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package com.yuxue.train;
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import java.io.File;
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import java.util.List;
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import org.bytedeco.javacpp.opencv_core;
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import org.bytedeco.javacpp.opencv_core.Mat;
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import org.bytedeco.javacpp.opencv_core.TermCriteria;
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import org.bytedeco.javacpp.opencv_imgcodecs;
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import org.bytedeco.javacpp.opencv_imgproc;
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import org.bytedeco.javacpp.opencv_ml.SVM;
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import org.bytedeco.javacpp.opencv_ml.TrainData;
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import org.opencv.ml.Ml;
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import com.yuxue.constant.Constant;
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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.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 2020-05-13 10:10
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*/
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public class PlateRecoTrain1 {
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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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public static void main(String[] arg) {
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// 训练， 生成svm.xml库文件
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// train();
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// 识别，判断样本文件是否是车牌
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pridect();
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}
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public static void train() {
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// 正样本 // 136 × 36 像素 训练的源图像文件要相同大小
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List<File> imgList0 = FileUtil.listFile(new File(DEFAULT_PATH + "/learn/HasPlate"), Constant.DEFAULT_TYPE, false);
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// 负样本 // 136 × 36 像素 训练的源图像文件要相同大小
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List<File> imgList1 = FileUtil.listFile(new File(DEFAULT_PATH + "/learn/NoPlate"), Constant.DEFAULT_TYPE, false);
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// 标记：正样本用 0 表示，负样本用 1 表示。
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int labels[] = createLabelArray(imgList0.size(), imgList1.size());
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int sample_num = labels.length; // 图片数量
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// 用于存放所有样本的矩阵
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Mat trainingDataMat = null;
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// 存放标记的Mat,每个图片都要给一个标记
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Mat labelsMat = new Mat(sample_num, 1, opencv_core.CV_32FC1);
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// labelsMat.put(0, 0, labels);
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for (int i = 0; i < sample_num; i++) { // 遍历所有的正负样本，处理样本用于生成训练的库文件
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String path = "";
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if(i < imgList0.size()) {
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path = imgList0.get(i).getAbsolutePath();
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} else {
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path = imgList1.get(i - imgList0.size()).getAbsolutePath();
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}
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Mat inMat = opencv_imgcodecs.imread(path); // 读取样本文件
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// 创建一个行数为sample_num, 列数为 rows*cols 的矩阵; 用于存放样本
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if (trainingDataMat == null) {
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trainingDataMat = new Mat(sample_num, inMat.rows() * inMat.cols(), opencv_core.CV_32FC1);// CV_32FC1 是规定的训练用的图片格式。
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}
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// 样本文件处理，这里是为了过滤不需要的特征，减少训练时间 // 根据实际情况需要进行处理
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Mat greyMat = new Mat();
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opencv_imgproc.cvtColor(inMat, greyMat, opencv_imgproc.COLOR_BGR2GRAY); // 转成灰度图
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Mat dst = new Mat(inMat.rows(), inMat.cols(), inMat.type());
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opencv_imgproc.Canny(greyMat, dst, 130, 250); // 边缘检测
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// 将样本矩阵转换成只有一行的矩阵，保存为float数组
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float[] arr = new float[dst.rows() * dst.cols()];
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int l = 0;
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for (int j = 0; j < dst.rows(); j++) { // 遍历行
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for (int k = 0; k < dst.cols(); k++) { // 遍历列
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double[] a = null;//dst.get(j, k);
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arr[l] = (float) a[0];
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l++;
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}
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}
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// trainingDataMat.put(i, 0, arr); // 多张图合并到一张
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}
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// opencv_imgcodecs.imwrite(DEFAULT_PATH + "trainingDataMat.jpg", trainingDataMat);
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// 配置SVM训练器参数
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TermCriteria criteria = new TermCriteria(TermCriteria.EPS + TermCriteria.MAX_ITER, 20000, 0.0001);
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SVM svm = SVM.create();
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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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TrainData td = TrainData.create(trainingDataMat, Ml.ROW_SAMPLE, labelsMat);// 类封装的训练数据
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boolean success = svm.train(td.getSamples(), Ml.ROW_SAMPLE, td.getResponses());// 训练统计模型
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System.out.println("svm training result: " + success);
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svm.save(MODEL_PATH);// 保存模型
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}
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public static void pridect() {
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// 加载训练得到的 xml 模型文件
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SVM svm = SVM.load(MODEL_PATH);
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// 136 × 36 像素 需要跟训练的源图像文件保持相同大小
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doPridect(svm, DEFAULT_PATH + "test/A01_NMV802_0.jpg");
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doPridect(svm, DEFAULT_PATH + "test/debug_resize_1.jpg");
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doPridect(svm, DEFAULT_PATH + "test/debug_resize_2.jpg");
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doPridect(svm, DEFAULT_PATH + "test/debug_resize_3.jpg");
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doPridect(svm, DEFAULT_PATH + "test/S22_KG2187_3.jpg");
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doPridect(svm, DEFAULT_PATH + "test/S22_KG2187_5.jpg");
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doPridect(svm, DEFAULT_PATH + "test/result_0.png");
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doPridect(svm, DEFAULT_PATH + "test/result_1.png");
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doPridect(svm, DEFAULT_PATH + "test/result_2.png");
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doPridect(svm, DEFAULT_PATH + "test/result_3.png");
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doPridect(svm, DEFAULT_PATH + "test/result_4.png");
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doPridect(svm, DEFAULT_PATH + "test/result_5.png");
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doPridect(svm, DEFAULT_PATH + "test/result_6.png");
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doPridect(svm, DEFAULT_PATH + "test/result_7.png");
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doPridect(svm, DEFAULT_PATH + "test/result_8.png");
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}
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public static void doPridect(SVM svm, String imgPath) {
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Mat src = opencv_imgcodecs.imread(imgPath);// 图片大小要和样本一致
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opencv_imgproc.cvtColor(src, src, opencv_imgproc.CV_RGB2GRAY);
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Mat dst = new Mat();
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opencv_imgproc.Canny(src, dst, 130, 250);
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Mat samples = dst.reshape(1, 1);
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samples.convertTo(samples, opencv_core.CV_32FC1);
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// opencv_imgcodecs.imwrite(DEFAULT_PATH + "test_1.jpg", samples);
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// 如果训练时使用这个标识，那么符合的图像会返回9.0
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float flag = svm.predict(samples);
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System.err.println(flag);
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if (flag == 0) {
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System.err.println(imgPath + "： 目标符合");
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}
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if (flag == 1) {
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System.out.println(imgPath + "： 目标不符合");
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}
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}
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public static int[] createLabelArray(Integer i1, Integer i2) {
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int labels[] = new int[i1 + i2];
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for (int i = 0; i < labels.length; i++) {
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if(i < i1) {
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labels[i] = 0;
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} else {
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labels[i] = 1;
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}
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}
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return labels;
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}
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}
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yuxue
5 years ago