use of georegression.fitting.affine.ModelManagerAffine2D_F64 in project BoofCV by lessthanoptimal.
the class FactoryMotion2D method createMotion2D.
/**
* Estimates the 2D motion of an image using different models.
*
* @param ransacIterations Number of RANSAC iterations
* @param inlierThreshold Threshold which defines an inlier.
* @param outlierPrune If a feature is an outlier for this many turns in a row it is dropped. Try 2
* @param absoluteMinimumTracks New features will be respawned if the number of inliers drop below this number.
* @param respawnTrackFraction If the fraction of current inliers to the original number of inliers drops below
* this fraction then new features are spawned. Try 0.3
* @param respawnCoverageFraction If the area covered drops by this fraction then spawn more features. Try 0.8
* @param refineEstimate Should it refine the model estimate using all inliers.
* @param tracker Point feature tracker.
* @param motionModel Instance of the model model used. Affine2D_F64 or Homography2D_F64
* @param <I> Image input type.
* @param <IT> Model model
* @return ImageMotion2D
*/
@SuppressWarnings({ "unchecked", "rawtypes" })
public static <I extends ImageBase<I>, IT extends InvertibleTransform<IT>> ImageMotion2D<I, IT> createMotion2D(int ransacIterations, double inlierThreshold, int outlierPrune, int absoluteMinimumTracks, double respawnTrackFraction, double respawnCoverageFraction, boolean refineEstimate, PointTracker<I> tracker, IT motionModel) {
ModelManager<IT> manager;
Factory<ModelGenerator<IT, AssociatedPair>> fitter;
Factory<DistanceFromModel<IT, AssociatedPair>> distance;
ModelFitter<IT, AssociatedPair> modelRefiner = null;
if (motionModel instanceof Homography2D_F64) {
manager = (ModelManager) new ModelManagerHomography2D_F64();
if (refineEstimate)
modelRefiner = (ModelFitter) new GenerateHomographyLinear(true);
fitter = () -> (ModelGenerator) new GenerateHomographyLinear(true);
distance = () -> (DistanceFromModel) new DistanceHomographySq();
} else if (motionModel instanceof Affine2D_F64) {
manager = (ModelManager) new ModelManagerAffine2D_F64();
if (refineEstimate)
modelRefiner = (ModelFitter) new GenerateAffine2D();
fitter = () -> (ModelGenerator) new GenerateAffine2D();
distance = () -> (DistanceFromModel) new DistanceAffine2DSq();
} else if (motionModel instanceof Se2_F64) {
manager = (ModelManager) new ModelManagerSe2_F64();
fitter = () -> (ModelGenerator) new GenerateSe2_AssociatedPair(new MotionSe2PointSVD_F64());
distance = () -> (DistanceFromModel) new DistanceSe2Sq();
// no refine, already optimal
} else {
throw new RuntimeException("Unknown model type: " + motionModel.getClass().getSimpleName());
}
ModelMatcherPost<IT, AssociatedPair> modelMatcher = new Ransac<>(123123, ransacIterations, inlierThreshold, manager, AssociatedPair.class);
modelMatcher.setModel(fitter, distance);
ImageMotionPointTrackerKey<I, IT> lowlevel = new ImageMotionPointTrackerKey<>(tracker, modelMatcher, modelRefiner, motionModel, outlierPrune);
ImageMotionPtkSmartRespawn<I, IT> smartRespawn = new ImageMotionPtkSmartRespawn<>(lowlevel, absoluteMinimumTracks, respawnTrackFraction, respawnCoverageFraction);
return new WrapImageMotionPtkSmartRespawn<>(smartRespawn);
}
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