no commit message

src/main/java/com/yuxue/constant/Constant.java

@@ -23,7 +23,8 @@ public class Constant {
     // 车牌识别，默认处理图片类型
     public static String DEFAULT_TYPE = "png,jpg,jpeg";
 
-    public static String DEFAULT_ANN_PATH = "res/model/ann.xml";
+    // public static String DEFAULT_ANN_PATH = "res/model/ann.xml";
+    public static String DEFAULT_ANN_PATH = "D:/PlateDetect/train/chars_recognise_ann/ann.xml";
 
   
 

src/main/java/com/yuxue/easypr/core/CharsIdentify.java

@@ -1,6 +1,7 @@
 package com.yuxue.easypr.core;
 
 import org.bytedeco.javacpp.opencv_core;
+import org.bytedeco.javacpp.opencv_imgcodecs;
 import org.bytedeco.javacpp.opencv_core.Mat;
 import org.bytedeco.javacpp.opencv_ml.ANN_MLP;
 
@@ -36,23 +37,29 @@ public class CharsIdentify {
      */
     public String charsIdentify(final Mat input, final Boolean isChinese, final Boolean isSpeci) {
         String result = "";
-
+        
-        Mat f = CoreFunc.features(input, Constant.predictSize);
+        String name = "D:/PlateDetect/train/chars_recognise_ann/" + System.currentTimeMillis() + ".jpg";
+        opencv_imgcodecs.imwrite(name, input);
+        Mat img = opencv_imgcodecs.imread(name);
+        Mat f = CoreFunc.features(img, Constant.predictSize);
 
         int index = this.classify(f, isChinese, isSpeci);
-
+        
-        if (!isChinese) {
+        System.err.print(index);
+        if (index < Constant.numCharacter) {
             result = String.valueOf(Constant.strCharacters[index]);
         } else {
             String s = Constant.strChinese[index - Constant.numCharacter];
             result = Constant.KEY_CHINESE_MAP.get(s);   // 编码转中文
         }
+        System.err.println(result);
         return result;
     }
-
+    
     private int classify(final Mat f, final Boolean isChinses, final Boolean isSpeci) {
         int result = -1;
-        Mat output = new Mat(1, Constant.numAll, opencv_core.CV_32FC1);
+        
+        Mat output = new Mat(1, 140, opencv_core.CV_32F);
 
         ann.predict(f, output, 0);  // 预测结果
 
@@ -71,4 +78,5 @@ public class CharsIdentify {
         return result;
     }
 
+
 }

src/main/java/com/yuxue/easypr/core/Features.java

@@ -65,7 +65,7 @@ public class Features implements SVMCallback {
     /**
      * 本函数是获取SITF特征子的回调函数
      * 
-     * @param image
+     * @param image  
      * @return
      */
     @Override

src/main/java/com/yuxue/easypr/core/PlateJudge.java

@@ -52,7 +52,6 @@ public class PlateJudge {
         Mat p = features.reshape(1, 1);
         p.convertTo(p, opencv_core.CV_32FC1);
         ret = (int) svm.predict(features);
-        System.err.println(ret);
         return ret;
         
         

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

@@ -167,6 +167,8 @@ public class ANNTrain {
         layers.put(0, 0, samples.cols());
         layers.put(0, 1, _neurons);
         layers.put(0, 2, classes.cols());
+        
+        System.out.println(layers);
 
         ann.setLayerSizes(layers);
         ann.setActivationFunction(ANN_MLP.SIGMOID_SYM, 1, 1);
@@ -211,7 +213,7 @@ public class ANNTrain {
         // 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();
         

src/main/java/com/yuxue/train/ANNTrain1.java

@@ -0,0 +1,161 @@
+package com.yuxue.train;
+
+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;
+
+/**
+ * 基于org.bytedeco.javacpp包实现的训练
+ * 
+ * 图片文字识别训练
+ * 训练出来的库文件，用于识别图片中的文字
+ * 
+ * 训练的ann.xml应用：
+ * 1、替换res/model/ann.xml文件
+ * 2、修改com.yuxue.easypr.core.CharsIdentify.charsIdentify(Mat, Boolean, Boolean)方法
+ * 
+ * @author yuxue
+ * @date 2020-05-14 22:16
+ */
+public class ANNTrain1 {
+
+    private ANN_MLP ann = ANN_MLP.create();
+
+    // 默认的训练操作的根目录
+    private static final String DEFAULT_PATH = "D:/PlateDetect/train/chars_recognise_ann/";
+
+    // 训练模型文件保存位置
+    private static final String MODEL_PATH = "res/model/ann.xml";
+    
+    
+    public void train(int _predictsize, int _neurons) {
+        Mat samples = new Mat(); // 使用push_back，行数列数不能赋初始值
+        Vector<Integer> trainingLabels = new Vector<Integer>();
+        // 加载数字及字母字符
+        for (int i = 0; i < Constant.numCharacter; i++) {
+            String str = DEFAULT_PATH + "learn/" + Constant.strCharacters[i];
+            Vector<String> files = new Vector<String>();
+            FileUtil.getFiles(str, files);
+
+            int size = (int) files.size();
+            for (int j = 0; j < size; j++) {
+                Mat img = opencv_imgcodecs.imread(files.get(j), 0);
+                // System.err.println(files.get(j)); // 文件名不能包含中文
+                Mat f = CoreFunc.features(img, _predictsize);
+                samples.push_back(f);
+                trainingLabels.add(i); // 每一幅字符图片所对应的字符类别索引下标
+            }
+        }
+
+        // 加载汉字字符
+        for (int i = 0; i < Constant.strChinese.length; i++) {
+            String str = DEFAULT_PATH + "learn/" + Constant.strChinese[i];
+            Vector<String> files = new Vector<String>();
+            FileUtil.getFiles(str, files);
+
+            int size = (int) files.size();
+            for (int j = 0; j < size; j++) {
+                Mat img = opencv_imgcodecs.imread(files.get(j), 0);
+                // System.err.println(files.get(j));   // 文件名不能包含中文
+                Mat f = CoreFunc.features(img, _predictsize);
+                samples.push_back(f);
+                trainingLabels.add(i + Constant.numCharacter);
+            }
+        }
+
+
+        //440   vhist.length + hhist.length + lowData.cols() * lowData.rows();
+        // CV_32FC1 CV_32SC1 CV_32F
+        Mat classes = new Mat(trainingLabels.size(), Constant.numAll, CV_32F);
+        
+        float[] labels = new float[trainingLabels.size()];
+        for (int i = 0; i < labels.length; ++i) {
+            classes.ptr(i, trainingLabels.get(i)).putFloat(1.f);
+            
+        }
+
+        // samples.type() == CV_32F || samples.type() == CV_32S 
+        TrainData train_data = TrainData.create(samples, ROW_SAMPLE, classes);
+
+        ann.clear();
+        Mat layers = new Mat(1, 3, CV_32SC1);
+        layers.ptr(0, 0).putInt(samples.cols());
+        layers.ptr(0, 1).putInt(_neurons);
+        layers.ptr(0, 2).putInt(classes.cols());
+
+        System.out.println(layers);
+        
+        ann.setLayerSizes(layers);
+        ann.setActivationFunction(ANN_MLP.SIGMOID_SYM, 1, 1);
+        ann.setTrainMethod(ANN_MLP.BACKPROP);
+        TermCriteria criteria = new TermCriteria(TermCriteria.EPS + TermCriteria.MAX_ITER, 30000, 0.0001);
+        ann.setTermCriteria(criteria);
+        ann.setBackpropWeightScale(0.1);
+        ann.setBackpropMomentumScale(0.1);
+        ann.train(train_data);
+
+        //FileStorage fsto = new FileStorage(MODEL_PATH, FileStorage.WRITE);
+        //ann.write(fsto, "ann");
+        ann.save(MODEL_PATH);
+    }
+    
+    
+    public void predict() {
+        ann.clear();
+        ann = ANN_MLP.load(MODEL_PATH);
+        //ann = ANN_MLP.loadANN_MLP(MODEL_PATH, "ann");
+        Vector<String> files = new Vector<String>();
+        FileUtil.getFiles(DEFAULT_PATH + "test/", files);
+        
+        for (String string : files) {
+            Mat img = opencv_imgcodecs.imread(string);
+            Mat f = CoreFunc.features(img, Constant.predictSize);
+            
+            // 140 predictSize = 10; vhist.length + hhist.length + lowData.cols() * lowData.rows();
+            // 440 predictSize = 20;
+            Mat output = new Mat(1, 140, CV_32F);
+            //ann.predict(f, output, 0);  // 预测结果
+            // System.err.println(string + "===>" + (int) ann.predict(f, output, 0));
+
+            int index = (int) ann.predict(f, output, 0);
+            
+            String result = "";
+            if (index <= Constant.numCharacter) {
+                result = String.valueOf(Constant.strCharacters[index]);
+            } else {
+                String s = Constant.strChinese[index - Constant.numCharacter];
+                result = Constant.KEY_CHINESE_MAP.get(s);   // 编码转中文
+            }
+            System.err.println(string + "===>" + result);
+            
+            // ann.predict(f, output, 0);
+            // System.err.println(string + "===>" + output.get(0, 0)[0]);
+            
+        }
+    }
+
+    public static void main(String[] args) {
+        
+        ANNTrain1 annT = new 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.predict();
+        
+        System.out.println("The end.");
+    }
+
+
+}