COMPMID-597: Port HOGMultiDetection to new framework

Change-Id: I4b31b7f052a06bea4154d04c9926a0e076e28d73
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/126555
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: John Richardson <john.richardson@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 279 insertions, 0 deletions

diff --git a/tests/validation/reference/HOGMultiDetection.cpp b/tests/validation/reference/HOGMultiDetection.cpp
new file mode 100644
index 0000000000..2f5e4395a0
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+++ b/tests/validation/reference/HOGMultiDetection.cpp
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+/*
+ * Copyright (c) 2017-2018 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 "HOGMultiDetection.h"
+
+#include "Derivative.h"
+#include "HOGDescriptor.h"
+#include "HOGDetector.h"
+#include "Magnitude.h"
+#include "Phase.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+namespace
+{
+void validate_models(const std::vector<HOGInfo> &models)
+{
+    ARM_COMPUTE_ERROR_ON(0 == models.size());
+
+    for(size_t i = 1; i < models.size(); ++i)
+    {
+        ARM_COMPUTE_ERROR_ON_MSG(models[0].phase_type() != models[i].phase_type(),
+                                 "All HOG parameters must have the same phase type");
+
+        ARM_COMPUTE_ERROR_ON_MSG(models[0].normalization_type() != models[i].normalization_type(),
+                                 "All HOG parameters must have the same normalization_type");
+
+        ARM_COMPUTE_ERROR_ON_MSG((models[0].l2_hyst_threshold() != models[i].l2_hyst_threshold()) && (models[0].normalization_type() == arm_compute::HOGNormType::L2HYS_NORM),
+                                 "All HOG parameters must have the same l2 hysteresis threshold if you use L2 hysteresis normalization type");
+    }
+}
+} // namespace
+
+void detection_windows_non_maxima_suppression(std::vector<DetectionWindow> &multi_windows, float min_distance)
+{
+    const size_t num_candidates = multi_windows.size();
+    size_t       num_detections = 0;
+
+    // Sort by idx_class first and by score second
+    std::sort(multi_windows.begin(), multi_windows.end(), [](const DetectionWindow & lhs, const DetectionWindow & rhs)
+    {
+        if(lhs.idx_class < rhs.idx_class)
+        {
+            return true;
+        }
+        if(rhs.idx_class < lhs.idx_class)
+        {
+            return false;
+        }
+
+        // idx_classes are equal so compare by score
+        if(lhs.score > rhs.score)
+        {
+            return true;
+        }
+        if(rhs.score > lhs.score)
+        {
+            return false;
+        }
+
+        return false;
+    });
+
+    const float min_distance_pow2 = min_distance * min_distance;
+
+    // Euclidean distance
+    for(size_t i = 0; i < num_candidates; ++i)
+    {
+        if(0.0f != multi_windows.at(i).score)
+        {
+            DetectionWindow cur;
+            cur.x         = multi_windows.at(i).x;
+            cur.y         = multi_windows.at(i).y;
+            cur.width     = multi_windows.at(i).width;
+            cur.height    = multi_windows.at(i).height;
+            cur.idx_class = multi_windows.at(i).idx_class;
+            cur.score     = multi_windows.at(i).score;
+
+            // Store window
+            multi_windows.at(num_detections) = cur;
+            ++num_detections;
+
+            const float xc = cur.x + cur.width * 0.5f;
+            const float yc = cur.y + cur.height * 0.5f;
+
+            for(size_t k = i + 1; k < (num_candidates) && (cur.idx_class == multi_windows.at(k).idx_class); ++k)
+            {
+                const float xn = multi_windows.at(k).x + multi_windows.at(k).width * 0.5f;
+                const float yn = multi_windows.at(k).y + multi_windows.at(k).height * 0.5f;
+
+                const float dx = std::fabs(xn - xc);
+                const float dy = std::fabs(yn - yc);
+
+                if(dx < min_distance && dy < min_distance)
+                {
+                    const float d = dx * dx + dy * dy;
+
+                    if(d < min_distance_pow2)
+                    {
+                        // Invalidate detection window
+                        multi_windows.at(k).score = 0.0f;
+                    }
+                }
+            }
+        }
+    }
+
+    multi_windows.resize(num_detections);
+}
+
+template <typename T>
+std::vector<DetectionWindow> hog_multi_detection(const SimpleTensor<T> &src, BorderMode border_mode, T constant_border_value,
+                                                 const std::vector<HOGInfo> &models, std::vector<std::vector<float>> descriptors,
+                                                 unsigned int max_num_detection_windows, float threshold, bool non_maxima_suppression, float min_distance)
+{
+    ARM_COMPUTE_ERROR_ON(descriptors.size() != models.size());
+    validate_models(models);
+
+    const size_t width      = src.shape().x();
+    const size_t height     = src.shape().y();
+    const size_t num_models = models.size();
+
+    // Initialize previous values
+    size_t prev_num_bins     = models[0].num_bins();
+    Size2D prev_cell_size    = models[0].cell_size();
+    Size2D prev_block_size   = models[0].block_size();
+    Size2D prev_block_stride = models[0].block_stride();
+
+    std::vector<size_t> input_orient_bin;
+    std::vector<size_t> input_hog_detect;
+    std::vector<std::pair<size_t, size_t>> input_block_norm;
+
+    input_orient_bin.push_back(0);
+    input_hog_detect.push_back(0);
+    input_block_norm.emplace_back(0, 0);
+
+    // Iterate through the number of models and check if orientation binning
+    // and block normalization steps can be skipped
+    for(size_t i = 1; i < num_models; ++i)
+    {
+        size_t cur_num_bins     = models[i].num_bins();
+        Size2D cur_cell_size    = models[i].cell_size();
+        Size2D cur_block_size   = models[i].block_size();
+        Size2D cur_block_stride = models[i].block_stride();
+
+        // Check if binning and normalization steps are required
+        if((cur_num_bins != prev_num_bins) || (cur_cell_size.width != prev_cell_size.width) || (cur_cell_size.height != prev_cell_size.height))
+        {
+            prev_num_bins     = cur_num_bins;
+            prev_cell_size    = cur_cell_size;
+            prev_block_size   = cur_block_size;
+            prev_block_stride = cur_block_stride;
+
+            // Compute orientation binning and block normalization. Update input to process
+            input_orient_bin.push_back(i);
+            input_block_norm.emplace_back(i, input_orient_bin.size() - 1);
+        }
+        else if((cur_block_size.width != prev_block_size.width) || (cur_block_size.height != prev_block_size.height) || (cur_block_stride.width != prev_block_stride.width)
+                || (cur_block_stride.height != prev_block_stride.height))
+        {
+            prev_block_size   = cur_block_size;
+            prev_block_stride = cur_block_stride;
+
+            // Compute block normalization. Update input to process
+            input_block_norm.emplace_back(i, input_orient_bin.size() - 1);
+        }
+
+        // Update input to process for hog detector
+        input_hog_detect.push_back(input_block_norm.size() - 1);
+    }
+
+    size_t num_orient_bin = input_orient_bin.size();
+    size_t num_block_norm = input_block_norm.size();
+    size_t num_hog_detect = input_hog_detect.size();
+
+    std::vector<SimpleTensor<float>> hog_spaces(num_orient_bin);
+    std::vector<SimpleTensor<float>> hog_norm_spaces(num_block_norm);
+
+    // Calculate derivative
+    SimpleTensor<int16_t> grad_x;
+    SimpleTensor<int16_t> grad_y;
+    std::tie(grad_x, grad_y) = derivative<int16_t>(src, border_mode, constant_border_value, GradientDimension::GRAD_XY);
+
+    // Calculate magnitude and phase
+    SimpleTensor<int16_t> _mag   = magnitude(grad_x, grad_y, MagnitudeType::L2NORM);
+    SimpleTensor<uint8_t> _phase = phase(grad_x, grad_y, models[0].phase_type());
+
+    // Calculate Tensors for the HOG space and orientation binning
+    for(size_t i = 0; i < num_orient_bin; ++i)
+    {
+        const size_t idx_multi_hog = input_orient_bin[i];
+
+        const size_t num_bins    = models[idx_multi_hog].num_bins();
+        const size_t num_cells_x = width / models[idx_multi_hog].cell_size().width;
+        const size_t num_cells_y = height / models[idx_multi_hog].cell_size().height;
+
+        // TensorShape of hog space
+        TensorShape hog_space_shape(num_cells_x, num_cells_y);
+
+        // Initialise HOG space
+        TensorInfo info_hog_space(hog_space_shape, num_bins, DataType::F32);
+        hog_spaces.at(i) = SimpleTensor<float>(info_hog_space.tensor_shape(), DataType::F32, info_hog_space.num_channels());
+
+        // For each cell create histogram based on magnitude and phase
+        hog_orientation_binning(_mag, _phase, hog_spaces[i], models[idx_multi_hog]);
+    }
+
+    // Calculate Tensors for the normalized HOG space and block normalization
+    for(size_t i = 0; i < num_block_norm; ++i)
+    {
+        const size_t idx_multi_hog  = input_block_norm[i].first;
+        const size_t idx_orient_bin = input_block_norm[i].second;
+
+        // Create tensor info for HOG descriptor
+        TensorInfo tensor_info(models[idx_multi_hog], src.shape().x(), src.shape().y());
+        hog_norm_spaces.at(i) = SimpleTensor<float>(tensor_info.tensor_shape(), DataType::F32, tensor_info.num_channels());
+
+        // Normalize histograms based on block size
+        hog_block_normalization(hog_norm_spaces[i], hog_spaces[idx_orient_bin], models[idx_multi_hog]);
+    }
+
+    std::vector<DetectionWindow> multi_windows;
+
+    // Calculate Detection Windows for HOG detector
+    for(size_t i = 0; i < num_hog_detect; ++i)
+    {
+        const size_t idx_block_norm = input_hog_detect[i];
+
+        // NOTE: Detection window stride fixed to block stride
+        const Size2D detection_window_stride = models[i].block_stride();
+
+        std::vector<DetectionWindow> windows = hog_detector(hog_norm_spaces[idx_block_norm], descriptors[i],
+                                                            max_num_detection_windows, models[i], detection_window_stride, threshold, i);
+
+        multi_windows.insert(multi_windows.end(), windows.begin(), windows.end());
+    }
+
+    // Suppress Non-maxima detection windows
+    if(non_maxima_suppression)
+    {
+        detection_windows_non_maxima_suppression(multi_windows, min_distance);
+    }
+
+    return multi_windows;
+}
+
+template std::vector<DetectionWindow> hog_multi_detection(const SimpleTensor<uint8_t> &src, BorderMode border_mode, uint8_t constant_border_value,
+                                                          const std::vector<HOGInfo> &models, std::vector<std::vector<float>> descriptors,
+                                                          unsigned int max_num_detection_windows, float threshold, bool non_maxima_suppression, float min_distance);
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute