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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.transform.pyramid;

import boofcv.abst.filter.convolve.ConvolveDown;
import boofcv.factory.filter.convolve.FactoryConvolveDown;
import boofcv.struct.border.BorderType;
import boofcv.struct.convolve.Kernel1D;
import boofcv.struct.image.ImageBase;
import boofcv.struct.image.ImageType;
import boofcv.struct.pyramid.ConfigDiscreteLevels;
import boofcv.struct.pyramid.ImagePyramid;
import boofcv.struct.pyramid.PyramidDiscrete;
import org.jetbrains.annotations.Nullable;

/**
 * 

* Convolves a re-normalizable blur kernel across the image before down sampling. This is useful for creating * a Gaussian pyramid as well as other standard pyramids. *

* *

* NOTE: This pyramid cannot be configured such that blur will be applied to the input image. It can be done by * the user before the image is passed in. *

* *

* NOTE: This blur magnitude is constant for each level in the pyramid. In general it is desirable to * have it dependent on each level's scale factor. *

* * @author Peter Abeles */ @SuppressWarnings({"unchecked", "NullAway.Init"}) public class PyramidDiscreteSampleBlur> extends PyramidDiscrete { // stores the results from the first convolution private T temp; ConvolveDown horizontal; ConvolveDown vertical; // amount of blur applied to each layer double[] sigmas; private double sigma; /** * * @param kernel A blur kernel * @param sigma The effective amount of Gaussian blur the kernel applies * @param imageType Type of image processed * @param saveOriginalReference If a reference to the full resolution image should be saved instead of copied. * Set to false if you don't know what you are doing. * @param configLayers Specifies how the levels are computed */ public PyramidDiscreteSampleBlur(Kernel1D kernel, double sigma, ImageType imageType, boolean saveOriginalReference, @Nullable ConfigDiscreteLevels configLayers) { super(imageType,saveOriginalReference,configLayers); horizontal = FactoryConvolveDown.convolve(kernel, BorderType.NORMALIZED, true, 1, imageType,imageType); vertical = FactoryConvolveDown.convolve(kernel, BorderType.NORMALIZED, false, 1, imageType,imageType); this.sigma = sigma; } private void lazyDeclareSigmas(double sigma) { if( this.sigmas != null ) return; sigmas = new double[ super.levelScales.length ]; sigmas[0] = 0; for( int i = 1; i < sigmas.length; i++ ) { // blur in previous layer double prev = sigmas[i-1]; // the effective amount of blur applied to previous layer while being down sampled double applied = sigma*super.levelScales[i-1]; // The amount of blur which has been applied to this layer sigmas[i] = Math.sqrt(prev*prev + applied*applied); } } protected PyramidDiscreteSampleBlur( PyramidDiscreteSampleBlur orig ) { super(orig); this.horizontal = orig.horizontal; this.vertical = orig.vertical; this.sigma = orig.sigma; } @Override public void process(T input) { super.initialize(input.width,input.height); lazyDeclareSigmas(this.sigma); if( temp == null ) { // declare it to be the latest image that it might need to be, resize below temp = (T)input.createNew(1,1); } if (levelScales[0] == 1) { if (isSaveOriginalReference()) { setFirstLayer(input); } else { getLayer(0).setTo(input); } } else { int skip = levelScales[0]; horizontal.setSkip(skip); vertical.setSkip(skip); temp.reshape(input.width/skip,input.height); horizontal.process(input,temp); vertical.process(temp,getLayer(0)); } for (int index = 1; index < getNumLayers(); index++) { int skip = levelScales[index]/ levelScales[index-1]; T prev = getLayer(index-1); temp.reshape(prev.width/skip,prev.height); horizontal.setSkip(skip); vertical.setSkip(skip); horizontal.process(prev,temp); vertical.process(temp,getLayer(index)); } } /** * There is no offset since a symmetric kernel is applied starting at pixel (0,0) * * @param layer Layer in the pyramid * @return offset */ @Override public double getSampleOffset(int layer) { return 0; } @Override public double getSigma(int layer) { return sigmas[layer]; } @Override public ImagePyramid copyStructure() { return new PyramidDiscreteSampleBlur<>(this); } }




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