@Override public boolean process(List<Point> _dataSet ) { // see if it has the minimum number of points if (_dataSet.size() < modelGenerator.getMinimumPoints() ) return false; // the data set will be modified so a copy is needed. Otherwise indexes of match set will not // be correct dataSet.clear(); dataSet.addAll(_dataSet); // configure internal data structures initialize(dataSet); // iterate until it has exhausted all iterations or stop if the entire data set // is in the inlier set for (int i = 0; i < maxIterations && bestFitPoints.size() != dataSet.size(); i++) { // sample the a small set of points randomDraw(dataSet, sampleSize, initialSample, rand); // get the candidate(s) for this sample set if( modelGenerator.generate(initialSample, candidateParam ) ) { // see if it can find a model better than the current best one selectMatchSet(_dataSet, thresholdFit, candidateParam); // save this results if (bestFitPoints.size() < candidatePoints.size()) { swapCandidateWithBest(); } } } return bestFitPoints.size() > 0; }
this.sampleSize = generator.getMinimumPoints();
Ransac.randomDraw(dataSet, sampleSize, smallSet, rand); if( generator.generate(smallSet, candidate) ) { errorMetric.setModel(candidate); errorMetric.computeDistance(_dataSet,errors);
this.sampleSize = generator.getMinimumPoints();
if( model.modelGenerator.generate(initialSample.toList(), param ) ) {
@Override public boolean process(List<Point> _dataSet ) { // see if it has the minimum number of points if (_dataSet.size() < modelGenerator.getMinimumPoints() ) return false; // the data set will be modified so a copy is needed. Otherwise indexes of match set will not // be correct dataSet.clear(); dataSet.addAll(_dataSet); // configure internal data structures initialize(dataSet); // iterate until it has exhausted all iterations or stop if the entire data set // is in the inlier set for (int i = 0; i < maxIterations && bestFitPoints.size() != dataSet.size(); i++) { // sample the a small set of points randomDraw(dataSet, sampleSize, initialSample, rand); // get the candidate(s) for this sample set if( modelGenerator.generate(initialSample, candidateParam ) ) { // see if it can find a model better than the current best one selectMatchSet(_dataSet, thresholdFit, candidateParam); // save this results if (bestFitPoints.size() < candidatePoints.size()) { swapCandidateWithBest(); } } } return bestFitPoints.size() > 0; }
/** * Creates a new instance of the ransac algorithm. The number of points sampled will default to the * minimum number. To override this default invoke {@link #setSampleSize(int)}. * * @param randSeed The random seed used by the random number generator. * @param modelGenerator Creates new model(s) given a small number of points. * @param modelDistance Computes the difference between a point an a model. * @param maxIterations The maximum number of iterations the RANSAC algorithm will perform. * @param thresholdFit How close of a fit a points needs to be to the model to be considered a fit. */ public Ransac(long randSeed, ModelManager<Model> modelManager, ModelGenerator<Model, Point> modelGenerator, DistanceFromModel<Model, Point> modelDistance, int maxIterations, double thresholdFit) { this.modelGenerator = modelGenerator; this.modelDistance = modelDistance; this.rand = new Random(randSeed); this.maxIterations = maxIterations; this.bestFitParam = modelManager.createModelInstance(); this.candidateParam = modelManager.createModelInstance(); this.sampleSize = modelGenerator.getMinimumPoints(); this.thresholdFit = thresholdFit; }
if( model.modelGenerator.generate(initialSample.toList(), param ) ) {
/** * Creates a new instance of the ransac algorithm. The number of points sampled will default to the * minimum number. To override this default invoke {@link #setSampleSize(int)}. * * @param randSeed The random seed used by the random number generator. * @param modelGenerator Creates new model(s) given a small number of points. * @param modelDistance Computes the difference between a point an a model. * @param maxIterations The maximum number of iterations the RANSAC algorithm will perform. * @param thresholdFit How close of a fit a points needs to be to the model to be considered a fit. */ public Ransac(long randSeed, ModelManager<Model> modelManager, ModelGenerator<Model, Point> modelGenerator, DistanceFromModel<Model, Point> modelDistance, int maxIterations, double thresholdFit) { this.modelGenerator = modelGenerator; this.modelDistance = modelDistance; this.rand = new Random(randSeed); this.maxIterations = maxIterations; this.bestFitParam = modelManager.createModelInstance(); this.candidateParam = modelManager.createModelInstance(); this.sampleSize = modelGenerator.getMinimumPoints(); this.thresholdFit = thresholdFit; }
Ransac.randomDraw(dataSet, sampleSize, smallSet, rand); if( generator.generate(smallSet, candidate) ) { errorMetric.setModel(candidate); errorMetric.computeDistance(_dataSet,errors);
o.sampleSize = o.modelGenerator.getMinimumPoints();
o.sampleSize = o.modelGenerator.getMinimumPoints();