添加ann训练

src/main/java/com/yuxue/train/ANNTrain.java

@@ -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',
+    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 */
-            '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' };
-            '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 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 ,
+    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_hu"  沪 , "zh_ji"  冀 , "zh_jl"  吉 , "zh_jin"  津 , "zh_jing"  京 , "zh_shan"  陕 ,
+            "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_liao"  辽 , "zh_lu"  鲁 , "zh_min"  闽 , "zh_ning"  宁 , "zh_su"  苏 , "zh_sx"  晋 ,
+            "zh_zhe" /* 浙 */ };
-            "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));
+                // System.out.println(files.get(j));
-                Mat img = imread(files.get(j), 0);
+                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));
+                // System.out.println(files.get(j));
-                Mat img = imread(files.get(j), 0);
+                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.write("TrainingDataF5", trainingDataf5);
-        fs.writeObj("TrainingDataF10", trainingDataf10.data());
+        fs.write("TrainingDataF10", trainingDataf10);
-        fs.writeObj("TrainingDataF15", trainingDataf15.data());
+        fs.write("TrainingDataF15", trainingDataf15);
-        fs.writeObj("TrainingDataF20", trainingDataf20.data());
+        fs.write("TrainingDataF20", trainingDataf20);
-        fs.writeObj("classes", classes.data());
+        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("完成耗时: " + Long.valueOf((end - start) / 1000).toString());
-
+        
-        System.out.println("End the saveModelChar");
-
         String model_name = "res/train/ann.xml";
-
+        
-        // if(1)
+        FileStorage fsto = new FileStorage(model_name, FileStorage.WRITE);
-        // {
-        // 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);
         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 ++)
+        /*for (int i = 2; i <= 2; i++) {
-        // {
+            int size = i * 5;
-        // int size = i * 5;
+            for (int j = 5; j <= 10; j++) {
-        // for (int j = 5; j <= 10; j++)
+                int neurons = j * 10;
-        // {
+                saveModel(size, neurons);
-        // 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;
-    }*/
+    }
-}
+}

src/main/java/com/yuxue/train/SVMTrain.java

@@ -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 {