133 lines
6.2 KiB
C++
133 lines
6.2 KiB
C++
/*
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* Copyright (c) 2018-2019 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 "HOGDetector.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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/** Computes the number of detection windows to iterate over in the feature vector. */
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Size2D num_detection_windows(const TensorShape &shape, const Size2D &window_step, const HOGInfo &hog_info)
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{
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const size_t num_block_strides_width = hog_info.detection_window_size().width / hog_info.block_stride().width;
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const size_t num_block_strides_height = hog_info.detection_window_size().height / hog_info.block_stride().height;
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return Size2D{ floor_to_multiple(shape.x() - num_block_strides_width, window_step.width) + window_step.width,
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floor_to_multiple(shape.y() - num_block_strides_height, window_step.height) + window_step.height };
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}
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} // namespace
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template <typename T>
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std::vector<DetectionWindow> hog_detector(const SimpleTensor<T> &src, const std::vector<T> &descriptor, unsigned int max_num_detection_windows,
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const HOGInfo &hog_info, const Size2D &detection_window_stride, float threshold, uint16_t idx_class)
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{
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ARM_COMPUTE_ERROR_ON_MSG((detection_window_stride.width % hog_info.block_stride().width != 0),
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"Detection window stride width must be multiple of block stride width");
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ARM_COMPUTE_ERROR_ON_MSG((detection_window_stride.height % hog_info.block_stride().height != 0),
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"Detection window stride height must be multiple of block stride height");
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// Create vector for identifying each detection window
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std::vector<DetectionWindow> windows;
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// Calculate detection window step
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const Size2D window_step(detection_window_stride.width / hog_info.block_stride().width,
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detection_window_stride.height / hog_info.block_stride().height);
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// Calculate number of detection windows
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const Size2D num_windows = num_detection_windows(src.shape(), window_step, hog_info);
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// Calculate detection window and row offsets in feature vector
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const size_t src_offset_x = window_step.width * hog_info.num_bins() * hog_info.num_cells_per_block().area();
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const size_t src_offset_y = window_step.height * hog_info.num_bins() * hog_info.num_cells_per_block().area() * src.shape().x();
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const size_t src_offset_row = src.num_channels() * src.shape().x();
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// Calculate detection window attributes
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const Size2D num_block_positions_per_detection_window = hog_info.num_block_positions_per_image(hog_info.detection_window_size());
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const unsigned int num_bins_per_descriptor_x = num_block_positions_per_detection_window.width * src.num_channels();
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const unsigned int num_blocks_per_descriptor_y = num_block_positions_per_detection_window.height;
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ARM_COMPUTE_ERROR_ON((num_bins_per_descriptor_x * num_blocks_per_descriptor_y + 1) != hog_info.descriptor_size());
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size_t win_id = 0;
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// Traverse feature vector in detection window steps
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for(auto win_y = 0u, offset_y = 0u; win_y < num_windows.height; win_y += window_step.height, offset_y += src_offset_y)
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{
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for(auto win_x = 0u, offset_x = 0u; win_x < num_windows.width; win_x += window_step.width, offset_x += src_offset_x)
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{
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// Reset the score
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float score = 0.0f;
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// Traverse detection window
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for(auto y = 0u, offset_row = 0u; y < num_blocks_per_descriptor_y; ++y, offset_row += src_offset_row)
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{
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const int bin_offset = y * num_bins_per_descriptor_x;
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for(auto x = 0u; x < num_bins_per_descriptor_x; ++x)
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{
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// Compute Linear SVM
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const float a = src[x + offset_x + offset_y + offset_row];
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const float b = descriptor[x + bin_offset];
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score += a * b;
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}
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}
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// Add the bias. The bias is located at the position (descriptor_size() - 1)
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score += descriptor[num_bins_per_descriptor_x * num_blocks_per_descriptor_y];
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if(score > threshold)
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{
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DetectionWindow window;
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if(win_id++ < max_num_detection_windows)
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{
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window.x = win_x * hog_info.block_stride().width;
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window.y = win_y * hog_info.block_stride().height;
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window.width = hog_info.detection_window_size().width;
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window.height = hog_info.detection_window_size().height;
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window.idx_class = idx_class;
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window.score = score;
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windows.push_back(window);
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}
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}
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}
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}
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return windows;
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}
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template std::vector<DetectionWindow> hog_detector(const SimpleTensor<float> &src, const std::vector<float> &descriptor, unsigned int max_num_detection_windows,
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const HOGInfo &hog_info, const Size2D &detection_window_stride, float threshold, uint16_t idx_class);
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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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