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Diffstat (limited to 'tests/validation/reference/HOGDetector.cpp')
| -rw-r--r-- | tests/validation/reference/HOGDetector.cpp | 132 |
1 files changed, 0 insertions, 132 deletions
diff --git a/tests/validation/reference/HOGDetector.cpp b/tests/validation/reference/HOGDetector.cpp deleted file mode 100644 index 798c3fc142..0000000000 --- a/tests/validation/reference/HOGDetector.cpp +++ /dev/null @@ -1,132 +0,0 @@ -/* - * Copyright (c) 2018-2019 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 "HOGDetector.h" - -namespace arm_compute -{ -namespace test -{ -namespace validation -{ -namespace reference -{ -namespace -{ -/** Computes the number of detection windows to iterate over in the feature vector. */ -Size2D num_detection_windows(const TensorShape &shape, const Size2D &window_step, const HOGInfo &hog_info) -{ - const size_t num_block_strides_width = hog_info.detection_window_size().width / hog_info.block_stride().width; - const size_t num_block_strides_height = hog_info.detection_window_size().height / hog_info.block_stride().height; - - return Size2D{ floor_to_multiple(shape.x() - num_block_strides_width, window_step.width) + window_step.width, - floor_to_multiple(shape.y() - num_block_strides_height, window_step.height) + window_step.height }; -} -} // namespace - -template <typename T> -std::vector<DetectionWindow> hog_detector(const SimpleTensor<T> &src, const std::vector<T> &descriptor, unsigned int max_num_detection_windows, - const HOGInfo &hog_info, const Size2D &detection_window_stride, float threshold, uint16_t idx_class) -{ - ARM_COMPUTE_ERROR_ON_MSG((detection_window_stride.width % hog_info.block_stride().width != 0), - "Detection window stride width must be multiple of block stride width"); - ARM_COMPUTE_ERROR_ON_MSG((detection_window_stride.height % hog_info.block_stride().height != 0), - "Detection window stride height must be multiple of block stride height"); - - // Create vector for identifying each detection window - std::vector<DetectionWindow> windows; - - // Calculate detection window step - const Size2D window_step(detection_window_stride.width / hog_info.block_stride().width, - detection_window_stride.height / hog_info.block_stride().height); - - // Calculate number of detection windows - const Size2D num_windows = num_detection_windows(src.shape(), window_step, hog_info); - - // Calculate detection window and row offsets in feature vector - const size_t src_offset_x = window_step.width * hog_info.num_bins() * hog_info.num_cells_per_block().area(); - const size_t src_offset_y = window_step.height * hog_info.num_bins() * hog_info.num_cells_per_block().area() * src.shape().x(); - const size_t src_offset_row = src.num_channels() * src.shape().x(); - - // Calculate detection window attributes - const Size2D num_block_positions_per_detection_window = hog_info.num_block_positions_per_image(hog_info.detection_window_size()); - const unsigned int num_bins_per_descriptor_x = num_block_positions_per_detection_window.width * src.num_channels(); - const unsigned int num_blocks_per_descriptor_y = num_block_positions_per_detection_window.height; - - ARM_COMPUTE_ERROR_ON((num_bins_per_descriptor_x * num_blocks_per_descriptor_y + 1) != hog_info.descriptor_size()); - - size_t win_id = 0; - - // Traverse feature vector in detection window steps - for(auto win_y = 0u, offset_y = 0u; win_y < num_windows.height; win_y += window_step.height, offset_y += src_offset_y) - { - for(auto win_x = 0u, offset_x = 0u; win_x < num_windows.width; win_x += window_step.width, offset_x += src_offset_x) - { - // Reset the score - float score = 0.0f; - - // Traverse detection window - for(auto y = 0u, offset_row = 0u; y < num_blocks_per_descriptor_y; ++y, offset_row += src_offset_row) - { - const int bin_offset = y * num_bins_per_descriptor_x; - - for(auto x = 0u; x < num_bins_per_descriptor_x; ++x) - { - // Compute Linear SVM - const float a = src[x + offset_x + offset_y + offset_row]; - const float b = descriptor[x + bin_offset]; - score += a * b; - } - } - - // Add the bias. The bias is located at the position (descriptor_size() - 1) - score += descriptor[num_bins_per_descriptor_x * num_blocks_per_descriptor_y]; - - if(score > threshold) - { - DetectionWindow window; - - if(win_id++ < max_num_detection_windows) - { - window.x = win_x * hog_info.block_stride().width; - window.y = win_y * hog_info.block_stride().height; - window.width = hog_info.detection_window_size().width; - window.height = hog_info.detection_window_size().height; - window.idx_class = idx_class; - window.score = score; - - windows.push_back(window); - } - } - } - } - - return windows; -} - -template std::vector<DetectionWindow> hog_detector(const SimpleTensor<float> &src, const std::vector<float> &descriptor, unsigned int max_num_detection_windows, - const HOGInfo &hog_info, const Size2D &detection_window_stride, float threshold, uint16_t idx_class); -} // namespace reference -} // namespace validation -} // namespace test -} // namespace arm_compute |