/**
* Copyright 2010 Neuroph Project http://neuroph.sourceforge.net
*
* 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.neuroph.contrib.imgrec;
import java.awt.Dimension;
import java.util.List;
import java.util.Map;
import java.util.Vector;
import java.util.Map.Entry;
import org.neuroph.core.NeuralNetwork;
import org.neuroph.core.Neuron;
import org.neuroph.core.exceptions.VectorSizeMismatchException;
import org.neuroph.core.learning.SupervisedTrainingElement;
import org.neuroph.core.learning.TrainingSet;
import org.neuroph.nnet.MultiLayerPerceptron;
import org.neuroph.nnet.learning.MomentumBackpropagation;
import org.neuroph.util.TransferFunctionType;
import org.neuroph.util.VectorParser;
import org.neuroph.util.plugins.LabelsPlugin;
import org.neuroph.util.plugins.PluginBase;
/**
* Provides methods to create neural network and training set for image recognition.
* This class is mostly based on the code from tileclassification utility by Jon Tait
* @author Jon Tait
* @author Zoran Sevarac
*/
public class ImageRecognitionHelper {
/**
* Creates and returns new neural network for image recognition.
* Assumes that all of the FractionRgbData objects in the given map have identical
* length arrays in them so that the input layer of the neural network can be
* created here.
*
* @param label neural network label
* @param samplingResolution sampling resolution (image size)
* @param imageLabels image labels
* @param layersNeuronsCount neuron counts in hidden layers
* @param transferFunctionType type of transfer function to use for neurons in network
* @param colorMode color mode
*/
public static NeuralNetwork createNewNeuralNetwork(String label, Dimension samplingResolution, ColorMode colorMode, List<String> imageLabels, Vector<Integer> layersNeuronsCount, TransferFunctionType transferFunctionType) {
int numberOfInputNeurons;
if (colorMode == ColorMode.FULL_COLOR)
numberOfInputNeurons = 3 * samplingResolution.width * samplingResolution.height;
else
numberOfInputNeurons = samplingResolution.width * samplingResolution.height;
int numberOfOuputNeurons = imageLabels.size();
layersNeuronsCount.add(0, numberOfInputNeurons);
layersNeuronsCount.add(numberOfOuputNeurons);
System.out.println("Neuron layer size counts vector = " + layersNeuronsCount);
NeuralNetwork neuralNetwork = new MultiLayerPerceptron(layersNeuronsCount, transferFunctionType);
LabelsPlugin labelsPlugin = (LabelsPlugin) neuralNetwork.getPlugin(LabelsPlugin.LABELS_PLUGIN_NAME);
labelsPlugin.setLabel(neuralNetwork, label);
PluginBase imageRecognitionPlugin = new ImageRecognitionPlugin(samplingResolution, colorMode);
neuralNetwork.addPlugin(imageRecognitionPlugin);
assignLabelsToOutputNeurons(neuralNetwork, imageLabels);
neuralNetwork.setLearningRule(new MomentumBackpropagation());
return neuralNetwork;
}
/**
* Assign labels to output neurons
* @param neuralNetwork neural network
* @param imageLabels image labels
*/
private static void assignLabelsToOutputNeurons(NeuralNetwork neuralNetwork, List<String> imageLabels) {
List<Neuron> outputNeurons = neuralNetwork.getOutputNeurons();
LabelsPlugin labelsPlugin = (LabelsPlugin) neuralNetwork.getPlugin(LabelsPlugin.LABELS_PLUGIN_NAME);
for(int i=0; i<outputNeurons.size(); i++) {
Neuron neuron = outputNeurons.get(i);
String label = imageLabels.get(i);
labelsPlugin.setLabel(neuron, label);
}
}
/**
* Creates training set for the specified image labels and rgb data
* @param imageLabels image labels
* @param rgbDataMap map collection of rgb data
* @return training set for the specified image data
*/
public static TrainingSet createTrainingSet(List<String> imageLabels, Map<String, FractionRgbData> rgbDataMap) {
TrainingSet trainingSet = new TrainingSet();
for (Entry<String, FractionRgbData> entry : rgbDataMap.entrySet()) {
double[] input = entry.getValue().getFlattenedRgbValues();
double[] response = createResponse(entry.getKey(), imageLabels);
trainingSet.addElement(new SupervisedTrainingElement(
VectorParser.convertToVector(input),
VectorParser.convertToVector(response)));
}
return trainingSet;
}
/**
* Creates binary black and white training set for the specified image labels and rgb data
* @param imageLabels image labels
* @param rgbDataMap map collection of rgb data
* @return binary black and white training set for the specified image data
*/
public static TrainingSet createBlackAndWhiteTrainingSet(List<String> imageLabels, Map<String, FractionRgbData> rgbDataMap) throws VectorSizeMismatchException
{
TrainingSet trainingSet = new TrainingSet();
for (Entry<String, FractionRgbData> entry : rgbDataMap.entrySet()) {
double[] inputRGB = entry.getValue().getFlattenedRgbValues();
double[] inputBW = FractionRgbData.convertRgbInputToBinaryBlackAndWhite(inputRGB);
double[] response = createResponse(entry.getKey(), imageLabels);
trainingSet.addElement(new SupervisedTrainingElement(
VectorParser.convertToVector(inputBW),
VectorParser.convertToVector(response)));
}
return trainingSet;
}
/**
* Creates network output vector (response) for the specified image data
* @param inputLabel label of the input image
* @param imageLabels labels used for output neurons
* @return network response for the specified input
*/
private static double[] createResponse(String inputLabel, List<String> imageLabels)
{
double[] response = new double[imageLabels.size()];
int i=0;
for(String imageLabel : imageLabels) {
if(inputLabel.startsWith(imageLabel)) {
response[i] = 1d;
} else {
response[i] = 0d;
}
i++;
}
return response;
}
}