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#include "model_adapters.h"
#include <algorithm>
#include <numeric>
#include <cstdio>
namespace neuralnet_tracker_tests
{
void assert_(bool ok, const std::string& msg)
{
if (ok)
return;
std::cout << msg << std::endl;
std::exit(-1);
}
void test_find_input_intensity_quantile()
{
cv::Mat data(10,10, CV_8UC1);
std::iota(data.begin<uint8_t>(), data.end<uint8_t>(), 0);
const float pct = 90;
const int val = neuralnet_tracker_ns::find_input_intensity_quantile(data, pct);
assert_(val == int(10*10*pct/100.f), "test_find_input_intensity_quantile failed");
}
void test_normalize_brightness()
{
cv::Mat data(10,10, CV_8UC1);
std::iota(data.begin<uint8_t>(), data.end<uint8_t>(), 0);
cv::Mat out;
neuralnet_tracker_ns::normalize_brightness(data, out);
auto [minit,maxit] = std::minmax_element(out.begin<float>(),out.end<float>());
const auto minval = *minit;
const auto maxval = *maxit;
assert_(std::abs(minval + 0.5f) < 0.02, "test_normalize_brightness failed");
// If the brightest value is lower than half-max, it will be boosted to half-max.
// Otherwise it will just be rescaled to [-.5, 0.5 ]. Here we have the low-brightness case.
assert_(std::abs(maxval - 0.0f) < 0.02, "test_normalize_brightness failed");
}
void run()
{
test_find_input_intensity_quantile();
test_normalize_brightness();
}
}
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