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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
namespace opencv_test {
Ptr<AdaptiveManifoldFilter> createAMFilterRefImpl(double sigma_s, double sigma_r, bool adjust_outliers = false);
namespace {
#ifndef SQR
#define SQR(x) ((x)*(x))
#endif
static string getOpenCVExtraDir()
{
return cvtest::TS::ptr()->get_data_path();
}
static void checkSimilarity(InputArray res, InputArray ref, double maxNormInf = 1, double maxNormL2 = 1.0 / 64)
{
double normInf = cvtest::norm(res, ref, NORM_INF);
double normL2 = cvtest::norm(res, ref, NORM_L2) / res.total();
if (maxNormInf >= 0) { EXPECT_LE(normInf, maxNormInf); }
if (maxNormL2 >= 0) { EXPECT_LE(normL2, maxNormL2); }
}
TEST(AdaptiveManifoldTest, SplatSurfaceAccuracy)
{
RNG rnd(0);
for (int i = 0; i < 5; i++)
{
Size sz(rnd.uniform(512, 1024), rnd.uniform(512, 1024));
int guideCn = rnd.uniform(1, 8);
Mat guide(sz, CV_MAKE_TYPE(CV_32F, guideCn));
randu(guide, 0, 1);
Scalar surfaceValue;
int srcCn = rnd.uniform(1, 4);
rnd.fill(surfaceValue, RNG::UNIFORM, 0, 255);
Mat src(sz, CV_MAKE_TYPE(CV_8U, srcCn), surfaceValue);
double sigma_s = rnd.uniform(1.0, 50.0);
double sigma_r = rnd.uniform(0.1, 0.9);
Mat res;
amFilter(guide, src, res, sigma_s, sigma_r, false);
double normInf = cvtest::norm(src, res, NORM_INF);
EXPECT_EQ(normInf, 0);
}
}
TEST(AdaptiveManifoldTest, AuthorsReferenceAccuracy)
{
String srcImgPath = "cv/edgefilter/kodim23.png";
String refPaths[] =
{
"cv/edgefilter/amf/kodim23_amf_ss5_sr0.3_ref.png",
"cv/edgefilter/amf/kodim23_amf_ss30_sr0.1_ref.png",
"cv/edgefilter/amf/kodim23_amf_ss50_sr0.3_ref.png"
};
pair<double, double> refParams[] =
{
make_pair(5.0, 0.3),
make_pair(30.0, 0.1),
make_pair(50.0, 0.3)
};
String refOutliersPaths[] =
{
"cv/edgefilter/amf/kodim23_amf_ss5_sr0.1_outliers_ref.png",
"cv/edgefilter/amf/kodim23_amf_ss15_sr0.3_outliers_ref.png",
"cv/edgefilter/amf/kodim23_amf_ss50_sr0.5_outliers_ref.png"
};
pair<double, double> refOutliersParams[] =
{
make_pair(5.0, 0.1),
make_pair(15.0, 0.3),
make_pair(50.0, 0.5),
};
Mat srcImg = imread(getOpenCVExtraDir() + srcImgPath);
ASSERT_TRUE(!srcImg.empty());
for (int i = 0; i < 3; i++)
{
Mat refRes = imread(getOpenCVExtraDir() + refPaths[i]);
double sigma_s = refParams[i].first;
double sigma_r = refParams[i].second;
ASSERT_TRUE(!refRes.empty());
Mat res;
Ptr<AdaptiveManifoldFilter> amf = createAMFilter(sigma_s, sigma_r, false);
amf->setUseRNG(false);
amf->filter(srcImg, res, srcImg);
amf->collectGarbage();
checkSimilarity(res, refRes);
}
for (int i = 0; i < 3; i++)
{
Mat refRes = imread(getOpenCVExtraDir() + refOutliersPaths[i]);
double sigma_s = refOutliersParams[i].first;
double sigma_r = refOutliersParams[i].second;
ASSERT_TRUE(!refRes.empty());
Mat res;
Ptr<AdaptiveManifoldFilter> amf = createAMFilter(sigma_s, sigma_r, true);
amf->setUseRNG(false);
amf->filter(srcImg, res, srcImg);
amf->collectGarbage();
checkSimilarity(res, refRes);
}
}
typedef tuple<string, string> AMRefTestParams;
typedef TestWithParam<AMRefTestParams> AdaptiveManifoldRefImplTest;
TEST_P(AdaptiveManifoldRefImplTest, RefImplAccuracy)
{
AMRefTestParams params = GetParam();
string guideFileName = get<0>(params);
string srcFileName = get<1>(params);
Mat guide = imread(getOpenCVExtraDir() + guideFileName);
Mat src = imread(getOpenCVExtraDir() + srcFileName);
ASSERT_TRUE(!guide.empty() && !src.empty());
int seed = 10 * (int)guideFileName.length() + (int)srcFileName.length();
RNG rnd(seed);
//inconsistent downsample/upsample operations in reference implementation
Size dstSize((guide.cols + 15) & ~15, (guide.rows + 15) & ~15);
resize(guide, guide, dstSize, 0, 0, INTER_LINEAR_EXACT);
resize(src, src, dstSize, 0, 0, INTER_LINEAR_EXACT);
int nThreads = cv::getNumThreads();
if (nThreads == 1)
throw SkipTestException("Single thread environment");
for (int iter = 0; iter < 4; iter++)
{
double sigma_s = rnd.uniform(1.0, 50.0);
double sigma_r = rnd.uniform(0.1, 0.9);
bool adjust_outliers = (iter % 2 == 0);
cv::setNumThreads(nThreads);
Mat res;
amFilter(guide, src, res, sigma_s, sigma_r, adjust_outliers);
cv::setNumThreads(1);
Mat resRef;
Ptr<AdaptiveManifoldFilter> amf = createAMFilterRefImpl(sigma_s, sigma_r, adjust_outliers);
amf->filter(src, resRef, guide);
//results of reference implementation may differ on small sigma_s into small isolated region
//due to low single-precision floating point numbers accuracy
//therefore the threshold of inf norm was increased
checkSimilarity(res, resRef, 25);
}
}
INSTANTIATE_TEST_CASE_P(TypicalSet, AdaptiveManifoldRefImplTest,
Combine(
Values("cv/edgefilter/kodim23.png", "cv/npr/test4.png"),
Values("cv/edgefilter/kodim23.png", "cv/npr/test4.png")
));
}} // namespace
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