double StatsCalc::CalcCorrelation() // r
{
double cov = CalcCovariance();
double correlation = cov / (x.CalcStdDeviation() * y.CalcStdDeviation());
return correlation;
}
double StatsCalc::CalcCovariance()
{
double xmean = x.CalcMean();
double ymean = y.CalcMean();
double total = 0;
for(int i = 0; i < max; i++)
total += (XSeries[i] - xmean) * (YSeries[i] - ymean);
return total / max;
}
void StatsCalc::SetValues(double *xvalues, double *yvalues, int count)
{
for(int i = 0; i < count; i++)
{
XSeries[i] = xvalues[i];
YSeries[i] = yvalues[i];
}
x.SetValues(xvalues, count);
y.SetValues(yvalues, count);
max = count; // n
}
NormalDistribution::NormalDistribution(const Mean& m,
const StdDeviation& sigma)
: DistributionBase(), mean_(m.value()), stdDev_(sigma.value())
{
if(stdDev_ < 0.0)
throw runtime_error("NormalDistribution : in ctor, sigma must be positive.");
lower_ = mean_ - stdDev_*2;
upper_ = mean_ + stdDev_*2;
pLower_ = getUntruncatedCDF(lower_);
pUpper_ = getUntruncatedCDF(upper_);
}
NormalDistribution::NormalDistribution(const Mean& m,
const StdDeviation& sigma,
double numDeviations)
: DistributionBase(), mean_(m.value()), stdDev_(sigma.value())
{
if(stdDev_ < 0.0)
throw std::runtime_error("NormalDistribution : in ctor, sigma must be positive.");
if(numDeviations < 0.0)
throw runtime_error("NormalDistribution : in ctor, numDevations must be positive.");
lower_ = mean_ - numDeviations*stdDev_;
upper_ = mean_ + numDeviations*stdDev_;
pLower_ = getUntruncatedCDF(lower_);
pUpper_ = getUntruncatedCDF(upper_);
}
pcl::PointCloud<briskDepth> BDMatch(pcl::PointCloud<briskDepth> a, pcl::PointCloud<briskDepth> b)
{
std::cout << "The count is: " << count << std::endl;
pcl::PointCloud<briskDepth> pclMatch;
try
{
cv::Mat descriptorsA;
cv::Mat descriptorsB;
for(int i =0; i < a.size(); i++)
{
descriptorsA.push_back(a[i].descriptor);
}
for(int i =0; i < b.size(); i++)
{
descriptorsB.push_back(b[i].descriptor);
}
cv::BFMatcher matcher(cv::NORM_HAMMING);
std::vector< cv::DMatch > matches;
matcher.match( descriptorsA, descriptorsB, matches );
double max_dist = 0; double min_dist = 1000;
StdDeviation sd;
double temp[descriptorsA.rows];
for (int i =0; i < descriptorsA.rows;i++)
{
double dist = matches[i].distance;
if(max_dist<dist) max_dist = dist;
if(min_dist>dist) min_dist = dist;
//std::cout << dist << "\t";
temp[i] = dist;
}
//std::cout << std::endl;
// std::cout << " Brisk max dist " << max_dist << std::endl;
// std::cout << " Brisk mins dist " << min_dist << std::endl;
sd.SetValues(temp, descriptorsA.rows);
double mean = sd.CalculateMean();
double variance = sd.CalculateVariane();
double samplevariance = sd.CalculateSampleVariane();
double sampledevi = sd.GetSampleStandardDeviation();
double devi = sd.GetStandardDeviation();
std::cout << "Brisk\t" << descriptorsA.rows << "\t"
<< mean << "\t"
<< variance << "\t"
<< samplevariance << "\t"
<< devi << "\t"
<< sampledevi << "\n";
std::vector< cv::DMatch > good_matches;
for (int i=0;i<descriptorsA.rows;i++)
{
//if( matches[i].distance<10)
if( matches[i].distance<max_dist/2)
{
good_matches.push_back(matches[i]);
pclMatch.push_back(a[i]);
}
}
cv::Mat img_matches;
cv::drawMatches( brisk_lastImg, brisk_lastKeypoints, brisk_currentImg, brisk_lastKeypoints,
good_matches, img_matches, cv::Scalar::all(-1), cv::Scalar::all(-1),
std::vector<char>(), cv::DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
// cv::imshow("Brisk Matches", img_matches);
std::vector<cv::Point2f> obj;
std::vector<cv::Point2f> scene;
for( int i = 0; i < good_matches.size(); i++ )
{
//-- Get the keypoints from the good matches
obj.push_back( brisk_lastKeypoints[ good_matches[i].queryIdx ].pt );
scene.push_back( brisk_currentKeypoints[ good_matches[i].trainIdx ].pt );
}
cv::Mat H = findHomography( obj, scene, CV_RANSAC );
//-- Get the corners from the image_1 ( the object to be "detected" )
std::vector<cv::Point2f> obj_corners(4);
obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( brisk_lastImg.cols, 0 );
obj_corners[2] = cvPoint( brisk_lastImg.cols, brisk_lastImg.rows ); obj_corners[3] = cvPoint( 0, brisk_lastImg.rows );
std::vector<cv::Point2f> scene_corners(4);
perspectiveTransform( obj_corners, scene_corners, H);
//-- Draw lines between the corners (the mapped object in the scene - image_2 )
line( img_matches, scene_corners[0] + cv::Point2f( brisk_lastImg.cols, 0), scene_corners[1] + cv::Point2f( brisk_lastImg.cols, 0), cv::Scalar(0, 255, 0), 4 );
line( img_matches, scene_corners[1] + cv::Point2f( brisk_lastImg.cols, 0), scene_corners[2] + cv::Point2f( brisk_lastImg.cols, 0), cv::Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[2] + cv::Point2f( brisk_lastImg.cols, 0), scene_corners[3] + cv::Point2f( brisk_lastImg.cols, 0), cv::Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[3] + cv::Point2f( brisk_lastImg.cols, 0), scene_corners[0] + cv::Point2f( brisk_lastImg.cols, 0), cv::Scalar( 0, 255, 0), 4 );
//-- Show detected matches
cv::imshow( "Good brisk Matches & Object detection", img_matches );
cv::waitKey(50);
// std::cout << good_matches.size() << " Brisk features matched from, " << a.size() << ", " << b.size() << " sets." << std::endl;
}
catch (const std::exception &exc)
{
// catch anything thrown within try block that derives from std::exception
std::cerr << exc.what();
}
return pclMatch;
}
