Difference between revisions of "Example SURF Feature"

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Speeded Up Robust Feature (SURF) is a region descriptor and interest point detector.  Two different ways of using SURF are demonstrated in this example.  The easy way uses a high level interface that is easy to work with, but sacrifices flexibility.  The harder way directly creates the SURF classes, is more complex, and requires a better understanding of how the code works.
Speeded Up Robust Feature (SURF) is a region descriptor and interest point detector.  Two different ways of using SURF are demonstrated in this example.  The easy way uses a high level interface that is easy to work with, but sacrifices flexibility.  The harder way directly creates the SURF classes, is more complex, and requires a better understanding of how the code works.


Example File: [https://github.com/lessthanoptimal/BoofCV/blob/v0.25/examples/src/boofcv/examples/features/ExampleFeatureSurf.java ExampleFeatureSurf.java]
Example File: [https://github.com/lessthanoptimal/BoofCV/blob/v0.37/examples/src/main/java/boofcv/examples/features/ExampleFeatureSurf.java ExampleFeatureSurf.java]


Concepts:
Concepts:
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  */
  */
public class ExampleFeatureSurf {
public class ExampleFeatureSurf {
/**
/**
* Use generalized interfaces for working with SURF.  This removes much of the drudgery, but also reduces flexibility
* Use generalized interfaces for working with SURF.  This removes much of the drudgery, but also reduces flexibility
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public static void easy( GrayF32 image ) {
public static void easy( GrayF32 image ) {
// create the detector and descriptors
// create the detector and descriptors
DetectDescribePoint<GrayF32,BrightFeature> surf = FactoryDetectDescribe.
ConfigFastHessian configDetector = new ConfigFastHessian();
surfStable(new ConfigFastHessian(0, 2, 200, 2, 9, 4, 4), null, null,GrayF32.class);
configDetector.extract = new ConfigExtract(2, 0, 5, true);
configDetector.maxFeaturesPerScale = 200;
configDetector.initialSampleStep = 2;
 
DetectDescribePoint<GrayF32,TupleDesc_F64> surf = FactoryDetectDescribe.
surfStable(configDetector, null, null,GrayF32.class);


// specify the image to process
// specify the image to process
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/**
/**
* Configured exactly the same as the easy example above, but require a lot more code and a more in depth
* Configured exactly the same as the easy example above, but require a lot more code and a more in depth
* understanding of how SURF works and is configured.  Instead of TupleDesc_F64, SurfFeature are computed in
* understanding of how SURF works and is configured.  Each sub-problem which composes "SURF" is now explicitly
* this case.  They are almost the same as TupleDesc_F64, but contain the Laplacian's sign which can be used
* created and configured independently. This allows an advance user to tune it for a specific problem.
* to speed up association. That is an example of how using less generalized interfaces can improve performance.
*  
*  
* @param image Input image type. DOES NOT NEED TO BE GrayF32, GrayU8 works too
* @param image Input image type. DOES NOT NEED TO BE GrayF32, GrayU8 works too
*/
*/
public static <II extends ImageGray> void harder(GrayF32 image ) {
public static <II extends ImageGray<II>> void harder(GrayF32 image ) {
// SURF works off of integral images
// SURF works off of integral images
Class<II> integralType = GIntegralImageOps.getIntegralType(GrayF32.class);
Class<II> integralType = GIntegralImageOps.getIntegralType(GrayF32.class);
// define the feature detection algorithm
// define the feature detection algorithm
NonMaxSuppression extractor =
ConfigFastHessian config = new ConfigFastHessian();
FactoryFeatureExtractor.nonmax(new ConfigExtract(2, 0, 5, true));
config.extract = new ConfigExtract(2, 0, 5, true);
FastHessianFeatureDetector<II> detector =
config.maxFeaturesPerScale = 200;
new FastHessianFeatureDetector<>(extractor, 200, 2, 9, 4, 4, 6);
config.initialSampleStep = 2;
FastHessianFeatureDetector<II> detector = FactoryInterestPointAlgs.fastHessian(config);


// estimate orientation
// estimate orientation
OrientationIntegral<II> orientation =  
OrientationIntegral<II> orientation = FactoryOrientationAlgs.sliding_ii(null, integralType);
FactoryOrientationAlgs.sliding_ii(null, integralType);


DescribePointSurf<II> descriptor = FactoryDescribePointAlgs.<II>surfStability(null,integralType);
DescribePointSurf<II> descriptor = FactoryDescribePointAlgs.surfStability(null,integralType);
// compute the integral image of 'image'
// compute the integral image of 'image'
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descriptor.setImage(integral);
descriptor.setImage(integral);


List<ScalePoint> points = detector.getFoundPoints();
List<ScalePoint> points = detector.getFoundFeatures();


List<BrightFeature> descriptions = new ArrayList<>();
List<TupleDesc_F64> descriptions = new ArrayList<>();


for( ScalePoint p : points ) {
for( ScalePoint p : points ) {
// estimate orientation
// estimate orientation
orientation.setObjectRadius( p.scale*BoofDefaults.SURF_SCALE_TO_RADIUS);
orientation.setObjectRadius( p.scale*BoofDefaults.SURF_SCALE_TO_RADIUS);
double angle = orientation.compute(p.x,p.y);
double angle = orientation.compute(p.pixel.x,p.pixel.y);
// extract the SURF description for this region
// extract the SURF description for this region
BrightFeature desc = descriptor.createDescription();
TupleDesc_F64 desc = descriptor.createDescription();
descriptor.describe(p.x,p.y,angle,p.scale,desc);
descriptor.describe(p.pixel.x,p.pixel.y,angle,p.scale, true, desc);


// save everything for processing later on
// save everything for processing later on
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}
}


public static void main( String args[] ) {
public static void main( String[] args ) {
// Need to turn off concurrency since the order in which feature are returned
// is not deterministic if turned on
BoofConcurrency.USE_CONCURRENT = false;
 
GrayF32 image = UtilImageIO.loadImage(UtilIO.pathExample("particles01.jpg"), GrayF32.class);
GrayF32 image = UtilImageIO.loadImage(UtilIO.pathExample("particles01.jpg"), GrayF32.class);
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System.out.println("Done!");
System.out.println("Done!");
}
}
}
}
</syntaxhighlight>
</syntaxhighlight>

Revision as of 13:07, 21 December 2020

Speeded Up Robust Feature (SURF) is a region descriptor and interest point detector. Two different ways of using SURF are demonstrated in this example. The easy way uses a high level interface that is easy to work with, but sacrifices flexibility. The harder way directly creates the SURF classes, is more complex, and requires a better understanding of how the code works.

Example File: ExampleFeatureSurf.java

Concepts:

  • SURF
  • Region Descriptor
  • Interest Point

Example Code

/**
 * Example of how to use SURF detector and descriptors in BoofCV. 
 * 
 * @author Peter Abeles
 */
public class ExampleFeatureSurf {
	/**
	 * Use generalized interfaces for working with SURF.  This removes much of the drudgery, but also reduces flexibility
	 * and slightly increases memory and computational requirements.
	 * 
	 *  @param image Input image type. DOES NOT NEED TO BE GrayF32, GrayU8 works too
	 */
	public static void easy( GrayF32 image ) {
		// create the detector and descriptors
		ConfigFastHessian configDetector = new ConfigFastHessian();
		configDetector.extract = new ConfigExtract(2, 0, 5, true);
		configDetector.maxFeaturesPerScale = 200;
		configDetector.initialSampleStep = 2;

		DetectDescribePoint<GrayF32,TupleDesc_F64> surf = FactoryDetectDescribe.
				surfStable(configDetector, null, null,GrayF32.class);

		 // specify the image to process
		surf.detect(image);

		System.out.println("Found Features: "+surf.getNumberOfFeatures());
		System.out.println("First descriptor's first value: "+surf.getDescription(0).value[0]);
	}

	/**
	 * Configured exactly the same as the easy example above, but require a lot more code and a more in depth
	 * understanding of how SURF works and is configured.  Each sub-problem which composes "SURF" is now explicitly
	 * created and configured independently. This allows an advance user to tune it for a specific problem.
	 * 
	 * @param image Input image type. DOES NOT NEED TO BE GrayF32, GrayU8 works too
	 */
	public static <II extends ImageGray<II>> void harder(GrayF32 image ) {
		// SURF works off of integral images
		Class<II> integralType = GIntegralImageOps.getIntegralType(GrayF32.class);
		
		// define the feature detection algorithm
		ConfigFastHessian config = new ConfigFastHessian();
		config.extract = new ConfigExtract(2, 0, 5, true);
		config.maxFeaturesPerScale = 200;
		config.initialSampleStep = 2;
		FastHessianFeatureDetector<II> detector = FactoryInterestPointAlgs.fastHessian(config);

		// estimate orientation
		OrientationIntegral<II> orientation =  FactoryOrientationAlgs.sliding_ii(null, integralType);

		DescribePointSurf<II> descriptor = FactoryDescribePointAlgs.surfStability(null,integralType);
		
		// compute the integral image of 'image'
		II integral = GeneralizedImageOps.createSingleBand(integralType,image.width,image.height);
		GIntegralImageOps.transform(image, integral);

		// detect fast hessian features
		detector.detect(integral);
		// tell algorithms which image to process
		orientation.setImage(integral);
		descriptor.setImage(integral);

		List<ScalePoint> points = detector.getFoundFeatures();

		List<TupleDesc_F64> descriptions = new ArrayList<>();

		for( ScalePoint p : points ) {
			// estimate orientation
			orientation.setObjectRadius( p.scale*BoofDefaults.SURF_SCALE_TO_RADIUS);
			double angle = orientation.compute(p.pixel.x,p.pixel.y);
			
			// extract the SURF description for this region
			TupleDesc_F64 desc = descriptor.createDescription();
			descriptor.describe(p.pixel.x,p.pixel.y,angle,p.scale, true, desc);

			// save everything for processing later on
			descriptions.add(desc);
		}
		
		System.out.println("Found Features: "+points.size());
		System.out.println("First descriptor's first value: "+descriptions.get(0).value[0]);
	}

	public static void main( String[] args ) {
		// Need to turn off concurrency since the order in which feature are returned
		// is not deterministic if turned on
		BoofConcurrency.USE_CONCURRENT = false;

		GrayF32 image = UtilImageIO.loadImage(UtilIO.pathExample("particles01.jpg"), GrayF32.class);
		
		// run each example
		ExampleFeatureSurf.easy(image);
		ExampleFeatureSurf.harder(image);
		
		System.out.println("Done!");
	}
}