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217 lines
6.2 KiB
217 lines
6.2 KiB
// This file is part of OpenCV project. |
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// It is subject to the license terms in the LICENSE file found in the top-level directory |
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// of this distribution and at http://opencv.org/license.html. |
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#include "test_precomp.hpp" |
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namespace opencv_test { namespace { |
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static string getOpenCVExtraDir() |
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{ |
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return cvtest::TS::ptr()->get_data_path(); |
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} |
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CV_ENUM(SupportedTypes, CV_8UC1, CV_8UC3, CV_32FC1); // reduced set |
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CV_ENUM(ModeType, DTF_NC, DTF_IC, DTF_RF) |
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typedef tuple<Size, ModeType, SupportedTypes, SupportedTypes> DTParams; |
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Mat convertTypeAndSize(Mat src, int dstType, Size dstSize) |
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{ |
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Mat dst; |
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CV_Assert(src.channels() == 3); |
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int dstChannels = CV_MAT_CN(dstType); |
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if (dstChannels == 1) |
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{ |
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cvtColor(src, dst, COLOR_BGR2GRAY); |
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} |
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else if (dstChannels == 2) |
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{ |
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Mat srcCn[3]; |
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split(src, srcCn); |
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merge(srcCn, 2, dst); |
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} |
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else if (dstChannels == 3) |
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{ |
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dst = src.clone(); |
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} |
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else if (dstChannels == 4) |
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{ |
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Mat srcCn[4]; |
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split(src, srcCn); |
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srcCn[3] = srcCn[0].clone(); |
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merge(srcCn, 4, dst); |
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} |
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dst.convertTo(dst, dstType); |
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resize(dst, dst, dstSize, 0, 0, dstType == CV_32FC1 ? INTER_LINEAR : INTER_LINEAR_EXACT); |
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return dst; |
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} |
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TEST(DomainTransformTest, SplatSurfaceAccuracy) |
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{ |
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static int dtModes[] = {DTF_NC, DTF_RF, DTF_IC}; |
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RNG rnd(0); |
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for (int i = 0; i < 15; i++) |
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{ |
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Size sz(rnd.uniform(512, 1024), rnd.uniform(512, 1024)); |
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int guideCn = rnd.uniform(1, 4); |
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Mat guide(sz, CV_MAKE_TYPE(CV_32F, guideCn)); |
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randu(guide, 0, 255); |
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Scalar surfaceValue; |
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int srcCn = rnd.uniform(1, 4); |
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rnd.fill(surfaceValue, RNG::UNIFORM, 0, 255); |
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Mat src(sz, CV_MAKE_TYPE(CV_8U, srcCn), surfaceValue); |
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double sigma_s = rnd.uniform(1.0, 100.0); |
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double sigma_r = rnd.uniform(1.0, 100.0); |
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int mode = dtModes[i%3]; |
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Mat res; |
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dtFilter(guide, src, res, sigma_s, sigma_r, mode, 1); |
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double normL1 = cvtest::norm(src, res, NORM_L1)/src.total()/src.channels(); |
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EXPECT_LE(normL1, 1.0/64); |
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} |
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} |
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typedef TestWithParam<DTParams> DomainTransformTest; |
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TEST_P(DomainTransformTest, MultiThreadReproducibility) |
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{ |
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if (cv::getNumberOfCPUs() == 1) |
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return; |
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double MAX_DIF = 1.0; |
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double MAX_MEAN_DIF = 1.0 / 256.0; |
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int loopsCount = 2; |
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RNG rng(0); |
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DTParams params = GetParam(); |
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Size size = get<0>(params); |
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int mode = get<1>(params); |
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int guideType = get<2>(params); |
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int srcType = get<3>(params); |
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Mat original = imread(getOpenCVExtraDir() + "cv/edgefilter/statue.png"); |
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Mat guide = convertTypeAndSize(original, guideType, size); |
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Mat src = convertTypeAndSize(original, srcType, size); |
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int nThreads = cv::getNumThreads(); |
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if (nThreads == 1) |
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throw SkipTestException("Single thread environment"); |
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for (int iter = 0; iter <= loopsCount; iter++) |
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{ |
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double ss = rng.uniform(0.0, 100.0); |
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double sc = rng.uniform(0.0, 100.0); |
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cv::setNumThreads(nThreads); |
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Mat resMultithread; |
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dtFilter(guide, src, resMultithread, ss, sc, mode); |
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cv::setNumThreads(1); |
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Mat resSingleThread; |
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dtFilter(guide, src, resSingleThread, ss, sc, mode); |
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EXPECT_LE(cv::norm(resSingleThread, resMultithread, NORM_INF), MAX_DIF); |
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EXPECT_LE(cv::norm(resSingleThread, resMultithread, NORM_L1), MAX_MEAN_DIF*src.total()); |
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} |
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} |
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INSTANTIATE_TEST_CASE_P(FullSet, DomainTransformTest, |
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Combine(Values(szODD, szQVGA), ModeType::all(), SupportedTypes::all(), SupportedTypes::all()) |
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); |
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template<typename SrcVec> |
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Mat getChessMat1px(Size sz, double whiteIntensity = 255) |
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{ |
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typedef typename DataType<SrcVec>::channel_type SrcType; |
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Mat dst(sz, traits::Type<SrcVec>::value); |
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SrcVec black = SrcVec::all(0); |
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SrcVec white = SrcVec::all((SrcType)whiteIntensity); |
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for (int i = 0; i < dst.rows; i++) |
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for (int j = 0; j < dst.cols; j++) |
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dst.at<SrcVec>(i, j) = ((i + j) % 2) ? white : black; |
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return dst; |
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} |
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TEST(DomainTransformTest, ChessBoard_NC_accuracy) |
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{ |
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RNG rng(0); |
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double MAX_DIF = 1; |
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Size sz = szVGA; |
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double ss = 80; |
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double sc = 60; |
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Mat srcb = randomMat(rng, sz, CV_8UC4, 0, 255, true); |
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Mat srcf = randomMat(rng, sz, CV_32FC4, 0, 255, true); |
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Mat chessb = getChessMat1px<Vec3b>(sz); |
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Mat dstb, dstf; |
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dtFilter(chessb, srcb.clone(), dstb, ss, sc, DTF_NC); |
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dtFilter(chessb, srcf.clone(), dstf, ss, sc, DTF_NC); |
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EXPECT_LE(cv::norm(srcb, dstb, NORM_INF), MAX_DIF); |
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EXPECT_LE(cv::norm(srcf, dstf, NORM_INF), MAX_DIF); |
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} |
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TEST(DomainTransformTest, BoxFilter_NC_accuracy) |
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{ |
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double MAX_DIF = 1; |
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int radius = 5; |
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double sc = 1.0; |
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double ss = 1.01*radius / sqrt(3.0); |
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Mat src = imread(getOpenCVExtraDir() + "cv/edgefilter/statue.png"); |
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ASSERT_TRUE(!src.empty()); |
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Mat1b guide(src.size(), 200); |
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Mat res_dt, res_box; |
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blur(src, res_box, Size(2 * radius + 1, 2 * radius + 1)); |
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dtFilter(guide, src, res_dt, ss, sc, DTF_NC, 1); |
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EXPECT_LE(cv::norm(res_dt, res_box, NORM_L2), MAX_DIF*src.total()); |
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} |
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TEST(DomainTransformTest, AuthorReferenceAccuracy) |
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{ |
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string dir = getOpenCVExtraDir() + "cv/edgefilter"; |
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double ss = 30; |
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double sc = 0.2 * 255; |
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Mat src = imread(dir + "/statue.png"); |
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Mat ref_NC = imread(dir + "/dt/authors_statue_NC_ss30_sc0.2.png"); |
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Mat ref_IC = imread(dir + "/dt/authors_statue_IC_ss30_sc0.2.png"); |
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Mat ref_RF = imread(dir + "/dt/authors_statue_RF_ss30_sc0.2.png"); |
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ASSERT_FALSE(src.empty()); |
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ASSERT_FALSE(ref_NC.empty()); |
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ASSERT_FALSE(ref_IC.empty()); |
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ASSERT_FALSE(ref_RF.empty()); |
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Mat res_NC, res_IC, res_RF; |
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dtFilter(src, src, res_NC, ss, sc, DTF_NC); |
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dtFilter(src, src, res_IC, ss, sc, DTF_IC); |
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dtFilter(src, src, res_RF, ss, sc, DTF_RF); |
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double totalMaxError = 1.0/64.0*src.total(); |
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EXPECT_LE(cvtest::norm(res_NC, ref_NC, NORM_L2), totalMaxError); |
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EXPECT_LE(cvtest::norm(res_NC, ref_NC, NORM_INF), 1); |
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EXPECT_LE(cvtest::norm(res_IC, ref_IC, NORM_L2), totalMaxError); |
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EXPECT_LE(cvtest::norm(res_IC, ref_IC, NORM_INF), 1); |
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EXPECT_LE(cvtest::norm(res_RF, ref_RF, NORM_L2), totalMaxError); |
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EXPECT_LE(cvtest::norm(res_IC, ref_IC, NORM_INF), 1); |
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} |
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}} // namespace
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