280 lines
11 KiB
C++
280 lines
11 KiB
C++
/*
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* Copyright (c) 2017-2018 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "HOGMultiDetection.h"
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#include "Derivative.h"
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#include "HOGDescriptor.h"
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#include "HOGDetector.h"
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#include "Magnitude.h"
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#include "Phase.h"
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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namespace reference
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{
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namespace
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{
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void validate_models(const std::vector<HOGInfo> &models)
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{
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ARM_COMPUTE_ERROR_ON(0 == models.size());
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for(size_t i = 1; i < models.size(); ++i)
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{
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ARM_COMPUTE_ERROR_ON_MSG(models[0].phase_type() != models[i].phase_type(),
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"All HOG parameters must have the same phase type");
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ARM_COMPUTE_ERROR_ON_MSG(models[0].normalization_type() != models[i].normalization_type(),
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"All HOG parameters must have the same normalization_type");
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ARM_COMPUTE_ERROR_ON_MSG((models[0].l2_hyst_threshold() != models[i].l2_hyst_threshold()) && (models[0].normalization_type() == arm_compute::HOGNormType::L2HYS_NORM),
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"All HOG parameters must have the same l2 hysteresis threshold if you use L2 hysteresis normalization type");
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}
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}
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} // namespace
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void detection_windows_non_maxima_suppression(std::vector<DetectionWindow> &multi_windows, float min_distance)
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{
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const size_t num_candidates = multi_windows.size();
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size_t num_detections = 0;
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// Sort by idx_class first and by score second
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std::sort(multi_windows.begin(), multi_windows.end(), [](const DetectionWindow & lhs, const DetectionWindow & rhs)
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{
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if(lhs.idx_class < rhs.idx_class)
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{
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return true;
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}
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if(rhs.idx_class < lhs.idx_class)
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{
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return false;
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}
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// idx_classes are equal so compare by score
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if(lhs.score > rhs.score)
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{
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return true;
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}
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if(rhs.score > lhs.score)
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{
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return false;
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}
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return false;
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});
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const float min_distance_pow2 = min_distance * min_distance;
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// Euclidean distance
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for(size_t i = 0; i < num_candidates; ++i)
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{
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if(0.0f != multi_windows.at(i).score)
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{
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DetectionWindow cur;
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cur.x = multi_windows.at(i).x;
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cur.y = multi_windows.at(i).y;
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cur.width = multi_windows.at(i).width;
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cur.height = multi_windows.at(i).height;
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cur.idx_class = multi_windows.at(i).idx_class;
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cur.score = multi_windows.at(i).score;
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// Store window
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multi_windows.at(num_detections) = cur;
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++num_detections;
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const float xc = cur.x + cur.width * 0.5f;
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const float yc = cur.y + cur.height * 0.5f;
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for(size_t k = i + 1; k < (num_candidates) && (cur.idx_class == multi_windows.at(k).idx_class); ++k)
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{
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const float xn = multi_windows.at(k).x + multi_windows.at(k).width * 0.5f;
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const float yn = multi_windows.at(k).y + multi_windows.at(k).height * 0.5f;
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const float dx = std::fabs(xn - xc);
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const float dy = std::fabs(yn - yc);
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if(dx < min_distance && dy < min_distance)
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{
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const float d = dx * dx + dy * dy;
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if(d < min_distance_pow2)
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{
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// Invalidate detection window
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multi_windows.at(k).score = 0.0f;
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}
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}
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}
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}
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}
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multi_windows.resize(num_detections);
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}
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template <typename T>
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std::vector<DetectionWindow> hog_multi_detection(const SimpleTensor<T> &src, BorderMode border_mode, T constant_border_value,
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const std::vector<HOGInfo> &models, std::vector<std::vector<float>> descriptors,
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unsigned int max_num_detection_windows, float threshold, bool non_maxima_suppression, float min_distance)
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{
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ARM_COMPUTE_ERROR_ON(descriptors.size() != models.size());
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validate_models(models);
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const size_t width = src.shape().x();
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const size_t height = src.shape().y();
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const size_t num_models = models.size();
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// Initialize previous values
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size_t prev_num_bins = models[0].num_bins();
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Size2D prev_cell_size = models[0].cell_size();
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Size2D prev_block_size = models[0].block_size();
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Size2D prev_block_stride = models[0].block_stride();
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std::vector<size_t> input_orient_bin;
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std::vector<size_t> input_hog_detect;
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std::vector<std::pair<size_t, size_t>> input_block_norm;
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input_orient_bin.push_back(0);
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input_hog_detect.push_back(0);
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input_block_norm.emplace_back(0, 0);
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// Iterate through the number of models and check if orientation binning
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// and block normalization steps can be skipped
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for(size_t i = 1; i < num_models; ++i)
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{
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size_t cur_num_bins = models[i].num_bins();
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Size2D cur_cell_size = models[i].cell_size();
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Size2D cur_block_size = models[i].block_size();
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Size2D cur_block_stride = models[i].block_stride();
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// Check if binning and normalization steps are required
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if((cur_num_bins != prev_num_bins) || (cur_cell_size.width != prev_cell_size.width) || (cur_cell_size.height != prev_cell_size.height))
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{
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prev_num_bins = cur_num_bins;
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prev_cell_size = cur_cell_size;
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prev_block_size = cur_block_size;
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prev_block_stride = cur_block_stride;
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// Compute orientation binning and block normalization. Update input to process
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input_orient_bin.push_back(i);
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input_block_norm.emplace_back(i, input_orient_bin.size() - 1);
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}
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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)
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|| (cur_block_stride.height != prev_block_stride.height))
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{
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prev_block_size = cur_block_size;
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prev_block_stride = cur_block_stride;
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// Compute block normalization. Update input to process
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input_block_norm.emplace_back(i, input_orient_bin.size() - 1);
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}
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// Update input to process for hog detector
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input_hog_detect.push_back(input_block_norm.size() - 1);
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}
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size_t num_orient_bin = input_orient_bin.size();
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size_t num_block_norm = input_block_norm.size();
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size_t num_hog_detect = input_hog_detect.size();
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std::vector<SimpleTensor<float>> hog_spaces(num_orient_bin);
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std::vector<SimpleTensor<float>> hog_norm_spaces(num_block_norm);
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// Calculate derivative
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SimpleTensor<int16_t> grad_x;
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SimpleTensor<int16_t> grad_y;
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std::tie(grad_x, grad_y) = derivative<int16_t>(src, border_mode, constant_border_value, GradientDimension::GRAD_XY);
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// Calculate magnitude and phase
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SimpleTensor<int16_t> _mag = magnitude(grad_x, grad_y, MagnitudeType::L2NORM);
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SimpleTensor<uint8_t> _phase = phase(grad_x, grad_y, models[0].phase_type());
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// Calculate Tensors for the HOG space and orientation binning
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for(size_t i = 0; i < num_orient_bin; ++i)
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{
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const size_t idx_multi_hog = input_orient_bin[i];
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const size_t num_bins = models[idx_multi_hog].num_bins();
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const size_t num_cells_x = width / models[idx_multi_hog].cell_size().width;
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const size_t num_cells_y = height / models[idx_multi_hog].cell_size().height;
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// TensorShape of hog space
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TensorShape hog_space_shape(num_cells_x, num_cells_y);
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// Initialise HOG space
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TensorInfo info_hog_space(hog_space_shape, num_bins, DataType::F32);
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hog_spaces.at(i) = SimpleTensor<float>(info_hog_space.tensor_shape(), DataType::F32, info_hog_space.num_channels());
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// For each cell create histogram based on magnitude and phase
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hog_orientation_binning(_mag, _phase, hog_spaces[i], models[idx_multi_hog]);
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}
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// Calculate Tensors for the normalized HOG space and block normalization
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for(size_t i = 0; i < num_block_norm; ++i)
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{
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const size_t idx_multi_hog = input_block_norm[i].first;
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const size_t idx_orient_bin = input_block_norm[i].second;
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// Create tensor info for HOG descriptor
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TensorInfo tensor_info(models[idx_multi_hog], src.shape().x(), src.shape().y());
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hog_norm_spaces.at(i) = SimpleTensor<float>(tensor_info.tensor_shape(), DataType::F32, tensor_info.num_channels());
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// Normalize histograms based on block size
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hog_block_normalization(hog_norm_spaces[i], hog_spaces[idx_orient_bin], models[idx_multi_hog]);
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}
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std::vector<DetectionWindow> multi_windows;
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// Calculate Detection Windows for HOG detector
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for(size_t i = 0; i < num_hog_detect; ++i)
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{
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const size_t idx_block_norm = input_hog_detect[i];
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// NOTE: Detection window stride fixed to block stride
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const Size2D detection_window_stride = models[i].block_stride();
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std::vector<DetectionWindow> windows = hog_detector(hog_norm_spaces[idx_block_norm], descriptors[i],
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max_num_detection_windows, models[i], detection_window_stride, threshold, i);
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multi_windows.insert(multi_windows.end(), windows.begin(), windows.end());
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}
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// Suppress Non-maxima detection windows
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if(non_maxima_suppression)
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{
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detection_windows_non_maxima_suppression(multi_windows, min_distance);
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}
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return multi_windows;
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}
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template std::vector<DetectionWindow> hog_multi_detection(const SimpleTensor<uint8_t> &src, BorderMode border_mode, uint8_t constant_border_value,
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const std::vector<HOGInfo> &models, std::vector<std::vector<float>> descriptors,
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unsigned int max_num_detection_windows, float threshold, bool non_maxima_suppression, float min_distance);
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} // namespace reference
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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