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package com.yuxue.train;
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import java.util.*;
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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 com.yuxue.easypr.core.Features;
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import com.yuxue.easypr.core.SVMCallback;
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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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* JavaCPP 是一个开源库，它提供了在 Java 中高效访问本地 C++的方法
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*
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* 图片识别车牌训练
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* 训练出来的库文件，用于判断切图是否包含车牌
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*
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* 训练的svm.xml应用：
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* 1、替换res/model/svm.xml文件
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* 2、修改com.yuxue.easypr.core.PlateJudge.plateJudge(Mat) 方法
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* 将样本处理方法切换一下，即将对应被注释掉的模块代码取消注释
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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 SVMTrain1 {
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private SVMCallback callback = new Features();
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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/plate_detect_svm/";
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// 训练模型文件保存位置
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private static final String MODEL_PATH = DEFAULT_PATH + "svm.xml";
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private static final String hasPlate = "HasPlate";
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private static final String noPlate = "NoPlate";
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public SVMTrain1() {
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}
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public SVMTrain1(SVMCallback callback) {
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this.callback = callback;
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}
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/**
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* 将learn文件夹下的图片，转存到tain test文件夹下，区分hasPalte noPlate
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* 随机选取bound%作为训练数据，30%作为测试数据
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* @param bound
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* @param name
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*/
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private void learn2Plate(float bound, final String name) {
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final String filePath = DEFAULT_PATH + "learn/" + name;
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Vector<String> files = new Vector<String>();
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//// 获取该路径下的所有文件
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FileUtil.getFiles(filePath, files);
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int size = files.size();
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if (0 == size) {
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System.err.println("当前目录下没有文件: " + filePath);
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return;
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}
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Collections.shuffle(files, new Random(new Date().getTime()));
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//// 随机选取70%作为训练数据，30%作为测试数据
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int boundry = (int) (bound * size);
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// 重新创建目录
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FileUtil.recreateDir(DEFAULT_PATH + "train/" + name);
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FileUtil.recreateDir(DEFAULT_PATH + "test/" + name);
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for (int i = 0; i < boundry; i++) {
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Mat img = opencv_imgcodecs.imread(files.get(i));
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String str = DEFAULT_PATH + "train/" + name + "/" + name + "_" + Integer.valueOf(i).toString() + ".jpg";
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opencv_imgcodecs.imwrite(str, img);
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}
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for (int i = boundry; i < size; i++) {
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Mat img = opencv_imgcodecs.imread(files.get(i));
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String str = DEFAULT_PATH + "test/" + name + "/" + name + "_" + Integer.valueOf(i).toString() + ".jpg";
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opencv_imgcodecs.imwrite(str, img);
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}
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}
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/**
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* 获取训练图片
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* @param trainingImages
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* @param trainingLabels
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* @param name
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*/
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private void getPlateTrain(Mat trainingImages, Vector<Integer> trainingLabels, final String name, int label) {
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// int label = 1;
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final String filePath = DEFAULT_PATH + "train/" + name;
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Vector<String> files = new Vector<String>();
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// 获取该路径下的所有文件
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FileUtil.getFiles(filePath, files);
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int size = files.size();
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if (null == files || size <= 0) {
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System.out.println("File not found in " + filePath);
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return;
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}
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for (int i = 0; i < size; i++) {
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// System.out.println(files.get(i));
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Mat inMat = opencv_imgcodecs.imread(files.get(i));
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// 调用回调函数决定特征
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// Mat features = this.callback.getHisteqFeatures(inMat);
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Mat features = this.callback.getHistogramFeatures(inMat);
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// 通过直方图均衡化后的彩色图进行预测
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Mat p = features.reshape(1, 1);
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p.convertTo(p, opencv_core.CV_32F);
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// 136 36 14688 1 变换尺寸
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// System.err.println(inMat.cols() + "\t" + inMat.rows() + "\t" + p.cols() + "\t" + p.rows());
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trainingImages.push_back(p); // 合并成一张图片
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trainingLabels.add(label);
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}
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}
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private void getPlateTest(MatVector testingImages, Vector<Integer> testingLabels, final String name, int label) {
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// int label = 1;
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final String filePath = DEFAULT_PATH + "test/" + name;
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Vector<String> files = new Vector<String>();
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FileUtil.getFiles(filePath, files);
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int size = files.size();
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if (0 == size) {
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System.out.println("File not found in " + filePath);
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return;
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}
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System.out.println("get " + name + " test!");
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for (int i = 0; i < size; i++) {
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Mat inMat = opencv_imgcodecs.imread(files.get(i));
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testingImages.push_back(inMat);
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testingLabels.add(label);
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}
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}
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// ! 测试SVM的准确率，回归率以及FScore
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public void getAccuracy(Mat testingclasses_preditc, Mat testingclasses_real) {
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int channels = testingclasses_preditc.channels();
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System.out.println("channels: " + Integer.valueOf(channels).toString());
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int nRows = testingclasses_preditc.rows();
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System.out.println("nRows: " + Integer.valueOf(nRows).toString());
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int nCols = testingclasses_preditc.cols() * channels;
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System.out.println("nCols: " + Integer.valueOf(nCols).toString());
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int channels_real = testingclasses_real.channels();
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System.out.println("channels_real: " + Integer.valueOf(channels_real).toString());
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int nRows_real = testingclasses_real.rows();
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System.out.println("nRows_real: " + Integer.valueOf(nRows_real).toString());
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int nCols_real = testingclasses_real.cols() * channels;
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System.out.println("nCols_real: " + Integer.valueOf(nCols_real).toString());
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double count_all = 0;
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double ptrue_rtrue = 0;
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double ptrue_rfalse = 0;
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double pfalse_rtrue = 0;
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double pfalse_rfalse = 0;
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for (int i = 0; i < nRows; i++) {
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final float predict = Convert.toFloat(testingclasses_preditc.ptr(i));
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final float real = Convert.toFloat(testingclasses_real.ptr(i));
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count_all++;
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// System.out.println("predict:" << predict).toString());
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// System.out.println("real:" << real).toString());
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if (predict == 1.0 && real == 1.0)
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ptrue_rtrue++;
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if (predict == 1.0 && real == 0)
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ptrue_rfalse++;
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if (predict == 0 && real == 1.0)
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pfalse_rtrue++;
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if (predict == 0 && real == 0)
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pfalse_rfalse++;
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}
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System.out.println("count_all: " + Double.valueOf(count_all).toString());
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System.out.println("ptrue_rtrue: " + Double.valueOf(ptrue_rtrue).toString());
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System.out.println("ptrue_rfalse: " + Double.valueOf(ptrue_rfalse).toString());
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System.out.println("pfalse_rtrue: " + Double.valueOf(pfalse_rtrue).toString());
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System.out.println("pfalse_rfalse: " + Double.valueOf(pfalse_rfalse).toString());
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double precise = 0;
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if (ptrue_rtrue + ptrue_rfalse != 0) {
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precise = ptrue_rtrue / (ptrue_rtrue + ptrue_rfalse);
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System.out.println("precise: " + Double.valueOf(precise).toString());
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} else {
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System.out.println("precise: NA");
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}
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double recall = 0;
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if (ptrue_rtrue + pfalse_rtrue != 0) {
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recall = ptrue_rtrue / (ptrue_rtrue + pfalse_rtrue);
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System.out.println("recall: " + Double.valueOf(recall).toString());
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} else {
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System.out.println("recall: NA");
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}
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if (precise + recall != 0) {
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double F = (precise * recall) / (precise + recall);
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System.out.println("F: " + Double.valueOf(F).toString());
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} else {
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System.out.println("F: NA");
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}
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}
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/**
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* 训练
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* @param dividePrepared
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* @return
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*/
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public int svmTrain(boolean dividePrepared) {
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Mat classes = new Mat();
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Mat trainingData = new Mat();
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Mat trainingImages = new Mat();
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Vector<Integer> trainingLabels = new Vector<Integer>();
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// 分割learn里的数据到train和test里 // 从库里面选取训练样本
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if (!dividePrepared) {
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learn2Plate(0.1f, hasPlate); // 性能不好的机器，最好不要挑选太多的样本，这个方案太消耗资源了。
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learn2Plate(0.1f, noPlate);
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}
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// System.err.println("Begin to get train data to memory");
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getPlateTrain(trainingImages, trainingLabels, hasPlate, 0);
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getPlateTrain(trainingImages, trainingLabels, noPlate, 1);
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// System.err.println(trainingImages.cols());
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trainingImages.copyTo(trainingData);
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trainingData.convertTo(trainingData, CV_32F);
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int[] labels = new int[trainingLabels.size()];
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for (int i = 0; i < trainingLabels.size(); ++i) {
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labels[i] = trainingLabels.get(i).intValue();
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}
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new Mat(labels).copyTo(classes);
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TrainData train_data = TrainData.create(trainingData, ROW_SAMPLE, classes);
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SVM svm = SVM.create();
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try {
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TermCriteria criteria = new TermCriteria(TermCriteria.EPS + TermCriteria.MAX_ITER, 20000, 0.0001);
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svm.setTermCriteria(criteria); // 指定
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svm.setKernel(SVM.RBF); // 使用预先定义的内核初始化
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svm.setType(SVM.C_SVC); // SVM的类型,默认是：SVM.C_SVC
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svm.setGamma(0.1); // 核函数的参数
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svm.setNu(0.1); // SVM优化问题参数
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svm.setC(1); // SVM优化问题的参数C
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svm.setP(0.1);
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svm.setDegree(0.1);
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svm.setCoef0(0.1);
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svm.trainAuto(train_data, 10,
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SVM.getDefaultGrid(SVM.C),
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SVM.getDefaultGrid(SVM.GAMMA),
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SVM.getDefaultGrid(SVM.P),
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SVM.getDefaultGrid(SVM.NU),
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SVM.getDefaultGrid(SVM.COEF),
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SVM.getDefaultGrid(SVM.DEGREE),
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true);
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} catch (Exception err) {
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System.out.println(err.getMessage());
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}
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System.out.println("Svm generate done!");
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/*FileStorage fsTo = new FileStorage(MODEL_PATH, FileStorage.WRITE);
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svm.write(fsTo, "svm");*/
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svm.save(MODEL_PATH);
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return 0;
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}
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// 测试
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public int svmPredict() {
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SVM svm = SVM.create();
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try {
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svm.clear();
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// svm = SVM.loadSVM(MODEL_PATH, "svm");
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svm = SVM.load(MODEL_PATH);
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} catch (Exception err) {
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System.err.println(err.getMessage());
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return 0; // next predict requires svm
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}
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System.out.println("Begin to predict");
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// Test SVM
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MatVector testingImages = new MatVector();
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Vector<Integer> testingLabels_real = new Vector<Integer>();
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// 将测试数据加载入内存
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getPlateTest(testingImages, testingLabels_real, hasPlate, 0);
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getPlateTest(testingImages, testingLabels_real, noPlate, 1);
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double count_all = 0;
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double ptrue_rtrue = 0;
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double ptrue_rfalse = 0;
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double pfalse_rtrue = 0;
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double pfalse_rfalse = 0;
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long size = testingImages.size();
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System.err.println(size);
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for (int i = 0; i < size; i++) {
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Mat inMat = testingImages.get(i);
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// Mat features = callback.getHisteqFeatures(inMat);
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Mat features = callback.getHistogramFeatures(inMat);
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Mat p = features.reshape(1, 1);
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p.convertTo(p, opencv_core.CV_32F);
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// System.out.println(p.cols() + "\t" + p.rows() + "\t" + p.type());
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// samples.cols == var_count && samples.type() == CV_32F
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// var_count 的值会在svm.xml库文件中有体现
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float predoct = svm.predict(features);
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int predict = (int) predoct; // 预期值
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int real = testingLabels_real.get(i); // 实际值
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if (predict == 1 && real == 1)
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ptrue_rtrue++;
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if (predict == 1 && real == 0)
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ptrue_rfalse++;
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if (predict == 0 && real == 1)
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pfalse_rtrue++;
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if (predict == 0 && real == 0)
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pfalse_rfalse++;
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}
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count_all = size;
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System.out.println("Get the Accuracy!");
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System.out.println("count_all: " + Double.valueOf(count_all).toString());
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System.out.println("ptrue_rtrue: " + Double.valueOf(ptrue_rtrue).toString());
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System.out.println("ptrue_rfalse: " + Double.valueOf(ptrue_rfalse).toString());
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System.out.println("pfalse_rtrue: " + Double.valueOf(pfalse_rtrue).toString());
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System.out.println("pfalse_rfalse: " + Double.valueOf(pfalse_rfalse).toString());
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double precise = 0;
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if (ptrue_rtrue + ptrue_rfalse != 0) {
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precise = ptrue_rtrue / (ptrue_rtrue + ptrue_rfalse);
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System.out.println("precise: " + Double.valueOf(precise).toString());
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} else
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System.out.println("precise: NA");
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double recall = 0;
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if (ptrue_rtrue + pfalse_rtrue != 0) {
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recall = ptrue_rtrue / (ptrue_rtrue + pfalse_rtrue);
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System.out.println("recall: " + Double.valueOf(recall).toString());
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} else
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System.out.println("recall: NA");
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double Fsocre = 0;
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if (precise + recall != 0) {
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Fsocre = 2 * (precise * recall) / (precise + recall);
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System.out.println("Fsocre: " + Double.valueOf(Fsocre).toString());
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} else
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System.out.println("Fsocre: NA");
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return 0;
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}
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public static void main(String[] args) {
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SVMTrain1 s = new SVMTrain1();
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s.svmTrain(true);
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s.svmPredict();
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}
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}
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