) \|\| (cur_block_stride.width != prev_block_stride.width)
	\|\| (cur_block_stride.height != prev_block_stride.height))
	{
	prev_block_size = cur_block_size;
	prev_block_stride = cur_block_stride;

	// Compute block normalization. Update input to process
	input_block_norm.emplace_back(i, input_orient_bin.size() - 1);
	}

	// Update input to process for hog detector
	input_hog_detect.push_back(input_block_norm.size() - 1);
	}

	size_t num_orient_bin = input_orient_bin.size();
	size_t num_block_norm = input_block_norm.size();
	size_t num_hog_detect = input_hog_detect.size();

	std::vector<SimpleTensor<float>> hog_spaces(num_orient_bin);
	std::vector<SimpleTensor<float>> hog_norm_spaces(num_block_norm);

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

	// Calculate magnitude and phase
	SimpleTensor<int16_t> _mag = magnitude(grad_x, grad_y, MagnitudeType::L2NORM);
	SimpleTensor<uint8_t> _phase = phase(grad_x, grad_y, models[0].phase_type());

	// Calculate Tensors for the HOG space and orientation binning
	for(size_t i = 0; i < num_orient_bin; ++i)
	{
	const size_t idx_multi_hog = input_orient_bin[i];

	const size_t num_bins = models[idx_multi_hog].num_bins();
	const size_t num_cells_x = width / models[idx_multi_hog].cell_size().width;
	const size_t num_cells_y = height / models[idx_multi_hog].cell_size().height;

	// TensorShape of hog space
	TensorShape hog_space_shape(num_cells_x, num_cells_y);

	// Initialise HOG space
	TensorInfo info_hog_space(hog_space_shape, num_bins, DataType::F32);
	hog_spaces.at(i) = SimpleTensor<float>(info_hog_space.tensor_shape(), DataType::F32, info_hog_space.num_channels());

	// For each cell create histogram based on magnitude and phase
	hog_orientation_binning(_mag, _phase, hog_spaces[i], models[idx_multi_hog]);
	}

	// Calculate Tensors for the normalized HOG space and block normalization
	for(size_t i = 0; i < num_block_norm; ++i)
	{
	const size_t idx_multi_hog = input_block_norm[i].first;
	const size_t idx_orient_bin = input_block_norm[i].second;

	// Create tensor info for HOG descriptor
	TensorInfo tensor_info(models[idx_multi_hog], src.shape().x(), src.shape().y());
	hog_norm_spaces.at(i) = SimpleTensor<float>(tensor_info.tensor_shape(), DataType::F32, tensor_info.num_channels());

	// Normalize histograms based on block size
	hog_block_normalization(hog_norm_spaces[i], hog_spaces[idx_orient_bin], models[idx_multi_hog]);
	}

	std::vector<DetectionWindow> multi_windows;

	// Calculate Detection Windows for HOG detector
	for(size_t i = 0; i < num_hog_detect; ++i)
	{
	const size_t idx_block_norm = input_hog_detect[i];

	// NOTE: Detection window stride fixed to block stride
	const Size2D detection_window_stride = models[i].block_stride();

	std::vector<DetectionWindow> windows = hog_detector(hog_norm_spaces[idx_block_norm], descriptors[i],
	max_num_detection_windows, models[i], detection_window_stride, threshold, i);

	multi_windows.insert(multi_windows.end(), windows.begin(), windows.end());
	}

	// Suppress Non-maxima detection windows
	if(non_maxima_suppression)
	{
	detection_windows_non_maxima_suppression(multi_windows, min_distance);
	}

	return multi_windows;
	}

	template std::vector<DetectionWindow> hog_multi_detection(const SimpleTensor<uint8_t> &src, BorderMode border_mode, uint8_t constant_border_value,
	const std::vector<HOGInfo> &models, std::vector<std::vector<float>> descriptors,
	unsigned int max_num_detection_windows, float threshold, bool non_maxima_suppression, float min_distance);
	} // namespace reference
	} // namespace validation
	} // namespace test
	} // namespace arm_compute