/*
 * 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.examples.imageprocessing;


import boofcv.abst.filter.blur.BlurFilter;
import boofcv.abst.filter.derivative.ImageGradient;
import boofcv.alg.filter.blur.BlurImageOps;
import boofcv.alg.filter.blur.GBlurImageOps;
import boofcv.alg.filter.derivative.DerivativeType;
import boofcv.alg.filter.derivative.GImageDerivativeOps;
import boofcv.alg.filter.derivative.GradientSobel;
import boofcv.core.image.GeneralizedImageOps;
import boofcv.core.image.border.BorderType;
import boofcv.core.image.border.FactoryImageBorderAlgs;
import boofcv.factory.filter.blur.FactoryBlurFilter;
import boofcv.factory.filter.derivative.FactoryDerivative;
import boofcv.gui.ListDisplayPanel;
import boofcv.gui.image.ShowImages;
import boofcv.gui.image.VisualizeImageData;
import boofcv.io.UtilIO;
import boofcv.io.image.ConvertBufferedImage;
import boofcv.io.image.UtilImageIO;
import boofcv.struct.image.GrayF32;
import boofcv.struct.image.GrayS16;
import boofcv.struct.image.GrayU8;
import boofcv.struct.image.ImageGray;

import java.awt.image.BufferedImage;

/**
 * An introductory example designed to introduce basic BoofCV concepts.  Each function
 * shows how to perform basic filtering and display operations using different techniques.
 *
 * @author Peter Abeles
 */
@SuppressWarnings({"unchecked"})
public class ExampleImageFilter {

	private static int blurRadius = 10;

	private static ListDisplayPanel panel = new ListDisplayPanel();

	public static void procedural( GrayU8 input )
	{
		GrayU8 blurred = new GrayU8(input.width,input.height);
		GrayS16 derivX = new GrayS16(input.width,input.height);
		GrayS16 derivY = new GrayS16(input.width,input.height);

		// Gaussian blur: Convolve a Gaussian kernel
		BlurImageOps.gaussian(input,blurred,-1,blurRadius,null);

		// Calculate image's derivative
		GradientSobel.process(blurred, derivX, derivY, FactoryImageBorderAlgs.extend(input));

		// display the results
		BufferedImage outputImage = VisualizeImageData.colorizeGradient(derivX, derivY, -1);
		panel.addImage(outputImage,"Procedural Fixed Type");
	}

	public static 
	void generalized( T input )
	{
		Class inputType = (Class)input.getClass();
		Class derivType = GImageDerivativeOps.getDerivativeType(inputType);

		T blurred = (T)input.createSameShape();
		D derivX = GeneralizedImageOps.createSingleBand(derivType, input.width, input.height);
		D derivY = GeneralizedImageOps.createSingleBand(derivType, input.width, input.height);

		// Gaussian blur: Convolve a Gaussian kernel
		GBlurImageOps.gaussian(input, blurred, -1, blurRadius, null);

		// Calculate image's derivative
		GImageDerivativeOps.gradient(DerivativeType.SOBEL,blurred, derivX, derivY, BorderType.EXTENDED);

		// display the results
		BufferedImage outputImage = VisualizeImageData.colorizeGradient(derivX, derivY,-1);
		panel.addImage(outputImage,"Generalized "+inputType.getSimpleName());
	}

	public static 
	void filter( T input )
	{
		Class inputType = (Class)input.getClass();
		Class derivType = GImageDerivativeOps.getDerivativeType(inputType);

		T blurred = (T)input.createSameShape();
		D derivX = GeneralizedImageOps.createSingleBand(derivType, input.width, input.height);
		D derivY = GeneralizedImageOps.createSingleBand(derivType, input.width, input.height);

		// declare image filters
		BlurFilter filterBlur = FactoryBlurFilter.gaussian(inputType, -1, blurRadius);
		ImageGradient gradient = FactoryDerivative.sobel(inputType, derivType);

		// process the image
		filterBlur.process(input,blurred);
		gradient.process(blurred,derivX,derivY);

		// display the results
		BufferedImage outputImage = VisualizeImageData.colorizeGradient(derivX, derivY, -1);
		panel.addImage(outputImage,"Filter "+inputType.getSimpleName());
	}

	public static void nogenerics( ImageGray input )
	{
		Class inputType = input.getClass();
		Class derivType = GImageDerivativeOps.getDerivativeType(inputType);

		ImageGray blurred = (ImageGray)input.createSameShape();
		ImageGray derivX = GeneralizedImageOps.createSingleBand(derivType, input.width, input.height);
		ImageGray derivY = GeneralizedImageOps.createSingleBand(derivType, input.width, input.height);

		// Gaussian blur: Convolve a Gaussian kernel
		GBlurImageOps.gaussian(input, blurred, -1, blurRadius, null);

		// Calculate image's derivative
		GImageDerivativeOps.gradient(DerivativeType.SOBEL,blurred, derivX, derivY, BorderType.EXTENDED);

		// display the results
		BufferedImage outputImage = VisualizeImageData.colorizeGradient(derivX, derivY,-1);
		panel.addImage(outputImage,"Generalized "+inputType.getSimpleName());
	}

	public static void main( String args[] ) {

		BufferedImage image = UtilImageIO.loadImage(UtilIO.pathExample("standard/lena512.jpg"));

		// produces the same results
		procedural(ConvertBufferedImage.convertFromSingle(image, null, GrayU8.class));
		generalized(ConvertBufferedImage.convertFromSingle(image, null, GrayU8.class));
		filter(ConvertBufferedImage.convertFromSingle(image, null, GrayU8.class));
		nogenerics(ConvertBufferedImage.convertFromSingle(image, null, GrayU8.class));

		// try another image data type
		generalized(ConvertBufferedImage.convertFromSingle(image, null, GrayF32.class));

		ShowImages.showWindow(panel,"Image Filter Examples", true);
	}
}