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BoofCV is an open source Java library for real-time computer vision and robotics applications.
/*
* Copyright (c) 2011-2018, 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.feature.tracker;
import boofcv.abst.feature.associate.*;
import boofcv.abst.feature.describe.ConfigSurfDescribe;
import boofcv.abst.feature.describe.DescribeRegionPoint;
import boofcv.abst.feature.describe.WrapDescribeBrief;
import boofcv.abst.feature.describe.WrapDescribePixelRegionNCC;
import boofcv.abst.feature.detdesc.DetectDescribeFusion;
import boofcv.abst.feature.detdesc.DetectDescribePoint;
import boofcv.abst.feature.detect.interest.ConfigFastCorner;
import boofcv.abst.feature.detect.interest.ConfigFastHessian;
import boofcv.abst.feature.detect.interest.ConfigGeneralDetector;
import boofcv.abst.feature.detect.interest.InterestPointDetector;
import boofcv.abst.feature.orientation.ConfigAverageIntegral;
import boofcv.abst.feature.orientation.ConfigSlidingIntegral;
import boofcv.abst.feature.orientation.OrientationImage;
import boofcv.abst.feature.orientation.OrientationIntegral;
import boofcv.abst.feature.tracker.*;
import boofcv.abst.filter.derivative.ImageGradient;
import boofcv.alg.feature.associate.AssociateSurfBasic;
import boofcv.alg.feature.describe.DescribePointBrief;
import boofcv.alg.feature.describe.DescribePointPixelRegionNCC;
import boofcv.alg.feature.describe.DescribePointSurf;
import boofcv.alg.feature.describe.brief.FactoryBriefDefinition;
import boofcv.alg.feature.detect.intensity.FastCornerDetector;
import boofcv.alg.feature.detect.intensity.GradientCornerIntensity;
import boofcv.alg.feature.detect.intensity.ShiTomasiCornerIntensity;
import boofcv.alg.feature.detect.interest.EasyGeneralFeatureDetector;
import boofcv.alg.feature.detect.interest.GeneralFeatureDetector;
import boofcv.alg.filter.derivative.GImageDerivativeOps;
import boofcv.alg.interpolate.InterpolateRectangle;
import boofcv.alg.tracker.combined.CombinedTrackerScalePoint;
import boofcv.alg.tracker.klt.PkltConfig;
import boofcv.alg.transform.ii.GIntegralImageOps;
import boofcv.factory.feature.associate.FactoryAssociation;
import boofcv.factory.feature.describe.FactoryDescribePointAlgs;
import boofcv.factory.feature.describe.FactoryDescribeRegionPoint;
import boofcv.factory.feature.detdesc.FactoryDetectDescribe;
import boofcv.factory.feature.detect.intensity.FactoryIntensityPointAlg;
import boofcv.factory.feature.detect.interest.FactoryDetectPoint;
import boofcv.factory.feature.detect.interest.FactoryInterestPoint;
import boofcv.factory.feature.orientation.FactoryOrientation;
import boofcv.factory.feature.orientation.FactoryOrientationAlgs;
import boofcv.factory.filter.blur.FactoryBlurFilter;
import boofcv.factory.filter.derivative.FactoryDerivative;
import boofcv.factory.interpolate.FactoryInterpolation;
import boofcv.factory.tracker.FactoryTrackerAlg;
import boofcv.factory.transform.pyramid.FactoryPyramid;
import boofcv.struct.feature.*;
import boofcv.struct.image.ImageGray;
import boofcv.struct.image.ImageType;
import boofcv.struct.pyramid.PyramidDiscrete;
import java.util.Random;
/**
* Factory for creating trackers which implement {@link boofcv.abst.feature.tracker.PointTracker}. These trackers
* are intended for use in SFM applications. Some features which individual trackers can provide are lost when
* using the high level interface {@link PointTracker}. To create low level tracking algorithms see
* {@link FactoryTrackerAlg}
*
* @see FactoryTrackerAlg
*
* @author Peter Abeles
*/
public class FactoryPointTracker {
/**
* Pyramid KLT feature tracker.
*
* @see boofcv.alg.tracker.klt.PyramidKltTracker
*
* @param scaling Scales in the image pyramid. Recommend [1,2,4] or [2,4]
* @param configExtract Configuration for extracting features
* @param featureRadius Size of the tracked feature. Try 3 or 5
* @param imageType Input image type.
* @param derivType Image derivative type.
* @return KLT based tracker.
*/
public static , D extends ImageGray>
PointTracker klt(int scaling[], ConfigGeneralDetector configExtract, int featureRadius,
Class imageType, Class derivType) {
PkltConfig config = new PkltConfig();
config.pyramidScaling = scaling;
config.templateRadius = featureRadius;
return klt(config, configExtract, imageType, derivType );
}
/**
* Pyramid KLT feature tracker.
*
* @see boofcv.alg.tracker.klt.PyramidKltTracker
*
* @param config Config for the tracker. Try PkltConfig.createDefault().
* @param configExtract Configuration for extracting features
* @return KLT based tracker.
*/
public static , D extends ImageGray>
PointTracker klt(PkltConfig config, ConfigGeneralDetector configExtract,
Class imageType, Class derivType ) {
if( derivType == null )
derivType = GImageDerivativeOps.getDerivativeType(imageType);
if( config == null ) {
config = new PkltConfig();
}
if( configExtract == null ) {
configExtract = new ConfigGeneralDetector();
}
GeneralFeatureDetector detector = createShiTomasi(configExtract, derivType);
InterpolateRectangle interpInput = FactoryInterpolation.bilinearRectangle(imageType);
InterpolateRectangle interpDeriv = FactoryInterpolation.bilinearRectangle(derivType);
ImageGradient gradient = FactoryDerivative.sobel(imageType, derivType);
PyramidDiscrete pyramid = FactoryPyramid.discreteGaussian(config.pyramidScaling,-1,2,true, ImageType.single(imageType));
return new PointTrackerKltPyramid<>(config.config, config.templateRadius, pyramid, detector,
gradient, interpInput, interpDeriv, derivType);
}
/**
* Creates a tracker which detects Fast-Hessian features and describes them with SURF using the faster variant
* of SURF.
*
* @see DescribePointSurf
* @see boofcv.abst.feature.tracker.DdaManagerDetectDescribePoint
*
* @param configDetector Configuration for SURF detector
* @param configDescribe Configuration for SURF descriptor
* @param configOrientation Configuration for orientation
* @param imageType Type of image the input is.
* @return SURF based tracker.
*/
// TODO remove maxTracks? Use number of detected instead
public static >
PointTracker dda_FH_SURF_Fast(
ConfigFastHessian configDetector ,
ConfigSurfDescribe.Speed configDescribe ,
ConfigAverageIntegral configOrientation ,
Class imageType)
{
ScoreAssociation score = FactoryAssociation.scoreEuclidean(TupleDesc_F64.class, true);
AssociateSurfBasic assoc = new AssociateSurfBasic(FactoryAssociation.greedy(score, 5, true));
AssociateDescription2D generalAssoc =
new AssociateDescTo2D<>(new WrapAssociateSurfBasic(assoc));
DetectDescribePoint fused =
FactoryDetectDescribe.surfFast(configDetector, configDescribe, configOrientation,imageType);
DdaManagerDetectDescribePoint manager = new DdaManagerDetectDescribePoint<>(fused);
return new DetectDescribeAssociate<>(manager, generalAssoc, false);
}
/**
* Creates a tracker which detects Fast-Hessian features and describes them with SURF using the faster variant
* of SURF.
*
* @see DescribePointSurf
* @see boofcv.abst.feature.tracker.DdaManagerDetectDescribePoint
*
* @param configDetector Configuration for SURF detector
* @param configDescribe Configuration for SURF descriptor
* @param configOrientation Configuration for orientation
* @param imageType Type of image the input is.
* @return SURF based tracker.
*/
// TODO remove maxTracks? Use number of detected instead
public static >
PointTracker dda_FH_SURF_Stable(
ConfigFastHessian configDetector ,
ConfigSurfDescribe.Stability configDescribe ,
ConfigSlidingIntegral configOrientation ,
Class imageType)
{
ScoreAssociation score = FactoryAssociation.scoreEuclidean(TupleDesc_F64.class, true);
AssociateSurfBasic assoc = new AssociateSurfBasic(FactoryAssociation.greedy(score, 5, true));
AssociateDescription2D generalAssoc =
new AssociateDescTo2D<>(new WrapAssociateSurfBasic(assoc));
DetectDescribePoint fused =
FactoryDetectDescribe.surfStable(configDetector,configDescribe,configOrientation,imageType);
DdaManagerDetectDescribePoint manager = new DdaManagerDetectDescribePoint<>(fused);
return new DetectDescribeAssociate<>(manager, generalAssoc, false);
}
/**
* Creates a tracker which detects Shi-Tomasi corner features and describes them with BRIEF.
*
* @see ShiTomasiCornerIntensity
* @see DescribePointBrief
* @see boofcv.abst.feature.tracker.DdaManagerDetectDescribePoint
*
* @param maxAssociationError Maximum allowed association error. Try 200.
* @param configExtract Configuration for extracting features
* @param imageType Type of image being processed.
* @param derivType Type of image used to store the image derivative. null == use default
*/
public static , D extends ImageGray>
PointTracker dda_ST_BRIEF(int maxAssociationError,
ConfigGeneralDetector configExtract,
Class imageType, Class derivType)
{
if( derivType == null )
derivType = GImageDerivativeOps.getDerivativeType(imageType);
DescribePointBrief brief = FactoryDescribePointAlgs.brief(FactoryBriefDefinition.gaussian2(new Random(123), 16, 512),
FactoryBlurFilter.gaussian(ImageType.single(imageType), 0, 4));
GeneralFeatureDetector detectPoint = createShiTomasi(configExtract, derivType);
EasyGeneralFeatureDetector easy = new EasyGeneralFeatureDetector<>(detectPoint, imageType, derivType);
ScoreAssociateHamming_B score = new ScoreAssociateHamming_B();
AssociateDescription2D association =
new AssociateDescTo2D<>(FactoryAssociation.greedy(score, maxAssociationError, true));
DdaManagerGeneralPoint manager =
new DdaManagerGeneralPoint<>(easy, new WrapDescribeBrief<>(brief, imageType), 1.0);
return new DetectDescribeAssociate<>(manager, association, false);
}
/**
* Creates a tracker which detects FAST corner features and describes them with BRIEF.
*
* @see FastCornerDetector
* @see DescribePointBrief
* @see boofcv.abst.feature.tracker.DdaManagerDetectDescribePoint
*
* @param configFast Configuration for FAST detector
* @param configExtract Configuration for extracting features
* @param maxAssociationError Maximum allowed association error. Try 200.
* @param imageType Type of image being processed.
*/
public static , D extends ImageGray>
PointTracker dda_FAST_BRIEF(ConfigFastCorner configFast,
ConfigGeneralDetector configExtract,
int maxAssociationError,
Class imageType )
{
DescribePointBrief brief = FactoryDescribePointAlgs.brief(FactoryBriefDefinition.gaussian2(new Random(123), 16, 512),
FactoryBlurFilter.gaussian(ImageType.single(imageType), 0, 4));
GeneralFeatureDetector corner = FactoryDetectPoint.createFast(configFast, configExtract, imageType);
EasyGeneralFeatureDetector easy = new EasyGeneralFeatureDetector<>(corner, imageType, null);
ScoreAssociateHamming_B score = new ScoreAssociateHamming_B();
AssociateDescription2D association =
new AssociateDescTo2D<>(
FactoryAssociation.greedy(score, maxAssociationError, true));
DdaManagerGeneralPoint manager =
new DdaManagerGeneralPoint<>(easy, new WrapDescribeBrief<>(brief, imageType), 1.0);
return new DetectDescribeAssociate<>(manager, association, false);
}
/**
* Creates a tracker which detects Shi-Tomasi corner features and describes them with NCC.
*
* @see ShiTomasiCornerIntensity
* @see DescribePointPixelRegionNCC
* @see boofcv.abst.feature.tracker.DdaManagerDetectDescribePoint
*
* @param configExtract Configuration for extracting features
* @param describeRadius Radius of the region being described. Try 2.
* @param imageType Type of image being processed.
* @param derivType Type of image used to store the image derivative. null == use default */
public static , D extends ImageGray>
PointTracker dda_ST_NCC(ConfigGeneralDetector configExtract, int describeRadius,
Class imageType, Class derivType) {
if( derivType == null )
derivType = GImageDerivativeOps.getDerivativeType(imageType);
int w = 2*describeRadius+1;
DescribePointPixelRegionNCC alg = FactoryDescribePointAlgs.pixelRegionNCC(w, w, imageType);
GeneralFeatureDetector corner = createShiTomasi(configExtract, derivType);
EasyGeneralFeatureDetector easy = new EasyGeneralFeatureDetector<>(corner, imageType, derivType);
ScoreAssociateNccFeature score = new ScoreAssociateNccFeature();
AssociateDescription2D association =
new AssociateDescTo2D<>(
FactoryAssociation.greedy(score, Double.MAX_VALUE, true));
DdaManagerGeneralPoint manager =
new DdaManagerGeneralPoint<>(easy, new WrapDescribePixelRegionNCC<>(alg, imageType), 1.0);
return new DetectDescribeAssociate<>(manager, association, false);
}
/**
* Creates a tracker which uses the detect, describe, associate architecture.
*
* @param detector Interest point detector.
* @param orientation Optional orientation estimation algorithm. Can be null.
* @param describe Region description.
* @param associate Description association.
* @param updateDescription After a track has been associated should the description be changed? Try false.
* @param Type of input image.
* @param Type of region description
* @return tracker
*/
public static , Desc extends TupleDesc>
DetectDescribeAssociate dda(InterestPointDetector detector,
OrientationImage orientation ,
DescribeRegionPoint describe,
AssociateDescription2D associate ,
boolean updateDescription ) {
DetectDescribeFusion fused =
new DetectDescribeFusion<>(detector, orientation, describe);
DdaManagerDetectDescribePoint manager =
new DdaManagerDetectDescribePoint<>(fused);
DetectDescribeAssociate dat =
new DetectDescribeAssociate<>(manager, associate, updateDescription);
return dat;
}
public static , Desc extends TupleDesc>
DetectDescribeAssociate dda( DetectDescribePoint detDesc,
AssociateDescription2D associate ,
boolean updateDescription ) {
DdaManagerDetectDescribePoint manager =
new DdaManagerDetectDescribePoint<>(detDesc);
DetectDescribeAssociate dat =
new DetectDescribeAssociate<>(manager, associate, updateDescription);
return dat;
}
/**
* Creates a tracker which detects Fast-Hessian features, describes them with SURF, nominally tracks them using KLT.
*
* @see DescribePointSurf
* @see boofcv.abst.feature.tracker.DdaManagerDetectDescribePoint
*
* @param kltConfig Configuration for KLT tracker
* @param reactivateThreshold Tracks are reactivated after this many have been dropped. Try 10% of maxMatches
* @param configDetector Configuration for SURF detector
* @param configDescribe Configuration for SURF descriptor
* @param configOrientation Configuration for region orientation
* @param imageType Type of image the input is.
* @param Input image type.
* @return SURF based tracker.
*/
public static >
PointTracker combined_FH_SURF_KLT( PkltConfig kltConfig ,
int reactivateThreshold ,
ConfigFastHessian configDetector ,
ConfigSurfDescribe.Stability configDescribe ,
ConfigSlidingIntegral configOrientation ,
Class imageType) {
ScoreAssociation score = FactoryAssociation.defaultScore(TupleDesc_F64.class);
AssociateSurfBasic assoc = new AssociateSurfBasic(FactoryAssociation.greedy(score, 100000, true));
AssociateDescription generalAssoc = new WrapAssociateSurfBasic(assoc);
DetectDescribePoint fused =
FactoryDetectDescribe.surfStable(configDetector, configDescribe, configOrientation,imageType);
return combined(fused,generalAssoc, kltConfig,reactivateThreshold, imageType);
}
/**
* Creates a tracker which detects Shi-Tomasi corner features, describes them with SURF, and
* nominally tracks them using KLT.
*
* @see ShiTomasiCornerIntensity
* @see DescribePointSurf
* @see boofcv.abst.feature.tracker.DdaManagerDetectDescribePoint
*
* @param configExtract Configuration for extracting features
* @param kltConfig Configuration for KLT
* @param reactivateThreshold Tracks are reactivated after this many have been dropped. Try 10% of maxMatches
* @param configDescribe Configuration for SURF descriptor
* @param configOrientation Configuration for region orientation. If null then orientation isn't estimated
* @param imageType Type of image the input is.
* @param derivType Image derivative type. @return SURF based tracker.
*/
public static , D extends ImageGray>
PointTracker combined_ST_SURF_KLT(ConfigGeneralDetector configExtract,
PkltConfig kltConfig,
int reactivateThreshold,
ConfigSurfDescribe.Stability configDescribe,
ConfigSlidingIntegral configOrientation,
Class imageType,
Class derivType) {
if( derivType == null )
derivType = GImageDerivativeOps.getDerivativeType(imageType);
GeneralFeatureDetector corner = createShiTomasi(configExtract, derivType);
InterestPointDetector detector = FactoryInterestPoint.wrapPoint(corner, 1, imageType, derivType);
DescribeRegionPoint regionDesc
= FactoryDescribeRegionPoint.surfStable(configDescribe, imageType);
ScoreAssociation score = FactoryAssociation.scoreEuclidean(TupleDesc_F64.class, true);
AssociateSurfBasic assoc = new AssociateSurfBasic(FactoryAssociation.greedy(score, 100000, true));
AssociateDescription generalAssoc = new WrapAssociateSurfBasic(assoc);
OrientationImage orientation = null;
if( configOrientation != null ) {
Class integralType = GIntegralImageOps.getIntegralType(imageType);
OrientationIntegral orientationII = FactoryOrientationAlgs.sliding_ii(configOrientation, integralType);
orientation = FactoryOrientation.convertImage(orientationII,imageType);
}
return combined(detector,orientation,regionDesc,generalAssoc, kltConfig,reactivateThreshold,
imageType);
}
/**
* Creates a tracker that is a hybrid between KLT and Detect-Describe-Associate (DDA) trackers.
*
* @see CombinedTrackerScalePoint
*
* @param detector Feature detector.
* @param orientation Optional feature orientation. Can be null.
* @param describe Feature description
* @param associate Association algorithm.
* @param kltConfig Configuration for KLT tracker
* @param reactivateThreshold Tracks are reactivated after this many have been dropped. Try 10% of maxMatches
* @param imageType Input image type. @return Feature tracker
*/
public static , Desc extends TupleDesc>
PointTracker combined(InterestPointDetector detector,
OrientationImage orientation,
DescribeRegionPoint describe,
AssociateDescription associate,
PkltConfig kltConfig ,
int reactivateThreshold,
Class imageType)
{
DetectDescribeFusion fused = new DetectDescribeFusion<>(detector, orientation, describe);
return combined(fused,associate, kltConfig, reactivateThreshold,imageType);
}
/**
* Creates a tracker that is a hybrid between KLT and Detect-Describe-Associate (DDA) trackers.
*
* @see CombinedTrackerScalePoint
*
* @param detector Feature detector and describer.
* @param associate Association algorithm.
* @param kltConfig Configuration for KLT tracker
* @param reactivateThreshold Tracks are reactivated after this many have been dropped. Try 10% of maxMatches
* @param imageType Input image type. @return Feature tracker
*/
public static , D extends ImageGray, Desc extends TupleDesc>
PointTracker combined(DetectDescribePoint detector,
AssociateDescription associate,
PkltConfig kltConfig ,
int reactivateThreshold, Class imageType )
{
Class derivType = GImageDerivativeOps.getDerivativeType(imageType);
if( kltConfig == null ) {
kltConfig = new PkltConfig();
}
CombinedTrackerScalePoint tracker =
FactoryTrackerAlg.combined(detector,associate, kltConfig,imageType,derivType);
return new PointTrackerCombined<>(tracker, reactivateThreshold, imageType, derivType);
}
public static , D extends ImageGray, Desc extends TupleDesc>
PointTracker dda(GeneralFeatureDetector detector,
DescribeRegionPoint describe,
AssociateDescription2D associate,
double scale,
Class imageType) {
EasyGeneralFeatureDetector easy = new EasyGeneralFeatureDetector<>(detector, imageType, null);
DdaManagerGeneralPoint manager =
new DdaManagerGeneralPoint<>(easy, describe, scale);
return new DetectDescribeAssociate<>(manager, associate, false);
}
/**
* Creates a Shi-Tomasi corner detector specifically designed for SFM. Smaller feature radius work better.
* Variable detectRadius to control the number of features. When larger features are used weighting should
* be set to true, but because this is so small, it is set to false
*/
public static , D extends ImageGray>
GeneralFeatureDetector createShiTomasi(ConfigGeneralDetector config ,
Class derivType)
{
GradientCornerIntensity cornerIntensity = FactoryIntensityPointAlg.shiTomasi(1, false, derivType);
return FactoryDetectPoint.createGeneral(cornerIntensity, config );
}
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