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/*
* 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_OPTICAL_FLOW
#define ARM_COMPUTE_TEST_OPTICAL_FLOW
#include "arm_compute/core/PyramidInfo.h"
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "tests/AssetsLibrary.h"
#include "tests/Globals.h"
#include "tests/IAccessor.h"
#include "tests/Types.h"
#include "tests/framework/Asserts.h"
#include "tests/framework/Fixture.h"
#include "tests/validation/reference/OpticalFlow.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
template <typename TensorType,
typename AccessorType,
typename ArrayType,
typename ArrayAccessorType,
typename FunctionType,
typename PyramidType,
typename PyramidFunctionType,
typename T>
class OpticalFlowValidationFixture : public framework::Fixture
{
public:
template <typename...>
void setup(std::string old_image_name, std::string new_image_name, OpticalFlowParameters params,
size_t num_levels, size_t num_keypoints, Format format, BorderMode border_mode)
{
std::mt19937 gen(library->seed());
std::uniform_int_distribution<uint8_t> int_dist(0, 255);
const uint8_t constant_border_value = int_dist(gen);
// Create keypoints
std::vector<KeyPoint> old_keypoints = generate_random_keypoints(library->get_image_shape(old_image_name), num_keypoints, library->seed(), num_levels);
std::vector<KeyPoint> new_keypoints_estimates = old_keypoints;
_target = compute_target(old_image_name, new_image_name, params, num_levels, old_keypoints, new_keypoints_estimates, format, border_mode, constant_border_value);
_reference = compute_reference(old_image_name, new_image_name, params, num_levels, old_keypoints, new_keypoints_estimates, format, border_mode, constant_border_value);
}
protected:
template <typename V>
void fill(V &&tensor, const std::string image, Format format)
{
library->fill(tensor, image, format);
}
ArrayType compute_target(std::string old_image_name, std::string new_image_name, OpticalFlowParameters params, size_t num_levels,
std::vector<KeyPoint> &old_keypoints, std::vector<KeyPoint> &new_keypoints_estimates,
Format format, BorderMode border_mode, uint8_t constant_border_value)
{
// Get image shapes
TensorShape old_shape = library->get_image_shape(old_image_name);
TensorShape new_shape = library->get_image_shape(new_image_name);
// Create tensors
auto old_image = create_tensor<TensorType>(old_shape, format);
auto new_image = create_tensor<TensorType>(new_shape, format);
// Load keypoints
ArrayType old_points(old_keypoints.size());
ArrayType new_points_estimates(new_keypoints_estimates.size());
ArrayType new_points(old_keypoints.size());
fill_array(ArrayAccessorType(old_points), old_keypoints);
fill_array(ArrayAccessorType(new_points_estimates), new_keypoints_estimates);
// Create pyramid images
PyramidInfo pyramid_info(num_levels, SCALE_PYRAMID_HALF, old_image.info()->tensor_shape(), format);
PyramidType old_pyramid = create_pyramid<PyramidType>(pyramid_info);
PyramidType new_pyramid = create_pyramid<PyramidType>(pyramid_info);
// Create and configure pyramid functions
PyramidFunctionType old_gp;
old_gp.configure(&old_image, &old_pyramid, border_mode, constant_border_value);
PyramidFunctionType new_gp;
new_gp.configure(&new_image, &new_pyramid, border_mode, constant_border_value);
for(size_t i = 0; i < pyramid_info.num_levels(); ++i)
{
ARM_COMPUTE_EXPECT(old_pyramid.get_pyramid_level(i)->info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(new_pyramid.get_pyramid_level(i)->info()->is_resizable(), framework::LogLevel::ERRORS);
}
// Create and configure optical flow function
FunctionType optical_flow;
optical_flow.configure(&old_pyramid,
&new_pyramid,
&old_points,
&new_points_estimates,
&new_points,
params.termination,
params.epsilon,
params.num_iterations,
params.window_dimension,
params.use_initial_estimate,
border_mode,
constant_border_value);
ARM_COMPUTE_EXPECT(old_image.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(new_image.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate input tensors
old_image.allocator()->allocate();
new_image.allocator()->allocate();
// Allocate pyramids
old_pyramid.allocate();
new_pyramid.allocate();
ARM_COMPUTE_EXPECT(!old_image.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!new_image.info()->is_resizable(), framework::LogLevel::ERRORS);
for(size_t i = 0; i < pyramid_info.num_levels(); ++i)
{
ARM_COMPUTE_EXPECT(!old_pyramid.get_pyramid_level(i)->info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!new_pyramid.get_pyramid_level(i)->info()->is_resizable(), framework::LogLevel::ERRORS);
}
// Fill tensors
fill(AccessorType(old_image), old_image_name, format);
fill(AccessorType(new_image), new_image_name, format);
// Compute functions
old_gp.run();
new_gp.run();
optical_flow.run();
return new_points;
}
std::vector<KeyPoint> compute_reference(std::string old_image_name, std::string new_image_name,
OpticalFlowParameters params, size_t num_levels,
std::vector<KeyPoint> &old_keypoints, std::vector<KeyPoint> &new_keypoints_estimates,
Format format, BorderMode border_mode, uint8_t constant_border_value)
{
SimpleTensor<T> old_image{ library->get_image_shape(old_image_name), data_type_from_format(format) };
SimpleTensor<T> new_image{ library->get_image_shape(new_image_name), data_type_from_format(format) };
fill(old_image, old_image_name, format);
fill(new_image, new_image_name, format);
return reference::optical_flow<T>(old_image, new_image, params, num_levels, old_keypoints, new_keypoints_estimates,
border_mode, constant_border_value);
}
ArrayType _target{};
std::vector<KeyPoint> _reference{};
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_OPTICAL_FLOW */
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