/*
 * Copyright (c) 2011-2016, 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.demonstrations.feature.detect.interest;

import boofcv.abst.feature.detect.extract.ConfigExtract;
import boofcv.abst.feature.detect.extract.NonMaxLimiter;
import boofcv.abst.feature.detect.extract.NonMaxSuppression;
import boofcv.alg.feature.detect.interest.SiftDetector;
import boofcv.alg.feature.detect.interest.SiftScaleSpace;
import boofcv.factory.feature.detect.extract.FactoryFeatureExtractor;
import boofcv.gui.feature.VisualizeFeatures;
import boofcv.gui.image.ShowImages;
import boofcv.io.UtilIO;
import boofcv.io.image.ConvertBufferedImage;
import boofcv.io.image.UtilImageIO;
import boofcv.struct.image.GrayF32;

import java.awt.image.BufferedImage;

/**
 * @author Peter Abeles
 */
public class DebugSiftDetectorApp {

	public static void main( String args[] ) {
		BufferedImage input = UtilImageIO.loadImage(UtilIO.pathExample("sunflowers.jpg"));
//		BufferedImage input = UtilImageIO.loadImage(UtilIO.pathExample("shapes/shapes01.png");

		GrayF32 gray = ConvertBufferedImage.convertFromSingle(input, null, GrayF32.class);


		NonMaxSuppression nonmax = FactoryFeatureExtractor.nonmax(new ConfigExtract(3,1,1,true,true,true));
		NonMaxLimiter extractor = new NonMaxLimiter(nonmax,400);
		SiftScaleSpace imageSS = new SiftScaleSpace(-1,5,3,2.75);
		SiftDetector alg = new SiftDetector(imageSS,10,extractor);


		alg.process(gray);

		System.out.println("total features found: " + alg.getDetections().size());

		VisualizeFeatures.drawScalePoints(input.createGraphics(),
				alg.getDetections().toList(),1);


//		ListDisplayPanel dog = new ListDisplayPanel();
//		for( int i = 0; i < alg.getScaleSpace().getDog().length; i++ ) {
//			int scale = i % (alg.getScaleSpace().getNumScales()-1);
//			int octave = i / (alg.getScaleSpace().getNumScales()-1);
//
//			BufferedImage img = VisualizeImageData.colorizeSign(alg.getScaleSpace().getDog()[i],null,-1);
//			dog.addImage(img,octave+"  "+scale);
//		}
//
//		ListDisplayPanel ss = new ListDisplayPanel();
//		for( int i = 0; i < alg.getScaleSpace().getScale().length; i++ ) {
//			int scale = i % alg.getScaleSpace().getNumScales();
//			int octave = i / alg.getScaleSpace().getNumScales();
//
//			BufferedImage img = VisualizeImageData.grayMagnitude(alg.getScaleSpace().getScale()[i],null,255);
//			ss.addImage(img,octave+"  "+scale);
//		}
//		ShowImages.showWindow(dog, "Octave DOG");
//		ShowImages.showWindow(ss, "Octave Scales");
		ShowImages.showWindow(input, "Found Features", true);

		System.out.println("Done");
	}
}