From 7f4a8191a0fff69ec6c819e8d785a2c780388feb Mon Sep 17 00:00:00 2001 From: John Richardson Date: Mon, 5 Feb 2018 15:12:22 +0000 Subject: COMPMID-597: Port HOGMultiDetection to new framework Change-Id: I4b31b7f052a06bea4154d04c9926a0e076e28d73 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/126555 Tested-by: Jenkins Reviewed-by: John Richardson Reviewed-by: Gian Marco Iodice Reviewed-by: Anthony Barbier --- tests/validation/reference/HOGMultiDetection.cpp | 279 +++++++++++++++++++++++ tests/validation/reference/HOGMultiDetection.h | 48 ++++ 2 files changed, 327 insertions(+) create mode 100644 tests/validation/reference/HOGMultiDetection.cpp create mode 100644 tests/validation/reference/HOGMultiDetection.h (limited to 'tests/validation/reference') diff --git a/tests/validation/reference/HOGMultiDetection.cpp b/tests/validation/reference/HOGMultiDetection.cpp new file mode 100644 index 0000000000..2f5e4395a0 --- /dev/null +++ b/tests/validation/reference/HOGMultiDetection.cpp @@ -0,0 +1,279 @@ +/* + * 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 &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 &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 +std::vector hog_multi_detection(const SimpleTensor &src, BorderMode border_mode, T constant_border_value, + const std::vector &models, std::vector> 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 input_orient_bin; + std::vector input_hog_detect; + std::vector> 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> hog_spaces(num_orient_bin); + std::vector> hog_norm_spaces(num_block_norm); + + // Calculate derivative + SimpleTensor grad_x; + SimpleTensor grad_y; + std::tie(grad_x, grad_y) = derivative(src, border_mode, constant_border_value, GradientDimension::GRAD_XY); + + // Calculate magnitude and phase + SimpleTensor _mag = magnitude(grad_x, grad_y, MagnitudeType::L2NORM); + SimpleTensor _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(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(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 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 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 hog_multi_detection(const SimpleTensor &src, BorderMode border_mode, uint8_t constant_border_value, + const std::vector &models, std::vector> 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 diff --git a/tests/validation/reference/HOGMultiDetection.h b/tests/validation/reference/HOGMultiDetection.h new file mode 100644 index 0000000000..6d75bf4a8e --- /dev/null +++ b/tests/validation/reference/HOGMultiDetection.h @@ -0,0 +1,48 @@ +/* + * 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. + */ +#ifndef __ARM_COMPUTE_TEST_HOG_MULTI_DETECTION_H__ +#define __ARM_COMPUTE_TEST_HOG_MULTI_DETECTION_H__ + +#include "arm_compute/core/Types.h" +#include "tests/SimpleTensor.h" + +#include + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace reference +{ +template +std::vector hog_multi_detection(const SimpleTensor &src, BorderMode border_mode, T constant_border_value, + const std::vector &models, std::vector> descriptors, + unsigned int max_num_detection_windows, float threshold = 0.0f, bool non_maxima_suppression = false, float min_distance = 1.0f); +} // namespace reference +} // namespace validation +} // namespace test +} // namespace arm_compute +#endif /* __ARM_COMPUTE_TEST_HOG_MULTI_DETECTION_H__ */ -- cgit v1.2.1