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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 "tests/validation/Helpers.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
void fill_mask_from_pattern(uint8_t *mask, int cols, int rows, MatrixPattern pattern)
{
unsigned int v = 0;
std::mt19937 gen(library->seed());
std::bernoulli_distribution dist(0.5);
for(int r = 0; r < rows; ++r)
{
for(int c = 0; c < cols; ++c, ++v)
{
uint8_t val = 0;
switch(pattern)
{
case MatrixPattern::BOX:
val = 255;
break;
case MatrixPattern::CROSS:
val = ((r == (rows / 2)) || (c == (cols / 2))) ? 255 : 0;
break;
case MatrixPattern::DISK:
val = (((r - rows / 2.0f + 0.5f) * (r - rows / 2.0f + 0.5f)) / ((rows / 2.0f) * (rows / 2.0f)) + ((c - cols / 2.0f + 0.5f) * (c - cols / 2.0f + 0.5f)) / ((cols / 2.0f) *
(cols / 2.0f))) <= 1.0f ? 255 : 0;
break;
case MatrixPattern::OTHER:
val = (dist(gen) ? 0 : 255);
break;
default:
return;
}
mask[v] = val;
}
}
if(pattern == MatrixPattern::OTHER)
{
std::uniform_int_distribution<uint8_t> distribution_u8(0, ((cols * rows) - 1));
mask[distribution_u8(gen)] = 255;
}
}
TensorShape calculate_depth_concatenate_shape(const std::vector<TensorShape> &input_shapes)
{
ARM_COMPUTE_ERROR_ON(input_shapes.empty());
TensorShape out_shape = input_shapes[0];
size_t max_x = 0;
size_t max_y = 0;
size_t depth = 0;
for(const auto &shape : input_shapes)
{
max_x = std::max(shape.x(), max_x);
max_y = std::max(shape.y(), max_y);
depth += shape.z();
}
out_shape.set(0, max_x);
out_shape.set(1, max_y);
out_shape.set(2, depth);
return out_shape;
}
HarrisCornersParameters harris_corners_parameters()
{
HarrisCornersParameters params;
std::mt19937 gen(library->seed());
std::uniform_real_distribution<float> threshold_dist(0.f, 0.01f);
std::uniform_real_distribution<float> sensitivity(0.04f, 0.15f);
std::uniform_real_distribution<float> euclidean_distance(0.f, 30.f);
std::uniform_int_distribution<uint8_t> int_dist(0, 255);
params.threshold = threshold_dist(gen);
params.sensitivity = sensitivity(gen);
params.min_dist = euclidean_distance(gen);
params.constant_border_value = int_dist(gen);
return params;
}
} // namespace validation
} // namespace test
} // namespace arm_compute
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