From c6f9510bcb754afaadfe9477ff85d6c55ffcf43b Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Tue, 30 Mar 2021 10:03:01 +0100 Subject: Remove Computer Vision generic interfaces and types Removes: - reference validation routines - CV related types and structures - CV related interfaces Signed-off-by: Georgios Pinitas Change-Id: I3a203da12d9b04c154059b190aeba18a611149a9 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5340 Tested-by: Arm Jenkins Reviewed-by: Michele Di Giorgio Comments-Addressed: Arm Jenkins --- tests/validation/reference/HOGMultiDetection.cpp | 279 ----------------------- 1 file changed, 279 deletions(-) delete mode 100644 tests/validation/reference/HOGMultiDetection.cpp (limited to 'tests/validation/reference/HOGMultiDetection.cpp') diff --git a/tests/validation/reference/HOGMultiDetection.cpp b/tests/validation/reference/HOGMultiDetection.cpp deleted file mode 100644 index 50d846c0be..0000000000 --- a/tests/validation/reference/HOGMultiDetection.cpp +++ /dev/null @@ -1,279 +0,0 @@ -/* - * 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 -- cgit v1.2.1