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Gursimar Singh
425d5cfcf0
Adding color correction module to photo module from opencv_contrib #27051 This PR moved color correction module from opencv_contrib to main repo inside photo module. ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
302 lines
12 KiB
C++
302 lines
12 KiB
C++
// 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
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{
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namespace
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{
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Mat s = (Mat_<Vec3d>(24, 1) <<
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Vec3d(214.11, 98.67, 37.97),
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Vec3d(231.94, 153.1, 85.27),
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Vec3d(204.08, 143.71, 78.46),
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Vec3d(190.58, 122.99, 30.84),
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Vec3d(230.93, 148.46, 100.84),
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Vec3d(228.64, 206.97, 97.5),
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Vec3d(229.09, 137.07, 55.29),
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Vec3d(189.21, 111.22, 92.66),
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Vec3d(223.5, 96.42, 75.45),
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Vec3d(201.82, 69.71, 50.9),
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Vec3d(240.52, 196.47, 59.3),
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Vec3d(235.73, 172.13, 54.),
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Vec3d(131.6, 75.04, 68.86),
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Vec3d(189.04, 170.43, 42.05),
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Vec3d(222.23, 74., 71.95),
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Vec3d(241.01, 199.1, 61.15),
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Vec3d(224.99, 101.4, 100.24),
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Vec3d(174.58, 152.63, 91.52),
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Vec3d(248.06, 227.69, 140.5),
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Vec3d(241.15, 201.38, 115.58),
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Vec3d(236.49, 175.87, 88.86),
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Vec3d(212.19, 133.49, 54.79),
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Vec3d(181.17, 102.94, 36.18),
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Vec3d(115.1, 53.77, 15.23));
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TEST(Photo_ColorCorrection, test_model)
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{
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cv::ccm::ColorCorrectionModel model(s / 255, cv::ccm::COLORCHECKER_MACBETH);
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Mat colorCorrectionMat = model.compute();
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Mat srcRgbl = (Mat_<Vec3d>(24, 1) <<
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Vec3d(0.68078957, 0.12382801, 0.01514889),
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Vec3d(0.81177942, 0.32550452, 0.089818),
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Vec3d(0.61259378, 0.2831933, 0.07478902),
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Vec3d(0.52696493, 0.20105976, 0.00958657),
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Vec3d(0.80402284, 0.30419523, 0.12989841),
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Vec3d(0.78658646, 0.63184111, 0.12062068),
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Vec3d(0.78999637, 0.25520249, 0.03462853),
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Vec3d(0.51866697, 0.16114393, 0.1078387),
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Vec3d(0.74820768, 0.11770076, 0.06862177),
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Vec3d(0.59776825, 0.05765816, 0.02886627),
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Vec3d(0.8793145, 0.56346033, 0.0403954),
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Vec3d(0.84124847, 0.42120746, 0.03287592),
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Vec3d(0.23333214, 0.06780408, 0.05612276),
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Vec3d(0.5176423, 0.41210976, 0.01896255),
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Vec3d(0.73888613, 0.06575388, 0.06181293),
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Vec3d(0.88326036, 0.58018751, 0.04321991),
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Vec3d(0.75922531, 0.13149072, 0.1282041),
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Vec3d(0.4345097, 0.32331019, 0.10494139),
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Vec3d(0.94110142, 0.77941419, 0.26946323),
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Vec3d(0.88438952, 0.5949049 , 0.17536928),
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Vec3d(0.84722687, 0.44160449, 0.09834799),
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Vec3d(0.66743106, 0.24076803, 0.03394333),
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Vec3d(0.47141286, 0.13592419, 0.01362205),
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Vec3d(0.17377101, 0.03256864, 0.00203026));
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EXPECT_MAT_NEAR(srcRgbl, model.getSrcLinearRGB(), 1e-4);
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Mat dstRgbl = (Mat_<Vec3d>(24, 1) <<
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Vec3d(0.17303173, 0.08211037, 0.05672686),
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Vec3d(0.56832031, 0.29269488, 0.21835529),
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Vec3d(0.10365019, 0.19588357, 0.33140475),
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Vec3d(0.10159676, 0.14892193, 0.05188294),
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Vec3d(0.22159627, 0.21584476, 0.43461196),
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Vec3d(0.10806379, 0.51437196, 0.41264213),
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Vec3d(0.74736423, 0.20062878, 0.02807988),
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Vec3d(0.05757947, 0.10516793, 0.40296109),
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Vec3d(0.56676218, 0.08424805, 0.11969461),
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Vec3d(0.11099515, 0.04230796, 0.14292554),
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Vec3d(0.34546869, 0.50872001, 0.04944204),
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Vec3d(0.79461323, 0.35942459, 0.02051968),
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Vec3d(0.01710416, 0.05022043, 0.29220674),
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Vec3d(0.05598012, 0.30021149, 0.06871162),
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Vec3d(0.45585457, 0.03033727, 0.04085654),
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Vec3d(0.85737614, 0.56757335, 0.0068503),
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Vec3d(0.53348585, 0.08861148, 0.30750446),
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Vec3d(-0.0374061, 0.24699498, 0.40041217),
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Vec3d(0.91262695, 0.91493909, 0.89367049),
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Vec3d(0.57981916, 0.59200418, 0.59328881),
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Vec3d(0.35490581, 0.36544831, 0.36755375),
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Vec3d(0.19007357, 0.19186587, 0.19308397),
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Vec3d(0.08529188, 0.08887994, 0.09257601),
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Vec3d(0.0303193, 0.03113818, 0.03274845));
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EXPECT_MAT_NEAR(dstRgbl, model.getRefLinearRGB(), 1e-4);
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Mat mask = Mat::ones(24, 1, CV_8U);
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EXPECT_MAT_NEAR(model.getMask(), mask, 0.0);
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Mat refColorMat = (Mat_<double>(3, 3) <<
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0.37406520, 0.02066507, 0.05804047,
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0.12719672, 0.77389268, -0.01569404,
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-0.27627010, 0.00603427, 2.74272981);
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EXPECT_MAT_NEAR(colorCorrectionMat, refColorMat, 1e-4);
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}
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TEST(Photo_ColorCorrection, test_model_with_color_patches_mask)
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{
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Mat dstData = (Mat_<Vec3d>(24, 1) <<
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Vec3d(37.986, 13.555, 14.059),
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Vec3d(65.711, 18.13, 17.81),
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Vec3d(49.927, -4.88, -21.925),
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Vec3d(43.139, -13.095, 21.905),
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Vec3d(55.112, 8.843999999999999, -25.399),
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Vec3d(70.71899999999999, -33.397, -0.199),
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Vec3d(62.661, 36.067, 57.096),
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Vec3d(40.02, 10.41, -45.964),
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Vec3d(51.124, 48.239, 16.248),
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Vec3d(30.325, 22.976, -21.587),
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Vec3d(72.532, -23.709, 57.255),
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Vec3d(71.941, 19.363, 67.857),
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Vec3d(28.778, 14.179, -50.297),
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Vec3d(55.261, -38.342, 31.37),
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Vec3d(42.101, 53.378, 28.19),
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Vec3d(81.733, 4.039, 79.819),
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Vec3d(51.935, 49.986, -14.574),
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Vec3d(51.038, -28.631, -28.638),
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Vec3d(96.539, -0.425, 1.186),
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Vec3d(81.25700000000001, -0.638, -0.335),
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Vec3d(66.76600000000001, -0.734, -0.504),
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Vec3d(50.867, -0.153, -0.27),
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Vec3d(35.656, -0.421, -1.231),
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Vec3d(20.461, -0.079, -0.973)
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);
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Mat coloredMask = (Mat_<uchar>(24, 1) <<
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1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1, 1,
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0, 0, 0, 0, 0, 0);
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cv::ccm::ColorCorrectionModel model(s/255, dstData, cv::ccm::COLOR_SPACE_LAB_D50_2, coloredMask);
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Mat colorCorrectionMat = model.compute();
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Mat refColorMat = (Mat_<double>(3, 3) <<
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0.37406520, 0.02066507, 0.05804047,
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0.12719672, 0.77389268, -0.01569404,
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-0.27627010, 0.00603427, 2.74272981);
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EXPECT_MAT_NEAR(colorCorrectionMat, refColorMat, 1e-4);
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}
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TEST(Photo_ColorCorrection, test_masks_weights_1)
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{
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Mat weightsList_ = (Mat_<double>(24, 1) <<
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1.1, 0, 0, 1.2, 0, 0,
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1.3, 0, 0, 1.4, 0, 0,
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0.5, 0, 0, 0.6, 0, 0,
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0.7, 0, 0, 0.8, 0, 0);
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cv::ccm::ColorCorrectionModel model1(s / 255,cv::ccm::COLORCHECKER_MACBETH);
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model1.setColorSpace(cv::ccm::COLOR_SPACE_SRGB);
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model1.setCcmType(cv::ccm::CCM_LINEAR);
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model1.setDistance(cv::ccm::DISTANCE_CIE2000);
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model1.setLinearization(cv::ccm::LINEARIZATION_GAMMA);
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model1.setLinearizationGamma(2.2);
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model1.setLinearizationDegree(3);
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model1.setSaturatedThreshold(0, 0.98);
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model1.setWeightsList(weightsList_);
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model1.setWeightCoeff(1.5);
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Mat colorCorrectionMat = model1.compute();
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Mat weights = (Mat_<double>(8, 1) <<
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1.15789474, 1.26315789, 1.36842105, 1.47368421,
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0.52631579, 0.63157895, 0.73684211, 0.84210526);
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EXPECT_MAT_NEAR(model1.getWeights(), weights, 1e-4);
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Mat mask = (Mat_<uchar>(24, 1) <<
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true, false, false, true, false, false,
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true, false, false, true, false, false,
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true, false, false, true, false, false,
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true, false, false, true, false, false);
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EXPECT_MAT_NEAR(model1.getMask(), mask, 0.0);
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}
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TEST(Photo_ColorCorrection, test_masks_weights_2)
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{
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cv::ccm::ColorCorrectionModel model2(s / 255, cv::ccm::COLORCHECKER_MACBETH);
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model2.setCcmType(cv::ccm::CCM_LINEAR);
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model2.setDistance(cv::ccm::DISTANCE_CIE2000);
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model2.setLinearization(cv::ccm::LINEARIZATION_GAMMA);
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model2.setLinearizationGamma(2.2);
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model2.setLinearizationDegree(3);
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model2.setSaturatedThreshold(0.05, 0.93);
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model2.setWeightsList(Mat());
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model2.setWeightCoeff(1.5);
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Mat colorCorrectionMat = model2.compute();
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Mat weights = (Mat_<double>(20, 1) <<
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0.65554256, 1.49454705, 1.00499244, 0.79735434, 1.16327759,
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1.68623868, 1.37973155, 0.73213388, 1.0169629, 0.47430246,
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1.70312161, 0.45414218, 1.15910007, 0.7540434, 1.05049802,
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1.04551645, 1.54082353, 1.02453421, 0.6015915, 0.26154558);
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EXPECT_MAT_NEAR(model2.getWeights(), weights, 1e-4);
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Mat mask = (Mat_<uchar>(24, 1) <<
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true, true, true, true, true, true,
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true, true, true, true, false, true,
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true, true, true, false, true, true,
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false, false, true, true, true, true);
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EXPECT_MAT_NEAR(model2.getMask(), mask, 0.0);
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}
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TEST(Photo_ColorCorrection, compute_color_correction_matrix)
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{
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// read gold chartsRGB
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string path = cvtest::findDataFile("mcc/mcc_ccm_test.yml");
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FileStorage fs(path, FileStorage::READ);
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Mat chartsRGB;
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FileNode node = fs["chartsRGB"];
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node >> chartsRGB;
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ASSERT_FALSE(chartsRGB.empty()) << "chartsRGB is empty after loading from: " << path;
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// compute CCM
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cv::ccm::ColorCorrectionModel model(chartsRGB.col(1).clone().reshape(3, chartsRGB.rows/3) / 255., cv::ccm::COLORCHECKER_MACBETH);
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Mat colorCorrectionMat = model.compute();
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// read gold CCM
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node = fs["ccm"];
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ASSERT_FALSE(node.empty());
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Mat gold_ccm;
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node >> gold_ccm;
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fs.release();
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// check CCM
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EXPECT_MAT_NEAR(gold_ccm, colorCorrectionMat, 1e-8);
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const double gold_loss = 4.6386569120323129;
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// check loss
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const double loss = model.getLoss();
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EXPECT_NEAR(gold_loss, loss, 1e-8);
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}
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TEST(Photo_ColorCorrection, correct_image)
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{
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string path = cvtest::findDataFile("mcc/mcc_ccm_test.jpg");
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Mat img = imread(path, IMREAD_COLOR);
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// read gold calibrate img
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path = cvtest::findDataFile("mcc/mcc_ccm_test_res.png");
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Mat gold_img = imread(path);
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// read gold chartsRGB
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path = cvtest::findDataFile("mcc/mcc_ccm_test.yml");
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FileStorage fs(path, FileStorage::READ);
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Mat chartsRGB;
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FileNode node = fs["chartsRGB"];
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node >> chartsRGB;
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fs.release();
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ASSERT_FALSE(chartsRGB.empty()) << "chartsRGB is empty after loading from: " << path;
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// compute CCM
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cv::ccm::ColorCorrectionModel model(chartsRGB.col(1).clone().reshape(3, chartsRGB.rows/3) / 255., cv::ccm::COLORCHECKER_MACBETH);
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Mat colorCorrectionMat = model.compute();
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// compute calibrate image
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Mat calibratedImage;
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model.correctImage(img, calibratedImage);
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// check calibrated image
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EXPECT_MAT_NEAR(gold_img, calibratedImage, 0.1);
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}
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TEST(Photo_ColorCorrection, serialization)
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{
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auto path = cvtest::findDataFile("mcc/mcc_ccm_test.yml");
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FileStorage fs(path, FileStorage::READ);
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Mat chartsRGB;
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FileNode node = fs["chartsRGB"];
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node >> chartsRGB;
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fs.release();
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ASSERT_FALSE(chartsRGB.empty()) << "chartsRGB is empty after loading from: " << path;
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// compute CCM
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cv::ccm::ColorCorrectionModel model(chartsRGB.col(1).clone().reshape(3, chartsRGB.rows/3) / 255., cv::ccm::COLORCHECKER_MACBETH);
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Mat colorCorrectionMat = model.compute();
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//--- 1. write model to memory -------------------------------------------
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FileStorage fs1("", FileStorage::WRITE | FileStorage::MEMORY);
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model.write(fs1);
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std::string yaml1 = fs1.releaseAndGetString();
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//--- 2. read model back from memory -------------------------------------
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cv::ccm::ColorCorrectionModel model1;
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FileStorage fs2(yaml1, FileStorage::READ | FileStorage::MEMORY);
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model1.read(fs2["ColorCorrectionModel"]);
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fs2.release();
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//--- 3. write the re-loaded model again to memory -----------------------
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FileStorage fs3("", FileStorage::WRITE | FileStorage::MEMORY);
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model1.write(fs3);
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std::string yaml2 = fs3.releaseAndGetString();
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//--- 4. compare the two YAML strings ------------------------------------
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EXPECT_EQ(yaml1, yaml2);
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}
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} // namespace
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} // namespace opencv_test
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