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vendor: OpenCV 5.0.0 snapshot at 40738fb16ceddb5fb3fea747585f7ce6abb0605b

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/*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_MergeDebevec, regression_depth_consistency)
{
string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
vector<Mat> images8;
vector<float> times;
loadExposureSeq(test_path + "exposures/", images8, times);
vector<Mat> images16(images8.size()), images32(images8.size());
for (size_t i = 0; i < images8.size(); ++i)
{
images8[i].convertTo(images16[i], CV_16UC3, 257.0);
images8[i].convertTo(images32[i], CV_32FC3, 1.0 / 255.0);
}
Ptr<MergeDebevec> merge = createMergeDebevec();
Ptr<Tonemap> map = createTonemap();
Mat hdr8, hdr16, hdr32;
merge->process(images8, hdr8, times);
merge->process(images16, hdr16, times);
merge->process(images32, hdr32, times);
map->process(hdr8, hdr8);
map->process(hdr16, hdr16);
map->process(hdr32, hdr32);
checkEqual(hdr8, hdr16, 2e-2f, "Debevec realdata 16U vs 8U");
checkEqual(hdr8, hdr32, 2e-2f, "Debevec realdata 32F vs 8U");
}
TEST(Photo_MergeRobertson, regression_depth_consistency)
{
string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
vector<Mat> images8;
vector<float> times;
loadExposureSeq(test_path + "exposures/", images8, times);
vector<Mat> images16(images8.size()), images32(images8.size());
for (size_t i = 0; i < images8.size(); ++i)
{
images8[i].convertTo(images16[i], CV_16UC3, 257.0);
images8[i].convertTo(images32[i], CV_32FC3, 1.0 / 255.0);
}
Ptr<MergeRobertson> merge = createMergeRobertson();
Ptr<Tonemap> map = createTonemap();
Mat hdr8, hdr16, hdr32;
merge->process(images8, hdr8, times);
merge->process(images16, hdr16, times);
merge->process(images32, hdr32, times);
map->process(hdr8, hdr8);
map->process(hdr16, hdr16);
map->process(hdr32, hdr32);
checkEqual(hdr8, hdr16, 3e-2f, "Robertson realdata 16U vs 8U");
checkEqual(hdr8, hdr32, 3e-2f, "Robertson realdata 32F vs 8U");
}
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, 0.25);
#elif !defined(HAVE_IPP)
ASSERT_LT(max, 0.22);
#else
ASSERT_LT(max, 0.15);
#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));
}
TEST(Photo_CalibrateDebevec, bug_24966)
{
string test_path = string(cvtest::TS::ptr()->get_data_path()) + "hdr/";
vector<Mat> all_images;
vector<float> all_times;
loadExposureSeq(test_path + "exposures/", all_images, all_times);
// Use a balanced subset of exposures
vector<int> selected_indices = {1,2,3,4,5};
vector<Mat> images;
vector<float> times;
for (int idx : selected_indices) {
images.push_back(all_images[idx]);
times.push_back(all_times[idx]);
}
// Run CRF estimation for different sample points
vector<int> sample_points = {200,300,400};
vector<Mat> responses;
for (int samples : sample_points) {
Ptr<CalibrateDebevec> calibrate = createCalibrateDebevec(samples);
Mat response;
calibrate->process(images, response, times);
Mat roi = response.rowRange(15, 240); //Checking CRF only in the middle of the image
responses.push_back(roi);
}
// Compare consecutive pairs of CRFs
for (size_t i = 0; i < responses.size()-1; ++i) {
Mat diff = abs(responses[i] - responses[i+1]);
double max_diff;
minMaxLoc(diff, nullptr, &max_diff);
cout << "max_diff = " << max_diff << endl;
#if defined(__aarch64__) && defined(__APPLE__)
ASSERT_LT(max_diff, 10) << "CRF instability detected between samples="
<< sample_points[i] << " and " << sample_points[i+1]
<< " (max diff = " << max_diff << ")";
#else
ASSERT_LT(max_diff, 5) << "CRF instability detected between samples="
<< sample_points[i] << " and " << sample_points[i+1]
<< " (max diff = " << max_diff << ")";
#endif
}
}
}} // namespace