opencv/modules/photo/test/test_hdr.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
// 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.
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// 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.
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// 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;
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//M*/
#include "test_precomp.hpp"
using namespace cv;
using namespace std;
void loadImage(string path, Mat &img)
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{
img = imread(path, -1);
ASSERT_FALSE(img.empty()) << "Could not load input image " << path;
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}
void checkEqual(Mat img0, Mat img1, double threshold, const string& name)
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{
double max = 1.0;
minMaxLoc(abs(img0 - img1), NULL, &max);
ASSERT_FALSE(max > threshold) << "max=" << max << " threshold=" << threshold << " method=" << name;
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}
static vector<float> DEFAULT_VECTOR;
void loadExposureSeq(String path, vector<Mat>& images, vector<float>& times = DEFAULT_VECTOR)
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{
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();
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}
void loadResponseCSV(String path, Mat& response)
{
response = Mat(256, 1, CV_32FC3);
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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];
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resp_file.ignore(1);
}
}
resp_file.close();
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}
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TEST(Photo_Tonemap, regression)
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{
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;
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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<TonemapDurand> durand = createTonemapDurand(gamma);
durand->process(img, result);
loadImage(test_path + "durand.png", expected);
result.convertTo(result, CV_8UC3, 255);
checkEqual(result, expected, 3, "Durand");
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");
}
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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);
for(int i = 0; i < TESTS_COUNT; i++) {
Point shift(rand() % max_shift, rand() % max_shift);
Mat res;
align->shiftMat(img, res, shift);
Point calc = align->calculateShift(img, res);
errors += (calc != -shift);
}
ASSERT_TRUE(errors < 5) << errors << " errors";
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}
TEST(Photo_MergeMertens, regression)
{
string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
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vector<Mat> images;
loadExposureSeq((test_path + "exposures/").c_str() , images);
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Ptr<MergeMertens> merge = createMergeMertens();
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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");
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}
TEST(Photo_MergeDebevec, regression)
{
string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
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vector<Mat> images;
vector<float> times;
Mat response;
loadExposureSeq(test_path + "exposures/", images, times);
loadResponseCSV(test_path + "exposures/response.csv", response);
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Ptr<MergeDebevec> merge = createMergeDebevec();
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Mat result, expected;
loadImage(test_path + "merge/debevec.hdr", expected);
merge->process(images, result, times, response);
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Ptr<Tonemap> map = createTonemap();
map->process(result, result);
map->process(expected, expected);
checkEqual(expected, result, 1e-2f, "Debevec");
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}
TEST(Photo_MergeRobertson, regression)
{
string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
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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);
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#ifdef __aarch64__
const float eps = 6.f;
#else
const float eps = 5.f;
#endif
checkEqual(expected, result, eps, "MergeRobertson");
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}
TEST(Photo_CalibrateDebevec, regression)
{
string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
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vector<Mat> images;
vector<float> times;
Mat response, expected;
loadExposureSeq(test_path + "exposures/", images, times);
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loadResponseCSV(test_path + "calibrate/debevec.csv", expected);
Ptr<CalibrateDebevec> calibrate = createCalibrateDebevec();
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calibrate->process(images, response, times);
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Mat diff = abs(response - expected);
diff = diff.mul(1.0f / response);
double max;
minMaxLoc(diff, NULL, &max);
ASSERT_FALSE(max > 0.1);
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}
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TEST(Photo_CalibrateRobertson, regression)
{
string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
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vector<Mat> images;
vector<float> times;
Mat response, expected;
loadExposureSeq(test_path + "exposures/", images, times);
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loadResponseCSV(test_path + "calibrate/robertson.csv", expected);
Ptr<CalibrateRobertson> calibrate = createCalibrateRobertson();
calibrate->process(images, response, times);
checkEqual(expected, response, 1e-1f, "CalibrateRobertson");
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}