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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) 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.detect.interest;

import boofcv.abst.feature.describe.ConfigSiftScaleSpace;
import boofcv.abst.feature.detect.extract.ConfigExtract;
import boofcv.abst.feature.detect.extract.NonMaxLimiter;
import boofcv.abst.feature.detect.extract.NonMaxSuppression;
import boofcv.abst.feature.detect.intensity.GeneralFeatureIntensity;
import boofcv.abst.feature.detect.intensity.WrapperGradientCornerIntensity;
import boofcv.abst.feature.detect.intensity.WrapperHessianBlobIntensity;
import boofcv.abst.feature.detect.interest.ConfigFastHessian;
import boofcv.abst.feature.detect.interest.ConfigSiftDetector;
import boofcv.abst.filter.ImageFunctionSparse;
import boofcv.abst.filter.derivative.AnyImageDerivative;
import boofcv.alg.feature.detect.intensity.GradientCornerIntensity;
import boofcv.alg.feature.detect.intensity.HessianBlobIntensity;
import boofcv.alg.feature.detect.interest.*;
import boofcv.alg.filter.derivative.GImageDerivativeOps;
import boofcv.factory.feature.detect.extract.FactoryFeatureExtractor;
import boofcv.factory.feature.detect.intensity.FactoryIntensityPointAlg;
import boofcv.factory.filter.derivative.FactoryDerivativeSparse;
import boofcv.struct.image.ImageGray;

import javax.annotation.Nullable;

/**
 * Factory for non-generic specific implementations of interest point detection algorithms.
 *
 * @author Peter Abeles
 */
public class FactoryInterestPointAlgs {

	/**
	 * Creates a {@link FeaturePyramid} which is uses a hessian blob detector.
	 *
	 * @param extractRadius   Size of the feature used to detect the corners.
	 * @param detectThreshold Minimum corner intensity required
	 * @param maxFeatures     Max number of features that can be found.
	 * @param imageType       Type of input image.
	 * @param derivType       Image derivative type.
	 * @return CornerLaplaceScaleSpace
	 */
	public static , D extends ImageGray>
	FeaturePyramid hessianPyramid(int extractRadius,
										float detectThreshold,
										int maxFeatures,
										Class imageType,
										Class derivType) {
		GeneralFeatureIntensity intensity = new WrapperHessianBlobIntensity<>(HessianBlobIntensity.Type.DETERMINANT, derivType);
		NonMaxSuppression extractor = FactoryFeatureExtractor.nonmax(
				new ConfigExtract(extractRadius, detectThreshold, extractRadius, true));
		GeneralFeatureDetector detector = new GeneralFeatureDetector<>(intensity, extractor);
		detector.setMaxFeatures(maxFeatures);

		AnyImageDerivative deriv = GImageDerivativeOps.derivativeForScaleSpace(imageType, derivType);

		return new FeaturePyramid<>(detector, deriv, 2);
	}

	/**
	 * Creates a {@link FeaturePyramid} which is uses the Harris corner detector.
	 *
	 * @param extractRadius   Size of the feature used to detect the corners.
	 * @param detectThreshold Minimum corner intensity required
	 * @param maxFeatures     Max number of features that can be found.
	 * @param imageType       Type of input image.
	 * @param derivType       Image derivative type.
	 * @return CornerLaplaceScaleSpace
	 */
	public static , D extends ImageGray>
	FeaturePyramid harrisPyramid(int extractRadius,
									   float detectThreshold,
									   int maxFeatures,
									   Class imageType,
									   Class derivType) {
		GradientCornerIntensity harris = FactoryIntensityPointAlg.harris(extractRadius, 0.04f, false, derivType);
		GeneralFeatureIntensity intensity = new WrapperGradientCornerIntensity<>(harris);
		NonMaxSuppression extractor = FactoryFeatureExtractor.nonmax(
				new ConfigExtract(extractRadius, detectThreshold, extractRadius, true));
		GeneralFeatureDetector detector = new GeneralFeatureDetector<>(intensity, extractor);
		detector.setMaxFeatures(maxFeatures);

		AnyImageDerivative deriv = GImageDerivativeOps.derivativeForScaleSpace(imageType, derivType);

		return new FeaturePyramid<>(detector, deriv, 2);
	}

	/**
	 * Creates a {@link boofcv.alg.feature.detect.interest.FeatureLaplacePyramid} which is uses a hessian blob detector.
	 *
	 * @param extractRadius   Size of the feature used to detect the corners.
	 * @param detectThreshold Minimum corner intensity required
	 * @param maxFeatures     Max number of features that can be found.
	 * @param imageType       Type of input image.
	 * @param derivType       Image derivative type.
	 * @return CornerLaplaceScaleSpace
	 */
	public static , D extends ImageGray>
	FeatureLaplacePyramid hessianLaplace(int extractRadius,
											   float detectThreshold,
											   int maxFeatures,
											   Class imageType,
											   Class derivType) {
		GeneralFeatureIntensity intensity = new WrapperHessianBlobIntensity<>(HessianBlobIntensity.Type.DETERMINANT, derivType);
		NonMaxSuppression extractor = FactoryFeatureExtractor.nonmax(
				new ConfigExtract(extractRadius, detectThreshold, extractRadius, true));
		GeneralFeatureDetector detector = new GeneralFeatureDetector<>(intensity, extractor);
		detector.setMaxFeatures(maxFeatures);

		AnyImageDerivative deriv = GImageDerivativeOps.derivativeForScaleSpace(imageType, derivType);

		ImageFunctionSparse sparseLaplace = FactoryDerivativeSparse.createLaplacian(imageType, null);

		return new FeatureLaplacePyramid<>(detector, sparseLaplace, deriv, 2);
	}

	/**
	 * Creates a {@link FeatureLaplacePyramid} which is uses the Harris corner detector.
	 *
	 * @param extractRadius   Size of the feature used to detect the corners.
	 * @param detectThreshold Minimum corner intensity required
	 * @param maxFeatures     Max number of features that can be found.
	 * @param imageType       Type of input image.
	 * @param derivType       Image derivative type.
	 * @return CornerLaplaceScaleSpace
	 */
	public static , D extends ImageGray>
	FeatureLaplacePyramid harrisLaplace(int extractRadius,
											  float detectThreshold,
											  int maxFeatures,
											  Class imageType,
											  Class derivType) {
		GradientCornerIntensity harris = FactoryIntensityPointAlg.harris(extractRadius, 0.04f, false, derivType);
		GeneralFeatureIntensity intensity = new WrapperGradientCornerIntensity<>(harris);
		NonMaxSuppression extractor = FactoryFeatureExtractor.nonmax(
				new ConfigExtract(extractRadius, detectThreshold, extractRadius, true));
		GeneralFeatureDetector detector = new GeneralFeatureDetector<>(intensity, extractor);
		detector.setMaxFeatures(maxFeatures);

		AnyImageDerivative deriv = GImageDerivativeOps.derivativeForScaleSpace(imageType,derivType);
		ImageFunctionSparse sparseLaplace = FactoryDerivativeSparse.createLaplacian(imageType, null);

		return new FeatureLaplacePyramid<>(detector, sparseLaplace, deriv, 2);
	}

	/**
	 * Creates a Fast Hessian blob detector used by SURF.
	 *
	 * @param config Configuration for detector. Pass in null for default options.
	 * @param  Integral Image
	 * @return The feature detector
	 */
	public static >
	FastHessianFeatureDetector fastHessian( @Nullable ConfigFastHessian config ) {

		if( config == null )
			config = new ConfigFastHessian();
		config.checkValidity();

		// ignore border is overwritten by Fast Hessian at detection time
		NonMaxSuppression extractor = FactoryFeatureExtractor.nonmax(
				new ConfigExtract(config.extractRadius, config.detectThreshold, 0, true));
		return new FastHessianFeatureDetector<>(extractor, config.maxFeaturesPerScale,
				config.initialSampleSize, config.initialSize, config.numberScalesPerOctave,
				config.numberOfOctaves, config.scaleStepSize);
	}

	/**
	 * Creates a SIFT detector
	 */
	public static SiftDetector sift( @Nullable ConfigSiftScaleSpace configSS , @Nullable ConfigSiftDetector configDetector ) {

		if( configSS == null )
			configSS = new ConfigSiftScaleSpace();

		if( configDetector == null )
			configDetector = new ConfigSiftDetector();

		NonMaxLimiter nonmax = FactoryFeatureExtractor.nonmaxLimiter(
				configDetector.extract,configDetector.maxFeaturesPerScale);
		SiftScaleSpace ss = new SiftScaleSpace(configSS.firstOctave,configSS.lastOctave,
				configSS.numScales,configSS.sigma0);
		return new SiftDetector(ss,configDetector.edgeR,nonmax);
	}
}