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BoofCV is an open source Java library for real-time computer vision and robotics applications.
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/*
* Copyright (c) 2021, Peter Abeles. All Rights Reserved.
*
* This file is part of BoofCV (http://boofcv.org).
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package boofcv.factory.sfm;
import boofcv.abst.sfm.d2.ImageMotion2D;
import boofcv.abst.sfm.d2.WrapImageMotionPtkSmartRespawn;
import boofcv.abst.tracker.PointTracker;
import boofcv.alg.distort.ImageDistort;
import boofcv.alg.geo.robust.*;
import boofcv.alg.interpolate.InterpolatePixel;
import boofcv.alg.interpolate.InterpolationType;
import boofcv.alg.sfm.d2.*;
import boofcv.factory.distort.FactoryDistort;
import boofcv.factory.interpolate.FactoryInterpolation;
import boofcv.struct.border.BorderType;
import boofcv.struct.geo.AssociatedPair;
import boofcv.struct.image.ImageBase;
import boofcv.struct.image.ImageType;
import georegression.fitting.affine.ModelManagerAffine2D_F64;
import georegression.fitting.homography.ModelManagerHomography2D_F64;
import georegression.fitting.se.ModelManagerSe2_F64;
import georegression.fitting.se.MotionSe2PointSVD_F64;
import georegression.struct.InvertibleTransform;
import georegression.struct.affine.Affine2D_F64;
import georegression.struct.homography.Homography2D_F64;
import georegression.struct.se.Se2_F64;
import org.ddogleg.fitting.modelset.*;
import org.ddogleg.fitting.modelset.ransac.Ransac;
import org.ddogleg.struct.Factory;
/**
* Factory for creating algorithms related to 2D image motion. Typically used for image stabilization, mosaic, and
* motion detection in video feeds.
*
* @author Peter Abeles
*/
public class FactoryMotion2D {
/**
* 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 Image input type.
* @param Model model
* @return ImageMotion2D
*/
@SuppressWarnings({"unchecked", "rawtypes"})
public static , IT extends InvertibleTransform>
ImageMotion2D createMotion2D( int ransacIterations, double inlierThreshold, int outlierPrune,
int absoluteMinimumTracks, double respawnTrackFraction,
double respawnCoverageFraction,
boolean refineEstimate,
PointTracker tracker, IT motionModel ) {
ModelManager manager;
Factory> fitter;
Factory> distance;
ModelFitter 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 modelMatcher =
new Ransac<>(123123, ransacIterations, inlierThreshold, manager, AssociatedPair.class);
modelMatcher.setModel(fitter, distance);
ImageMotionPointTrackerKey lowlevel =
new ImageMotionPointTrackerKey<>(tracker, modelMatcher, modelRefiner, motionModel, outlierPrune);
ImageMotionPtkSmartRespawn smartRespawn =
new ImageMotionPtkSmartRespawn<>(lowlevel,
absoluteMinimumTracks, respawnTrackFraction, respawnCoverageFraction);
return new WrapImageMotionPtkSmartRespawn<>(smartRespawn);
}
/**
* Estimates the image motion then combines images together. Typically used for mosaics and stabilization.
*
* @param maxJumpFraction If the area changes by this much between two consecuative frames then the transform
* is reset.
* @param motion2D Estimates the image motion.
* @param imageType Type of image processed
* @param Image input type.
* @param Model model
* @return StitchingFromMotion2D
*/
@SuppressWarnings("unchecked")
public static , IT extends InvertibleTransform>
StitchingFromMotion2D
createVideoStitch( double maxJumpFraction, ImageMotion2D motion2D, ImageType imageType ) {
StitchingTransform transform;
if (motion2D.getTransformType() == Affine2D_F64.class) {
transform = (StitchingTransform)FactoryStitchingTransform.createAffine_F64();
} else {
transform = (StitchingTransform)FactoryStitchingTransform.createHomography_F64();
}
InterpolatePixel interp;
if (imageType.getFamily() == ImageType.Family.GRAY || imageType.getFamily() == ImageType.Family.PLANAR) {
interp = FactoryInterpolation.createPixelS(0, 255, InterpolationType.BILINEAR, BorderType.EXTENDED,
imageType.getImageClass());
} else {
throw new IllegalArgumentException("Unsupported image type");
}
ImageDistort distorter = FactoryDistort.distort(false, interp, imageType);
distorter.setRenderAll(false);
return new StitchingFromMotion2D<>(motion2D, distorter, transform, maxJumpFraction);
}
}
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