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/*
* 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
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