bool FrameMatchingStatistics::tryGetValue(StatisticElement element, float& value) const
{
if (!isValid)
return false;
switch (element)
{
case StatisticsElementPointsCount:
value = totalKeypoints;
return true;
case StatisticsElementPercentOfCorrectMatches:
value = correctMatchesPercent * 100;
return true;
case StatisticsElementPercentOfMatches:
value = percentOfMatches * 100;
return true;
case StatisticsElementMeanDistance:
value = meanDistance;
return true;
case StatisticsElementHomographyError:
value = homographyError;
return true;
case StatisticsElementMatchingRatio:
value = matchingRatio();
return true;
case StatisticsElementPatternLocalization:
value = patternLocalization();
return true;
default:
return false;
}
}
int main(int argc, const char* argv[])
{
// Print OpenCV build info:
std::cout << cv::getBuildInformation() << std::endl;
std::vector<FeatureAlgorithm> algorithms;
std::vector<cv::Ptr<ImageTransformation> > transformations;
bool useCrossCheck = true;
// Initialize list of algorithm tuples:
algorithms.push_back(FeatureAlgorithm("BRISK/BRISK/BF",
new cv::BriskFeatureDetector(60,4),
new cv::BriskDescriptorExtractor(),
new cv::BFMatcher(cv::NORM_HAMMING, useCrossCheck)));
algorithms.push_back(FeatureAlgorithm("ORB/ORB/BF",
new cv::ORB(),
new cv::ORB(),
new cv::BFMatcher(cv::NORM_HAMMING, useCrossCheck)));
algorithms.push_back(FeatureAlgorithm("SURF/BRISK/BF",
new cv::SurfFeatureDetector(),
new cv::BriskDescriptorExtractor(),
new cv::BFMatcher(cv::NORM_HAMMING, useCrossCheck)));
algorithms.push_back(FeatureAlgorithm("SURF/FREAK/BF",
new cv::SurfFeatureDetector(),
new cv::FREAK(),
new cv::BFMatcher(cv::NORM_HAMMING, useCrossCheck)));
algorithms.push_back(FeatureAlgorithm("ORB3/ORB3/BF",
new cv::ORB(500, 1.2f, 8,31, 0, 3),
new cv::ORB(500, 1.2f, 8,31, 0, 3),
new cv::BFMatcher(cv::NORM_HAMMING2, useCrossCheck)));
algorithms.push_back(FeatureAlgorithm("ORB4/ORB4/BF",
new cv::ORB(500, 1.2f, 8, 31, 0, 4),
new cv::ORB(500, 1.2f, 8, 31, 0, 4),
new cv::BFMatcher(cv::NORM_HAMMING2, useCrossCheck)));
algorithms.push_back(FeatureAlgorithm("FAST/BRIEF/BF",
new cv::FastFeatureDetector(50),
new cv::BriefDescriptorExtractor(),
new cv::BFMatcher(cv::NORM_HAMMING, useCrossCheck)));
algorithms.push_back(FeatureAlgorithm("ORB/FREAK/BF",
new cv::OrbFeatureDetector(),
new cv::FREAK(),
new cv::BFMatcher(cv::NORM_HAMMING, useCrossCheck)));
algorithms.push_back(FeatureAlgorithm("SURF/SURF/BF",
new cv::SurfFeatureDetector(),
new cv::SurfDescriptorExtractor(),
new cv::BFMatcher(cv::NORM_L2, useCrossCheck)));
algorithms.push_back(FeatureAlgorithm("SURF/SURF/FLANN",
new cv::SurfFeatureDetector(),
new cv::SurfDescriptorExtractor(),
new cv::FlannBasedMatcher()));
/**/
// Initialize list of used transformations:
if (USE_VERBOSE_TRANSFORMATIONS)
{
transformations.push_back(new GaussianBlurTransform(9));
transformations.push_back(new BrightnessImageTransform(-127, +127,1));
transformations.push_back(new ImageRotationTransformation(0, 360, 1, cv::Point2f(0.5f,0.5f)));
transformations.push_back(new ImageScalingTransformation(0.25f, 2.0f, 0.01f));
}
else
{
transformations.push_back(new GaussianBlurTransform(9));
transformations.push_back(new BrightnessImageTransform(-127, +127, 10));
transformations.push_back(new ImageRotationTransformation(0, 360, 10, cv::Point2f(0.5f,0.5f)));
transformations.push_back(new ImageScalingTransformation(0.25f, 2.0f, 0.1f));
}
if (argc < 2)
{
std::cout << "At least one input image should be passed" << std::endl;
}
for (int imageIndex = 1; imageIndex < argc; imageIndex++)
{
std::string testImagePath(argv[imageIndex]);
cv::Mat testImage = cv::imread(testImagePath);
CollectedStatistics fullStat;
if (testImage.empty())
{
std::cout << "Cannot read image from " << testImagePath << std::endl;
}
for (size_t algIndex = 0; algIndex < algorithms.size(); algIndex++)
{
const FeatureAlgorithm& alg = algorithms[algIndex];
std::cout << "Testing " << alg.name << "...";
for (size_t transformIndex = 0; transformIndex < transformations.size(); transformIndex++)
{
const ImageTransformation& trans = *transformations[transformIndex].obj;
performEstimation(alg, trans, testImage.clone(), fullStat.getStatistics(alg.name, trans.name));
}
std::cout << "done." << std::endl;
}
std::ofstream performance("Performance.csv");
fullStat.printPerformanceStatistics(performance);
std::ofstream matchingRatio("MatchingRatio.csv");
fullStat.printStatistics(matchingRatio,StatisticsElementMatchingRatio);
std::ofstream percentOfMatches("PercentOfMatches.csv");
fullStat.printStatistics(percentOfMatches,StatisticsElementPercentOfMatches);
std::ofstream percentOfCorrectMatches("PercentOfCorrectMatches.csv");
fullStat.printStatistics(percentOfCorrectMatches,StatisticsElementPercentOfCorrectMatches);
std::ofstream meanDistance("MeanDistance.csv");
fullStat.printStatistics(meanDistance,StatisticsElementMeanDistance);
}
return 0;
}
