p86743915
/
pp6hrvtlm
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

添加ann训练

Browse Source
devA
yuxue 5 years ago
parent d12b463fdd
commit b6154ef642
2 changed files with 73 additions and 64 deletions
Show Stats Download Patch File Download Diff File

135
src/test/java/com/yuxue/test/ANNTrain.java → src/main/java/com/yuxue/train/ANNTrain.java
Unescape View File

@ -1,49 +1,50 @@
package com.yuxue.test;
package com.yuxue.train;
import static org.bytedeco.javacpp.opencv_core.CV_32F;
import static org.bytedeco.javacpp.opencv_core.CV_32FC1;
import static org.bytedeco.javacpp.opencv_core.CV_32SC1;
import static org.bytedeco.javacpp.opencv_core.getTickCount;
import static org.bytedeco.javacpp.opencv_imgproc.resize;
import static org.bytedeco.javacpp.opencv_ml.ROW_SAMPLE;
import java.util.Vector;
import org.bytedeco.javacpp.opencv_core.CvMemStorage;
import org.bytedeco.javacpp.opencv_core.FileStorage;
import org.bytedeco.javacpp.opencv_core.Mat;
import org.bytedeco.javacpp.opencv_core.Scalar;
import org.bytedeco.javacpp.opencv_core.Size;
import org.bytedeco.javacpp.opencv_core.TermCriteria;
import org.bytedeco.javacpp.opencv_imgcodecs;
import org.bytedeco.javacpp.opencv_ml.ANN_MLP;
import org.bytedeco.javacpp.opencv_ml.TrainData;
import com.yuxue.easypr.core.CoreFunc;
import com.yuxue.enumtype.Direction;
import com.yuxue.util.Convert;
import com.yuxue.util.FileUtil;
import ch.qos.logback.classic.pattern.Util;
/*
/**
*
* @author yuxue
* @date 2020-05-14 22:16
*/
public class ANNTrain {
/*private ANN_MLP ann=ANN_MLP.create();
private ANN_MLP ann = ANN_MLP.create();
// 中国车牌
private final char strCharacters[] = { '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E',
'F', 'G', 'H', 没有I
'J', 'K', 'L', 'M', 'N', 没有O 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z' };
private final int numCharacter = 34; 没有I和0,10个数字与24个英文字符之和
private final char strCharacters[] = { '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', /* 没有I */
'J', 'K', 'L', 'M', 'N', /* 没有O */'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z' };
private final int numCharacter = 34; /* 没有I和0,10个数字与24个英文字符之和 */
// 以下都是我训练时用到的中文字符数据，并不全面，有些省份没有训练数据所以没有字符
// 有些后面加数字2的表示在训练时常看到字符的一种变形，也作为训练数据存储
private final String strChinese[] = { "zh_cuan" 川 , "zh_e" 鄂 , "zh_gan" 赣 , "zh_hei" 黑 ,
"zh_hu" 沪 , "zh_ji" 冀 , "zh_jl" 吉 , "zh_jin" 津 , "zh_jing" 京 , "zh_shan" 陕 ,
"zh_liao" 辽 , "zh_lu" 鲁 , "zh_min" 闽 , "zh_ning" 宁 , "zh_su" 苏 , "zh_sx" 晋 ,
"zh_wan" 皖 , "zh_yu" 豫 , "zh_yue" 粤 , "zh_zhe" 浙 };
private final String strChinese[] = { "zh_cuan" /* 川 */, "zh_e" /* 鄂 */, "zh_gan" /* 赣 */, "zh_hei" /* 黑 */, "zh_hu" /* 沪 */, "zh_ji" /* 冀 */, "zh_jl" /* 吉 */, "zh_jin" /* 津 */, "zh_jing" /* 京 */,
"zh_shan" /* 陕 */, "zh_liao" /* 辽 */, "zh_lu" /* 鲁 */, "zh_min" /* 闽 */, "zh_ning" /* 宁 */, "zh_su" /* 苏 */, "zh_sx" /* 晋 */, "zh_wan" /* 皖 */, "zh_yu" /* 豫 */, "zh_yue" /* 粤 */,
"zh_zhe" /* 浙 */ };
private final int numAll = 54; 34+20=54
private final int numAll = 54; /* 34+20=54 */
public Mat features(Mat in, int sizeData) {
// Histogram features
@ -72,23 +73,27 @@ public class ANNTrain {
out.ptr(j).put(Convert.getBytes(val));
}
}
// if(DEBUG)
// cout << out << "\n===========================================\n";
return out;
}
public void annTrain(Mat TrainData, Mat classes, int nNeruns) {
/**
*
* @param TrainData 训练样本数据
* @param classes 数据对应的标签
* @param nNeruns
*/
public void annTrain(Mat trainingData, Mat classes, int nNeruns) {
ann.clear();
Mat layers = new Mat(1, 3, CV_32SC1);
layers.ptr(0).put(Convert.getBytes(TrainData.cols()));
layers.ptr(0).put(Convert.getBytes(trainingData.cols()));
layers.ptr(1).put(Convert.getBytes(nNeruns));
layers.ptr(2).put(Convert.getBytes(numAll));
ann.create(layers, ANN_MLP.SIGMOID_SYM, 1, 1);
// Prepare trainClases
// Create a mat with n trained data by m classes
Mat trainClasses = new Mat();
trainClasses.create(TrainData.rows(), numAll, CV_32FC1);
trainClasses.create(trainingData.rows(), numAll, CV_32FC1);
for (int i = 0; i < trainClasses.rows(); i++) {
for (int k = 0; k < trainClasses.cols(); k++) {
// If class of data i is same than a k class
@ -98,12 +103,29 @@ public class ANNTrain {
trainClasses.ptr(i, k).put(Convert.getBytes(0f));
}
}
Mat weights = new Mat(1, TrainData.rows(), CV_32FC1, Scalar.all(1));
Mat weights = new Mat(1, trainingData.rows(), CV_32FC1, Scalar.all(1));
// Learn classifier
ann.train(TrainData, trainClasses, weights);
TrainData train_data = TrainData.create(trainingData, ROW_SAMPLE, trainClasses);
/*
ann_->setTrainMethod(cv::ml::ANN_MLP::TrainingMethods::BACKPROP);
ann_->setTermCriteria(cvTermCriteria(CV_TERMCRIT_ITER, 30000, 0.0001));
ann_->setBackpropWeightScale(0.1);
ann_->setBackpropMomentumScale(0.1);*/
ann.setLayerSizes(layers);
ann.setActivationFunction(ANN_MLP.SIGMOID_SYM, 1, 1);
ann.setTrainMethod(ANN_MLP.BACKPROP);
TermCriteria criteria = new TermCriteria(TermCriteria.MAX_ITER, 30000, 0.0001);
ann.setTermCriteria(criteria);
ann.setBackpropWeightScale(0.1);
ann.setBackpropMomentumScale(0.1);
ann.train(train_data);
}
public int saveTrainData() {
System.out.println("Begin saveTrainData");
Mat classes = new Mat();
Mat trainingDataf5 = new Mat();
@ -122,8 +144,8 @@ public class ANNTrain {
int size = (int) files.size();
for (int j = 0; j < size; j++) {
System.out.println(files.get(j));
Mat img = imread(files.get(j), 0);
// System.out.println(files.get(j));
Mat img = opencv_imgcodecs.imread(files.get(j), 0);
Mat f5 = features(img, 5);
Mat f10 = features(img, 10);
Mat f15 = features(img, 15);
@ -143,12 +165,12 @@ public class ANNTrain {
System.out.println("Character: " + strChinese[i]);
String str = path + '/' + strChinese[i];
Vector<String> files = new Vector<String>();
Util.getFiles(str, files);
FileUtil.getFiles(str, files);
int size = (int) files.size();
for (int j = 0; j < size; j++) {
System.out.println(files.get(j));
Mat img = imread(files.get(j), 0);
// System.out.println(files.get(j));
Mat img = opencv_imgcodecs.imread(files.get(j), 0);
Mat f5 = features(img, 5);
Mat f10 = features(img, 10);
Mat f15 = features(img, 15);
@ -172,11 +194,11 @@ public class ANNTrain {
new Mat(labels).copyTo(classes);
FileStorage fs = new FileStorage("res/train/ann_data.xml", FileStorage.WRITE);
fs.writeObj("TrainingDataF5", trainingDataf5.data());
fs.writeObj("TrainingDataF10", trainingDataf10.data());
fs.writeObj("TrainingDataF15", trainingDataf15.data());
fs.writeObj("TrainingDataF20", trainingDataf20.data());
fs.writeObj("classes", classes.data());
fs.write("TrainingDataF5", trainingDataf5);
fs.write("TrainingDataF10", trainingDataf10);
fs.write("TrainingDataF15", trainingDataf15);
fs.write("TrainingDataF20", trainingDataf20);
fs.write("classes", classes);
fs.release();
System.out.println("End saveTrainData");
@ -184,32 +206,21 @@ public class ANNTrain {
}
public void saveModel(int _predictsize, int _neurons) {
// 样本文件数据已经保存到xml
FileStorage fs = new FileStorage("res/train/ann_data.xml", FileStorage.READ);
String training = "TrainingDataF" + _predictsize;
Mat TrainingData = new Mat(fs.get(training).readObj());
Mat TrainingData = new Mat(fs.get(training));
Mat Classes = new Mat(fs.get("classes"));
// train the Ann
System.out.println("Begin to saveModelChar predictSize:" + Integer.valueOf(_predictsize).toString());
System.out.println(" neurons:" + Integer.valueOf(_neurons).toString());
long start = getTickCount();
annTrain(TrainingData, Classes, _neurons);
long end = getTickCount();
System.out.println("GetTickCount:" + Long.valueOf((end - start) / 1000).toString());
System.out.println("End the saveModelChar");
System.out.println("完成耗时: " + Long.valueOf((end - start) / 1000).toString());
String model_name = "res/train/ann.xml";
// if(1)
// {
// String str =
// String.format("ann_prd:%d\tneu:%d",_predictsize,_neurons);
// model_name = str;
// }
CvFileStorage fsto = CvFileStorage.open(model_name, CvMemStorage.create(), CV_STORAGE_WRITE);
FileStorage fsto = new FileStorage(model_name, FileStorage.WRITE);
ann.write(fsto, "ann");
}
@ -219,15 +230,13 @@ public class ANNTrain {
saveTrainData();
// 可根据需要训练不同的predictSize或者neurons的ANN模型
// for (int i = 2; i <= 2; i ++)
// {
// int size = i * 5;
// for (int j = 5; j <= 10; j++)
// {
// int neurons = j * 10;
// saveModel(size, neurons);
// }
// }
/*for (int i = 2; i <= 2; i++) {
int size = i * 5;
for (int j = 5; j <= 10; j++) {
int neurons = j * 10;
saveModel(size, neurons);
}
}*/
// 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。
// 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。
@ -235,5 +244,5 @@ public class ANNTrain {
System.out.println("To be end.");
return 0;
}*/
}
}
}

2
src/main/java/com/yuxue/train/SVMTrain.java
Unescape View File

@ -16,7 +16,7 @@ import com.yuxue.util.FileUtil;
/**
*
* @author yuxue
* @date 2020-05-14 11:37
* @date 2020-05-14 22:16
*/
public class SVMTrain {

Write Preview
Loading…
Cancel
Save