arm_compute v18.05
diff --git a/tests/validation/reference/HOGDetector.cpp b/tests/validation/reference/HOGDetector.cpp
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+/*
+ * 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