/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2013, OpenCV Foundation, all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "test_precomp.hpp" namespace opencv_test { namespace { void loadImage(string path, Mat &img) { img = imread(path, -1); ASSERT_FALSE(img.empty()) << "Could not load input image " << path; } void checkEqual(Mat img0, Mat img1, double threshold, const string& name) { double max = 1.0; minMaxLoc(abs(img0 - img1), NULL, &max); ASSERT_FALSE(max > threshold) << "max=" << max << " threshold=" << threshold << " method=" << name; } static vector<float> DEFAULT_VECTOR; void loadExposureSeq(String path, vector<Mat>& images, vector<float>& times = DEFAULT_VECTOR) { std::ifstream list_file((path + "list.txt").c_str()); ASSERT_TRUE(list_file.is_open()); string name; float val; while(list_file >> name >> val) { Mat img = imread(path + name); ASSERT_FALSE(img.empty()) << "Could not load input image " << path + name; images.push_back(img); times.push_back(1 / val); } list_file.close(); } void loadResponseCSV(String path, Mat& response) { response = Mat(256, 1, CV_32FC3); std::ifstream resp_file(path.c_str()); for(int i = 0; i < 256; i++) { for(int c = 0; c < 3; c++) { resp_file >> response.at<Vec3f>(i)[c]; resp_file.ignore(1); } } resp_file.close(); } TEST(Photo_Tonemap, regression) { string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/tonemap/"; Mat img, expected, result; loadImage(test_path + "image.hdr", img); float gamma = 2.2f; Ptr<Tonemap> linear = createTonemap(gamma); linear->process(img, result); loadImage(test_path + "linear.png", expected); result.convertTo(result, CV_8UC3, 255); checkEqual(result, expected, 3, "Simple"); Ptr<TonemapDrago> drago = createTonemapDrago(gamma); drago->process(img, result); loadImage(test_path + "drago.png", expected); result.convertTo(result, CV_8UC3, 255); checkEqual(result, expected, 3, "Drago"); Ptr<TonemapReinhard> reinhard = createTonemapReinhard(gamma); reinhard->process(img, result); loadImage(test_path + "reinhard.png", expected); result.convertTo(result, CV_8UC3, 255); checkEqual(result, expected, 3, "Reinhard"); Ptr<TonemapMantiuk> mantiuk = createTonemapMantiuk(gamma); mantiuk->process(img, result); loadImage(test_path + "mantiuk.png", expected); result.convertTo(result, CV_8UC3, 255); checkEqual(result, expected, 3, "Mantiuk"); } TEST(Photo_AlignMTB, regression) { const int TESTS_COUNT = 100; string folder = string(cvtest::TS::ptr()->get_data_path()) + "shared/"; string file_name = folder + "lena.png"; Mat img; loadImage(file_name, img); cvtColor(img, img, COLOR_RGB2GRAY); int max_bits = 5; int max_shift = 32; srand(static_cast<unsigned>(time(0))); int errors = 0; Ptr<AlignMTB> align = createAlignMTB(max_bits); RNG rng = theRNG(); for(int i = 0; i < TESTS_COUNT; i++) { Point shift(rng.uniform(0, max_shift), rng.uniform(0, max_shift)); Mat res; align->shiftMat(img, res, shift); Point calc = align->calculateShift(img, res); errors += (calc != -shift); } ASSERT_TRUE(errors < 5) << errors << " errors"; } TEST(Photo_MergeMertens, regression) { string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/"; vector<Mat> images; loadExposureSeq((test_path + "exposures/").c_str() , images); Ptr<MergeMertens> merge = createMergeMertens(); Mat result, expected; loadImage(test_path + "merge/mertens.png", expected); merge->process(images, result); result.convertTo(result, CV_8UC3, 255); checkEqual(expected, result, 3, "Mertens"); Mat uniform(100, 100, CV_8UC3); uniform = Scalar(0, 255, 0); images.clear(); images.push_back(uniform); merge->process(images, result); result.convertTo(result, CV_8UC3, 255); checkEqual(uniform, result, 1e-2f, "Mertens"); } TEST(Photo_MergeDebevec, regression) { string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/"; vector<Mat> images; vector<float> times; Mat response; loadExposureSeq(test_path + "exposures/", images, times); loadResponseCSV(test_path + "exposures/response.csv", response); Ptr<MergeDebevec> merge = createMergeDebevec(); Mat result, expected; loadImage(test_path + "merge/debevec.hdr", expected); merge->process(images, result, times, response); Ptr<Tonemap> map = createTonemap(); map->process(result, result); map->process(expected, expected); checkEqual(expected, result, 1e-2f, "Debevec"); } TEST(Photo_MergeRobertson, regression) { string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/"; vector<Mat> images; vector<float> times; loadExposureSeq(test_path + "exposures/", images, times); Ptr<MergeRobertson> merge = createMergeRobertson(); Mat result, expected; loadImage(test_path + "merge/robertson.hdr", expected); merge->process(images, result, times); const float eps = 6.f; checkEqual(expected, result, eps, "MergeRobertson"); } TEST(Photo_CalibrateDebevec, regression) { string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/"; vector<Mat> images; vector<float> times; Mat response, expected; loadExposureSeq(test_path + "exposures/", images, times); loadResponseCSV(test_path + "calibrate/debevec.csv", expected); Ptr<CalibrateDebevec> calibrate = createCalibrateDebevec(); calibrate->process(images, response, times); Mat diff = abs(response - expected); diff = diff.mul(1.0f / response); double max; minMaxLoc(diff, NULL, &max); #if defined(__arm__) || defined(__aarch64__) ASSERT_LT(max, 0.131); #else ASSERT_LT(max, 0.1); #endif } TEST(Photo_CalibrateRobertson, regression) { string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/"; vector<Mat> images; vector<float> times; Mat response, expected; loadExposureSeq(test_path + "exposures/", images, times); loadResponseCSV(test_path + "calibrate/robertson.csv", expected); Ptr<CalibrateRobertson> calibrate = createCalibrateRobertson(); calibrate->process(images, response, times); checkEqual(expected, response, 1e-1f, "CalibrateRobertson"); } TEST(Photo_CalibrateRobertson, bug_18180) { vector<Mat> images; vector<cv::String> fn; string test_path = cvtest::TS::ptr()->get_data_path() + "hdr/exposures/bug_18180/"; for(int i = 1; i <= 4; ++i) images.push_back(imread(test_path + std::to_string(i) + ".jpg")); vector<float> times {15.0f, 2.5f, 0.25f, 0.33f}; Mat response, expected; Ptr<CalibrateRobertson> calibrate = createCalibrateRobertson(2, 0.01f); calibrate->process(images, response, times); Mat response_no_nans = response.clone(); patchNaNs(response_no_nans); // since there should be no NaNs, original response vs. response with NaNs patched should be identical EXPECT_EQ(0.0, cv::norm(response, response_no_nans, NORM_L2)); } }} // namespace