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Java example source code file (ImageVectorizer.java)

This example Java source code file (ImageVectorizer.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

dataset, file, imageloader, imagevectorizer, indarray, override, runtimeexception

The ImageVectorizer.java Java example source code

/*
 *
 *  * Copyright 2015 Skymind,Inc.
 *  *
 *  *    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 org.deeplearning4j.datasets.vectorizer;

import java.io.File;

import org.canova.image.loader.ImageLoader;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.util.FeatureUtil;

/**
 * An image vectorizer takes an input image (RGB) and
 * transforms it in to a data applyTransformToDestination
 * @author Adam Gibson
 *
 */
public class ImageVectorizer implements Vectorizer {

	private File image;
	private ImageLoader loader = new ImageLoader();
	private boolean binarize;
	private boolean normalize;
	private int label;
	private int numLabels;

	/**
	 * Baseline knowledge needed for the vectorizer
	 * @param image the input image to convert
	 * @param numLabels the number of labels
	 * @param label the label of this image
	 */
	public ImageVectorizer(File image,int numLabels,int label) {
		super();
		this.image = image;
		this.numLabels = numLabels;
		this.label = label;
	}


	/**
	 * Binarize the data based on the threshold (anything < threshold is zero)
	 * This  is used for making the image brightness agnostic.
	 * @return builder pattern
	 */
	public ImageVectorizer binarize(int threshold) {
		this.binarize = true;
		this.normalize = false;
		return this;
	}
	
	/**
	 * Binarize the data based on the threshold (anything < threshold is zero)
	 * This  is used for making the image brightness agnostic.
	 * Equivalent to calling (binarze(30))
	 * @return builder pattern
	 */
	public ImageVectorizer binarize() {
		return binarize(30);
	}

	/**
	 * Normalize the input image by row sums
	 * @return builder pattern
	 */
	public ImageVectorizer normalize() {
		this.binarize = false;
		this.normalize = true;
		return this;
	}


	@Override
	public DataSet vectorize() {
		try {
			INDArray d = loader.asMatrix(image);
			INDArray label2 = FeatureUtil.toOutcomeVector(label, numLabels);
			if(normalize) {
				d = d.div(255);
			}
			else if(binarize) {
				for(int i = 0; i < d.length(); i++) {
					double curr = (double) d.getScalar(i).element();
					int threshold = 30;
					if(curr > threshold) {
						d.putScalar(i, 1);
					}
					else 
						d.putScalar(i, 0);


				}
			}


			return new DataSet(d,label2);
		} catch (Exception e) {
			throw new RuntimeException(e);
		}

	}


}

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