boofcv.alg.background.stationary.BackgroundStationaryGmm_SB_MT Maven / Gradle / Ivy

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/*
 * Copyright (c) 2021, 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 javax.annotation.Generated;
import boofcv.alg.background.BackgroundAlgorithmGmm;
import boofcv.alg.misc.ImageMiscOps;
import boofcv.struct.image.GrayU8;
import boofcv.struct.image.ImageGray;
import boofcv.struct.image.ImageType;
import org.jetbrains.annotations.Nullable;

import boofcv.concurrency.BoofConcurrency;

/**
 * Implementation of {@link BackgroundAlgorithmGmm} for {@link ImageGray}.
 *
 * @author Peter Abeles
 */
@Generated("boofcv.alg.background.stationary.BackgroundStationaryGmm_SB")
public class BackgroundStationaryGmm_SB_MT> extends BackgroundStationaryGmm {
	/**
	 * @param learningPeriod Specifies how fast it will adjust to changes in the image. Must be greater than zero.
	 * @param decayCoef Determines how quickly a Gaussian is forgotten
	 * @param maxGaussians Maximum number of Gaussians in a mixture for a pixel
	 * @param imageType Type of image it's processing.
	 */
	public BackgroundStationaryGmm_SB_MT( float learningPeriod, float decayCoef,
									   int maxGaussians, ImageType imageType ) {
		super(learningPeriod, decayCoef, maxGaussians, imageType);
	}

	@Override public void updateBackground( T frame, @Nullable GrayU8 mask ) {
		super.updateBackground(frame, mask);

		common.inputWrapperG.wrap(frame);

		BoofConcurrency.loopFor(0, common.imageHeight, row -> {
			int inputIndex = frame.startIndex + row*frame.stride;
			float[] dataRow = common.model.data[row];

			if (mask == null) {
				for (int col = 0; col < common.imageWidth; col++) {
					float pixelValue = common.inputWrapperG.getF(inputIndex++);
					int modelIndex = col*common.modelStride;

					common.updateMixture(pixelValue, dataRow, modelIndex);
				}
			} else {
				int indexMask = mask.startIndex + row*mask.stride;
				for (int col = 0; col < common.imageWidth; col++) {
					float pixelValue = common.inputWrapperG.getF(inputIndex++);
					int modelIndex = col*common.modelStride;

					mask.data[indexMask++] = (byte)common.updateMixture(pixelValue, dataRow, modelIndex);
				}
			}
		});
	}

	@Override public void segment( T frame, GrayU8 segmented ) {
		segmented.reshape(frame.width, frame.height);
		if (common.imageWidth != frame.width || common.imageHeight != frame.height) {
			ImageMiscOps.fill(segmented, unknownValue);
			return;
		}

		common.unknownValue = unknownValue;
		common.inputWrapperG.wrap(frame);

		BoofConcurrency.loopFor(0, common.imageHeight, row -> {
			int indexIn = frame.startIndex + row*frame.stride;
			int indexOut = segmented.startIndex + row*segmented.stride;
			float[] dataRow = common.model.data[row];

			for (int col = 0; col < common.imageWidth; col++) {
				float pixelValue = common.inputWrapperG.getF(indexIn++);
				int modelIndex = col*common.modelStride;

				segmented.data[indexOut++] = (byte)common.checkBackground(pixelValue, dataRow, modelIndex);
			}
		});
	}
}