| /* |
| * Copyright (c) 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 "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 |