John Richardson | 684cb0f | 2018-01-09 11:17:00 +0000 | [diff] [blame] | 1 | /* |
Michele Di Giorgio | d9eaf61 | 2020-07-08 11:12:57 +0100 | [diff] [blame] | 2 | * Copyright (c) 2018-2019 Arm Limited. |
John Richardson | 684cb0f | 2018-01-09 11:17:00 +0000 | [diff] [blame] | 3 | * |
| 4 | * SPDX-License-Identifier: MIT |
| 5 | * |
| 6 | * Permission is hereby granted, free of charge, to any person obtaining a copy |
| 7 | * of this software and associated documentation files (the "Software"), to |
| 8 | * deal in the Software without restriction, including without limitation the |
| 9 | * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| 10 | * sell copies of the Software, and to permit persons to whom the Software is |
| 11 | * furnished to do so, subject to the following conditions: |
| 12 | * |
| 13 | * The above copyright notice and this permission notice shall be included in all |
| 14 | * copies or substantial portions of the Software. |
| 15 | * |
| 16 | * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| 17 | * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| 18 | * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| 19 | * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| 20 | * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| 21 | * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| 22 | * SOFTWARE. |
| 23 | */ |
| 24 | #include "HOGDetector.h" |
| 25 | |
| 26 | namespace arm_compute |
| 27 | { |
| 28 | namespace test |
| 29 | { |
| 30 | namespace validation |
| 31 | { |
| 32 | namespace reference |
| 33 | { |
| 34 | namespace |
| 35 | { |
| 36 | /** Computes the number of detection windows to iterate over in the feature vector. */ |
| 37 | Size2D num_detection_windows(const TensorShape &shape, const Size2D &window_step, const HOGInfo &hog_info) |
| 38 | { |
| 39 | const size_t num_block_strides_width = hog_info.detection_window_size().width / hog_info.block_stride().width; |
| 40 | const size_t num_block_strides_height = hog_info.detection_window_size().height / hog_info.block_stride().height; |
| 41 | |
Michalis Spyrou | bcfd09a | 2019-05-01 13:03:59 +0100 | [diff] [blame] | 42 | return Size2D{ floor_to_multiple(shape.x() - num_block_strides_width, window_step.width) + window_step.width, |
| 43 | floor_to_multiple(shape.y() - num_block_strides_height, window_step.height) + window_step.height }; |
John Richardson | 684cb0f | 2018-01-09 11:17:00 +0000 | [diff] [blame] | 44 | } |
| 45 | } // namespace |
| 46 | |
| 47 | template <typename T> |
| 48 | std::vector<DetectionWindow> hog_detector(const SimpleTensor<T> &src, const std::vector<T> &descriptor, unsigned int max_num_detection_windows, |
| 49 | const HOGInfo &hog_info, const Size2D &detection_window_stride, float threshold, uint16_t idx_class) |
| 50 | { |
| 51 | ARM_COMPUTE_ERROR_ON_MSG((detection_window_stride.width % hog_info.block_stride().width != 0), |
| 52 | "Detection window stride width must be multiple of block stride width"); |
| 53 | ARM_COMPUTE_ERROR_ON_MSG((detection_window_stride.height % hog_info.block_stride().height != 0), |
| 54 | "Detection window stride height must be multiple of block stride height"); |
| 55 | |
| 56 | // Create vector for identifying each detection window |
| 57 | std::vector<DetectionWindow> windows; |
| 58 | |
| 59 | // Calculate detection window step |
| 60 | const Size2D window_step(detection_window_stride.width / hog_info.block_stride().width, |
| 61 | detection_window_stride.height / hog_info.block_stride().height); |
| 62 | |
| 63 | // Calculate number of detection windows |
| 64 | const Size2D num_windows = num_detection_windows(src.shape(), window_step, hog_info); |
| 65 | |
| 66 | // Calculate detection window and row offsets in feature vector |
| 67 | const size_t src_offset_x = window_step.width * hog_info.num_bins() * hog_info.num_cells_per_block().area(); |
| 68 | const size_t src_offset_y = window_step.height * hog_info.num_bins() * hog_info.num_cells_per_block().area() * src.shape().x(); |
| 69 | const size_t src_offset_row = src.num_channels() * src.shape().x(); |
| 70 | |
| 71 | // Calculate detection window attributes |
| 72 | const Size2D num_block_positions_per_detection_window = hog_info.num_block_positions_per_image(hog_info.detection_window_size()); |
| 73 | const unsigned int num_bins_per_descriptor_x = num_block_positions_per_detection_window.width * src.num_channels(); |
| 74 | const unsigned int num_blocks_per_descriptor_y = num_block_positions_per_detection_window.height; |
| 75 | |
| 76 | ARM_COMPUTE_ERROR_ON((num_bins_per_descriptor_x * num_blocks_per_descriptor_y + 1) != hog_info.descriptor_size()); |
| 77 | |
| 78 | size_t win_id = 0; |
| 79 | |
| 80 | // Traverse feature vector in detection window steps |
| 81 | for(auto win_y = 0u, offset_y = 0u; win_y < num_windows.height; win_y += window_step.height, offset_y += src_offset_y) |
| 82 | { |
| 83 | for(auto win_x = 0u, offset_x = 0u; win_x < num_windows.width; win_x += window_step.width, offset_x += src_offset_x) |
| 84 | { |
| 85 | // Reset the score |
| 86 | float score = 0.0f; |
| 87 | |
| 88 | // Traverse detection window |
| 89 | for(auto y = 0u, offset_row = 0u; y < num_blocks_per_descriptor_y; ++y, offset_row += src_offset_row) |
| 90 | { |
| 91 | const int bin_offset = y * num_bins_per_descriptor_x; |
| 92 | |
| 93 | for(auto x = 0u; x < num_bins_per_descriptor_x; ++x) |
| 94 | { |
| 95 | // Compute Linear SVM |
| 96 | const float a = src[x + offset_x + offset_y + offset_row]; |
| 97 | const float b = descriptor[x + bin_offset]; |
| 98 | score += a * b; |
| 99 | } |
| 100 | } |
| 101 | |
| 102 | // Add the bias. The bias is located at the position (descriptor_size() - 1) |
| 103 | score += descriptor[num_bins_per_descriptor_x * num_blocks_per_descriptor_y]; |
| 104 | |
| 105 | if(score > threshold) |
| 106 | { |
| 107 | DetectionWindow window; |
| 108 | |
| 109 | if(win_id++ < max_num_detection_windows) |
| 110 | { |
| 111 | window.x = win_x * hog_info.block_stride().width; |
| 112 | window.y = win_y * hog_info.block_stride().height; |
| 113 | window.width = hog_info.detection_window_size().width; |
| 114 | window.height = hog_info.detection_window_size().height; |
| 115 | window.idx_class = idx_class; |
| 116 | window.score = score; |
| 117 | |
| 118 | windows.push_back(window); |
| 119 | } |
| 120 | } |
| 121 | } |
| 122 | } |
| 123 | |
| 124 | return windows; |
| 125 | } |
| 126 | |
| 127 | template std::vector<DetectionWindow> hog_detector(const SimpleTensor<float> &src, const std::vector<float> &descriptor, unsigned int max_num_detection_windows, |
| 128 | const HOGInfo &hog_info, const Size2D &detection_window_stride, float threshold, uint16_t idx_class); |
| 129 | } // namespace reference |
| 130 | } // namespace validation |
| 131 | } // namespace test |
| 132 | } // namespace arm_compute |