bool RandomFeatureFinder::find( Pose_d& pose )
{
if( !m_configured )
throw std::runtime_error("RandomFeatureFinder::find: not configured.");
// find circles in pose
std::vector<Eigen::Vector2d> posePoints = findCircles( *pose.image );
if( posePoints.size() < m_minPoints )
return false;
// generate descriptors for detected points
RFD poseRFD(true);
poseRFD( posePoints );
// compare the resulting descriptors with the configured
m_patternRFD.match( poseRFD, pose );
// assemble the result
if( pose.pointIndices.size() >= m_minPoints )
{
// set max number of points
pose.pointsMax = m_points3D.size();
// run over the indices and set the 3D points
pose.points3D.clear();
for( size_t i=0; i<pose.pointIndices.size(); i++ )
pose.points3D.push_back( m_points3D[pose.pointIndices[i] ] );
// we are happy
return true;
}
else
return false;
}
void circles()
{
// Find edges
Image i = readImage("circles.ppm");
GradientAndEdges gradientAndEdges = findGradientAndEdges(i, 5, 5, 3, 20);
// Create buffer
int bufferSize = 50;
Image result = newImage(1, i.w + bufferSize * 2, i.h + bufferSize * 2);
clearImage(result);
// Find circles
findCircles(gradientAndEdges, result, 32, 12, bufferSize, 15, 10);
findCircles(gradientAndEdges, result, 16, 5, bufferSize, 15, 10);
findCircles(gradientAndEdges, result, 48, 3, bufferSize, 51, 20);
// Show result
writeImage(result, "result.pgm");
return;
}
std::vector<std::vector<struct ColouredCircle> > findColouredCirclesInFrame(cv::InputArray frame, cv::InputArray background) {
cv::UMat foreground;
cv::UMat processed;
cv::Ptr<cv::BackgroundSubtractorMOG2> bs = cv::createBackgroundSubtractorMOG2(1, 254, false); // HEURISTIC: 254 = parameter for background subtractor
bs->apply(background, foreground, 1);
bs->apply(frame, foreground, 0);
frame.copyTo(processed, foreground);
cv::medianBlur(processed, processed, 9); // HEURISTIC: 9 = amount of blur applied before colour convertion
cv::cvtColor(processed, processed, cv::COLOR_BGR2HSV);
std::array<cv::UMat, 5> masks; // order: ball, blue, yellow, pink, green
cv::inRange(processed, cv::Scalar(0, 100, 100), cv::Scalar(10, 255, 255), masks[0]); // HEURISTIC: range of HSV values
cv::inRange(processed, cv::Scalar(76, 90, 90), cv::Scalar(146, 255, 255), masks[1]); // HEURISTIC: range of HSV values
cv::inRange(processed, cv::Scalar(24, 200, 200), cv::Scalar(44, 255, 255), masks[2]); // HEURISTIC: range of HSV values
cv::inRange(processed, cv::Scalar(165, 90, 90), cv::Scalar(180, 255, 255), masks[3]); // HEURISTIC: range of HSV values
cv::inRange(processed, cv::Scalar(50, 200, 200), cv::Scalar(70, 255, 255), masks[4]); // HEURISTIC: range of HSV values
std::array<std::vector<std::vector<cv::Point> >, 5> contours; // order: ball, blue, yellow, pink, green
// TODO: potentially can be vectorized
for(size_t i = 0; i < masks.size(); i++) {
cv::findContours(masks[i], contours[i], cv::RETR_LIST, cv::CHAIN_APPROX_SIMPLE);
}
std::vector<std::vector<struct ColouredCircle> > circles;
// TODO: potentially can be vectorized
for(size_t i = 0; i < contours.size(); i++) {
std::vector<struct ColouredCircle> c;
findCircles(contours[i], c, 1, 5); // HEURISTIC: deviation of circle and ellipse center and radius
for(size_t j = 0; j < c.size(); j++) {
c[j].colour = int(i);
}
circles.push_back(c);
}
return circles;
}
