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package com.yuxue.train;
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import java.util.Vector;
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import static org.bytedeco.javacpp.opencv_core.*;
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import static org.bytedeco.javacpp.opencv_ml.*;
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import org.bytedeco.javacpp.opencv_core;
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import org.bytedeco.javacpp.opencv_imgcodecs;
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import org.bytedeco.javacpp.opencv_core.Mat;
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import com.yuxue.constant.Constant;
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import com.yuxue.easypr.core.CoreFunc;
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import com.yuxue.util.Convert;
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import com.yuxue.util.FileUtil;
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/**
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* 基于org.bytedeco.javacpp包实现的训练
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*
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* 图片文字识别训练
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* 训练出来的库文件，用于识别图片中的文字
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*
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* 训练的ann.xml应用：
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* 1、替换res/model/ann.xml文件
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* 2、修改com.yuxue.easypr.core.CharsIdentify.charsIdentify(Mat, Boolean, Boolean)方法
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*
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* @author yuxue
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* @date 2020-05-14 22:16
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*/
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public class ANNTrain1 {
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private ANN_MLP ann = ANN_MLP.create();
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// 默认的训练操作的根目录
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private static final String DEFAULT_PATH = "D:/PlateDetect/train/chars_recognise_ann/";
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// 训练模型文件保存位置
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private static final String MODEL_PATH = "res/model/ann.xml";
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public void train(int _predictsize, int _neurons) {
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Mat samples = new Mat(); // 使用push_back，行数列数不能赋初始值
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Vector<Integer> trainingLabels = new Vector<Integer>();
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// 加载数字及字母字符
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for (int i = 0; i < Constant.numCharacter; i++) {
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String str = DEFAULT_PATH + "learn/" + Constant.strCharacters[i];
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Vector<String> files = new Vector<String>();
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FileUtil.getFiles(str, files);
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int size = (int) files.size();
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for (int j = 0; j < size; j++) {
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Mat img = opencv_imgcodecs.imread(files.get(j), 0);
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// System.err.println(files.get(j)); // 文件名不能包含中文
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Mat f = CoreFunc.features(img, _predictsize);
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samples.push_back(f);
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trainingLabels.add(i); // 每一幅字符图片所对应的字符类别索引下标
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}
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}
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// 加载汉字字符
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for (int i = 0; i < Constant.strChinese.length; i++) {
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String str = DEFAULT_PATH + "learn/" + Constant.strChinese[i];
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Vector<String> files = new Vector<String>();
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FileUtil.getFiles(str, files);
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int size = (int) files.size();
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for (int j = 0; j < size; j++) {
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Mat img = opencv_imgcodecs.imread(files.get(j), 0);
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// System.err.println(files.get(j)); // 文件名不能包含中文
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Mat f = CoreFunc.features(img, _predictsize);
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samples.push_back(f);
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trainingLabels.add(i + Constant.numCharacter);
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}
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}
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//440 vhist.length + hhist.length + lowData.cols() * lowData.rows();
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// CV_32FC1 CV_32SC1 CV_32F
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Mat classes = new Mat(trainingLabels.size(), Constant.numAll, CV_32F);
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float[] labels = new float[trainingLabels.size()];
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for (int i = 0; i < labels.length; ++i) {
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classes.ptr(i, trainingLabels.get(i)).putFloat(1.f);
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}
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// samples.type() == CV_32F || samples.type() == CV_32S
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TrainData train_data = TrainData.create(samples, ROW_SAMPLE, classes);
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ann.clear();
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Mat layers = new Mat(1, 3, CV_32SC1);
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layers.ptr(0, 0).putInt(samples.cols());
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layers.ptr(0, 1).putInt(_neurons);
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layers.ptr(0, 2).putInt(classes.cols());
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System.out.println(layers);
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ann.setLayerSizes(layers);
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ann.setActivationFunction(ANN_MLP.SIGMOID_SYM, 1, 1);
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ann.setTrainMethod(ANN_MLP.BACKPROP);
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TermCriteria criteria = new TermCriteria(TermCriteria.EPS + TermCriteria.MAX_ITER, 30000, 0.0001);
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ann.setTermCriteria(criteria);
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ann.setBackpropWeightScale(0.1);
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ann.setBackpropMomentumScale(0.1);
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ann.train(train_data);
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//FileStorage fsto = new FileStorage(MODEL_PATH, FileStorage.WRITE);
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//ann.write(fsto, "ann");
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ann.save(MODEL_PATH);
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}
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public void predict() {
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ann.clear();
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ann = ANN_MLP.load(MODEL_PATH);
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//ann = ANN_MLP.loadANN_MLP(MODEL_PATH, "ann");
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Vector<String> files = new Vector<String>();
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FileUtil.getFiles(DEFAULT_PATH + "test/", files);
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for (String string : files) {
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Mat img = opencv_imgcodecs.imread(string);
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Mat f = CoreFunc.features(img, Constant.predictSize);
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// 140 predictSize = 10; vhist.length + hhist.length + lowData.cols() * lowData.rows();
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// 440 predictSize = 20;
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Mat output = new Mat(1, 140, CV_32F);
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//ann.predict(f, output, 0); // 预测结果
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// System.err.println(string + "===>" + (int) ann.predict(f, output, 0));
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int index = (int) ann.predict(f, output, 0);
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String result = "";
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if (index <= Constant.numCharacter) {
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result = String.valueOf(Constant.strCharacters[index]);
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} else {
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String s = Constant.strChinese[index - Constant.numCharacter];
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result = Constant.KEY_CHINESE_MAP.get(s); // 编码转中文
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}
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System.err.println(string + "===>" + result);
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// ann.predict(f, output, 0);
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// System.err.println(string + "===>" + output.get(0, 0)[0]);
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}
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}
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public static void main(String[] args) {
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ANNTrain1 annT = new ANNTrain1();
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// 这里演示只训练model文件夹下的ann.xml，此模型是一个predictSize=10,neurons=40的ANN模型
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// 可根据需要训练不同的predictSize或者neurons的ANN模型
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// 根据机器的不同，训练时间不一样，但一般需要10分钟左右，所以慢慢等一会吧。
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// annT.train(Constant.predictSize, Constant.neurons);
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annT.predict();
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System.out.println("The end.");
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}
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}
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yuxue
5 years ago