Example Associate Three View

From BoofCV
Jump to navigationJump to search
Associated features across three views
Three view feature association from using this example.

Associating features across three views is useful for 3D reconstruction as it produces a more stable solution than 2-views.

Example Code:

Concepts:

  • Describe point features
  • Associate descriptions

Example Code

/**
 * Common matches between sets of three views are important in SFM as they filter out even more false positives
 * and three view geometry, unlike two view-geometry, has a unique projection in each image. This makes it even
 * easier to remove false matches using geometric constraints. In BoofCV's reconstruction pipeline three views
 * are always used over two views whenever possible due to the added robustness.
 *
 * In this example, association is first done pairwise between each image pair. Then the matches are traversed
 * to find features which form a "ring", that is that when traversed from image 1 -> 2 - > 3 -> 1 you wind up
 * back at the same location.
 */
public class ExampleAssociateThreeView {

	// Stores image pixel coordinate
	public final DogArray<Point2D_F64> locations01 = new DogArray<>(Point2D_F64::new);
	public final DogArray<Point2D_F64> locations02 = new DogArray<>(Point2D_F64::new);
	public final DogArray<Point2D_F64> locations03 = new DogArray<>(Point2D_F64::new);

	// Stores the descriptor for each feature
	public DogArray<TupleDesc_F64> features01, features02, features03;

	// Indicates which "set" a feature belongs in. SURF can be white or black. Using sets simplifies
	// feature association since only features in the same set can be matched
	public final DogArray_I32 featureSet01 = new DogArray_I32();
	public final DogArray_I32 featureSet02 = new DogArray_I32();
	public final DogArray_I32 featureSet03 = new DogArray_I32();

	// Reference to the feature detector/descriptor
	DetectDescribePoint<GrayU8, TupleDesc_F64> detDesc;

	// Create lists when accessing by index makes more sense
	List<DogArray<Point2D_F64>> listLocations = BoofMiscOps.asList(locations01, locations02, locations03);
	List<DogArray_I32> listFeatureSets = BoofMiscOps.asList(featureSet01, featureSet02, featureSet03);
	List<DogArray<TupleDesc_F64>> listFeatures;

	/**
	 * Initializes data structures to use the feature descriptor
	 */
	public <T extends ImageBase<T>> void initialize( DetectDescribePoint<GrayU8, TupleDesc_F64> detDesc ) {
		this.detDesc = detDesc;
		features01 = UtilFeature.createArray(detDesc, 100);
		features02 = UtilFeature.createArray(detDesc, 100);
		features03 = UtilFeature.createArray(detDesc, 100);
		listFeatures = BoofMiscOps.asList(features01, features02, features03);
	}

	/**
	 * Detects and saves features in the specified image
	 */
	public void detectFeatures( GrayU8 gray, int which ) {
		DogArray<Point2D_F64> locations = listLocations.get(which);
		DogArray<TupleDesc_F64> features = listFeatures.get(which);
		DogArray_I32 featureSet = listFeatureSets.get(which);

		detDesc.detect(gray);
		for (int i = 0; i < detDesc.getNumberOfFeatures(); i++) {
			Point2D_F64 pixel = detDesc.getLocation(i);
			locations.grow().setTo(pixel.x, pixel.y);
			features.grow().setTo(detDesc.getDescription(i));
			featureSet.add(detDesc.getSet(i));
		}
	}

	/**
	 * BoofCV comes with a class which does all the three view matching for you. Which association and scoring
	 * function are used is all configurable.
	 */
	public DogArray<AssociatedTripleIndex> threeViewPairwiseAssociate() {
		ScoreAssociation<TupleDesc_F64> scorer =
				FactoryAssociation.scoreEuclidean(TupleDesc_F64.class, true);
		AssociateDescription<TupleDesc_F64> associate =
				FactoryAssociation.greedy(new ConfigAssociateGreedy(true, 0.1), scorer);

		var associateThree = new AssociateThreeByPairs<>(associate);

		associateThree.initialize(detDesc.getNumberOfSets());
		associateThree.setFeaturesA(features01, featureSet01);
		associateThree.setFeaturesB(features02, featureSet02);
		associateThree.setFeaturesC(features03, featureSet03);

		associateThree.associate();

		return associateThree.getMatches();
	}

	public static void main( String[] args ) {
		String name = "rock_leaves_";
		GrayU8 gray01 = UtilImageIO.loadImage(UtilIO.pathExample("triple/" + name + "01.jpg"), GrayU8.class);
		GrayU8 gray02 = UtilImageIO.loadImage(UtilIO.pathExample("triple/" + name + "02.jpg"), GrayU8.class);
		GrayU8 gray03 = UtilImageIO.loadImage(UtilIO.pathExample("triple/" + name + "03.jpg"), GrayU8.class);

		// Using SURF features. Robust and fairly fast to compute
		DetectDescribePoint<GrayU8, TupleDesc_F64> detDesc = FactoryDetectDescribe.surfStable(
				new ConfigFastHessian(0, 4, 1000, 1, 9, 4, 2), null, null, GrayU8.class);

		ExampleAssociateThreeView example = new ExampleAssociateThreeView();
		example.initialize(detDesc);

		// Compute and describe features inside the image
		example.detectFeatures(gray01, 0);
		example.detectFeatures(gray02, 1);
		example.detectFeatures(gray03, 2);

		System.out.println("features01.size = " + example.features01.size);
		System.out.println("features02.size = " + example.features02.size);
		System.out.println("features03.size = " + example.features03.size);

		// Find features for an association ring across all the views. This removes most false positives.
		DogArray<AssociatedTripleIndex> associatedIdx = example.threeViewPairwiseAssociate();

		// Convert the matched indexes into AssociatedTriple which contain the actual pixel coordinates
		var associated = new DogArray<>(AssociatedTriple::new);
		associatedIdx.forEach(p -> associated.grow().setTo(
				example.locations01.get(p.a), example.locations02.get(p.b), example.locations03.get(p.c)));

		System.out.println("Total Matched Triples = " + associated.size);

		// Show remaining associations from RANSAC
		var triplePanel = new AssociatedTriplePanel();
		triplePanel.setImages(
				UtilImageIO.loadImageNotNull(UtilIO.pathExample("triple/" + name + "01.jpg")),
				UtilImageIO.loadImageNotNull(UtilIO.pathExample("triple/" + name + "02.jpg")),
				UtilImageIO.loadImageNotNull(UtilIO.pathExample("triple/" + name + "03.jpg")));
		triplePanel.setAssociation(associated.toList());
		ShowImages.showWindow(triplePanel, "Associations", true);
	}
}