!30 修改分水岭

Merge pull request !30 from 逐光/test

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

@@ -4,4 +4,5 @@ public class Classifier {//分类器
     public static final int LVQ = 1;//LVQ分类 你的训练模版量非常少 比如 一种只有几十一百张照片/分类少
     public static final int DNN = 2; //使用DNN分类 训练量足够大，一个种类1500+训练图片
     public static final int VAvg = 3;//使用特征向量均值分类 一种只有几十一百张照片
+    public static final int KNN = 4;//KNN分类器
 }

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

@@ -126,12 +126,15 @@ public class Convolution extends Frequency {
         meanClustering.start();
         List<RGBNorm> rgbNorms = meanClustering.getMatrices();
         Collections.sort(rgbNorms, rgbSort);
-        List<Double> feature = new ArrayList<>();
+        List<Double> features = new ArrayList<>();
         for (int i = 0; i < sqNub; i++) {
-            feature.add(rgbNorms.get(i).getNorm());
+            double[] rgb = rgbNorms.get(i).getRgb();
+            for (int j = 0; j < 3; j++) {
+                features.add(rgb[j]);
+            }
         }
         //System.out.println("feature==" + feature);
-        return feature;
+        return features;
     }
 
     private void regression(XYBody xyBody) {

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

@@ -18,10 +18,7 @@ import org.wlld.nerveEntity.SensoryNerve;
 import org.wlld.tools.ArithUtil;
 import org.wlld.tools.IdCreator;
 
-import java.util.ArrayList;
+import java.util.*;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
 
 public class Operation {//进行计算
     private TempleConfig templeConfig;//配置初始化参数模板
@@ -75,12 +72,13 @@ public class Operation {//进行计算
         if (templeConfig.isShowLog()) {
             System.out.println(feature);
         }
+        //System.out.println("=====================================");
         int classifier = templeConfig.getClassifier();
         switch (classifier) {
             case Classifier.DNN:
                 Map<Integer, Double> map = new HashMap<>();
                 map.put(tag, 1.0);
-                if (templeConfig.getSensoryNerves().size() == templeConfig.getFeatureNub()) {
+                if (templeConfig.getSensoryNerves().size() == templeConfig.getFeatureNub() * 3) {
                     intoDnnNetwork(1, feature, templeConfig.getSensoryNerves(), true, map, null);
                 } else {
                     throw new Exception("nerves number is not equal featureNub");
@@ -94,7 +92,16 @@ public class Operation {//进行计算
                 Matrix vec = MatrixOperation.listToRowVector(feature);
                 avgStudy(tag, vec);
                 break;
+            case Classifier.KNN:
+                Matrix veck = MatrixOperation.listToRowVector(feature);
+                knnStudy(tag, veck);
+                break;
+        }
     }
+
+    private void knnStudy(int tagging, Matrix vector) throws Exception {
+        Knn knn = templeConfig.getKnn();
+        knn.insertMatrix(vector, tagging);
     }
 
     private void avgStudy(int tagging, Matrix myMatrix) throws Exception {//特征矩阵均值学习
@@ -135,7 +142,7 @@ public class Operation {//进行计算
                     id = getIdByLVQ(myMatrix);
                     break;
                 case Classifier.DNN:
-                    if (templeConfig.getSensoryNerves().size() == templeConfig.getFeatureNub()) {
+                    if (templeConfig.getSensoryNerves().size() == templeConfig.getFeatureNub() * 3) {
                         intoDnnNetwork(IdCreator.get().nextId(), feature, templeConfig.getSensoryNerves(), false, null, maxPoint);
                         id = maxPoint.getId();
                     } else {
@@ -146,8 +153,14 @@ public class Operation {//进行计算
                     Matrix myMatrix1 = MatrixOperation.listToRowVector(feature);
                     id = getIdByVag(myMatrix1);
                     break;
+                case Classifier.KNN:
+                    Matrix myMatrix2 = MatrixOperation.listToRowVector(feature);
+                    Knn knn = templeConfig.getKnn();
+                    id = knn.getType(myMatrix2);
+                    break;
             }
             regionBody.setType(id);
+            //System.out.println("类别" + id);
         }
         return regionList;
     }
@@ -159,12 +172,13 @@ public class Operation {//进行计算
         for (Map.Entry<Integer, Matrix> entry : matrixK.entrySet()) {
             Matrix matrix = entry.getValue();
             double dist = MatrixOperation.getEDist(matrix, myVector);
-            //System.out.println("距离===" + dist + ",类别==" + entry.getKey());
+            //System.out.println("距离===" + dist + ",类别==" + entry.getKey()+",核心:"+matrix.getString());
             if (minDist == 0 || dist < minDist) {
                 minDist = dist;
                 id = entry.getKey();
             }
         }
+        //System.out.println("=======================");
         return id;
     }
 

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

@@ -45,7 +45,7 @@ public class TempleConfig {
     private double th = -1;//标准阈值
     private boolean boxReady = false;//边框已经学习完毕
     private double iouTh = 0.5;//IOU阈值
-    private int lvqNub = 10;//lvq循环次数，默认30
+    private int lvqNub = 200;//lvq循环次数，默认30
     private VectorK vectorK;//特征向量均值类
     private boolean isThreeChannel = false;//是否启用三通道
     private int classifier = Classifier.VAvg;//默认分类类别使用的是向量均值分类
@@ -53,7 +53,7 @@ public class TempleConfig {
     private int sensoryNerveNub = 9;//输入神经元个数
     private boolean isShowLog = false;
     private ActiveFunction activeFunction = new Tanh();
-    private double studyPoint = 0;
+    private double studyPoint = 0.1;
     private double matrixWidth = 5;//期望矩阵间隔
     private int rzType = RZ.NOT_RZ;//正则化类型，默认不进行正则化
     private double lParam = 0;//正则参数
@@ -64,6 +64,20 @@ public class TempleConfig {
     private double hTh = 0.88;//灰度阈值
     private double maxRain = 340;//不降雨RGB阈值
     private int featureNub = 4;//聚类特征数量
+    private Knn knn;//KNN分类器
+    private int knnNub = 3;//KNN投票人数
+
+    public Knn getKnn() {
+        return knn;
+    }
+
+    public int getKnnNub() {
+        return knnNub;
+    }
+
+    public void setKnnNub(int knnNub) {
+        this.knnNub = knnNub;
+    }
 
     public int getFeatureNub() {
         return featureNub;
@@ -298,13 +312,16 @@ public class TempleConfig {
             case StudyPattern.Cover_Pattern://覆盖学习模式
                 switch (classifier) {
                     case Classifier.DNN:
-                        initNerveManager(initPower, sensoryNerveNub, deep, studyPoint);
+                        initNerveManager(initPower, sensoryNerveNub * 3, deep, studyPoint);
                         break;
                     case Classifier.LVQ:
                         lvq = new LVQ(classificationNub, lvqNub, studyPoint);
                         break;
                     case Classifier.VAvg:
-                        vectorK = new VectorK(sensoryNerveNub);
+                        vectorK = new VectorK(featureNub * 3);
+                        break;
+                    case Classifier.KNN:
+                        knn = new Knn(knnNub);
                         break;
                 }
                 break;

src/main/java/org/wlld/imageRecognition/border/Knn.java

@@ -0,0 +1,101 @@
+package org.wlld.imageRecognition.border;
+
+import org.wlld.MatrixTools.Matrix;
+import org.wlld.MatrixTools.MatrixOperation;
+
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+public class Knn {//KNN分类器
+    private Map<Integer, List<Matrix>> featureMap = new HashMap<>();
+    private int length;//向量长度(需要返回)
+    private int nub;//选择几个人投票
+
+    public Knn(int nub) {
+        this.nub = nub;
+    }
+
+    public void insertMatrix(Matrix vector, int tag) throws Exception {
+        if (vector.isVector() && vector.isRowVector()) {
+            if (featureMap.size() == 0) {
+                List<Matrix> list = new ArrayList<>();
+                list.add(vector);
+                featureMap.put(tag, list);
+                length = vector.getY();
+            } else {
+                if (length == vector.getY()) {
+                    if (featureMap.containsKey(tag)) {
+                        featureMap.get(tag).add(vector);
+                    } else {
+                        List<Matrix> list = new ArrayList<>();
+                        list.add(vector);
+                        featureMap.put(tag, list);
+                    }
+                } else {
+                    throw new Exception("vector length is different");
+                }
+            }
+        } else {
+            throw new Exception("this matrix is not vector or rowVector");
+        }
+    }
+
+    private void compare(double[] values, int[] types, double value, int type) {
+        for (int i = 0; i < values.length; i++) {
+            double val = values[i];
+            if (val < 0) {
+                values[i] = value;
+                types[i] = type;
+                break;
+            } else {
+                if (value < val) {
+                    for (int j = values.length - 2; j >= i; j--) {
+                        values[j + 1] = values[j];
+                        types[j + 1] = types[j];
+                    }
+                    values[i] = value;
+                    types[i] = type;
+                    break;
+                }
+            }
+        }
+    }
+
+    public int getType(Matrix vector) throws Exception {//识别分类
+        int ty = 0;
+        double[] dists = new double[nub];
+        int[] types = new int[nub];
+        for (int i = 0; i < nub; i++) {
+            dists[i] = -1;
+        }
+        for (Map.Entry<Integer, List<Matrix>> entry : featureMap.entrySet()) {
+            int type = entry.getKey();
+            List<Matrix> matrices = entry.getValue();
+            for (Matrix matrix : matrices) {
+                double dist = MatrixOperation.getEDist(matrix, vector);
+                compare(dists, types, dist, type);
+            }
+        }
+        Map<Integer, Integer> map = new HashMap<>();
+        for (int i = 0; i < nub; i++) {
+            int type = types[i];
+            if (map.containsKey(type)) {
+                map.put(type, map.get(type) + 1);
+            } else {
+                map.put(type, 1);
+            }
+        }
+        int max = 0;
+        for (Map.Entry<Integer, Integer> entry : map.entrySet()) {
+            int value = entry.getValue();
+            int type = entry.getKey();
+            if (value > max) {
+                ty = type;
+            }
+        }
+        return ty;
+    }
+
+}

src/main/java/org/wlld/imageRecognition/border/LVQ.java

@@ -137,6 +137,7 @@ public class LVQ {
         }
         //初始化完成
         for (int i = 0; i < lvqNub; i++) {
+            //System.out.println("================================");
             study();
         }
         isReady = true;

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

@@ -20,7 +20,7 @@ public class Watershed {
     private double th = Kernel.th;//灰度阈值
     private int regionNub = Kernel.Region_Nub;//一张图分多少份
     private Map<Integer, RegionBody> regionBodyMap = new HashMap<>();
-    private double rainTh = 2;
+    private double rainTh = 0;
     private int xMax;
     private int yMax;
     private double maxRain;

src/test/java/coverTest/CoverTest.java

@@ -23,12 +23,12 @@ import java.util.Map;
  */
 public class CoverTest {
     public static void main(String[] args) throws Exception {
-        double a = 220;
+//        double a = 220;
-        double b = 220;
+//        double b = 220;
-        double c = 220;
+//        double c = 220;
-        double d = Math.sqrt(Math.pow(a, 2) + Math.pow(b, 2) + Math.pow(c, 2));
+//        double d = Math.sqrt(Math.pow(a, 2) + Math.pow(b, 2) + Math.pow(c, 2));
-        System.out.println(d);
+//        System.out.println(d);
-        //cover();
+        cover();
     }
 
     public static void insertModel(String model) throws Exception {//注入模型
@@ -64,7 +64,7 @@ public class CoverTest {
             operation = getModel();
         }
         for (int i = 1; i < 100; i++) {
-            String na = "D:\\pic\\test/" + name + i + ".jpg";
+            String na = "D:\\share\\cai/" + name + i + ".jpg";
             //System.out.println("name======================" + na);
             ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix(na);
             Map<Integer, Double> map1 = operation.coverPoint(threeChannelMatrix, poolSize, sqlNub, regionSize);
@@ -108,19 +108,28 @@ public class CoverTest {
         templeConfig.setSensoryNerveNub(3);//多出来的
         templeConfig.setRzType(RZ.L1);//不动//3 18
         templeConfig.setlParam(0.015);//不动
-        templeConfig.init(StudyPattern.Cover_Pattern, true, 400, 400, 2);
+        templeConfig.setClassifier(Classifier.DNN);
+        templeConfig.init(StudyPattern.Cover_Pattern, true, 400, 400, 4);
         Operation operation = new Operation(templeConfig);
         for (int i = 1; i < 100; i++) {
             Map<Integer, ThreeChannelMatrix> matrixMap = new HashMap<>();
-            ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("D:\\pic\\test/b" + i + ".jpg");
+            ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("D:\\share\\cai/a" + i + ".jpg");
-            ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix("D:\\pic\\test/d" + 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);//桔梗覆盖
             matrixMap.put(2, threeChannelMatrix2);//土地
+            matrixMap.put(3, threeChannelMatrix3);//桔梗覆盖
+            matrixMap.put(4, threeChannelMatrix4);//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, 18, "d", 2);
+        test(operation, 2, 3, 18, "a", 1);
+        test(operation, 2, 3, 18, "b", 2);
+        test(operation, 2, 3, 18, "c", 3);
+        test(operation, 2, 3, 18, "d", 4);
+
     }
 }

src/test/java/coverTest/FoodTest.java

@@ -8,8 +8,6 @@ import org.wlld.config.StudyPattern;
 import org.wlld.imageRecognition.*;
 import org.wlld.imageRecognition.segmentation.RegionBody;
 import org.wlld.imageRecognition.segmentation.Specifications;
-import org.wlld.imageRecognition.segmentation.Watershed;
-import org.wlld.nerveCenter.NerveManager;
 import org.wlld.nerveEntity.ModelParameter;
 import org.wlld.tools.ArithUtil;
 
@@ -19,41 +17,40 @@ import java.util.List;
 public class FoodTest {
 
     public static void main(String[] args) throws Exception {
-        //food();
+        //test2();
-        //rain();
+        test();
-        test2();
     }
 
-    public static void test2() throws Exception {
+    public static void test2(TempleConfig templeConfig) throws Exception {
         //test();
-        TempleConfig templeConfig = new TempleConfig();
         Picture picture = new Picture();
-        templeConfig.setSensoryNerveNub(4);
-        templeConfig.setStudyPoint(0.01);
-        templeConfig.sethTh(0.86);
-        templeConfig.setRegionNub(200);
-        templeConfig.setMaxRain(360);
-        templeConfig.setSoftMax(true);
         List<Specifications> specificationsList = new ArrayList<>();
         Specifications specifications = new Specifications();
         specifications.setWidth(400);
         specifications.setHeight(400);
         specificationsList.add(specifications);
-        templeConfig.setClassifier(Classifier.LVQ);
-        templeConfig.init(StudyPattern.Cover_Pattern, true, 400, 400, 3);
         Operation operation = new Operation(templeConfig);
-        ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix("D:\\cai\\e/e1.jpg");
+        for (int i = 6; i <= 7; i++) {
-        operation.colorLook(threeChannelMatrix, specificationsList);
+            ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("D:\\pic\\f/f" + i + ".jpg");
+            List<RegionBody> regionBody = operation.colorLook(threeChannelMatrix1, specificationsList);
+            for (int j = 0; j < regionBody.size(); j++) {
+                System.out.println(regionBody.get(j).getType());
+            }
+            System.out.println("=============================" + i);
+        }
     }
 
     public static void test() throws Exception {
         TempleConfig templeConfig = new TempleConfig();
         Picture picture = new Picture();
-        templeConfig.setSensoryNerveNub(4);
+        templeConfig.isShowLog(true);
+        templeConfig.setMaxRain(320);
+        templeConfig.setFeatureNub(3);
+        templeConfig.sethTh(0.88);
+        templeConfig.setPoolSize(2);
+        templeConfig.setSensoryNerveNub(3);
         templeConfig.setStudyPoint(0.01);
-        templeConfig.setSoftMax(true);
+        templeConfig.setClassifier(Classifier.VAvg);
-
-        templeConfig.setClassifier(Classifier.LVQ);
         templeConfig.init(StudyPattern.Cover_Pattern, true, 400, 400, 3);
         Operation operation = new Operation(templeConfig);
         List<Specifications> specificationsList = new ArrayList<>();
@@ -63,15 +60,17 @@ public class FoodTest {
         specificationsList.add(specifications);
         for (int j = 0; j < 1; j++) {
             for (int i = 1; i <= 10; i++) {
-                ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("D:\\cai/a/a" + i + ".jpg");
+                ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("D:\\pic/a" + i + ".jpg");
-                ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix("D:\\cai/b/b" + i + ".jpg");
+                ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix("D:\\pic/b" + i + ".jpg");
-                ThreeChannelMatrix threeChannelMatrix3 = picture.getThreeMatrix("D:\\cai/c/c" + i + ".jpg");
+                ThreeChannelMatrix threeChannelMatrix3 = picture.getThreeMatrix("D:\\pic/c" + i + ".jpg");
                 operation.colorStudy(threeChannelMatrix1, 1, specificationsList);
                 operation.colorStudy(threeChannelMatrix2, 2, specificationsList);
                 operation.colorStudy(threeChannelMatrix3, 3, specificationsList);
-                System.out.println("=======================================");
+                System.out.println("=======================================" + i);
             }
         }
+        templeConfig.finishStudy();
+        test2(templeConfig);
     }
 
     public static void study() throws Exception {
@@ -89,8 +88,7 @@ public class FoodTest {
         Picture picture = new Picture();
         Convolution convolution = new Convolution();
         ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix("");
-        List<Double> feature = convolution.getCenterColor(threeChannelMatrix, 2, 4);
+        // List<Double> feature = convolution.getCenterColor(threeChannelMatrix, 2, 4);
-
     }
 
     public static void food() throws Exception {