分水岭修改

src/main/java/org/wlld/config/Kernel.java

@@ -10,6 +10,7 @@ public class Kernel {
     private static final String All_Number2 = "[-1,0,-1]#[0,4,0]#[-1,0,-1]#";
     public static final int rainNub = 10;//分水岭初始降雨点的数量
     public static final int th = 150;//分水岭灰度阈值
+    public static final double rgbN = 441.6729559300637;//RGB范数归一化最大值
     public static Matrix Vertical;
     public static Matrix Horizontal;
     public static Matrix All;

src/main/java/org/wlld/imageRecognition/Convolution.java

@@ -40,7 +40,7 @@ public class Convolution extends Frequency {
         return matrix;
     }
 
-    private  List<ThreeChannelMatrix> regionThreeChannelMatrix(ThreeChannelMatrix threeChannelMatrix, int size) {
+    private List<ThreeChannelMatrix> regionThreeChannelMatrix(ThreeChannelMatrix threeChannelMatrix, int size) {
         List<ThreeChannelMatrix> threeChannelMatrixList = new ArrayList<>();
         Matrix matrixRAll = threeChannelMatrix.getMatrixR();
         Matrix matrixGAll = threeChannelMatrix.getMatrixG();
@@ -63,17 +63,17 @@ public class Convolution extends Frequency {
     }
 
 //    public static void main(String[] args) throws Exception {
-//        Picture picture = new Picture();
-//        for (int i = 1; i < 10; i++) {
-//            ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("D:\\share\\cai/a" + i + ".jpg");
-//            ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix("D:\\share\\cai/b" + i + ".jpg");
-//            kc(threeChannelMatrix1, 2, 3, 40);
-//            kc(threeChannelMatrix2, 2, 3, 40);
-//            System.out.println("============================================");
-//        }
+//        Picture picture = new Picture();//imageTrance
+//        ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix("D:\\share/a.png");
+//        ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix("D:\\share/b.png");
+//        XYBody xyBody = imageTrance(threeChannelMatrix.getMatrixRGB(), 40);
+//        XYBody xyBody2 = imageTrance(threeChannelMatrix2.getMatrixRGB(), 40);
+//        regression(xyBody);
+//        RegressionBody regressionBody = xyBody.getRegressionBody();
+//        regressionBody.getMaxDis(xyBody2.getY(), xyBody2.getX());//av==0.8493586867
 //    }
 
-    public  List<List<Double>> kAvg(ThreeChannelMatrix threeMatrix, int poolSize, int sqNub
+    public List<List<Double>> kAvg(ThreeChannelMatrix threeMatrix, int poolSize, int sqNub
             , int regionSize) throws Exception {
         RGBSort rgbSort = new RGBSort();
         List<List<Double>> features = new ArrayList<>();
@@ -111,13 +111,13 @@ public class Convolution extends Frequency {
             for (int t = 0; t < dm.length; t++) {
                 dm[t] = rgbNorms.get(t).getNorm();
             }
-            System.out.println(Arrays.toString(dm));
+            //System.out.println(Arrays.toString(dm));
             features.add(feature);
         }
         return features;
     }
 
-    public  List<List<Double>> kc(ThreeChannelMatrix threeChannelMatrix, int poolSize, int sqNub
+    public List<List<Double>> kc(ThreeChannelMatrix threeChannelMatrix, int poolSize, int sqNub
             , int regionSize) throws Exception {
         Matrix matrixR = threeChannelMatrix.getMatrixR();
         Matrix matrixG = threeChannelMatrix.getMatrixG();
@@ -214,36 +214,44 @@ public class Convolution extends Frequency {
         return list;
     }
 
-    public List<List<Double>> imageTrance(Matrix matrix, int size, int featureNub) throws Exception {//矩阵和卷积核大小
+    private  void regression(XYBody xyBody) {
+        //计算当前图形的线性回归
+        RegressionBody regressionBody = new RegressionBody();
+        regressionBody.lineRegression(xyBody.getY(), xyBody.getX(),this);
+        xyBody.setRegressionBody(regressionBody);
+    }
+
+    public  XYBody imageTrance(Matrix matrix, int size) throws Exception {//矩阵和卷积核大小
         int xn = matrix.getX();
         int yn = matrix.getY();
         int xSize = xn / size;//求导后矩阵的行数
         int ySize = yn / size;//求导后矩阵的列数
         double[] Y = new double[xSize * ySize];
         double[] X = new double[xSize * ySize];
+        double rgbN = Kernel.rgbN;
         for (int i = 0; i < xn - size; i += size) {
             for (int j = 0; j < yn - size; j += size) {
                 Matrix matrix1 = matrix.getSonOfMatrix(i, j, size, size);
                 double[] nubs = new double[size * size];//平均值数组
                 for (int t = 0; t < size; t++) {
                     for (int k = 0; k < size; k++) {
-                        double nub = matrix1.getNumber(t, k) / 255;
+                        double nub = matrix1.getNumber(t, k) / rgbN;
                         nubs[t * size + k] = nub;
                     }
                 }
                 double avg = average(nubs);//平均值
-                double dc = dcByAvg(nubs, avg);//当前离散系数
-                //double va = varianceByAve(nubs, avg);//方差
+                //double dc = frequency.dcByAvg(nubs, avg);//当前离散系数
+                double va = varianceByAve(nubs, avg);//方差
                 //离散系数作为X，AVG作为Y
                 int t = i / size * ySize + j / size;
                 Y[t] = avg;
-                X[t] = dc;
+                X[t] = va;
             }
         }
-        //计算当前图形的线性回归
-        RegressionBody regressionBody = new RegressionBody();
-        regressionBody.lineRegression(Y, X, this);
-        return regressionBody.mappingMatrix(featureNub);
+        XYBody xyBody = new XYBody();
+        xyBody.setX(X);
+        xyBody.setY(Y);
+        return xyBody;
     }
 
 
@@ -325,7 +333,7 @@ public class Convolution extends Frequency {
         return myMatrix;
     }
 
-    protected  Matrix late(Matrix matrix, int size) throws Exception {//迟化处理
+    protected Matrix late(Matrix matrix, int size) throws Exception {//迟化处理
         int xn = matrix.getX();
         int yn = matrix.getY();
         int x = xn / size;//求导后矩阵的行数

src/main/java/org/wlld/imageRecognition/Operation.java

@@ -57,78 +57,6 @@ public class Operation {//进行计算
         return sub(matrix1);
     }
 
-    public void coverStudyOne(Map<Integer, ThreeChannelMatrix> matrixMap, int regionSize) throws Exception {//先学卷积核
-        Frame frame = new Frame();
-        frame.setHeight(regionSize);
-        frame.setWidth(regionSize);
-        frame.setLengthHeight(regionSize);
-        frame.setLengthWidth(regionSize);
-        List<SensoryNerve> sensoryNerve = templeConfig.getConvolutionNerveManager().getSensoryNerves();
-        for (Map.Entry<Integer, ThreeChannelMatrix> entry : matrixMap.entrySet()) {
-            Matrix matrix = entry.getValue().getH();
-            List<FrameBody> frameBodies = convolution.getRegion(matrix, frame);
-            int key = entry.getKey();
-            int size = frameBodies.size();
-            for (int i = 0; i < size; i++) {
-                FrameBody frameBody = frameBodies.get(i);
-                intoConvolutionNetwork(1, frameBody.getMatrix(), sensoryNerve, true, key, null);
-            }
-        }
-    }
-
-    public void coverStudyTwo(Map<Integer, ThreeChannelMatrix> matrixMap, int poolSize, int sqNub, int regionSize,
-                              int times) throws Exception {
-        int size = templeConfig.getSensoryNerves().size();
-        List<SensoryNerve> sensoryNerve = templeConfig.getConvolutionNerveManager().getSensoryNerves();
-        Frame frame = new Frame();
-        frame.setHeight(regionSize);
-        frame.setWidth(regionSize);
-        frame.setLengthHeight(regionSize);
-        frame.setLengthWidth(regionSize);
-        List<CoverBody> coverBodies = new ArrayList<>();
-        for (Map.Entry<Integer, ThreeChannelMatrix> entry : matrixMap.entrySet()) {
-            List<FrameBody> frameBodies = convolution.getRegion(entry.getValue().getH(), frame);
-            int key = entry.getKey();
-            int sizeA = frameBodies.size();
-            List<List<Double>> featureList = new ArrayList<>();
-            for (int i = 0; i < sizeA; i++) {
-                FrameBody frameBody = frameBodies.get(i);
-                MatrixBack matrixBack = new MatrixBack();
-                intoConvolutionNetwork(1, frameBody.getMatrix(), sensoryNerve, false, 0, matrixBack);
-                Matrix matrix1 = matrixBack.getMatrix();
-                List<Double> feature = getFeatures(matrix1);
-                featureList.add(feature);
-            }
-            CoverBody coverBody = new CoverBody();
-            Map<Integer, Double> tag = new HashMap<>();
-            tag.put(entry.getKey(), 1.0);
-            //聚类RGB特征
-            List<List<Double>> lists = convolution.kAvg(entry.getValue(), poolSize, sqNub, regionSize);
-            coverBody.setcFeature(featureList);
-            coverBody.setFeature(lists);
-            coverBody.setTag(tag);
-            coverBodies.add(coverBody);
-        }
-        for (int j = 0; j < times; j++) {
-            for (int i = 0; i < size; i++) {
-                List<Double> list;
-                for (CoverBody coverBody : coverBodies) {
-                    List<Double> list1 = coverBody.getFeature().get(i);
-                    List<Double> list2 = coverBody.getcFeature().get(i);
-                    if (i < list1.size()) {
-                        list = list1;
-                    } else {
-                        list = list2;
-                    }
-                    if (templeConfig.isShowLog()) {
-                        System.out.println("feature:" + list);
-                    }
-                    intoDnnNetwork(1, list, templeConfig.getSensoryNerves(), true, coverBody.getTag(), null);
-                }
-            }
-        }
-    }
-
     public void coverStudy(Map<Integer, ThreeChannelMatrix> matrixMap, int poolSize, int sqNub, int regionSize,
                            int times) throws Exception {
         if (templeConfig.getStudyPattern() == StudyPattern.Cover_Pattern) {

src/main/java/org/wlld/imageRecognition/Picture.java

@@ -77,16 +77,23 @@ public class Picture {
         ThreeChannelMatrix threeChannelMatrix = new ThreeChannelMatrix();
         Matrix matrixR = new Matrix(height, width);//行，列
         Matrix matrixG = new Matrix(height, width);//行，列
-        Matrix matrixB = new Matrix(height, width);//行，列
+        Matrix matrixB = new Matrix(height, width);//行，
+        Matrix matrixRGB = new Matrix(height, width);
         threeChannelMatrix.setMatrixR(matrixR);
         threeChannelMatrix.setMatrixG(matrixG);
         threeChannelMatrix.setMatrixB(matrixB);
+        threeChannelMatrix.setMatrixRGB(matrixRGB);
         for (int i = 0; i < height; i++) {
             for (int j = 0; j < width; j++) {
                 int pixel = bi.getRGB(j, i);// 下面三行代码将一个数字转换为RGB数字
-                matrixR.setNub(i, j, (pixel & 0xff0000) >> 16);
-                matrixG.setNub(i, j, (pixel & 0xff00) >> 8);
-                matrixB.setNub(i, j, (pixel & 0xff));
+                int r = (pixel & 0xff0000) >> 16;//R
+                int g = (pixel & 0xff00) >> 8;//G
+                int b = (pixel & 0xff);//B
+                double rgb = Math.sqrt(Math.pow(r, 2) + Math.pow(g, 2) + Math.pow(b, 2));
+                matrixRGB.setNub(i, j, rgb);
+                matrixR.setNub(i, j, r);
+                matrixG.setNub(i, j, g);
+                matrixB.setNub(i, j, b);
             }
         }
         return threeChannelMatrix;

src/main/java/org/wlld/imageRecognition/ThreeChannelMatrix.java

@@ -7,6 +7,15 @@ public class ThreeChannelMatrix {
     Matrix matrixG;
     Matrix matrixB;
     Matrix H;
+    Matrix matrixRGB;
+
+    public Matrix getMatrixRGB() {
+        return matrixRGB;
+    }
+
+    public void setMatrixRGB(Matrix matrixRGB) {
+        this.matrixRGB = matrixRGB;
+    }
 
     public Matrix getH() {
         return H;

src/main/java/org/wlld/imageRecognition/XYBody.java

@@ -0,0 +1,33 @@
+package org.wlld.imageRecognition;
+
+import org.wlld.imageRecognition.modelEntity.RegressionBody;
+
+public class XYBody {
+    private double[] X;
+    private double[] Y;
+    private RegressionBody regressionBody;
+
+    public RegressionBody getRegressionBody() {
+        return regressionBody;
+    }
+
+    public void setRegressionBody(RegressionBody regressionBody) {
+        this.regressionBody = regressionBody;
+    }
+
+    public double[] getX() {
+        return X;
+    }
+
+    public void setX(double[] x) {
+        X = x;
+    }
+
+    public double[] getY() {
+        return Y;
+    }
+
+    public void setY(double[] y) {
+        Y = y;
+    }
+}

src/main/java/org/wlld/imageRecognition/modelEntity/RegressionBody.java

@@ -9,26 +9,26 @@ import java.util.List;
 public class RegressionBody {
     private double w;
     private double b;
-    private double[] X;
+    private double maxDis;//最大距离
 
-    public List<List<Double>> mappingMatrix(int size) {
-        int len = X.length - size;
-        List<List<Double>> lists = new ArrayList<>();
-        for (int i = 0; i < len; i += size) {
-            List<Double> list = new ArrayList<>();
-            for (int t = i; t < i + size; t++) {
-                double nub = ArithUtil.add(ArithUtil.mul(w, X[t]), b);
-                list.add(nub);
+    public void getMaxDis(double[] Y, double[] X) {//获取当前的最大距离
+        double allNub = 0;
+        for (int i = 0; i < X.length; i++) {
+            double y = ArithUtil.add(ArithUtil.mul(X[i], w), b);
+            double dis = Math.abs(ArithUtil.sub(Y[i], y));
+            allNub = ArithUtil.add(allNub, dis);
+            System.out.println("dis======" + dis);
+            if (dis > maxDis) {
+                maxDis = dis;
             }
-            lists.add(list);
         }
-        return lists;
+        allNub = ArithUtil.div(allNub, X.length);//当前最大值：0.1955576793420405,allNub==0.035958733
+        System.out.println("当前最大值：" + maxDis + ",allNub==" + allNub);
     }
 
     public void lineRegression(double[] Y, double[] X, Frequency frequency) {//进行二元线性回归
         double avX = frequency.average(X);//平均值
         int len = Y.length;
-        this.X = X;
         double wFenZi = 0;
         double wFenMu;
         double xSigma = 0;//X求和
@@ -49,6 +49,5 @@ public class RegressionBody {
             bSigma = ArithUtil.add(ArithUtil.sub(y, ArithUtil.mul(w, x)), bSigma);
         }
         b = ArithUtil.div(bSigma, len);
-
     }
 }

src/main/java/org/wlld/imageRecognition/segmentation/Watershed.java

@@ -2,7 +2,6 @@ package org.wlld.imageRecognition.segmentation;
 
 import org.wlld.MatrixTools.Matrix;
 import org.wlld.config.Kernel;
-import org.wlld.imageRecognition.border.FrameBody;
 
 import java.util.*;
 
@@ -201,6 +200,9 @@ public class Watershed {
         int x = matrix.getX();
         int y = matrix.getY();
         int size = xSize * ySize;
+        double minPoint = 1;
+        int xr = 0;
+        int yr = 0;
         for (int i = 0; i <= x - xSize; i += xSize) {
             for (int j = 0; j <= y - ySize; j += ySize) {
                 Matrix myMatrix = rainfallMap.getSonOfMatrix(i, j, xSize, ySize);
@@ -213,9 +215,15 @@ public class Watershed {
                     }
                 }
                 double cover = (double) sigma / (double) size;//降雨率产生剧烈波动时则出现坐标
-                System.out.println("cover==" + cover + ",x==" + i + ",y==" + j);
+                if (cover < minPoint) {
+                    xr = i;
+                    yr = j;
+                    //System.out.println("cover==" + cover + ",x==" + i + ",y==" + j);
+                    minPoint = cover;
+                }
             }
         }
+        System.out.println("min==" + minPoint + ",x==" + xr + ",y==" + yr);
     }
 
     private int getMinIndex(double[] array, double mySelf) {//获取最小值

src/test/java/coverTest/CoverTest.java

@@ -34,7 +34,7 @@ public class CoverTest {
 //        for (int i = 1; i < 100; i++) {
 //            cover2(operation, "D:\\pic\\6\\a/a" + i + ".jpg");
 //        }
-         //test(null, 2, 3, 40, "c", 3);
+        //test(null, 2, 3, 40, "c", 3);
         cover();
     }
 
@@ -71,7 +71,7 @@ public class CoverTest {
             operation = getModel();
         }
         for (int i = 1; i < 100; i++) {
-            String na = "D:\\share\\cai/" + name + i + ".jpg";
+            String na = "D:\\pic\\test/" + name + i + ".jpg";
             //System.out.println("name======================" + na);
             ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix(na);
             Map<Integer, Double> map1 = operation.coverPoint(threeChannelMatrix, poolSize, sqlNub, regionSize);
@@ -80,7 +80,7 @@ public class CoverTest {
             for (Map.Entry<Integer, Double> entry : map1.entrySet()) {
                 int key = entry.getKey();
                 double value = entry.getValue();
-               // System.out.println("key==" + key + ",value==" + value);
+                // System.out.println("key==" + key + ",value==" + value);
                 if (value > point) {
                     point = value;
                     id = key;
@@ -108,31 +108,26 @@ public class CoverTest {
         //初始化模板 注意 width height参数是你训练图片的实际尺寸需要改，其他不用动
         //创建运算类进行标注
         templeConfig.isShowLog(true);
-        templeConfig.setStudyPoint(0.005);//不动
+        templeConfig.setStudyPoint(0.01);//不动
         templeConfig.setSoftMax(true);
         //templeConfig.setDeep(2);
         //templeConfig.setHiddenNerveNub(9);
-        templeConfig.setSensoryNerveNub(4);
+        templeConfig.setSensoryNerveNub(3);//多出来的
         templeConfig.setRzType(RZ.L1);//不动//3 18
-        templeConfig.setlParam(0.01);//不动
-        templeConfig.init(StudyPattern.Cover_Pattern, true, 400, 400, 4);
+        templeConfig.setlParam(0.015);//不动
+        templeConfig.init(StudyPattern.Cover_Pattern, true, 400, 400, 2);
         Operation operation = new Operation(templeConfig);
         for (int i = 1; i < 100; i++) {
             Map<Integer, ThreeChannelMatrix> matrixMap = new HashMap<>();
-            ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("D:\\share\\cai/a" + i + ".jpg");
-            ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix("D:\\share\\cai/b" + i + ".jpg");
-            ThreeChannelMatrix threeChannelMatrix3 = picture.getThreeMatrix("D:\\share\\cai/c" + i + ".jpg");
-            ThreeChannelMatrix threeChannelMatrix4 = picture.getThreeMatrix("D:\\share\\cai/d" + i + ".jpg");
-
-            matrixMap.put(1, threeChannelMatrix1);
-            matrixMap.put(2, threeChannelMatrix2);
-            matrixMap.put(3, threeChannelMatrix3);
-            matrixMap.put(4, threeChannelMatrix4);
-            operation.coverStudy(matrixMap, 5, 4, 10, 2);
+            ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("D:\\pic\\test/b" + i + ".jpg");
+            ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix("D:\\pic\\test/d" + i + ".jpg");
+            matrixMap.put(1, threeChannelMatrix1);//桔梗覆盖
+            matrixMap.put(2, threeChannelMatrix2);//土地
+            operation.coverStudy(matrixMap, 2, 3, 18, 2);
         }
         ModelParameter modelParameter = templeConfig.getModel();
         String model = JSON.toJSONString(modelParameter);
         System.out.println(model);
-       // test(operation, 2, 3, 40, "d", 4);
+         test(operation, 2, 3, 18, "d", 2);
     }
 }

src/test/java/coverTest/FoodTest.java

@@ -5,6 +5,8 @@ import org.wlld.MatrixTools.Matrix;
 import org.wlld.config.Classifier;
 import org.wlld.config.RZ;
 import org.wlld.config.StudyPattern;
+import org.wlld.function.Sigmod;
+import org.wlld.function.Tanh;
 import org.wlld.imageRecognition.*;
 import org.wlld.imageRecognition.segmentation.Watershed;
 import org.wlld.nerveEntity.ModelParameter;
@@ -19,59 +21,69 @@ public class FoodTest {
 
     public static void rain() throws Exception {//降雨
         Picture picture = new Picture();
-        Matrix matrix = picture.getImageMatrixByLocal("D:\\share\\cai/i1.jpg");
+        Matrix matrix = picture.getImageMatrixByLocal("D:\\share\\cai/d1.jpg");
         Watershed watershed = new Watershed(matrix);
         watershed.rainfall();
     }
 
     public static void food() throws Exception {
-        Picture picture = new Picture();
-        TempleConfig templeConfig = new TempleConfig();
-        templeConfig.setClassifier(Classifier.DNN);
-        templeConfig.isShowLog(true);
-        templeConfig.setSoftMax(true);
-        templeConfig.setTh(0);
-        //templeConfig.setMatrixWidth(5);
-        templeConfig.setStudyPoint(0.02);
-        templeConfig.setRzType(RZ.L1);
-        templeConfig.setlParam(0.016);//0.015
+        Picture picture = new Picture();//创建图片解析类
+        TempleConfig templeConfig = new TempleConfig();//创建配置模板类
+        templeConfig.setClassifier(Classifier.DNN);//使用DNN 分类器
+        //templeConfig.setActiveFunction(new Sigmod());//设置激活函数
+        templeConfig.setDeep(2);//设置深度 深度神经网络 深度越深速度越慢
+        //数量越大越准 但是影响量比较小 不绝对 盲试
+        templeConfig.setHiddenNerveNub(9);//设置隐层神经元数量
+        templeConfig.isShowLog(true);//输出打印数据
+        //
+        //templeConfig.setSoftMax(true);//启用最后一层的SOFTMAX
+        //templeConfig.setTh(-1);//设置阈值
+        templeConfig.setStudyPoint(0.012);//设置学习率 0-1
+        templeConfig.setRzType(RZ.L1);//设置正则函数
+        templeConfig.setlParam(0.015);//设置正则参数
+
         templeConfig.init(StudyPattern.Accuracy_Pattern, true, 640, 480, 2);
-        Operation operation = new Operation(templeConfig);
+        Operation operation = new Operation(templeConfig);//计算类
         // 一阶段
         for (int j = 0; j < 2; j++) {
-            for (int i = 1; i < 233; i++) {//一阶段
+            for (int i = 1; i < 101; i++) {//一阶段
                 System.out.println("study1===================" + i);
                 //读取本地URL地址图片,并转化成矩阵
-                Matrix a = picture.getImageMatrixByLocal("D:\\pic\\6\\a/a" + i + ".jpg");
-                Matrix b = picture.getImageMatrixByLocal("D:\\pic\\6\\b/b" + i + ".jpg");
+                Matrix a = picture.getImageMatrixByLocal("D:\\pic\\1/a" + i + ".jpg");
+                Matrix b = picture.getImageMatrixByLocal("D:\\pic\\2/b" + i + ".jpg");
                 operation.learning(a, 1, false);
                 operation.learning(b, 2, false);
             }
         }
-        // 二阶段
-        for (int i = 1; i < 233; i++) {
+        // 二阶段 归一化
+        for (int i = 1; i < 101; i++) {
             System.out.println("avg==" + i);
-            Matrix a = picture.getImageMatrixByLocal("D:\\pic\\6\\a/a" + i + ".jpg");
-            Matrix b = picture.getImageMatrixByLocal("D:\\pic\\6\\b/b" + i + ".jpg");
+            Matrix a = picture.getImageMatrixByLocal("D:\\pic\\1/a" + i + ".jpg");
+            Matrix b = picture.getImageMatrixByLocal("D:\\pic\\2/b" + i + ".jpg");
             operation.normalization(a, templeConfig.getConvolutionNerveManager());
             operation.normalization(b, templeConfig.getConvolutionNerveManager());
         }
         templeConfig.getNormalization().avg();
-        for (int j = 0; j < 5; j++) {
-            for (int i = 1; i < 233; i++) {
+
+        for (int j = 0; j < 3; j++) {
+            for (int i = 1; i < 101; i++) {
                 System.out.println("j==" + j + ",study2==================" + i);
-                Matrix a = picture.getImageMatrixByLocal("D:\\pic\\6\\a/a" + i + ".jpg");
-                Matrix b = picture.getImageMatrixByLocal("D:\\pic\\6\\b/b" + i + ".jpg");
+                Matrix a = picture.getImageMatrixByLocal("D:\\pic\\1/a" + i + ".jpg");
+                Matrix b = picture.getImageMatrixByLocal("D:\\pic\\2/b" + i + ".jpg");
                 operation.learning(a, 1, true);
                 operation.learning(b, 2, true);
             }
         }
         templeConfig.finishStudy();//结束学习
+        ModelParameter modelParameter = templeConfig.getModel();
+        String model = JSON.toJSONString(modelParameter);
+        System.out.println(model);
+
         int wrong = 0;
         int allNub = 0;
-        for (int i = 1; i <= 232; i++) {
+        for (int i = 1; i <= 100; i++) {
             //读取本地URL地址图片,并转化成矩阵
-            Matrix a = picture.getImageMatrixByLocal("D:\\pic\\6\\a/a" + i + ".jpg");
+            Matrix a = picture.getImageMatrixByLocal("D:\\pic\\1/a" + i + ".jpg");
             allNub++;
             int an = operation.toSee(a);
             if (an != 1) {
@@ -80,9 +92,7 @@ public class FoodTest {
         }
         double wrongPoint = ArithUtil.div(wrong, allNub);
         System.out.println("错误率：" + (wrongPoint * 100) + "%");
-        ModelParameter modelParameter = templeConfig.getModel();
-        String model = JSON.toJSONString(modelParameter);
-        System.out.println(model);
+
     }
 
 }