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Open Source Computer Vision Library
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249 lines
8.1 KiB
249 lines
8.1 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#include "test_precomp.hpp" |
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#include <string> |
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#include <algorithm> |
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#include <fstream> |
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using namespace cv; |
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using namespace std; |
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void loadImage(string path, Mat &img) |
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{ |
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img = imread(path, -1); |
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ASSERT_FALSE(img.empty()) << "Could not load input image " << path; |
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} |
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void checkEqual(Mat img0, Mat img1, double threshold) |
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{ |
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double max = 1.0; |
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minMaxLoc(abs(img0 - img1), NULL, &max); |
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ASSERT_FALSE(max > threshold) << max; |
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} |
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static vector<float> DEFAULT_VECTOR; |
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void loadExposureSeq(String path, vector<Mat>& images, vector<float>& times = DEFAULT_VECTOR) |
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{ |
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ifstream list_file((path + "list.txt").c_str()); |
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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(path + name); |
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ASSERT_FALSE(img.empty()) << "Could not load input image " << 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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void loadResponseCSV(String path, Mat& response) |
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{ |
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response = Mat(256, 1, CV_32FC3); |
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ifstream resp_file(path.c_str()); |
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for(int i = 0; i < 256; i++) { |
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for(int c = 0; c < 3; c++) { |
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resp_file >> response.at<Vec3f>(i)[c]; |
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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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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, expected, result; |
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loadImage(test_path + "image.hdr", img); |
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float gamma = 2.2f; |
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Ptr<Tonemap> linear = createTonemap(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, 3); |
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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, 3); |
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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, 3); |
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Ptr<TonemapReinhard> reinhard = createTonemapReinhard(gamma); |
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reinhard->process(img, result); |
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loadImage(test_path + "reinhard.png", expected); |
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result.convertTo(result, CV_8UC3, 255); |
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checkEqual(result, expected, 3); |
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Ptr<TonemapMantiuk> mantiuk = createTonemapMantiuk(gamma); |
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mantiuk->process(img, result); |
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loadImage(test_path + "mantiuk.png", expected); |
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result.convertTo(result, CV_8UC3, 255); |
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checkEqual(result, expected, 3); |
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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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string file_name = folder + "lena.png"; |
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Mat img; |
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loadImage(file_name, img); |
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cvtColor(img, img, COLOR_RGB2GRAY); |
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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<AlignMTB> align = createAlignMTB(max_bits); |
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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, res, shift); |
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Point calc = align->calculateShift(img, res); |
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errors += (calc != -shift); |
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} |
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ASSERT_TRUE(errors < 5) << errors << " errors"; |
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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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vector<Mat> images; |
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loadExposureSeq((test_path + "exposures/").c_str() , images); |
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Ptr<MergeMertens> merge = createMergeMertens(); |
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Mat result, expected; |
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loadImage(test_path + "merge/mertens.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, 3); |
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} |
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TEST(Photo_MergeDebevec, regression) |
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{ |
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string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/"; |
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vector<Mat> images; |
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vector<float> times; |
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Mat response; |
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loadExposureSeq(test_path + "exposures/", images, times); |
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loadResponseCSV(test_path + "exposures/response.csv", response); |
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Ptr<MergeDebevec> merge = createMergeDebevec(); |
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Mat result, expected; |
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loadImage(test_path + "merge/debevec.hdr", expected); |
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merge->process(images, result, times, response); |
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Ptr<Tonemap> map = createTonemap(); |
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map->process(result, result); |
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map->process(expected, expected); |
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checkEqual(expected, result, 1e-2f); |
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} |
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TEST(Photo_MergeRobertson, regression) |
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{ |
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string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/"; |
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vector<Mat> images; |
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vector<float> times; |
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loadExposureSeq(test_path + "exposures/", images, times); |
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Ptr<MergeRobertson> merge = createMergeRobertson(); |
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Mat result, expected; |
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loadImage(test_path + "merge/robertson.hdr", expected); |
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merge->process(images, result, times); |
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Ptr<Tonemap> map = createTonemap(); |
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map->process(result, result); |
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map->process(expected, expected); |
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checkEqual(expected, result, 1e-2f); |
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} |
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TEST(Photo_CalibrateDebevec, regression) |
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{ |
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string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/"; |
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vector<Mat> images; |
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vector<float> times; |
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Mat response, expected; |
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loadExposureSeq(test_path + "exposures/", images, times); |
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loadResponseCSV(test_path + "calibrate/debevec.csv", expected); |
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Ptr<CalibrateDebevec> calibrate = createCalibrateDebevec(); |
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calibrate->process(images, response, times); |
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Mat diff = abs(response - expected); |
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diff = diff.mul(1.0f / response); |
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double max; |
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minMaxLoc(diff, NULL, &max); |
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ASSERT_FALSE(max > 0.1); |
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} |
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TEST(Photo_CalibrateRobertson, regression) |
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{ |
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string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/"; |
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vector<Mat> images; |
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vector<float> times; |
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Mat response, expected; |
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loadExposureSeq(test_path + "exposures/", images, times); |
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loadResponseCSV(test_path + "calibrate/robertson.csv", expected); |
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Ptr<CalibrateRobertson> calibrate = createCalibrateRobertson(); |
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calibrate->process(images, response, times); |
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checkEqual(expected, response, 1e-3f); |
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}
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