| /* |
| * Copyright (c) 2017 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 "HarrisCornerDetector.h" |
| |
| #include "Utils.h" |
| #include "tests/validation/Helpers.h" |
| #include "tests/validation/reference/NonMaximaSuppression.h" |
| #include "tests/validation/reference/Sobel.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace reference |
| { |
| namespace |
| { |
| template <typename T> |
| std::tuple<SimpleTensor<T>, SimpleTensor<T>, float> compute_sobel(const SimpleTensor<uint8_t> &src, int gradient_size, int block_size, BorderMode border_mode, uint8_t constant_border_value) |
| { |
| SimpleTensor<T> grad_x; |
| SimpleTensor<T> grad_y; |
| float norm_factor = 0.f; |
| |
| std::tie(grad_x, grad_y) = sobel<T>(src, gradient_size, border_mode, constant_border_value, GradientDimension::GRAD_XY); |
| |
| switch(gradient_size) |
| { |
| case 3: |
| norm_factor = 1.f / (4 * 255 * block_size); |
| break; |
| case 5: |
| norm_factor = 1.f / (16 * 255 * block_size); |
| break; |
| case 7: |
| norm_factor = 1.f / (64 * 255 * block_size); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Gradient size not supported."); |
| } |
| |
| return std::make_tuple(grad_x, grad_y, norm_factor); |
| } |
| |
| template <typename T, typename U> |
| std::vector<KeyPoint> harris_corner_detector_impl(const SimpleTensor<U> &src, float threshold, float min_dist, float sensitivity, int gradient_size, int block_size, BorderMode border_mode, |
| U constant_border_value) |
| { |
| ARM_COMPUTE_ERROR_ON(block_size != 3 && block_size != 5 && block_size != 7); |
| |
| SimpleTensor<T> grad_x; |
| SimpleTensor<T> grad_y; |
| float norm_factor = 0.f; |
| |
| // Sobel |
| std::tie(grad_x, grad_y, norm_factor) = compute_sobel<T>(src, gradient_size, block_size, border_mode, constant_border_value); |
| |
| SimpleTensor<float> scores(src.shape(), DataType::F32); |
| ValidRegion scores_region = shape_to_valid_region(scores.shape(), border_mode == BorderMode::UNDEFINED, BorderSize(gradient_size / 2 + block_size / 2)); |
| |
| // Calculate scores |
| for(int i = 0; i < scores.num_elements(); ++i) |
| { |
| Coordinates src_coord = index2coord(src.shape(), i); |
| Coordinates block_top_left{ src_coord.x() - block_size / 2, src_coord.y() - block_size / 2 }; |
| Coordinates block_bottom_right{ src_coord.x() + block_size / 2, src_coord.y() + block_size / 2 }; |
| |
| if(!is_in_valid_region(scores_region, src_coord)) |
| { |
| scores[i] = 0.f; |
| continue; |
| } |
| |
| float Gx2 = 0.f; |
| float Gy2 = 0.f; |
| float Gxy = 0.f; |
| |
| // Calculate Gx^2, Gy^2 and Gxy within the given window |
| for(int y = block_top_left.y(); y <= block_bottom_right.y(); ++y) |
| { |
| for(int x = block_top_left.x(); x <= block_bottom_right.x(); ++x) |
| { |
| Coordinates block_coord(x, y); |
| |
| const float norm_x = tensor_elem_at(grad_x, block_coord, border_mode, static_cast<T>(constant_border_value)) * norm_factor; |
| const float norm_y = tensor_elem_at(grad_y, block_coord, border_mode, static_cast<T>(constant_border_value)) * norm_factor; |
| |
| Gx2 += std::pow(norm_x, 2); |
| Gy2 += std::pow(norm_y, 2); |
| Gxy += norm_x * norm_y; |
| } |
| } |
| |
| const float trace2 = std::pow(Gx2 + Gy2, 2); |
| const float det = Gx2 * Gy2 - std::pow(Gxy, 2); |
| const float response = det - sensitivity * trace2; |
| |
| if(response > threshold) |
| { |
| scores[i] = response; |
| } |
| else |
| { |
| scores[i] = 0.f; |
| } |
| } |
| |
| // Suppress non-maxima candidates |
| SimpleTensor<float> suppressed_scores = non_maxima_suppression(scores, border_mode != BorderMode::UNDEFINED ? BorderMode::CONSTANT : BorderMode::UNDEFINED, 0.f); |
| ValidRegion suppressed_scores_region = shape_to_valid_region(suppressed_scores.shape(), border_mode == BorderMode::UNDEFINED, BorderSize(gradient_size / 2 + block_size / 2 + 1)); |
| |
| // Create vector of candidate corners |
| std::vector<KeyPoint> corner_candidates; |
| |
| for(int i = 0; i < suppressed_scores.num_elements(); ++i) |
| { |
| Coordinates coord = index2coord(suppressed_scores.shape(), i); |
| |
| if(is_in_valid_region(suppressed_scores_region, coord) && suppressed_scores[i] != 0.f) |
| { |
| KeyPoint corner; |
| corner.x = coord.x(); |
| corner.y = coord.y(); |
| corner.tracking_status = 1; |
| corner.strength = suppressed_scores[i]; |
| corner.scale = 0.f; |
| corner.orientation = 0.f; |
| corner.error = 0.f; |
| |
| corner_candidates.emplace_back(corner); |
| } |
| } |
| |
| // Sort descending by strength |
| std::sort(corner_candidates.begin(), corner_candidates.end(), [](const KeyPoint & a, const KeyPoint & b) |
| { |
| return a.strength > b.strength; |
| }); |
| |
| std::vector<KeyPoint> corners; |
| corners.reserve(corner_candidates.size()); |
| |
| // Only add corner if there is no stronger within min_dist |
| for(const KeyPoint &point : corner_candidates) |
| { |
| const auto strongest = std::find_if(corners.begin(), corners.end(), [&](const KeyPoint & other) |
| { |
| return std::sqrt((std::pow(point.x - other.x, 2) + std::pow(point.y - other.y, 2))) < min_dist; |
| }); |
| |
| if(strongest == corners.end()) |
| { |
| corners.emplace_back(point); |
| } |
| } |
| |
| corners.shrink_to_fit(); |
| |
| return corners; |
| } |
| } // namespace |
| |
| template <typename T> |
| std::vector<KeyPoint> harris_corner_detector(const SimpleTensor<T> &src, float threshold, float min_dist, float sensitivity, int gradient_size, int block_size, BorderMode border_mode, |
| T constant_border_value) |
| { |
| if(gradient_size < 7) |
| { |
| return harris_corner_detector_impl<int16_t>(src, threshold, min_dist, sensitivity, gradient_size, block_size, border_mode, constant_border_value); |
| } |
| else |
| { |
| return harris_corner_detector_impl<int32_t>(src, threshold, min_dist, sensitivity, gradient_size, block_size, border_mode, constant_border_value); |
| } |
| } |
| |
| template std::vector<KeyPoint> harris_corner_detector(const SimpleTensor<uint8_t> &src, float threshold, float min_dist, float sensitivity, int gradient_size, int block_size, BorderMode border_mode, |
| uint8_t constant_border_value); |
| } // namespace reference |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |