Tonemap as 3.0 algorithm
This commit is contained in:
Fedor Morozov
@@ -61,98 +61,159 @@ void checkEqual(Mat img0, Mat img1, double threshold)
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ASSERT_FALSE(max > threshold);
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}
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TEST(Photo_HdrFusion, regression)
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{
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string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
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string fuse_path = test_path + "fusion/";
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vector<float> times;
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vector<Mat> images;
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ifstream list_file(fuse_path + "list.txt");
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ASSERT_TRUE(list_file.is_open());
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string name;
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float val;
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while(list_file >> name >> val) {
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Mat img = imread(fuse_path + name);
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ASSERT_FALSE(img.empty()) << "Could not load input image " << fuse_path + name;
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images.push_back(img);
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times.push_back(1 / val);
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}
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list_file.close();
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Mat response, expected(256, 3, CV_32F);
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ifstream resp_file(test_path + "response.csv");
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for(int i = 0; i < 256; i++) {
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for(int channel = 0; channel < 3; channel++) {
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resp_file >> expected.at<float>(i, channel);
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resp_file.ignore(1);
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}
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}
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resp_file.close();
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estimateResponse(images, times, response);
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checkEqual(expected, response, 0.001);
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Mat result;
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loadImage(test_path + "no_calibration.hdr", expected);
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makeHDR(images, times, result);
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checkEqual(expected, result, 0.01);
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loadImage(test_path + "rle.hdr", expected);
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makeHDR(images, times, result, response);
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checkEqual(expected, result, 0.01);
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loadImage(test_path + "exp_fusion.png", expected);
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exposureFusion(images, result);
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result.convertTo(result, CV_8UC3, 255);
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checkEqual(expected, result, 0);
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}
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TEST(Photo_Tonemap, regression)
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{
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string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/tonemap/";
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Mat img;
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loadImage(test_path + "../rle.hdr", img);
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ifstream list_file(test_path + "list.txt");
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ASSERT_TRUE(list_file.is_open());
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string name;
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while(list_file >> name) {
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Mat expected = imread(test_path + name + ".png");
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ASSERT_FALSE(img.empty()) << "Could not load input image " << test_path + name + ".png";
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Ptr<Tonemap> mapper = Tonemap::create(name);
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ASSERT_FALSE(mapper.empty()) << "Could not find mapper " << name;
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Mat result;
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mapper->process(img, result);
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result.convertTo(result, CV_8UC3, 255);
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checkEqual(expected, result, 0);
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}
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list_file.close();
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}
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TEST(Photo_Align, regression)
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{
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const int TESTS_COUNT = 100;
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string folder = string(cvtest::TS::ptr()->get_data_path()) + "shared/";
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string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
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string file_name = folder + "lena.png";
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Mat img = imread(file_name);
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ASSERT_FALSE(img.empty()) << "Could not load input image " << file_name;
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cvtColor(img, img, COLOR_RGB2GRAY);
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Mat img, expected, result;
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loadImage(test_path + "rle.hdr", img);
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float gamma = 2.2f;
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test_path += "tonemap/";
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Ptr<TonemapLinear> linear = createTonemapLinear(gamma);
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linear->process(img, result);
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loadImage(test_path + "linear.png", expected);
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result.convertTo(result, CV_8UC3, 255);
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checkEqual(result, expected, 0);
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int max_bits = 5;
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int max_shift = 32;
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srand(static_cast<unsigned>(time(0)));
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int errors = 0;
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Ptr<TonemapDrago> drago = createTonemapDrago(gamma);
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drago->process(img, result);
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loadImage(test_path + "drago.png", expected);
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result.convertTo(result, CV_8UC3, 255);
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checkEqual(result, expected, 0);
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for(int i = 0; i < TESTS_COUNT; i++) {
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Point shift(rand() % max_shift, rand() % max_shift);
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Mat res;
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shiftMat(img, shift, res);
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Point calc = getExpShift(img, res, max_bits);
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errors += (calc != -shift);
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}
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ASSERT_TRUE(errors < 5);
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Ptr<TonemapDurand> durand = createTonemapDurand(gamma);
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durand->process(img, result);
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loadImage(test_path + "durand.png", expected);
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result.convertTo(result, CV_8UC3, 255);
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checkEqual(result, expected, 0);
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Ptr<TonemapReinhardDevlin> reinhard_devlin = createTonemapReinhardDevlin(gamma);
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reinhard_devlin->process(img, result);
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loadImage(test_path + "reinhard_devlin.png", expected);
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result.convertTo(result, CV_8UC3, 255);
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checkEqual(result, expected, 0);
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}
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//void loadExposureSeq(String fuse_path, vector<Mat>& images, vector<float>& times = vector<float>())
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//{
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// ifstream list_file(fuse_path + "list.txt");
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// ASSERT_TRUE(list_file.is_open());
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// string name;
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// float val;
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// while(list_file >> name >> val) {
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// Mat img = imread(fuse_path + name);
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// ASSERT_FALSE(img.empty()) << "Could not load input image " << fuse_path + name;
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// images.push_back(img);
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// times.push_back(1 / val);
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// }
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// list_file.close();
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//}
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////
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////TEST(Photo_MergeMertens, regression)
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////{
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//// string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
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//// string fuse_path = test_path + "fusion/";
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////
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//// vector<Mat> images;
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//// loadExposureSeq(fuse_path, images);
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////
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//// MergeMertens merge;
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////
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//// Mat result, expected;
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//// loadImage(test_path + "exp_fusion.png", expected);
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//// merge.process(images, result);
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//// result.convertTo(result, CV_8UC3, 255);
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//// checkEqual(expected, result, 0);
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////}
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//
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//TEST(Photo_Debevec, regression)
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//{
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// string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
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// string fuse_path = test_path + "fusion/";
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//
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// vector<float> times;
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// vector<Mat> images;
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//
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// loadExposureSeq(fuse_path, images, times);
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//
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// Mat response, expected(256, 3, CV_32F);
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// ifstream resp_file(test_path + "response.csv");
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// for(int i = 0; i < 256; i++) {
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// for(int channel = 0; channel < 3; channel++) {
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// resp_file >> expected.at<float>(i, channel);
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// resp_file.ignore(1);
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// }
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// }
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// resp_file.close();
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//
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// CalibrateDebevec calib;
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// MergeDebevec merge;
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//
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// //calib.process(images, response, times);
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// //checkEqual(expected, response, 0.001);
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// //
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// Mat result;
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// loadImage(test_path + "no_calibration.hdr", expected);
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// merge.process(images, result, times);
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// checkEqual(expected, result, 0.01);
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//
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// //loadImage(test_path + "rle.hdr", expected);
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// //merge.process(images, result, times, response);
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// //checkEqual(expected, result, 0.01);
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//}
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//
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//TEST(Photo_Tonemap, regression)
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//{
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// initModule_photo();
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// string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/tonemap/";
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// Mat img;
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// loadImage(test_path + "../rle.hdr", img);
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//
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// vector<String> algorithms;
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// Algorithm::getList(algorithms);
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// for(size_t i = 0; i < algorithms.size(); i++) {
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// String str = algorithms[i];
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// size_t dot = str.find('.');
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// if(dot != String::npos && str.substr(0, dot).compare("Tonemap") == 0) {
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// String algo_name = str.substr(dot + 1, str.size());
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// Mat expected;
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// loadImage(test_path + algo_name.toLowerCase() + ".png", expected);
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// Ptr<Tonemap> mapper = Tonemap::create(algo_name);
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// ASSERT_FALSE(mapper.empty()) << algo_name;
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// Mat result;
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// mapper->process(img, result);
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// result.convertTo(result, CV_8UC3, 255);
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// checkEqual(expected, result, 0);
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// }
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// }
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////}
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////
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////TEST(Photo_AlignMTB, regression)
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////{
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//// const int TESTS_COUNT = 100;
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//// string folder = string(cvtest::TS::ptr()->get_data_path()) + "shared/";
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////
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//// string file_name = folder + "lena.png";
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//// Mat img = imread(file_name);
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//// ASSERT_FALSE(img.empty()) << "Could not load input image " << file_name;
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//// cvtColor(img, img, COLOR_RGB2GRAY);
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////
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//// int max_bits = 5;
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//// int max_shift = 32;
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//// srand(static_cast<unsigned>(time(0)));
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//// int errors = 0;
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////
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//// AlignMTB align(max_bits);
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////
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//// for(int i = 0; i < TESTS_COUNT; i++) {
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//// Point shift(rand() % max_shift, rand() % max_shift);
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//// Mat res;
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//// align.shiftMat(img, shift, res);
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//// Point calc = align.getExpShift(img, res);
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//// errors += (calc != -shift);
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//// }
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//// ASSERT_TRUE(errors < 5);
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////}
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