249 lines
8.2 KiB
C++
249 lines
8.2 KiB
C++
/*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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namespace opencv_test { namespace {
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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, const string& name)
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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=" << max << " threshold=" << threshold << " method=" << name;
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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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std::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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std::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, "Simple");
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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, "Drago");
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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, "Reinhard");
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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, "Mantiuk");
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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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RNG rng = theRNG();
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for(int i = 0; i < TESTS_COUNT; i++) {
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Point shift(rng.uniform(0, max_shift), rng.uniform(0, 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, "Mertens");
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Mat uniform(100, 100, CV_8UC3);
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uniform = Scalar(0, 255, 0);
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images.clear();
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images.push_back(uniform);
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merge->process(images, result);
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result.convertTo(result, CV_8UC3, 255);
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checkEqual(uniform, result, 1e-2f, "Mertens");
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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, "Debevec");
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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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const float eps = 6.f;
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checkEqual(expected, result, eps, "MergeRobertson");
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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-1f, "CalibrateRobertson");
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}
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}} // namespace
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