This source file includes following definitions.
- getRandInt
- reference_bilateral_filter
- prepare_test_case
- validate_test_results
- run_func
- TEST
#include "test_precomp.hpp"
using namespace cv;
namespace cvtest
{
class CV_BilateralFilterTest :
public cvtest::BaseTest
{
public:
enum
{
MAX_WIDTH = 1920, MIN_WIDTH = 1,
MAX_HEIGHT = 1080, MIN_HEIGHT = 1
};
CV_BilateralFilterTest();
~CV_BilateralFilterTest();
protected:
virtual void run_func();
virtual int prepare_test_case(int test_case_index);
virtual int validate_test_results(int test_case_index);
private:
void reference_bilateral_filter(const Mat& src, Mat& dst, int d, double sigma_color,
double sigma_space, int borderType = BORDER_DEFAULT);
int getRandInt(RNG& rng, int min_value, int max_value) const;
double _sigma_color;
double _sigma_space;
Mat _src;
Mat _parallel_dst;
int _d;
};
CV_BilateralFilterTest::CV_BilateralFilterTest() :
cvtest::BaseTest(), _src(), _parallel_dst(), _d()
{
test_case_count = 1000;
}
CV_BilateralFilterTest::~CV_BilateralFilterTest()
{
}
int CV_BilateralFilterTest::getRandInt(RNG& rng, int min_value, int max_value) const
{
double rand_value = rng.uniform(log((double)min_value), log((double)max_value + 1));
return cvRound(exp((double)rand_value));
}
void CV_BilateralFilterTest::reference_bilateral_filter(const Mat &src, Mat &dst, int d,
double sigma_color, double sigma_space, int borderType)
{
int cn = src.channels();
int i, j, k, maxk, radius;
double minValSrc = -1, maxValSrc = 1;
const int kExpNumBinsPerChannel = 1 << 12;
int kExpNumBins = 0;
float lastExpVal = 1.f;
float len, scale_index;
Size size = src.size();
dst.create(size, src.type());
CV_Assert( (src.type() == CV_32FC1 || src.type() == CV_32FC3) &&
src.type() == dst.type() && src.size() == dst.size() &&
src.data != dst.data );
if( sigma_color <= 0 )
sigma_color = 1;
if( sigma_space <= 0 )
sigma_space = 1;
double gauss_color_coeff = -0.5/(sigma_color*sigma_color);
double gauss_space_coeff = -0.5/(sigma_space*sigma_space);
if( d <= 0 )
radius = cvRound(sigma_space*1.5);
else
radius = d/2;
radius = MAX(radius, 1);
d = radius*2 + 1;
minMaxLoc( src.reshape(1), &minValSrc, &maxValSrc );
if(std::abs(minValSrc - maxValSrc) < FLT_EPSILON)
{
src.copyTo(dst);
return;
}
Mat temp;
copyMakeBorder( src, temp, radius, radius, radius, radius, borderType );
patchNaNs(temp);
vector<float> _space_weight(d*d);
vector<int> _space_ofs(d*d);
float* space_weight = &_space_weight[0];
int* space_ofs = &_space_ofs[0];
len = (float)(maxValSrc - minValSrc) * cn;
kExpNumBins = kExpNumBinsPerChannel * cn;
vector<float> _expLUT(kExpNumBins+2);
float* expLUT = &_expLUT[0];
scale_index = kExpNumBins/len;
for( i = 0; i < kExpNumBins+2; i++ )
{
if( lastExpVal > 0.f )
{
double val = i / scale_index;
expLUT[i] = (float)std::exp(val * val * gauss_color_coeff);
lastExpVal = expLUT[i];
}
else
expLUT[i] = 0.f;
}
for( i = -radius, maxk = 0; i <= radius; i++ )
for( j = -radius; j <= radius; j++ )
{
double r = std::sqrt((double)i*i + (double)j*j);
if( r > radius )
continue;
space_weight[maxk] = (float)std::exp(r*r*gauss_space_coeff);
space_ofs[maxk++] = (int)(i*(temp.step/sizeof(float)) + j*cn);
}
for( i = 0; i < size.height; i++ )
{
const float* sptr = temp.ptr<float>(i+radius) + radius*cn;
float* dptr = dst.ptr<float>(i);
if( cn == 1 )
{
for( j = 0; j < size.width; j++ )
{
float sum = 0, wsum = 0;
float val0 = sptr[j];
for( k = 0; k < maxk; k++ )
{
float val = sptr[j + space_ofs[k]];
float alpha = (float)(std::abs(val - val0)*scale_index);
int idx = cvFloor(alpha);
alpha -= idx;
float w = space_weight[k]*(expLUT[idx] + alpha*(expLUT[idx+1] - expLUT[idx]));
sum += val*w;
wsum += w;
}
dptr[j] = (float)(sum/wsum);
}
}
else
{
assert( cn == 3 );
for( j = 0; j < size.width*3; j += 3 )
{
float sum_b = 0, sum_g = 0, sum_r = 0, wsum = 0;
float b0 = sptr[j], g0 = sptr[j+1], r0 = sptr[j+2];
for( k = 0; k < maxk; k++ )
{
const float* sptr_k = sptr + j + space_ofs[k];
float b = sptr_k[0], g = sptr_k[1], r = sptr_k[2];
float alpha = (float)((std::abs(b - b0) +
std::abs(g - g0) + std::abs(r - r0))*scale_index);
int idx = cvFloor(alpha);
alpha -= idx;
float w = space_weight[k]*(expLUT[idx] + alpha*(expLUT[idx+1] - expLUT[idx]));
sum_b += b*w; sum_g += g*w; sum_r += r*w;
wsum += w;
}
wsum = 1.f/wsum;
b0 = sum_b*wsum;
g0 = sum_g*wsum;
r0 = sum_r*wsum;
dptr[j] = b0; dptr[j+1] = g0; dptr[j+2] = r0;
}
}
}
}
int CV_BilateralFilterTest::prepare_test_case(int )
{
const static int types[] = { CV_32FC1, CV_32FC3, CV_8UC1, CV_8UC3 };
RNG& rng = ts->get_rng();
Size size(getRandInt(rng, MIN_WIDTH, MAX_WIDTH), getRandInt(rng, MIN_HEIGHT, MAX_HEIGHT));
int type = types[rng(sizeof(types) / sizeof(types[0]))];
_d = rng.uniform(0., 1.) > 0.5 ? 5 : 3;
_src.create(size, type);
rng.fill(_src, RNG::UNIFORM, 0, 256);
_sigma_color = _sigma_space = 1.;
return 1;
}
int CV_BilateralFilterTest::validate_test_results(int test_case_index)
{
static const double eps = 4;
Mat reference_dst, reference_src;
if (_src.depth() == CV_32F)
reference_bilateral_filter(_src, reference_dst, _d, _sigma_color, _sigma_space);
else
{
int type = _src.type();
_src.convertTo(reference_src, CV_32F);
reference_bilateral_filter(reference_src, reference_dst, _d, _sigma_color, _sigma_space);
reference_dst.convertTo(reference_dst, type);
}
double e = cvtest::norm(reference_dst, _parallel_dst, NORM_L2);
if (e > eps)
{
ts->printf(cvtest::TS::CONSOLE, "actual error: %g, expected: %g", e, eps);
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
}
else
ts->set_failed_test_info(cvtest::TS::OK);
return BaseTest::validate_test_results(test_case_index);
}
void CV_BilateralFilterTest::run_func()
{
bilateralFilter(_src, _parallel_dst, _d, _sigma_color, _sigma_space);
}
TEST(Imgproc_BilateralFilter, accuracy)
{
CV_BilateralFilterTest test;
test.safe_run();
}
}