tests/validation/reference/HOGDetector.cpp

   1 /*
   2  * Copyright (c) 2018 ARM Limited.
   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
  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);
  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