/*
 * 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.alg.background.stationary;

import boofcv.alg.InputSanityCheck;
import boofcv.alg.misc.ImageMiscOps;
import boofcv.core.image.FactoryGImageMultiBand;
import boofcv.core.image.GImageMultiBand;
import boofcv.struct.image.GrayU8;
import boofcv.struct.image.ImageInterleaved;
import boofcv.struct.image.ImageType;
import boofcv.struct.image.InterleavedF32;

/**
 * Implementation of {@link BackgroundStationaryGaussian} for {@link ImageInterleaved}.
 *
 * @author Peter Abeles
 */
public class BackgroundStationaryGaussian_IL<T extends ImageInterleaved>
		extends BackgroundStationaryGaussian<T>
{

	// wrappers which provide abstraction across image types
	protected GImageMultiBand inputWrapper;
	protected GImageMultiBand bgWrapper;

	// storage for multi-band pixel values
	protected float[] inputPixel;
	protected float[] bgPixel;

	// background is composed of bands*2 channels.  even = mean, odd = variance
	InterleavedF32 background;

	/**
	 * Configurations background removal.
	 *
	 * @param learnRate Specifies how quickly the background is updated.  0 = static  1.0 = instant.  Try 0.05
	 * @param threshold Threshold for background.  Consult a chi-square table for reasonably values.
	 *                  10 to 16 for 1 to 3 bands.
	 * @param imageType Type of input image.
	 */
	public BackgroundStationaryGaussian_IL(float learnRate, float threshold,
										   ImageType<T> imageType)
	{
		super(learnRate, threshold, imageType);

		int numBands = imageType.getNumBands();

		background = new InterleavedF32(1,1,2*numBands);
		bgWrapper = FactoryGImageMultiBand.create(background.getImageType());
		bgWrapper.wrap(background);

		inputWrapper = FactoryGImageMultiBand.create(imageType);

		inputPixel = new float[numBands];
		bgPixel = new float[numBands*2];
	}

	@Override
	public void reset() {
		background.reshape(1,1);
	}

	@Override
	public void updateBackground( T frame) {
		inputWrapper.wrap(frame);

		if( background.width == 1 ) {
			background.reshape(frame.width, frame.height);

			for (int y = 0; y < frame.height; y++) {
				for (int x = 0; x < frame.width; x++) {
					inputWrapper.get(x,y,inputPixel);
					for (int i = 0; i < frame.numBands; i++) {
						bgPixel[i*2] = inputPixel[i];
						bgPixel[i*2+1] = initialVariance;
					}
					bgWrapper.set(x,y,bgPixel);
				}
			}
			return;
		} else {
			InputSanityCheck.checkSameShape(background, frame);
		}

		int numBands = background.getNumBands()/2;
		float minusLearn = 1.0f - learnRate;

		int indexBG = 0;
		for (int y = 0; y < background.height; y++) {
			int indexInput = frame.startIndex + y*frame.stride;
			int end = indexInput + frame.width*numBands;
			while( indexInput < end ) {
				inputWrapper.getF(indexInput, inputPixel);

				for (int band = 0; band < numBands; band++) {

					float inputValue = inputPixel[band];
					float meanBG = background.data[indexBG];
					float varianceBG = background.data[indexBG+1];

					float diff = meanBG-inputValue;
					background.data[indexBG++] = minusLearn*meanBG + learnRate*inputValue;
					background.data[indexBG++] = minusLearn*varianceBG + learnRate*diff*diff;
				}

				indexInput += frame.numBands;
			}
		}
	}

	@Override
	public void segment( T frame, GrayU8 segmented) {
		if( background.width == 1 ) {
			ImageMiscOps.fill(segmented, unknownValue);
			return;
		}
		inputWrapper.wrap(frame);

		final int numBands = background.getNumBands()/2;

		float adjustedMinimumDifference = minimumDifference*numBands;

		int indexBG = 0;
		for (int y = 0; y < frame.height; y++) {
			int indexInput = frame.startIndex + y*frame.stride;
			int indexSegmented = segmented.startIndex + y*segmented.stride;

			int end = indexInput + frame.width*frame.numBands;
			while( indexInput < end ) {
				inputWrapper.getF(indexInput, inputPixel);

				float mahalanobis = 0;
				for (int band = 0; band < numBands; band++) {

					int indexBG_band = indexBG + band*2;

					float meanBG = background.data[indexBG_band];
					float varBG  = background.data[indexBG_band+1];

					float diff = meanBG - inputPixel[band];
					mahalanobis += diff * diff / varBG;
				}

				if (mahalanobis <= threshold) {
					segmented.data[indexSegmented] = 0;
				} else {
					if( minimumDifference == 0) {
						segmented.data[indexSegmented] = 1;
					} else {
						float sumAbsDiff = 0;
						for (int band = 0; band < numBands; band++) {
							int indexBG_band = indexBG + band*2;
							sumAbsDiff += Math.abs(background.data[indexBG_band] - inputPixel[band]);
						}
						if (sumAbsDiff >= adjustedMinimumDifference)
							segmented.data[indexSegmented] = 1;
						else
							segmented.data[indexSegmented] = 0;
					}
				}

				indexInput     += frame.numBands;
				indexSegmented += 1;
				indexBG        += background.numBands;
			}
		}
	}
}