/* * Copyright (c) 2016-2019 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 "arm_compute/runtime/NEON/functions/NEHarrisCorners.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/NEON/kernels/NEFillBorderKernel.h" #include "arm_compute/core/NEON/kernels/NEHarrisCornersKernel.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Validate.h" #include "arm_compute/runtime/Array.h" #include "arm_compute/runtime/NEON/NEScheduler.h" #include "arm_compute/runtime/NEON/functions/NESobel3x3.h" #include "arm_compute/runtime/NEON/functions/NESobel5x5.h" #include "arm_compute/runtime/NEON/functions/NESobel7x7.h" #include "arm_compute/runtime/TensorAllocator.h" #include "support/ToolchainSupport.h" #include #include using namespace arm_compute; NEHarrisCorners::NEHarrisCorners(std::shared_ptr memory_manager) // NOLINT : _memory_group(std::move(memory_manager)), _sobel(), _harris_score(), _non_max_suppr(), _candidates(), _sort_euclidean(), _border_gx(), _border_gy(), _gx(), _gy(), _score(), _nonmax(), _corners_list(), _num_corner_candidates(0) { } void NEHarrisCorners::configure(IImage *input, float threshold, float min_dist, float sensitivity, int32_t gradient_size, int32_t block_size, KeyPointArray *corners, BorderMode border_mode, uint8_t constant_border_value) { ARM_COMPUTE_ERROR_ON_TENSOR_NOT_2D(input); ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8); ARM_COMPUTE_ERROR_ON(!(block_size == 3 || block_size == 5 || block_size == 7)); const TensorShape shape = input->info()->tensor_shape(); TensorInfo tensor_info_gxgy; if(gradient_size < 7) { tensor_info_gxgy.init(shape, Format::S16); } else { tensor_info_gxgy.init(shape, Format::S32); } _gx.allocator()->init(tensor_info_gxgy); _gy.allocator()->init(tensor_info_gxgy); // Manage intermediate buffers _memory_group.manage(&_gx); _memory_group.manage(&_gy); TensorInfo tensor_info_score(shape, Format::F32); _score.allocator()->init(tensor_info_score); _nonmax.allocator()->init(tensor_info_score); _corners_list = arm_compute::support::cpp14::make_unique(shape.x() * shape.y()); // Set/init Sobel kernel accordingly with gradient_size switch(gradient_size) { case 3: { auto k = arm_compute::support::cpp14::make_unique(); k->configure(input, &_gx, &_gy, border_mode, constant_border_value); _sobel = std::move(k); break; } case 5: { auto k = arm_compute::support::cpp14::make_unique(); k->configure(input, &_gx, &_gy, border_mode, constant_border_value); _sobel = std::move(k); break; } case 7: { auto k = arm_compute::support::cpp14::make_unique(); k->configure(input, &_gx, &_gy, border_mode, constant_border_value); _sobel = std::move(k); break; } default: ARM_COMPUTE_ERROR("Gradient size not implemented"); } // Normalization factor const float norm_factor = 1.0f / (255.0f * pow(4.0f, gradient_size / 2) * block_size); // Manage intermediate buffers _memory_group.manage(&_score); // Set/init Harris Score kernel accordingly with block_size switch(block_size) { case 3: { auto k = arm_compute::support::cpp14::make_unique>(); k->configure(&_gx, &_gy, &_score, norm_factor, threshold, sensitivity, border_mode == BorderMode::UNDEFINED); _harris_score = std::move(k); } break; case 5: { auto k = arm_compute::support::cpp14::make_unique>(); k->configure(&_gx, &_gy, &_score, norm_factor, threshold, sensitivity, border_mode == BorderMode::UNDEFINED); _harris_score = std::move(k); } break; case 7: { auto k = arm_compute::support::cpp14::make_unique>(); k->configure(&_gx, &_gy, &_score, norm_factor, threshold, sensitivity, border_mode == BorderMode::UNDEFINED); _harris_score = std::move(k); } default: break; } // Configure border filling before harris score _border_gx.configure(&_gx, _harris_score->border_size(), border_mode, constant_border_value); _border_gy.configure(&_gy, _harris_score->border_size(), border_mode, constant_border_value); // Allocate once all the configure methods have been called _gx.allocator()->allocate(); _gy.allocator()->allocate(); // Manage intermediate buffers _memory_group.manage(&_nonmax); // Init non-maxima suppression function _non_max_suppr.configure(&_score, &_nonmax, border_mode); // Allocate once all the configure methods have been called _score.allocator()->allocate(); // Init corner candidates kernel _candidates.configure(&_nonmax, _corners_list.get(), &_num_corner_candidates); // Allocate once all the configure methods have been called _nonmax.allocator()->allocate(); // Init euclidean distance _sort_euclidean.configure(_corners_list.get(), corners, &_num_corner_candidates, min_dist); } void NEHarrisCorners::run() { ARM_COMPUTE_ERROR_ON_MSG(_sobel == nullptr, "Unconfigured function"); MemoryGroupResourceScope scope_mg(_memory_group); // Init to 0 number of corner candidates _num_corner_candidates = 0; // Run Sobel kernel _sobel->run(); // Fill border before harris score kernel NEScheduler::get().schedule(&_border_gx, Window::DimZ); NEScheduler::get().schedule(&_border_gy, Window::DimZ); // Run harris score kernel NEScheduler::get().schedule(_harris_score.get(), Window::DimY); // Run non-maxima suppression _non_max_suppr.run(); // Run corner candidate kernel NEScheduler::get().schedule(&_candidates, Window::DimY); // Run sort & euclidean distance NEScheduler::get().schedule(&_sort_euclidean, Window::DimY); }