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yuxue 5 years ago
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src/main/java/com/yuxue/train/ANNTrain1.java
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@ -5,13 +5,11 @@ import java.util.Vector;
import static org.bytedeco.javacpp.opencv_core.*;
import static org.bytedeco.javacpp.opencv_ml.*;
import org.bytedeco.javacpp.opencv_core;
import org.bytedeco.javacpp.opencv_imgcodecs;
import org.bytedeco.javacpp.opencv_core.Mat;
import com.yuxue.constant.Constant;
import com.yuxue.easypr.core.CoreFunc;
import com.yuxue.util.Convert;
import com.yuxue.util.FileUtil;
/**
@ -150,7 +148,7 @@ public class ANNTrain1 {
// 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
// 可根据需要训练不同的predictSize或者neurons的ANN模型
// 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。
// annT.train(Constant.predictSize, Constant.neurons);
annT.train(Constant.predictSize, Constant.neurons);
annT.predict();

212
src/main/java/com/yuxue/train/PlateRecoTrain.java
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@ -1,212 +0,0 @@
package com.yuxue.train;
import java.io.File;
import java.util.List;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.TermCriteria;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.opencv.ml.Ml;
import org.opencv.ml.SVM;
import org.opencv.ml.TrainData;
import com.yuxue.constant.Constant;
import com.yuxue.util.FileUtil;
/**
* 基于org.opencv官方包实现的训练
*
*
* windows下环境配置：
* 1、官网下载对应版本的openvp：https://opencv.org/releases/page/2/ 当前使用4.0.1版本
* 2、双击exe文件安装，将 安装目录下\build\java\x64\opencv_java401.dll 拷贝到\build\x64\vc14\bin\目录下
* 3、eclipse添加User Libraries
* 4、项目右键build path，添加步骤三新增的lib
*
* 图片识别车牌训练
* 训练出来的库文件，用于判断切图是否包含车牌
*
* 训练的svm.xml应用：
* 1、替换res/model/svm.xml文件
* 2、修改com.yuxue.easypr.core.PlateJudge.plateJudge(Mat) 方法
* 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
* @author yuxue
* @date 2020-05-13 10:10
*/
public class PlateRecoTrain {
// 默认的训练操作的根目录
private static final String DEFAULT_PATH = "D:/PlateDetect/train/plate_detect_svm/";
// 训练模型文件保存位置
private static final String MODEL_PATH = DEFAULT_PATH + "svm.xml";
static {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
}
public static void main(String[] arg) {
// 训练， 生成svm.xml库文件
// train();
// 识别，判断样本文件是否是车牌
pridect();
}
public static void train() {
// 正样本 // 136 × 36 像素 训练的源图像文件要相同大小
List<File> imgList0 = FileUtil.listFile(new File(DEFAULT_PATH + "/learn/HasPlate"), Constant.DEFAULT_TYPE, false);
// 负样本 // 136 × 36 像素 训练的源图像文件要相同大小
List<File> imgList1 = FileUtil.listFile(new File(DEFAULT_PATH + "/learn/NoPlate"), Constant.DEFAULT_TYPE, false);
// 标记：正样本用 0 表示，负样本用 1 表示。
int labels[] = createLabelArray(imgList0.size(), imgList1.size());
int sample_num = labels.length; // 图片数量
// 用于存放所有样本的矩阵
Mat trainingDataMat = null;
// 存放标记的Mat,每个图片都要给一个标记
Mat labelsMat = new Mat(sample_num, 1, CvType.CV_32SC1);
labelsMat.put(0, 0, labels);
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
String path = "";
if(i < imgList0.size()) {
path = imgList0.get(i).getAbsolutePath();
} else {
path = imgList1.get(i - imgList0.size()).getAbsolutePath();
}
Mat inMat = Imgcodecs.imread(path); // 读取样本文件
// 创建一个行数为sample_num, 列数为 rows*cols 的矩阵; 用于存放样本
if (trainingDataMat == null) {
trainingDataMat = new Mat(sample_num, inMat.rows() * inMat.cols(), CvType.CV_32F);
}
// 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
Mat greyMat = new Mat();
Imgproc.cvtColor(inMat, greyMat, Imgproc.COLOR_BGR2GRAY); // 转成灰度图
Mat dst = new Mat(inMat.rows(), inMat.cols(), inMat.type());
Imgproc.Canny(greyMat, dst, 130, 250); // 边缘检测
// 将样本矩阵转换成只有一行的矩阵，保存为float数组
float[] arr = new float[dst.rows() * dst.cols()];
int l = 0;
for (int j = 0; j < dst.rows(); j++) { // 遍历行
for (int k = 0; k < dst.cols(); k++) { // 遍历列
double[] a = dst.get(j, k);
arr[l] = (float) a[0];
l++;
}
}
trainingDataMat.put(i, 0, arr); // 多张图合并到一张
}
// Imgcodecs.imwrite(DEFAULT_PATH + "trainingDataMat.jpg", trainingDataMat);
// 配置SVM训练器参数
TermCriteria criteria = new TermCriteria(TermCriteria.EPS + TermCriteria.MAX_ITER, 20000, 0.0001);
SVM svm = SVM.create();
svm.setTermCriteria(criteria); // 指定
svm.setKernel(SVM.RBF); // 使用预先定义的内核初始化
svm.setType(SVM.C_SVC); // SVM的类型,默认是：SVM.C_SVC
svm.setGamma(0.1); // 核函数的参数
svm.setNu(0.1); // SVM优化问题参数
svm.setC(1); // SVM优化问题的参数C
svm.setP(0.1);
svm.setDegree(0.1);
svm.setCoef0(0.1);
TrainData td = TrainData.create(trainingDataMat, Ml.ROW_SAMPLE, labelsMat);// 类封装的训练数据
boolean success = svm.train(td.getSamples(), Ml.ROW_SAMPLE, td.getResponses());// 训练统计模型
System.out.println("svm training result: " + success);
svm.save(MODEL_PATH);// 保存模型
}
public static void pridect() {
// 加载训练得到的 xml 模型文件
SVM svm = SVM.load(MODEL_PATH);
// 136 × 36 像素 需要跟训练的源图像文件保持相同大小
doPridect(svm, DEFAULT_PATH + "test/A01_NMV802_0.jpg");
doPridect(svm, DEFAULT_PATH + "test/debug_resize_1.jpg");
doPridect(svm, DEFAULT_PATH + "test/debug_resize_2.jpg");
doPridect(svm, DEFAULT_PATH + "test/debug_resize_3.jpg");
doPridect(svm, DEFAULT_PATH + "test/S22_KG2187_3.jpg");
doPridect(svm, DEFAULT_PATH + "test/S22_KG2187_5.jpg");
doPridect(svm, DEFAULT_PATH + "test/result_0.png");
doPridect(svm, DEFAULT_PATH + "test/result_1.png");
doPridect(svm, DEFAULT_PATH + "test/result_2.png");
doPridect(svm, DEFAULT_PATH + "test/result_3.png");
doPridect(svm, DEFAULT_PATH + "test/result_4.png");
doPridect(svm, DEFAULT_PATH + "test/result_5.png");
doPridect(svm, DEFAULT_PATH + "test/result_6.png");
doPridect(svm, DEFAULT_PATH + "test/result_7.png");
doPridect(svm, DEFAULT_PATH + "test/result_8.png");
}
public static void doPridect(SVM svm, String imgPath) {
Mat src = Imgcodecs.imread(imgPath);// 图片大小要和样本一致
Imgproc.cvtColor(src, src, Imgproc.COLOR_BGR2GRAY);
Mat dst = new Mat();
Imgproc.Canny(src, dst, 130, 250);
Mat samples = dst.reshape(1, 1);
samples.convertTo(samples, CvType.CV_32F);
// 等价于上面两行代码
/*Mat samples = new Mat(1, dst.cols() * dst.rows(), CvType.CV_32F);
float[] arr = new float[dst.cols() * dst.rows()];
int l = 0;
for (int j = 0; j < dst.rows(); j++) { // 遍历行
for (int k = 0; k < dst.cols(); k++) { // 遍历列
double[] a = dst.get(j, k);
arr[l] = (float) a[0];
l++;
}
}
samples.put(0, 0, arr);*/
// Imgcodecs.imwrite(DEFAULT_PATH + "test_1.jpg", samples);
// 如果训练时使用这个标识，那么符合的图像会返回9.0
float flag = svm.predict(samples);
System.err.println(flag);
if (flag == 0) {
System.err.println(imgPath + "： 目标符合");
}
if (flag == 1) {
System.out.println(imgPath + "： 目标不符合");
}
}
public static int[] createLabelArray(Integer i1, Integer i2) {
int labels[] = new int[i1 + i2];
for (int i = 0; i < labels.length; i++) {
if(i < i1) {
labels[i] = 0;
} else {
labels[i] = 1;
}
}
return labels;
}
}

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src/main/java/com/yuxue/train/SVMTrain.java
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@ -1,382 +1,212 @@
package com.yuxue.train;
import java.util.*;
import static org.bytedeco.javacpp.opencv_core.*;
import static org.bytedeco.javacpp.opencv_ml.*;
import org.bytedeco.javacpp.opencv_core;
import org.bytedeco.javacpp.opencv_imgcodecs;
import com.yuxue.easypr.core.Features;
import com.yuxue.easypr.core.SVMCallback;
import com.yuxue.util.Convert;
import com.yuxue.util.FileUtil;
/**
* 基于org.bytedeco.javacpp包实现的训练
* JavaCPP 是一个开源库，它提供了在 Java 中高效访问本地 C++的方法
*
* 图片识别车牌训练
* 训练出来的库文件，用于判断切图是否包含车牌
*
* 训练的svm.xml应用：
* 1、替换res/model/svm.xml文件
* 2、修改com.yuxue.easypr.core.PlateJudge.plateJudge(Mat) 方法
* 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
* @author yuxue
* @date 2020-05-14 22:16
*/
public class SVMTrain {
private SVMCallback callback = new Features();
// 默认的训练操作的根目录
private static final String DEFAULT_PATH = "D:/PlateDetect/train/plate_detect_svm/";
// 训练模型文件保存位置
private static final String MODEL_PATH = DEFAULT_PATH + "svm.xml";
private static final String hasPlate = "HasPlate";
private static final String noPlate = "NoPlate";
public SVMTrain() {
}
public SVMTrain(SVMCallback callback) {
this.callback = callback;
}
/**
* 将learn文件夹下的图片，转存到tain test文件夹下，区分hasPalte noPlate
* 随机选取bound%作为训练数据，30%作为测试数据
* @param bound
* @param name
*/
private void learn2Plate(float bound, final String name) {
final String filePath = DEFAULT_PATH + "learn/" + name;
Vector<String> files = new Vector<String>();
//// 获取该路径下的所有文件
FileUtil.getFiles(filePath, files);
int size = files.size();
if (0 == size) {
System.err.println("当前目录下没有文件: " + filePath);
return;
}
Collections.shuffle(files, new Random(new Date().getTime()));
//// 随机选取70%作为训练数据，30%作为测试数据
int boundry = (int) (bound * size);
// 重新创建目录
FileUtil.recreateDir(DEFAULT_PATH + "train/" + name);
FileUtil.recreateDir(DEFAULT_PATH + "test/" + name);
for (int i = 0; i < boundry; i++) {
Mat img = opencv_imgcodecs.imread(files.get(i));
String str = DEFAULT_PATH + "train/" + name + "/" + name + "_" + Integer.valueOf(i).toString() + ".jpg";
opencv_imgcodecs.imwrite(str, img);
}
for (int i = boundry; i < size; i++) {
Mat img = opencv_imgcodecs.imread(files.get(i));
String str = DEFAULT_PATH + "test/" + name + "/" + name + "_" + Integer.valueOf(i).toString() + ".jpg";
opencv_imgcodecs.imwrite(str, img);
}
}
/**
* 获取训练图片
* @param trainingImages
* @param trainingLabels
* @param name
*/
private void getPlateTrain(Mat trainingImages, Vector<Integer> trainingLabels, final String name, int label) {
// int label = 1;
final String filePath = DEFAULT_PATH + "train/" + name;
Vector<String> files = new Vector<String>();
// 获取该路径下的所有文件
FileUtil.getFiles(filePath, files);
int size = files.size();
if (null == files || size <= 0) {
System.out.println("File not found in " + filePath);
return;
}
for (int i = 0; i < size; i++) {
// System.out.println(files.get(i));
Mat inMat = opencv_imgcodecs.imread(files.get(i));
// 调用回调函数决定特征
// Mat features = this.callback.getHisteqFeatures(inMat);
Mat features = this.callback.getHistogramFeatures(inMat);
// 通过直方图均衡化后的彩色图进行预测
Mat p = features.reshape(1, 1);
p.convertTo(p, opencv_core.CV_32F);
// 136 36 14688 1 变换尺寸
// System.err.println(inMat.cols() + "\t" + inMat.rows() + "\t" + p.cols() + "\t" + p.rows());
trainingImages.push_back(p); // 合并成一张图片
trainingLabels.add(label);
}
}
private void getPlateTest(MatVector testingImages, Vector<Integer> testingLabels, final String name, int label) {
// int label = 1;
final String filePath = DEFAULT_PATH + "test/" + name;
Vector<String> files = new Vector<String>();
FileUtil.getFiles(filePath, files);
int size = files.size();
if (0 == size) {
System.out.println("File not found in " + filePath);
return;
}
System.out.println("get " + name + " test!");
for (int i = 0; i < size; i++) {
Mat inMat = opencv_imgcodecs.imread(files.get(i));
testingImages.push_back(inMat);
testingLabels.add(label);
}
}
// ! 测试SVM的准确率，回归率以及FScore
public void getAccuracy(Mat testingclasses_preditc, Mat testingclasses_real) {
int channels = testingclasses_preditc.channels();
System.out.println("channels: " + Integer.valueOf(channels).toString());
int nRows = testingclasses_preditc.rows();
System.out.println("nRows: " + Integer.valueOf(nRows).toString());
int nCols = testingclasses_preditc.cols() * channels;
System.out.println("nCols: " + Integer.valueOf(nCols).toString());
int channels_real = testingclasses_real.channels();
System.out.println("channels_real: " + Integer.valueOf(channels_real).toString());
int nRows_real = testingclasses_real.rows();
System.out.println("nRows_real: " + Integer.valueOf(nRows_real).toString());
int nCols_real = testingclasses_real.cols() * channels;
System.out.println("nCols_real: " + Integer.valueOf(nCols_real).toString());
double count_all = 0;
double ptrue_rtrue = 0;
double ptrue_rfalse = 0;
double pfalse_rtrue = 0;
double pfalse_rfalse = 0;
for (int i = 0; i < nRows; i++) {
final float predict = Convert.toFloat(testingclasses_preditc.ptr(i));
final float real = Convert.toFloat(testingclasses_real.ptr(i));
count_all++;
// System.out.println("predict:" << predict).toString());
// System.out.println("real:" << real).toString());
if (predict == 1.0 && real == 1.0)
ptrue_rtrue++;
if (predict == 1.0 && real == 0)
ptrue_rfalse++;
if (predict == 0 && real == 1.0)
pfalse_rtrue++;
if (predict == 0 && real == 0)
pfalse_rfalse++;
}
System.out.println("count_all: " + Double.valueOf(count_all).toString());
System.out.println("ptrue_rtrue: " + Double.valueOf(ptrue_rtrue).toString());
System.out.println("ptrue_rfalse: " + Double.valueOf(ptrue_rfalse).toString());
System.out.println("pfalse_rtrue: " + Double.valueOf(pfalse_rtrue).toString());
System.out.println("pfalse_rfalse: " + Double.valueOf(pfalse_rfalse).toString());
double precise = 0;
if (ptrue_rtrue + ptrue_rfalse != 0) {
precise = ptrue_rtrue / (ptrue_rtrue + ptrue_rfalse);
System.out.println("precise: " + Double.valueOf(precise).toString());
} else {
System.out.println("precise: NA");
}
double recall = 0;
if (ptrue_rtrue + pfalse_rtrue != 0) {
recall = ptrue_rtrue / (ptrue_rtrue + pfalse_rtrue);
System.out.println("recall: " + Double.valueOf(recall).toString());
} else {
System.out.println("recall: NA");
}
if (precise + recall != 0) {
double F = (precise * recall) / (precise + recall);
System.out.println("F: " + Double.valueOf(F).toString());
} else {
System.out.println("F: NA");
}
}
/**
* 训练
* @param dividePrepared
* @return
*/
public int svmTrain(boolean dividePrepared) {
Mat classes = new Mat();
Mat trainingData = new Mat();
Mat trainingImages = new Mat();
Vector<Integer> trainingLabels = new Vector<Integer>();
// 分割learn里的数据到train和test里 // 从库里面选取训练样本
if (!dividePrepared) {
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。
learn2Plate(0.1f, noPlate);
}
// System.err.println("Begin to get train data to memory");
getPlateTrain(trainingImages, trainingLabels, hasPlate, 0);
getPlateTrain(trainingImages, trainingLabels, noPlate, 1);
// System.err.println(trainingImages.cols());
trainingImages.copyTo(trainingData);
trainingData.convertTo(trainingData, CV_32F);
int[] labels = new int[trainingLabels.size()];
for (int i = 0; i < trainingLabels.size(); ++i) {
labels[i] = trainingLabels.get(i).intValue();
}
new Mat(labels).copyTo(classes);
TrainData train_data = TrainData.create(trainingData, ROW_SAMPLE, classes);
SVM svm = SVM.create();
try {
TermCriteria criteria = new TermCriteria(TermCriteria.EPS + TermCriteria.MAX_ITER, 20000, 0.0001);
svm.setTermCriteria(criteria); // 指定
svm.setKernel(SVM.RBF); // 使用预先定义的内核初始化
svm.setType(SVM.C_SVC); // SVM的类型,默认是：SVM.C_SVC
svm.setGamma(0.1); // 核函数的参数
svm.setNu(0.1); // SVM优化问题参数
svm.setC(1); // SVM优化问题的参数C
svm.setP(0.1);
svm.setDegree(0.1);
svm.setCoef0(0.1);
svm.trainAuto(train_data, 10,
SVM.getDefaultGrid(SVM.C),
SVM.getDefaultGrid(SVM.GAMMA),
SVM.getDefaultGrid(SVM.P),
SVM.getDefaultGrid(SVM.NU),
SVM.getDefaultGrid(SVM.COEF),
SVM.getDefaultGrid(SVM.DEGREE),
true);
} catch (Exception err) {
System.out.println(err.getMessage());
}
System.out.println("Svm generate done!");
/*FileStorage fsTo = new FileStorage(MODEL_PATH, FileStorage.WRITE);
svm.write(fsTo, "svm");*/
svm.save(MODEL_PATH);
return 0;
}
// 测试
public int svmPredict() {
SVM svm = SVM.create();
try {
svm.clear();
// svm = SVM.loadSVM(MODEL_PATH, "svm");
svm = SVM.load(MODEL_PATH);
} catch (Exception err) {
System.err.println(err.getMessage());
return 0; // next predict requires svm
}
System.out.println("Begin to predict");
// Test SVM
MatVector testingImages = new MatVector();
Vector<Integer> testingLabels_real = new Vector<Integer>();
// 将测试数据加载入内存
getPlateTest(testingImages, testingLabels_real, hasPlate, 0);
getPlateTest(testingImages, testingLabels_real, noPlate, 1);
double count_all = 0;
double ptrue_rtrue = 0;
double ptrue_rfalse = 0;
double pfalse_rtrue = 0;
double pfalse_rfalse = 0;
long size = testingImages.size();
System.err.println(size);
for (int i = 0; i < size; i++) {
Mat inMat = testingImages.get(i);
// Mat features = callback.getHisteqFeatures(inMat);
Mat features = callback.getHistogramFeatures(inMat);
Mat p = features.reshape(1, 1);
p.convertTo(p, opencv_core.CV_32F);
// System.out.println(p.cols() + "\t" + p.rows() + "\t" + p.type());
// samples.cols == var_count && samples.type() == CV_32F
// var_count 的值会在svm.xml库文件中有体现
float predoct = svm.predict(features);
int predict = (int) predoct; // 预期值
int real = testingLabels_real.get(i); // 实际值
if (predict == 1 && real == 1)
ptrue_rtrue++;
if (predict == 1 && real == 0)
ptrue_rfalse++;
if (predict == 0 && real == 1)
pfalse_rtrue++;
if (predict == 0 && real == 0)
pfalse_rfalse++;
}
count_all = size;
System.out.println("Get the Accuracy!");
System.out.println("count_all: " + Double.valueOf(count_all).toString());
System.out.println("ptrue_rtrue: " + Double.valueOf(ptrue_rtrue).toString());
System.out.println("ptrue_rfalse: " + Double.valueOf(ptrue_rfalse).toString());
System.out.println("pfalse_rtrue: " + Double.valueOf(pfalse_rtrue).toString());
System.out.println("pfalse_rfalse: " + Double.valueOf(pfalse_rfalse).toString());
double precise = 0;
if (ptrue_rtrue + ptrue_rfalse != 0) {
precise = ptrue_rtrue / (ptrue_rtrue + ptrue_rfalse);
System.out.println("precise: " + Double.valueOf(precise).toString());
} else
System.out.println("precise: NA");
double recall = 0;
if (ptrue_rtrue + pfalse_rtrue != 0) {
recall = ptrue_rtrue / (ptrue_rtrue + pfalse_rtrue);
System.out.println("recall: " + Double.valueOf(recall).toString());
} else
System.out.println("recall: NA");
double Fsocre = 0;
if (precise + recall != 0) {
Fsocre = 2 * (precise * recall) / (precise + recall);
System.out.println("Fsocre: " + Double.valueOf(Fsocre).toString());
} else
System.out.println("Fsocre: NA");
return 0;
}
public static void main(String[] args) {
SVMTrain s = new SVMTrain();
s.svmTrain(true);
s.svmPredict();
}
}
package com.yuxue.train;
import java.io.File;
import java.util.List;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.TermCriteria;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.opencv.ml.Ml;
import org.opencv.ml.SVM;
import org.opencv.ml.TrainData;
import com.yuxue.constant.Constant;
import com.yuxue.util.FileUtil;
/**
* 基于org.opencv官方包实现的训练
*
*
* windows下环境配置：
* 1、官网下载对应版本的openvp：https://opencv.org/releases/page/2/ 当前使用4.0.1版本
* 2、双击exe文件安装，将 安装目录下\build\java\x64\opencv_java401.dll 拷贝到\build\x64\vc14\bin\目录下
* 3、eclipse添加User Libraries
* 4、项目右键build path，添加步骤三新增的lib
*
* 图片识别车牌训练
* 训练出来的库文件，用于判断切图是否包含车牌
*
* 训练的svm.xml应用：
* 1、替换res/model/svm.xml文件
* 2、修改com.yuxue.easypr.core.PlateJudge.plateJudge(Mat) 方法
* 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
* @author yuxue
* @date 2020-05-13 10:10
*/
public class SVMTrain {
// 默认的训练操作的根目录
private static final String DEFAULT_PATH = "D:/PlateDetect/train/plate_detect_svm/";
// 训练模型文件保存位置
private static final String MODEL_PATH = DEFAULT_PATH + "svm.xml";
static {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
}
public static void main(String[] arg) {
// 训练， 生成svm.xml库文件
train();
// 识别，判断样本文件是否是车牌
pridect();
}
public static void train() {
// 正样本 // 136 × 36 像素 训练的源图像文件要相同大小
List<File> imgList0 = FileUtil.listFile(new File(DEFAULT_PATH + "/learn/HasPlate"), Constant.DEFAULT_TYPE, false);
// 负样本 // 136 × 36 像素 训练的源图像文件要相同大小
List<File> imgList1 = FileUtil.listFile(new File(DEFAULT_PATH + "/learn/NoPlate"), Constant.DEFAULT_TYPE, false);
// 标记：正样本用 0 表示，负样本用 1 表示。
int labels[] = createLabelArray(imgList0.size(), imgList1.size());
int sample_num = labels.length; // 图片数量
// 用于存放所有样本的矩阵
Mat trainingDataMat = null;
// 存放标记的Mat,每个图片都要给一个标记
Mat labelsMat = new Mat(sample_num, 1, CvType.CV_32SC1);
labelsMat.put(0, 0, labels);
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
String path = "";
if(i < imgList0.size()) {
path = imgList0.get(i).getAbsolutePath();
} else {
path = imgList1.get(i - imgList0.size()).getAbsolutePath();
}
Mat inMat = Imgcodecs.imread(path); // 读取样本文件
// 创建一个行数为sample_num, 列数为 rows*cols 的矩阵; 用于存放样本
if (trainingDataMat == null) {
trainingDataMat = new Mat(sample_num, inMat.rows() * inMat.cols(), CvType.CV_32F);
}
// 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
Mat greyMat = new Mat();
Imgproc.cvtColor(inMat, greyMat, Imgproc.COLOR_BGR2GRAY); // 转成灰度图
Mat dst = new Mat(inMat.rows(), inMat.cols(), inMat.type());
Imgproc.Canny(greyMat, dst, 130, 250); // 边缘检测
// 将样本矩阵转换成只有一行的矩阵，保存为float数组
float[] arr = new float[dst.rows() * dst.cols()];
int l = 0;
for (int j = 0; j < dst.rows(); j++) { // 遍历行
for (int k = 0; k < dst.cols(); k++) { // 遍历列
double[] a = dst.get(j, k);
arr[l] = (float) a[0];
l++;
}
}
trainingDataMat.put(i, 0, arr); // 多张图合并到一张
}
// Imgcodecs.imwrite(DEFAULT_PATH + "trainingDataMat.jpg", trainingDataMat);
// 配置SVM训练器参数
TermCriteria criteria = new TermCriteria(TermCriteria.EPS + TermCriteria.MAX_ITER, 20000, 0.0001);
SVM svm = SVM.create();
svm.setTermCriteria(criteria); // 指定
svm.setKernel(SVM.RBF); // 使用预先定义的内核初始化
svm.setType(SVM.C_SVC); // SVM的类型,默认是：SVM.C_SVC
svm.setGamma(0.1); // 核函数的参数
svm.setNu(0.1); // SVM优化问题参数
svm.setC(1); // SVM优化问题的参数C
svm.setP(0.1);
svm.setDegree(0.1);
svm.setCoef0(0.1);
TrainData td = TrainData.create(trainingDataMat, Ml.ROW_SAMPLE, labelsMat);// 类封装的训练数据
boolean success = svm.train(td.getSamples(), Ml.ROW_SAMPLE, td.getResponses());// 训练统计模型
System.out.println("svm training result: " + success);
svm.save(MODEL_PATH);// 保存模型
}
public static void pridect() {
// 加载训练得到的 xml 模型文件
SVM svm = SVM.load(MODEL_PATH);
// 136 × 36 像素 需要跟训练的源图像文件保持相同大小
doPridect(svm, DEFAULT_PATH + "test/A01_NMV802_0.jpg");
doPridect(svm, DEFAULT_PATH + "test/debug_resize_1.jpg");
doPridect(svm, DEFAULT_PATH + "test/debug_resize_2.jpg");
doPridect(svm, DEFAULT_PATH + "test/debug_resize_3.jpg");
doPridect(svm, DEFAULT_PATH + "test/S22_KG2187_3.jpg");
doPridect(svm, DEFAULT_PATH + "test/S22_KG2187_5.jpg");
doPridect(svm, DEFAULT_PATH + "test/result_0.png");
doPridect(svm, DEFAULT_PATH + "test/result_1.png");
doPridect(svm, DEFAULT_PATH + "test/result_2.png");
doPridect(svm, DEFAULT_PATH + "test/result_3.png");
doPridect(svm, DEFAULT_PATH + "test/result_4.png");
doPridect(svm, DEFAULT_PATH + "test/result_5.png");
doPridect(svm, DEFAULT_PATH + "test/result_6.png");
doPridect(svm, DEFAULT_PATH + "test/result_7.png");
doPridect(svm, DEFAULT_PATH + "test/result_8.png");
}
public static void doPridect(SVM svm, String imgPath) {
Mat src = Imgcodecs.imread(imgPath);// 图片大小要和样本一致
Imgproc.cvtColor(src, src, Imgproc.COLOR_BGR2GRAY);
Mat dst = new Mat();
Imgproc.Canny(src, dst, 130, 250);
Mat samples = dst.reshape(1, 1);
samples.convertTo(samples, CvType.CV_32F);
// 等价于上面两行代码
/*Mat samples = new Mat(1, dst.cols() * dst.rows(), CvType.CV_32F);
float[] arr = new float[dst.cols() * dst.rows()];
int l = 0;
for (int j = 0; j < dst.rows(); j++) { // 遍历行
for (int k = 0; k < dst.cols(); k++) { // 遍历列
double[] a = dst.get(j, k);
arr[l] = (float) a[0];
l++;
}
}
samples.put(0, 0, arr);*/
// Imgcodecs.imwrite(DEFAULT_PATH + "test_1.jpg", samples);
// 如果训练时使用这个标识，那么符合的图像会返回9.0
float flag = svm.predict(samples);
System.err.println(flag);
if (flag == 0) {
System.err.println(imgPath + "： 目标符合");
}
if (flag == 1) {
System.out.println(imgPath + "： 目标不符合");
}
}
public static int[] createLabelArray(Integer i1, Integer i2) {
int labels[] = new int[i1 + i2];
for (int i = 0; i < labels.length; i++) {
if(i < i1) {
labels[i] = 0;
} else {
labels[i] = 1;
}
}
return labels;
}
}

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src/main/java/com/yuxue/train/SVMTrain1.java
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@ -0,0 +1,382 @@
package com.yuxue.train;
import java.util.*;
import static org.bytedeco.javacpp.opencv_core.*;
import static org.bytedeco.javacpp.opencv_ml.*;
import org.bytedeco.javacpp.opencv_core;
import org.bytedeco.javacpp.opencv_imgcodecs;
import com.yuxue.easypr.core.Features;
import com.yuxue.easypr.core.SVMCallback;
import com.yuxue.util.Convert;
import com.yuxue.util.FileUtil;
/**
* 基于org.bytedeco.javacpp包实现的训练
* JavaCPP 是一个开源库，它提供了在 Java 中高效访问本地 C++的方法
*
* 图片识别车牌训练
* 训练出来的库文件，用于判断切图是否包含车牌
*
* 训练的svm.xml应用：
* 1、替换res/model/svm.xml文件
* 2、修改com.yuxue.easypr.core.PlateJudge.plateJudge(Mat) 方法
* 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
* @author yuxue
* @date 2020-05-14 22:16
*/
public class SVMTrain1 {
private SVMCallback callback = new Features();
// 默认的训练操作的根目录
private static final String DEFAULT_PATH = "D:/PlateDetect/train/plate_detect_svm/";
// 训练模型文件保存位置
private static final String MODEL_PATH = DEFAULT_PATH + "svm.xml";
private static final String hasPlate = "HasPlate";
private static final String noPlate = "NoPlate";
public SVMTrain() {
}
public SVMTrain(SVMCallback callback) {
this.callback = callback;
}
/**
* 将learn文件夹下的图片，转存到tain test文件夹下，区分hasPalte noPlate
* 随机选取bound%作为训练数据，30%作为测试数据
* @param bound
* @param name
*/
private void learn2Plate(float bound, final String name) {
final String filePath = DEFAULT_PATH + "learn/" + name;
Vector<String> files = new Vector<String>();
//// 获取该路径下的所有文件
FileUtil.getFiles(filePath, files);
int size = files.size();
if (0 == size) {
System.err.println("当前目录下没有文件: " + filePath);
return;
}
Collections.shuffle(files, new Random(new Date().getTime()));
//// 随机选取70%作为训练数据，30%作为测试数据
int boundry = (int) (bound * size);
// 重新创建目录
FileUtil.recreateDir(DEFAULT_PATH + "train/" + name);
FileUtil.recreateDir(DEFAULT_PATH + "test/" + name);
for (int i = 0; i < boundry; i++) {
Mat img = opencv_imgcodecs.imread(files.get(i));
String str = DEFAULT_PATH + "train/" + name + "/" + name + "_" + Integer.valueOf(i).toString() + ".jpg";
opencv_imgcodecs.imwrite(str, img);
}
for (int i = boundry; i < size; i++) {
Mat img = opencv_imgcodecs.imread(files.get(i));
String str = DEFAULT_PATH + "test/" + name + "/" + name + "_" + Integer.valueOf(i).toString() + ".jpg";
opencv_imgcodecs.imwrite(str, img);
}
}
/**
* 获取训练图片
* @param trainingImages
* @param trainingLabels
* @param name
*/
private void getPlateTrain(Mat trainingImages, Vector<Integer> trainingLabels, final String name, int label) {
// int label = 1;
final String filePath = DEFAULT_PATH + "train/" + name;
Vector<String> files = new Vector<String>();
// 获取该路径下的所有文件
FileUtil.getFiles(filePath, files);
int size = files.size();
if (null == files || size <= 0) {
System.out.println("File not found in " + filePath);
return;
}
for (int i = 0; i < size; i++) {
// System.out.println(files.get(i));
Mat inMat = opencv_imgcodecs.imread(files.get(i));
// 调用回调函数决定特征
// Mat features = this.callback.getHisteqFeatures(inMat);
Mat features = this.callback.getHistogramFeatures(inMat);
// 通过直方图均衡化后的彩色图进行预测
Mat p = features.reshape(1, 1);
p.convertTo(p, opencv_core.CV_32F);
// 136 36 14688 1 变换尺寸
// System.err.println(inMat.cols() + "\t" + inMat.rows() + "\t" + p.cols() + "\t" + p.rows());
trainingImages.push_back(p); // 合并成一张图片
trainingLabels.add(label);
}
}
private void getPlateTest(MatVector testingImages, Vector<Integer> testingLabels, final String name, int label) {
// int label = 1;
final String filePath = DEFAULT_PATH + "test/" + name;
Vector<String> files = new Vector<String>();
FileUtil.getFiles(filePath, files);
int size = files.size();
if (0 == size) {
System.out.println("File not found in " + filePath);
return;
}
System.out.println("get " + name + " test!");
for (int i = 0; i < size; i++) {
Mat inMat = opencv_imgcodecs.imread(files.get(i));
testingImages.push_back(inMat);
testingLabels.add(label);
}
}
// ! 测试SVM的准确率，回归率以及FScore
public void getAccuracy(Mat testingclasses_preditc, Mat testingclasses_real) {
int channels = testingclasses_preditc.channels();
System.out.println("channels: " + Integer.valueOf(channels).toString());
int nRows = testingclasses_preditc.rows();
System.out.println("nRows: " + Integer.valueOf(nRows).toString());
int nCols = testingclasses_preditc.cols() * channels;
System.out.println("nCols: " + Integer.valueOf(nCols).toString());
int channels_real = testingclasses_real.channels();
System.out.println("channels_real: " + Integer.valueOf(channels_real).toString());
int nRows_real = testingclasses_real.rows();
System.out.println("nRows_real: " + Integer.valueOf(nRows_real).toString());
int nCols_real = testingclasses_real.cols() * channels;
System.out.println("nCols_real: " + Integer.valueOf(nCols_real).toString());
double count_all = 0;
double ptrue_rtrue = 0;
double ptrue_rfalse = 0;
double pfalse_rtrue = 0;
double pfalse_rfalse = 0;
for (int i = 0; i < nRows; i++) {
final float predict = Convert.toFloat(testingclasses_preditc.ptr(i));
final float real = Convert.toFloat(testingclasses_real.ptr(i));
count_all++;
// System.out.println("predict:" << predict).toString());
// System.out.println("real:" << real).toString());
if (predict == 1.0 && real == 1.0)
ptrue_rtrue++;
if (predict == 1.0 && real == 0)
ptrue_rfalse++;
if (predict == 0 && real == 1.0)
pfalse_rtrue++;
if (predict == 0 && real == 0)
pfalse_rfalse++;
}
System.out.println("count_all: " + Double.valueOf(count_all).toString());
System.out.println("ptrue_rtrue: " + Double.valueOf(ptrue_rtrue).toString());
System.out.println("ptrue_rfalse: " + Double.valueOf(ptrue_rfalse).toString());
System.out.println("pfalse_rtrue: " + Double.valueOf(pfalse_rtrue).toString());
System.out.println("pfalse_rfalse: " + Double.valueOf(pfalse_rfalse).toString());
double precise = 0;
if (ptrue_rtrue + ptrue_rfalse != 0) {
precise = ptrue_rtrue / (ptrue_rtrue + ptrue_rfalse);
System.out.println("precise: " + Double.valueOf(precise).toString());
} else {
System.out.println("precise: NA");
}
double recall = 0;
if (ptrue_rtrue + pfalse_rtrue != 0) {
recall = ptrue_rtrue / (ptrue_rtrue + pfalse_rtrue);
System.out.println("recall: " + Double.valueOf(recall).toString());
} else {
System.out.println("recall: NA");
}
if (precise + recall != 0) {
double F = (precise * recall) / (precise + recall);
System.out.println("F: " + Double.valueOf(F).toString());
} else {
System.out.println("F: NA");
}
}
/**
* 训练
* @param dividePrepared
* @return
*/
public int svmTrain(boolean dividePrepared) {
Mat classes = new Mat();
Mat trainingData = new Mat();
Mat trainingImages = new Mat();
Vector<Integer> trainingLabels = new Vector<Integer>();
// 分割learn里的数据到train和test里 // 从库里面选取训练样本
if (!dividePrepared) {
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。
learn2Plate(0.1f, noPlate);
}
// System.err.println("Begin to get train data to memory");
getPlateTrain(trainingImages, trainingLabels, hasPlate, 0);
getPlateTrain(trainingImages, trainingLabels, noPlate, 1);
// System.err.println(trainingImages.cols());
trainingImages.copyTo(trainingData);
trainingData.convertTo(trainingData, CV_32F);
int[] labels = new int[trainingLabels.size()];
for (int i = 0; i < trainingLabels.size(); ++i) {
labels[i] = trainingLabels.get(i).intValue();
}
new Mat(labels).copyTo(classes);
TrainData train_data = TrainData.create(trainingData, ROW_SAMPLE, classes);
SVM svm = SVM.create();
try {
TermCriteria criteria = new TermCriteria(TermCriteria.EPS + TermCriteria.MAX_ITER, 20000, 0.0001);
svm.setTermCriteria(criteria); // 指定
svm.setKernel(SVM.RBF); // 使用预先定义的内核初始化
svm.setType(SVM.C_SVC); // SVM的类型,默认是：SVM.C_SVC
svm.setGamma(0.1); // 核函数的参数
svm.setNu(0.1); // SVM优化问题参数
svm.setC(1); // SVM优化问题的参数C
svm.setP(0.1);
svm.setDegree(0.1);
svm.setCoef0(0.1);
svm.trainAuto(train_data, 10,
SVM.getDefaultGrid(SVM.C),
SVM.getDefaultGrid(SVM.GAMMA),
SVM.getDefaultGrid(SVM.P),
SVM.getDefaultGrid(SVM.NU),
SVM.getDefaultGrid(SVM.COEF),
SVM.getDefaultGrid(SVM.DEGREE),
true);
} catch (Exception err) {
System.out.println(err.getMessage());
}
System.out.println("Svm generate done!");
/*FileStorage fsTo = new FileStorage(MODEL_PATH, FileStorage.WRITE);
svm.write(fsTo, "svm");*/
svm.save(MODEL_PATH);
return 0;
}
// 测试
public int svmPredict() {
SVM svm = SVM.create();
try {
svm.clear();
// svm = SVM.loadSVM(MODEL_PATH, "svm");
svm = SVM.load(MODEL_PATH);
} catch (Exception err) {
System.err.println(err.getMessage());
return 0; // next predict requires svm
}
System.out.println("Begin to predict");
// Test SVM
MatVector testingImages = new MatVector();
Vector<Integer> testingLabels_real = new Vector<Integer>();
// 将测试数据加载入内存
getPlateTest(testingImages, testingLabels_real, hasPlate, 0);
getPlateTest(testingImages, testingLabels_real, noPlate, 1);
double count_all = 0;
double ptrue_rtrue = 0;
double ptrue_rfalse = 0;
double pfalse_rtrue = 0;
double pfalse_rfalse = 0;
long size = testingImages.size();
System.err.println(size);
for (int i = 0; i < size; i++) {
Mat inMat = testingImages.get(i);
// Mat features = callback.getHisteqFeatures(inMat);
Mat features = callback.getHistogramFeatures(inMat);
Mat p = features.reshape(1, 1);
p.convertTo(p, opencv_core.CV_32F);
// System.out.println(p.cols() + "\t" + p.rows() + "\t" + p.type());
// samples.cols == var_count && samples.type() == CV_32F
// var_count 的值会在svm.xml库文件中有体现
float predoct = svm.predict(features);
int predict = (int) predoct; // 预期值
int real = testingLabels_real.get(i); // 实际值
if (predict == 1 && real == 1)
ptrue_rtrue++;
if (predict == 1 && real == 0)
ptrue_rfalse++;
if (predict == 0 && real == 1)
pfalse_rtrue++;
if (predict == 0 && real == 0)
pfalse_rfalse++;
}
count_all = size;
System.out.println("Get the Accuracy!");
System.out.println("count_all: " + Double.valueOf(count_all).toString());
System.out.println("ptrue_rtrue: " + Double.valueOf(ptrue_rtrue).toString());
System.out.println("ptrue_rfalse: " + Double.valueOf(ptrue_rfalse).toString());
System.out.println("pfalse_rtrue: " + Double.valueOf(pfalse_rtrue).toString());
System.out.println("pfalse_rfalse: " + Double.valueOf(pfalse_rfalse).toString());
double precise = 0;
if (ptrue_rtrue + ptrue_rfalse != 0) {
precise = ptrue_rtrue / (ptrue_rtrue + ptrue_rfalse);
System.out.println("precise: " + Double.valueOf(precise).toString());
} else
System.out.println("precise: NA");
double recall = 0;
if (ptrue_rtrue + pfalse_rtrue != 0) {
recall = ptrue_rtrue / (ptrue_rtrue + pfalse_rtrue);
System.out.println("recall: " + Double.valueOf(recall).toString());
} else
System.out.println("recall: NA");
double Fsocre = 0;
if (precise + recall != 0) {
Fsocre = 2 * (precise * recall) / (precise + recall);
System.out.println("Fsocre: " + Double.valueOf(Fsocre).toString());
} else
System.out.println("Fsocre: NA");
return 0;
}
public static void main(String[] args) {
SVMTrain s = new SVMTrain();
s.svmTrain(true);
s.svmPredict();
}
}
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