Peter at 20:31, 17 January 2022

2022-01-17T20:31:42Z

← Older revision		Revision as of 20:31, 17 January 2022
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	Example Code:		Example Code:
	* [https://github.com/lessthanoptimal/BoofCV/tree/v0.38/examples/src/main/java/boofcv/examples/features/ExampleAssociateThreeView.java ExampleAssociateThreeView.java]		* [https://github.com/lessthanoptimal/BoofCV/tree/v0.40/examples/src/main/java/boofcv/examples/features/ExampleAssociateThreeView.java ExampleAssociateThreeView.java]

	Concepts:		Concepts:
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	* Initializes data structures to use the feature descriptor		* Initializes data structures to use the feature descriptor
	*/		*/
	public<T extends ImageBase<T>> void initialize( DetectDescribePoint<GrayU8, TupleDesc_F64> detDesc ) {		public <T extends ImageBase<T>> void initialize( DetectDescribePoint<GrayU8, TupleDesc_F64> detDesc ) {
	this.detDesc = detDesc;		this.detDesc = detDesc;
	features01 = UtilFeature.createArray(detDesc, 100);		features01 = UtilFeature.createArray(detDesc, 100);
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	// Convert the matched indexes into AssociatedTriple which contain the actual pixel coordinates		// Convert the matched indexes into AssociatedTriple which contain the actual pixel coordinates
	var associated = new DogArray<>(AssociatedTriple::new);		var associated = new DogArray<>(AssociatedTriple::new);
	associatedIdx.forEach(p->associated.grow().setTo(		associatedIdx.forEach(p -> associated.grow().setTo(
	example.locations01.get(p.a), example.locations02.get(p.b), example.locations03.get(p.c)));		example.locations01.get(p.a), example.locations02.get(p.b), example.locations03.get(p.c)));

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	var triplePanel = new AssociatedTriplePanel();		var triplePanel = new AssociatedTriplePanel();
	triplePanel.setImages(		triplePanel.setImages(
	UtilImageIO.~~loadImage~~(UtilIO.pathExample("triple/" + name + "01.jpg")),		UtilImageIO.loadImageNotNull(UtilIO.pathExample("triple/" + name + "01.jpg")),
	UtilImageIO.~~loadImage~~(UtilIO.pathExample("triple/" + name + "02.jpg")),		UtilImageIO.loadImageNotNull(UtilIO.pathExample("triple/" + name + "02.jpg")),
	UtilImageIO.~~loadImage~~(UtilIO.pathExample("triple/" + name + "03.jpg")));		UtilImageIO.loadImageNotNull(UtilIO.pathExample("triple/" + name + "03.jpg")));
	triplePanel.setAssociation(associated.toList());		triplePanel.setAssociation(associated.toList());
	ShowImages.showWindow(triplePanel, "Associations", true);		ShowImages.showWindow(triplePanel, "Associations", true);

Peter at 17:34, 12 July 2021

2021-07-12T17:34:32Z

← Older revision		Revision as of 17:34, 12 July 2021
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	<center>		<center>
	{\|		{\|
	\| [[File:~~Example_associate_point~~.jpg\|Associated ~~feature between images using example code.~~]]		\| [[File:Example_three_view_association.jpg\|Associated features across three views\|800px]]
	\|-		\|-
	!~~Associated~~ feature ~~between images~~ using example ~~code~~.		!Three view feature association from using this example.
	\|}		\|}
	</center>		</center>

Peter: Created page with " {| | Associated feature between images using example code. |- !Associated feature between images using example code. |} ..."

2021-07-12T17:25:25Z

Created page with "<center> {| | Associated feature between images using example code. |- !Associated feature between images using example code. |} </center>..."

New page

<center>
{|
| [[File:Example_associate_point.jpg|Associated feature between images using example code.]]
|-
!Associated feature between images using example code.
|}
</center>

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

Example Code:
* [https://github.com/lessthanoptimal/BoofCV/tree/v0.38/examples/src/main/java/boofcv/examples/features/ExampleAssociateThreeView.java ExampleAssociateThreeView.java]

Concepts:
* Describe point features
* Associate descriptions

= Example Code =

<syntaxhighlight lang="java">
/**
* 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.loadImage(UtilIO.pathExample("triple/" + name + "01.jpg")),
UtilImageIO.loadImage(UtilIO.pathExample("triple/" + name + "02.jpg")),
UtilImageIO.loadImage(UtilIO.pathExample("triple/" + name + "03.jpg")));
triplePanel.setAssociation(associated.toList());
ShowImages.showWindow(triplePanel, "Associations", true);
}
}
</syntaxhighlight>

Example Associate Three View - Revision history

Peter at 20:31, 17 January 2022

Peter at 17:34, 12 July 2021

Peter: Created page with " {| | Associated feature between images using example code. |- !Associated feature between images using example code. |} ..."