/*
 * 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.associate;

import boofcv.abst.feature.describe.ConfigBrief;
import boofcv.abst.feature.describe.DescribeRegionPoint;
import boofcv.abst.feature.detect.interest.ConfigFastHessian;
import boofcv.abst.feature.detect.interest.ConfigGeneralDetector;
import boofcv.abst.feature.detect.interest.ConfigSiftDetector;
import boofcv.abst.feature.detect.interest.InterestPointDetector;
import boofcv.abst.feature.orientation.OrientationImage;
import boofcv.abst.feature.orientation.OrientationIntegral;
import boofcv.alg.feature.detect.interest.GeneralFeatureDetector;
import boofcv.alg.transform.ii.GIntegralImageOps;
import boofcv.core.image.GeneralizedImageOps;
import boofcv.factory.feature.describe.FactoryDescribeRegionPoint;
import boofcv.factory.feature.detect.interest.FactoryDetectPoint;
import boofcv.factory.feature.detect.interest.FactoryInterestPoint;
import boofcv.factory.feature.orientation.FactoryOrientation;
import boofcv.factory.feature.orientation.FactoryOrientationAlgs;
import boofcv.gui.SelectAlgorithmAndInputPanel;
import boofcv.gui.feature.AssociationScorePanel;
import boofcv.gui.image.ShowImages;
import boofcv.io.PathLabel;
import boofcv.io.UtilIO;
import boofcv.io.image.ConvertBufferedImage;
import boofcv.struct.feature.TupleDesc;
import boofcv.struct.image.GrayF32;
import boofcv.struct.image.ImageGray;
import georegression.struct.point.Point2D_F64;

import javax.swing.*;
import java.awt.*;
import java.awt.image.BufferedImage;
import java.util.ArrayList;
import java.util.List;


/**
 * Shows how tightly focused the score is around the best figure by showing the relative
 * size and number of features which have a similar score visually in the image.  For example,
 * lots of other features with similar sized circles means the distribution is spread widely
 * while only one or two small figures means it is very narrow.
 *
 * @author Peter Abeles
 */
public class VisualizeAssociationScoreApp
		extends SelectAlgorithmAndInputPanel implements VisualizeScorePanel.Listener {
	// These classes process the input images and compute association score
	InterestPointDetector detector;
	DescribeRegionPoint describe;
	OrientationImage orientation;

	// gray scale versions of input image
	T imageLeft;
	T imageRight;
	Class imageType;

	// visualizes association score
	AssociationScorePanel scorePanel;
	VisualizeScorePanel controlPanel;

	// has the image been processed yet
	boolean processedImage = false;

	public VisualizeAssociationScoreApp(Class imageType,
										Class derivType) {
		super(2);
		this.imageType = imageType;

		imageLeft = GeneralizedImageOps.createSingleBand(imageType, 1, 1);
		imageRight = GeneralizedImageOps.createSingleBand(imageType, 1, 1);

		GeneralFeatureDetector alg;

		addAlgorithm(0, "Fast Hessian", FactoryInterestPoint.fastHessian(new ConfigFastHessian(1, 2, 200, 1, 9, 4, 4)));
		addAlgorithm(0, "SIFT", FactoryInterestPoint.sift(null,new ConfigSiftDetector(500),imageType));
		alg = FactoryDetectPoint.createShiTomasi(new ConfigGeneralDetector(500,2,1), false, derivType);
		addAlgorithm(0, "Shi-Tomasi", FactoryInterestPoint.wrapPoint(alg, 1, imageType, derivType));

		addAlgorithm(1, "SURF", FactoryDescribeRegionPoint.surfStable(null, imageType));
		addAlgorithm(1, "SIFT", FactoryDescribeRegionPoint.sift(null,null, imageType));
		addAlgorithm(1, "BRIEF", FactoryDescribeRegionPoint.brief(new ConfigBrief(true), imageType));
		addAlgorithm(1, "BRIEFO", FactoryDescribeRegionPoint.brief(new ConfigBrief(false), imageType));
		addAlgorithm(1, "Pixel 11x11", FactoryDescribeRegionPoint.pixel(11, 11, imageType));
		addAlgorithm(1, "NCC 11x11", FactoryDescribeRegionPoint.pixelNCC(11, 11, imageType));

		// estimate orientation using this once since it is fast
		Class integralType = GIntegralImageOps.getIntegralType(imageType);
		OrientationIntegral orientationII = FactoryOrientationAlgs.sliding_ii(null, integralType);
		orientation = FactoryOrientation.convertImage(orientationII, imageType);

		controlPanel = new VisualizeScorePanel(this);
		scorePanel = new AssociationScorePanel(3);

		JPanel gui = new JPanel();
		gui.setLayout(new BorderLayout());

		gui.add(controlPanel, BorderLayout.WEST);
		gui.add(scorePanel, BorderLayout.CENTER);

		setMainGUI(gui);
	}

	public void process(BufferedImage buffLeft, BufferedImage buffRight) {
		// copy the input images
		imageLeft.reshape(buffLeft.getWidth(), buffLeft.getHeight());
		imageRight.reshape(buffRight.getWidth(), buffRight.getHeight());

		ConvertBufferedImage.convertFromSingle(buffLeft, imageLeft, imageType);
		ConvertBufferedImage.convertFromSingle(buffRight, imageRight, imageType);

		// update the GUI's background images
		scorePanel.setImages(buffLeft, buffRight);

		processedImage = true;

		// tell it to update everything
		doRefreshAll();
	}

	@Override
	public void loadConfigurationFile(String fileName) {
	}

	@Override
	public void refreshAll(Object[] cookies) {
		detector = (InterestPointDetector) cookies[0];
		describe = (DescribeRegionPoint) cookies[1];
		controlPanel.setFeatureType(describe.getDescriptionType());

		processImage();
	}

	@Override
	public void setActiveAlgorithm(int indexFamily, String name, Object cookie) {
		switch (indexFamily) {
			case 0:
				detector = (InterestPointDetector) cookie;
				break;

			case 1:
				describe = (DescribeRegionPoint) cookie;
				break;
		}

		controlPanel.setFeatureType(describe.getDescriptionType());

		processImage();
	}

	/**
	 * Extracts image information and then passes that info onto scorePanel for display.  Data is not
	 * recycled to avoid threading issues.
	 */
	private void processImage() {
		final List leftPts = new ArrayList();
		final List rightPts = new ArrayList();
		final List leftDesc = new ArrayList();
		final List rightDesc = new ArrayList();

		final ProgressMonitor progressMonitor = new ProgressMonitor(this,
				"Compute Feature Information",
				"", 0, 4);
		extractImageFeatures(progressMonitor, 0, imageLeft, leftDesc, leftPts);
		extractImageFeatures(progressMonitor, 2, imageRight, rightDesc, rightPts);

		SwingUtilities.invokeLater(new Runnable() {
			public void run() {
				progressMonitor.close();
				scorePanel.setScorer(controlPanel.getSelected());
				scorePanel.setLocation(leftPts, rightPts, leftDesc, rightDesc);
				repaint();
			}
		});
	}

	/**
	 * Detects the locations of the features in the image and extracts descriptions of each of
	 * the features.
	 */
	private void extractImageFeatures(final ProgressMonitor progressMonitor, final int progress,
									  T image,
									  List descs, List locs) {
		SwingUtilities.invokeLater(new Runnable() {
			public void run() {
				progressMonitor.setNote("Detecting");
			}
		});
		detector.detect(image);
		SwingUtilities.invokeLater(new Runnable() {
			public void run() {
				progressMonitor.setProgress(progress + 1);
				progressMonitor.setNote("Describing");
			}
		});
		describe.setImage(image);
		orientation.setImage(image);

		// See if the detector can detect the feature's scale
		if (detector.hasScale()) {
			for (int i = 0; i < detector.getNumberOfFeatures(); i++) {
				double yaw = 0;

				Point2D_F64 pt = detector.getLocation(i);
				double radius = detector.getRadius(i);
				if (describe.requiresOrientation()) {
					orientation.setObjectRadius(radius);
					yaw = orientation.compute(pt.x, pt.y);
				}

				TupleDesc d = describe.createDescription();
				if ( describe.process(pt.x, pt.y, yaw, radius, d) ) {
					descs.add(d);
					locs.add(pt.copy());
				}
			}
		} else {
			// just set the radius to one in this case
			orientation.setObjectRadius(1);
			for (int i = 0; i < detector.getNumberOfFeatures(); i++) {
				double yaw = 0;

				Point2D_F64 pt = detector.getLocation(i);
				if (describe.requiresOrientation()) {
					yaw = orientation.compute(pt.x, pt.y);
				}

				TupleDesc d = describe.createDescription();
				if (describe.process(pt.x, pt.y, yaw, 1, d)) {
					descs.add(d);
					locs.add(pt.copy());
				}
			}
		}
		SwingUtilities.invokeLater(new Runnable() {
			public void run() {
				progressMonitor.setProgress(progress + 2);
			}
		});
	}


	@Override
	public void changeInput(String name, int index) {
		BufferedImage left = media.openImage(inputRefs.get(index).getPath(0));
		BufferedImage right = media.openImage(inputRefs.get(index).getPath(1));

		process(left, right);
	}

	@Override
	public boolean getHasProcessedImage() {
		return processedImage;
	}

	@Override
	public void changedSetting() {
		SwingUtilities.invokeLater(new Runnable() {
			public void run() {
				scorePanel.setScorer(controlPanel.getSelected());
				repaint();
			}
		});
	}

	public static void main(String args[]) {

		Class imageType = GrayF32.class;
		Class derivType = GrayF32.class;

		VisualizeAssociationScoreApp app = new VisualizeAssociationScoreApp(imageType, derivType);

		List inputs = new ArrayList();
		inputs.add(new PathLabel("Cave",
				UtilIO.pathExample("stitch/cave_01.jpg"), UtilIO.pathExample("stitch/cave_02.jpg")));
		inputs.add(new PathLabel("Kayak",
				UtilIO.pathExample("stitch/kayak_02.jpg"), UtilIO.pathExample("stitch/kayak_03.jpg")));
		inputs.add(new PathLabel("Forest",
				UtilIO.pathExample("scale/rainforest_01.jpg"), UtilIO.pathExample("scale/rainforest_02.jpg")));
		inputs.add(new PathLabel("Building",
				UtilIO.pathExample("stitch/apartment_building_01.jpg"), UtilIO.pathExample("stitch/apartment_building_02.jpg")));
		inputs.add(new PathLabel("Trees Rotate",
				UtilIO.pathExample("stitch/trees_rotate_01.jpg"), UtilIO.pathExample("stitch/trees_rotate_03.jpg")));

		app.setPreferredSize(new Dimension(1000, 500));
		app.setSize(1000, 500);
		app.setInputList(inputs);

		// wait for it to process one image so that the size isn't all screwed up
		while (!app.getHasProcessedImage()) {
			Thread.yield();
		}

		ShowImages.showWindow(app, "Association Relative Score", true);

	}
}