增加粒子群

src/main/java/org/wlld/MatrixTools/Matrix.java

@@ -425,6 +425,7 @@ public class Matrix {
         if (this.x >= x && this.y >= y) {
             return matrix[x][y];
         } else {
+            //System.out.println("x==" + x + ",y==" + y);
             throw new Exception("matrix length too little");
         }
     }

src/main/java/org/wlld/i/PsoFunction.java

@@ -0,0 +1,7 @@
+package org.wlld.i;
+
+public interface PsoFunction {//粒子群回调函数
+
+    //根据参数返回函数值
+    double getResult(double[] parameter,int id) throws Exception;
+}

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

@@ -235,24 +235,24 @@ public class Convolution extends Frequency {
                         rgb[twoNub + index] = sonB.getNumber(t, k) / 255;
                     }
                 }
+                //900 200
                 double dispersed = variance(h);
                 if (dispersed < 900 && dispersed > 200) {
                     for (int m = 0; m < nub; m++) {
                         double[] color = new double[]{rgb[m], rgb[m + nub], rgb[m + twoNub]};
                         meanClustering.setColor(color);
                     }
-                    // meanClustering.setColor(rgb);
                 }
             }
         }
-        List<double[]> list = meanClustering.start(true);//开始聚类
+        //List<double[]> list = meanClustering.start(true);//开始聚类
-        if (tag == 0) {//识别
+        meanClustering.start(true);
-            templeConfig.getFood().getkNerveManger().look(list);
+//        if (tag == 0) {//识别
-        } else {//训练
+//            templeConfig.getFood().getkNerveManger().look(list);
-            templeConfig.getFood().getkNerveManger().setFeature(tag, list);
+//        } else {//训练
-        }
+//            templeConfig.getFood().getkNerveManger().setFeature(tag, list);
+//        }
         List<RGBNorm> rgbNorms = meanClustering.getMatrices();
-        Collections.sort(rgbNorms, rgbSort);
         List<Double> features = new ArrayList<>();
         for (int i = 0; i < sqNub; i++) {
             double[] rgb = rgbNorms.get(i).getRgb();

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

@@ -84,12 +84,43 @@ public class MeanClustering {
         }
     }
 
+    private double[] getListAvg(List<double[]> list) {
+        int len = list.get(0).length;
+        double[] sigma = new double[len];
+        for (double[] rgb : list) {
+            for (int i = 0; i < len; i++) {
+                sigma[i] = sigma[i] + rgb[i];
+            }
+        }
+        int size = list.size();
+        for (int i = 0; i < len; i++) {
+            sigma[i] = sigma[i] / size;
+        }
+        return sigma;
+    }
+
+    private List<double[]> listK(List<double[]> listOne, int nub) {
+        int size = listOne.size();
+        int oneSize = size / nub;//几份取一份平均值
+        //System.out.println("oneSize==" + oneSize);
+        List<double[]> allList = new ArrayList<>();
+        for (int i = 0; i <= size - oneSize; i += oneSize) {
+            double[] avg = getListAvg(listOne.subList(i, i + oneSize));
+            allList.add(avg);
+        }
+        return allList;
+    }
+
     private List<double[]> startBp() {
         int times = 2000;
+        int index = 0;
         List<double[]> features = new ArrayList<>();
         List<List<double[]>> lists = new ArrayList<>();
         for (int j = 0; j < matrices.size(); j++) {
-            List<double[]> list = matrices.get(j).getRgbs().subList(0, times);
+            List<double[]> listOne = matrices.get(j).getRgbs();
+            // List<double[]> list = listK(listOne, times);
+            //System.out.println(listOne.size());
+            List<double[]> list = listOne.subList(index, times + index);
             lists.add(list);
         }
         for (int j = 0; j < times; j++) {
@@ -137,7 +168,7 @@ public class MeanClustering {
         return features;
     }
 
-    public List<double[]> start(boolean isRegression) throws Exception {//开始聚类
+    public void start(boolean isRegression) throws Exception {//开始聚类
         if (matrixList.size() > 1) {
             Random random = new Random();
             for (int i = 0; i < speciesQuantity; i++) {//初始化均值向量
@@ -149,10 +180,10 @@ public class MeanClustering {
             }
             //进行两者的比较
             boolean isNext;
-            for (int i = 0; i < 40; i++) {
+            for (int i = 0; i < 50; i++) {
                 averageMatrix();
                 isNext = isNext();
-                if (isNext && i < 39) {
+                if (isNext && i < 49) {
                     clear();
                 } else {
                     break;
@@ -160,15 +191,10 @@ public class MeanClustering {
             }
             RGBSort rgbSort = new RGBSort();
             Collections.sort(matrices, rgbSort);
-            for (RGBNorm rgbNorm : matrices) {
+//            for (RGBNorm rgbNorm : matrices) {
-                rgbNorm.finish();
+//                rgbNorm.finish();
-            }
-//            if (isRegression) {
-//                return startRegression();
-//            } else {
-//                return null;
 //            }
-            return startBp();
+            // return startBp();
         } else {
             throw new Exception("matrixList number less than 2");
         }

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

@@ -121,9 +121,9 @@ public class RGBNorm {
                 o2Norm = o2Norm + Math.pow(o2[i], 2);
             }
             if (o1Norm > o2Norm) {
-                return -1;
-            } else if (o1Norm < o2Norm) {
                 return 1;
+            } else if (o1Norm < o2Norm) {
+                return -1;
             }
             return 0;
         }

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

@@ -101,7 +101,7 @@ public class Knn {//KNN分类器
                 compare(dists, types, dist, type);
             }
         }
-        //System.out.println(Arrays.toString(types));
+        System.out.println(Arrays.toString(types));
         Map<Integer, Integer> map = new HashMap<>();
         for (int i = 0; i < nub; i++) {
             int type = types[i];

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

@@ -11,10 +11,10 @@ import org.wlld.i.OutBack;
  */
 public class RgbBack implements OutBack {
     private int id = 0;
-    private double out = 0;
+    private double out = -2;
 
     public void clear() {
-        out = 0;
+        out = -2;
         id = 0;
     }
 

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

@@ -33,20 +33,13 @@ public class KNerveManger {
         this.times = times;
         nerveManager = new NerveManager(sensoryNerveNub, 24, speciesNub,
                 1, new Tanh(),//0.008  l1 0.02
-                false, false, 0.008, RZ.L1, 0.01);
+                false, false, 0.008, RZ.L1, 0.02);
         nerveManager.init(true, false, true, true);
     }
 
     private Map<Integer, Double> createTag(int tag) {//创建一个标注
         Map<Integer, Double> tagging = new HashMap<>();
-        Set<Integer> set = featureMap.keySet();
+        tagging.put(tag, 1.0);
-        for (int key : set) {
-            double value = 0.0;
-            if (key == tag) {
-                value = 1.0;
-            }
-            tagging.put(key, value);
-        }
         return tagging;
     }
 
@@ -78,6 +71,7 @@ public class KNerveManger {
     }
 
     public void startStudy() throws Exception {
+        for (int j = 0; j < 2; j++) {
             for (int i = 0; i < times; i++) {
                 for (Map.Entry<Integer, List<double[]>> entry : featureMap.entrySet()) {
                     int type = entry.getKey();
@@ -87,13 +81,14 @@ public class KNerveManger {
                     post(feature, tag, true);
                 }
             }
+        }
 
-//        for (Map.Entry<Integer, List<double[]>> entry : featureMap.entrySet()) {
+        for (Map.Entry<Integer, List<double[]>> entry : featureMap.entrySet()) {
-//            int type = entry.getKey();
+            int type = entry.getKey();
-//            System.out.println("=============================" + type);
+            System.out.println("=============================" + type);
-//            List<double[]> list = entry.getValue();
+            List<double[]> list = entry.getValue();
-//            look(list);
+            look(list);
-//        }
+        }
     }
 
     private void post(double[] data, Map<Integer, Double> tagging, boolean isStudy) throws Exception {

src/main/java/org/wlld/nerveEntity/OutNerve.java

@@ -58,7 +58,7 @@ public class OutNerve extends Nerve {
                     if (E.containsKey(getId())) {
                         this.E = E.get(getId());
                     } else {
-                        this.E = 0;
+                        this.E = -1;
                     }
                     if (isShowLog) {
                         System.out.println("E==" + this.E + ",out==" + out + ",nerveId==" + getId());

src/main/java/org/wlld/pso/PSO.java

@@ -0,0 +1,201 @@
+package org.wlld.pso;
+
+import org.wlld.i.PsoFunction;
+
+import javax.imageio.ImageIO;
+import java.awt.*;
+import java.awt.geom.Rectangle2D;
+import java.awt.image.BufferedImage;
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Random;
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description 粒子群
+ */
+public class PSO {
+    private double globalValue = -1;//当前全局最优值
+    private int times;//迭代次数
+    private List<Particle> allPar = new ArrayList<>();//全部粒子集合
+    private PsoFunction psoFunction;//粒子群执行函数
+    private double inertialFactor = 0.5;//惯性因子
+    private double selfStudyFactor = 2;//个体学习因子
+    private double socialStudyFactor = 2;//社会学习因子
+    private boolean isMax;//取最大值还是最小值
+    private double[] allBest;//全局最佳位置
+    private Random random = new Random();
+    private int[] minBorder, maxBorder;
+    private double maxSpeed;
+
+    public PSO(int dimensionNub, int[] minBorder, int[] maxBorder,
+               int times, int particleNub, PsoFunction psoFunction,
+               double inertialFactor, double selfStudyFactor, double socialStudyFactor
+            , boolean isMax, double maxSpeed) {
+        this.times = times;
+        this.psoFunction = psoFunction;
+        this.isMax = isMax;
+        allBest = new double[dimensionNub];
+        this.minBorder = minBorder;
+        this.maxBorder = maxBorder;
+        this.maxSpeed = maxSpeed;
+        if (inertialFactor > 0) {
+            this.inertialFactor = inertialFactor;
+        }
+        if (selfStudyFactor >= 0 && selfStudyFactor <= 4) {
+            this.selfStudyFactor = selfStudyFactor;
+        }
+        if (socialStudyFactor >= 0 && socialStudyFactor <= 4) {
+            this.socialStudyFactor = socialStudyFactor;
+        }
+        for (int i = 0; i < particleNub; i++) {//初始化生成粒子群
+            Particle particle = new Particle(dimensionNub);
+            allPar.add(particle);
+        }
+
+    }
+
+    public void setAllPar(List<Particle> allPar) {//外置粒子群注入
+        this.allPar = allPar;
+    }
+
+    public void start(int fatherX, int fatherY) throws Exception {//开始进行迭代
+        int size = allPar.size();
+        for (int i = 0; i < times; i++) {
+            for (int j = 0; j < size; j++) {
+                move(allPar.get(j), j);
+            }
+        }
+        //粒子群移动结束
+        draw("/Users/lidapeng/Desktop/test/testOne/e2.jpg", fatherX, fatherY);
+    }
+
+    private void draw(String path, int fatherX, int fatherY) throws Exception {
+        File file = new File(path);
+        FileInputStream fileInputStream = new FileInputStream(file);
+        BufferedImage image2 = ImageIO.read(fileInputStream);
+        int width = image2.getWidth();
+        int height = image2.getHeight();
+        BufferedImage bi = new BufferedImage(width, height, BufferedImage.TYPE_INT_RGB);
+        Graphics2D g2 = (Graphics2D) bi.getGraphics();
+        g2.setColor(Color.CYAN);
+        g2.drawImage(image2, 0, 0, width, height, null);
+        int size = allPar.size();
+        for (int j = 0; j < size; j++) {//输出
+            Particle particle = allPar.get(j);
+            double[] parameter = particle.getParameter();
+            int x = (int) (fatherX + parameter[0]);
+            int y = (int) (fatherY + parameter[1]);
+            Rectangle2D rect = new Rectangle2D.Double(y, x, 1, 1);//声明并创建矩形对象，矩形的左上角是(20，30)，宽是300，高是40
+            g2.draw(rect);
+        }
+        String savePath = "/Users/lidapeng/Desktop/test/testTwo/a.jpg";
+        ImageIO.write(bi, "JPEG", new FileOutputStream(savePath));
+    }
+
+    private void move(Particle particle, int id) throws Exception {//粒子群开始移动
+        double[] parameter = particle.getParameter();//当前粒子的位置
+        BestData[] bestData = particle.bestDataArray;//该粒子的信息
+        double value = psoFunction.getResult(parameter, id);
+        double selfValue = particle.selfBestValue;//局部最佳值
+        if (isMax) {//取最大值
+            if (value > globalValue) {//更新全局最大值
+                globalValue = value;
+                //更新全局最佳位置
+                for (int i = 0; i < allBest.length; i++) {
+                    allBest[i] = parameter[i];
+                }
+            }
+            if (value > selfValue) {//更新局部最大值
+                particle.selfBestValue = value;
+                //更新局部最佳位置
+                for (int i = 0; i < bestData.length; i++) {
+                    bestData[i].selfBestPosition = parameter[i];
+                }
+            }
+        } else {//取最小值
+            if (globalValue < 0 || value < globalValue) {//更新全局最小值
+                globalValue = value;
+                //更新全局最佳位置
+                for (int i = 0; i < allBest.length; i++) {
+                    allBest[i] = parameter[i];
+                }
+            }
+            if (selfValue < 0 || value < selfValue) {//更新全局最小值
+                particle.selfBestValue = value;
+                //更新局部最佳位置
+                for (int i = 0; i < bestData.length; i++) {
+                    bestData[i].selfBestPosition = parameter[i];
+                }
+            }
+        }
+        //先更新粒子每个维度的速度
+        for (int i = 0; i < bestData.length; i++) {
+            double speed = bestData[i].speed;//当前维度的速度
+            double pid = bestData[i].selfBestPosition;//当前自己的最佳位置
+            double selfPosition = parameter[i];//当前自己的位置
+            double pgd = allBest[i];//当前维度的全局最佳位置
+            //当前维度更新后的速度
+            speed = inertialFactor * speed + selfStudyFactor * random.nextDouble() * (pid - selfPosition)
+                    + socialStudyFactor * random.nextDouble() * (pgd - selfPosition);
+            if (Math.abs(speed) > maxSpeed) {
+                if (speed > 0) {
+                    speed = maxSpeed;
+                } else {
+                    speed = -maxSpeed;
+                }
+            }
+            bestData[i].speed = speed;
+            //更新该粒子该维度新的位置
+            double position = selfPosition + speed;
+            if (position < minBorder[i]) {
+                position = minBorder[i];
+            }
+            if (position > maxBorder[i]) {
+                position = maxBorder[i];
+            }
+            bestData[i].selfPosition = position;
+        }
+    }
+
+    class Particle {//粒子
+        private BestData[] bestDataArray;
+        private double selfBestValue = -1;//自身最优的值
+
+        private double[] getParameter() {//获取粒子位置信息
+            double[] parameter = new double[bestDataArray.length];
+            for (int i = 0; i < parameter.length; i++) {
+                parameter[i] = bestDataArray[i].selfPosition;
+            }
+            return parameter;
+        }
+
+        protected Particle(int dimensionNub) {//初始化随机位置
+            bestDataArray = new BestData[dimensionNub];
+            for (int i = 0; i < dimensionNub; i++) {
+                int min = minBorder[i];
+                int max = maxBorder[i];
+                int region = max - min + 1;
+                int position = random.nextInt(region) + min;//初始化该维度的位置
+                bestDataArray[i] = new BestData(position);
+            }
+        }
+    }
+
+    class BestData {//数据保存
+
+        private BestData(double selfPosition) {
+            this.selfBestPosition = selfPosition;
+            this.selfPosition = selfPosition;
+        }
+
+        private double speed = 1;//该粒子当前维度的速度
+        private double selfBestPosition;//当前维度自身最优的历史位置/自己最优位置的值
+        private double selfPosition;//当前维度自己现在的位置/也就是当前维度自己的值
+    }
+}

src/main/java/org/wlld/tools/Frequency.java

@@ -82,11 +82,14 @@ public abstract class Frequency {//统计频数
 
     public double dc(double... m) {//计算离散系数
         double ave = average(m);//先计算出平均值
+        double dc = 0;
+        if (ave > 0) {
             double allNub = 0;
             for (int i = 0; i < m.length; i++) {
                 allNub = allNub + Math.pow(m[i] - ave, 2);
             }
-        double dc = ArithUtil.div(Math.sqrt(ArithUtil.div(allNub, m.length)), ave);//离散系数
+            dc = ArithUtil.div(Math.sqrt(ArithUtil.div(allNub, m.length)), ave);//离散系数
+        }
         return dc;
     }
 

src/test/java/coverTest/DataObservation.java

@@ -24,17 +24,17 @@ public class DataObservation {
     public static void main(String[] args) throws Exception {
         //372,330,右 最大值 147.44
         //377 ,330右 最大值 69.6
-        int xp = 123;
+        int xp = 100;
-        int yp = 165;//290
+        int yp = 720;//290
-        observation2("/Users/lidapeng/Desktop/test/testOne/a0.jpg", xp, yp);
+        observation2("/Users/lidapeng/Desktop/test/testOne/e2.jpg", xp, yp);
     }
 
     public static void observation2(String url, int xp, int yp) throws Exception {
         Picture picture = new Picture();
         ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix(url);
-        ThreeChannelMatrix myThreeChannelMatrix = convolution.getRegionMatrix(threeChannelMatrix, xp, yp, 270, 274);
+        ThreeChannelMatrix myThreeChannelMatrix = convolution.getRegionMatrix(threeChannelMatrix, xp, yp, 300, 300);
-        RegionFeature regionFeature = new RegionFeature(myThreeChannelMatrix, xp, yp);
+        RegionCut regionCut = new RegionCut(myThreeChannelMatrix, xp, yp, 30);
-        regionFeature.start();
+        regionCut.start();
     }
 
     public static void observation(String url, int xp, int yp, int size) throws Exception {

src/test/java/coverTest/FoodTest.java

@@ -68,16 +68,16 @@ public class FoodTest {
         cutting.setRegionNub(100);
         cutting.setMaxIou(2);
         //knn参数
-        templeConfig.setKnnNub(1);
+        templeConfig.setKnnNub(15);
         //池化比例
         templeConfig.setPoolSize(2);//缩小比例
         //聚类
-        templeConfig.setFeatureNub(3);//聚类特征数量
+        templeConfig.setFeatureNub(5);//聚类特征数量
         //菜品识别实体类
         food.setShrink(5);//缩紧像素
         food.setTimes(1);//聚类数据增强
         food.setRegionSize(5);
-        KNerveManger kNerveManger = new KNerveManger(9, 24, 2000);
+        KNerveManger kNerveManger = new KNerveManger(12, 24, 6000);
         food.setkNerveManger(kNerveManger);
         food.setRowMark(0.15);//0.12
         food.setColumnMark(0.15);//0.25
@@ -107,7 +107,7 @@ public class FoodTest {
 //        ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix("/Users/lidapeng/Desktop/myDocument/d.jpg");
 //        operation.setTray(threeChannelMatrix);
         String name = "/Users/lidapeng/Desktop/test/testOne/";
-        for (int i = 0; i < 1; i++) {
+        for (int i = 0; i < 3; i++) {
             System.out.println("轮数============================" + i);
             ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix(name + "a" + i + ".jpg");
             ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix(name + "b" + i + ".jpg");
@@ -160,62 +160,88 @@ public class FoodTest {
         }
         System.out.println("========================");
         kNerveManger.startStudy();
-        int i = 0;
+        int i = 3;
-        ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix(name + "a" + i + ".jpg");
+//        ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix(name + "a" + i + ".jpg");
-        ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix(name + "b" + i + ".jpg");
+//        ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix(name + "b" + i + ".jpg");
-        ThreeChannelMatrix threeChannelMatrix3 = picture.getThreeMatrix(name + "c" + i + ".jpg");
+//        ThreeChannelMatrix threeChannelMatrix3 = picture.getThreeMatrix(name + "c" + i + ".jpg");
-        ThreeChannelMatrix threeChannelMatrix4 = picture.getThreeMatrix(name + "d" + i + ".jpg");
+//        ThreeChannelMatrix threeChannelMatrix4 = picture.getThreeMatrix(name + "d" + i + ".jpg");
-        ThreeChannelMatrix threeChannelMatrix5 = picture.getThreeMatrix(name + "e" + i + ".jpg");
+//        ThreeChannelMatrix threeChannelMatrix5 = picture.getThreeMatrix(name + "e" + i + ".jpg");
-        ThreeChannelMatrix threeChannelMatrix6 = picture.getThreeMatrix(name + "f" + i + ".jpg");
+//        ThreeChannelMatrix threeChannelMatrix6 = picture.getThreeMatrix(name + "f" + i + ".jpg");
-        ThreeChannelMatrix threeChannelMatrix7 = picture.getThreeMatrix(name + "g" + i + ".jpg");
+//        ThreeChannelMatrix threeChannelMatrix7 = picture.getThreeMatrix(name + "g" + i + ".jpg");
-        ThreeChannelMatrix threeChannelMatrix8 = picture.getThreeMatrix(name + "h" + i + ".jpg");
+//        ThreeChannelMatrix threeChannelMatrix8 = picture.getThreeMatrix(name + "h" + i + ".jpg");
-        ThreeChannelMatrix threeChannelMatrix9 = picture.getThreeMatrix(name + "i" + i + ".jpg");
+//        ThreeChannelMatrix threeChannelMatrix9 = picture.getThreeMatrix(name + "i" + i + ".jpg");
-        ThreeChannelMatrix threeChannelMatrix10 = picture.getThreeMatrix(name + "j" + i + ".jpg");
+//        ThreeChannelMatrix threeChannelMatrix10 = picture.getThreeMatrix(name + "j" + i + ".jpg");
         ThreeChannelMatrix threeChannelMatrix11 = picture.getThreeMatrix(name + "k" + i + ".jpg");
-        ThreeChannelMatrix threeChannelMatrix12 = picture.getThreeMatrix(name + "l" + i + ".jpg");
+//        ThreeChannelMatrix threeChannelMatrix12 = picture.getThreeMatrix(name + "l" + i + ".jpg");
         ThreeChannelMatrix threeChannelMatrix13 = picture.getThreeMatrix(name + "m" + i + ".jpg");
-        ThreeChannelMatrix threeChannelMatrix14 = picture.getThreeMatrix(name + "n" + i + ".jpg");
+//        ThreeChannelMatrix threeChannelMatrix14 = picture.getThreeMatrix(name + "n" + i + ".jpg");
-
+//
-        ThreeChannelMatrix threeChannelMatrix15 = picture.getThreeMatrix(name + "o" + i + ".jpg");
+//        ThreeChannelMatrix threeChannelMatrix15 = picture.getThreeMatrix(name + "o" + i + ".jpg");
-        ThreeChannelMatrix threeChannelMatrix16 = picture.getThreeMatrix(name + "p" + i + ".jpg");
+//        ThreeChannelMatrix threeChannelMatrix16 = picture.getThreeMatrix(name + "p" + i + ".jpg");
-        ThreeChannelMatrix threeChannelMatrix17 = picture.getThreeMatrix(name + "q" + i + ".jpg");
+//        ThreeChannelMatrix threeChannelMatrix17 = picture.getThreeMatrix(name + "q" + i + ".jpg");
-        ThreeChannelMatrix threeChannelMatrix18 = picture.getThreeMatrix(name + "r" + i + ".jpg");
+//        ThreeChannelMatrix threeChannelMatrix18 = picture.getThreeMatrix(name + "r" + i + ".jpg");
-        ThreeChannelMatrix threeChannelMatrix19 = picture.getThreeMatrix(name + "s" + i + ".jpg");
+//        ThreeChannelMatrix threeChannelMatrix19 = picture.getThreeMatrix(name + "s" + i + ".jpg");
-        ThreeChannelMatrix threeChannelMatrix20 = picture.getThreeMatrix(name + "t" + i + ".jpg");
+//        ThreeChannelMatrix threeChannelMatrix20 = picture.getThreeMatrix(name + "t" + i + ".jpg");
-        ThreeChannelMatrix threeChannelMatrix21 = picture.getThreeMatrix(name + "u" + i + ".jpg");
+//        ThreeChannelMatrix threeChannelMatrix21 = picture.getThreeMatrix(name + "u" + i + ".jpg");
-        ThreeChannelMatrix threeChannelMatrix22 = picture.getThreeMatrix(name + "v" + i + ".jpg");
+//        ThreeChannelMatrix threeChannelMatrix22 = picture.getThreeMatrix(name + "v" + i + ".jpg");
-        ThreeChannelMatrix threeChannelMatrix23 = picture.getThreeMatrix(name + "w" + i + ".jpg");
+//        ThreeChannelMatrix threeChannelMatrix23 = picture.getThreeMatrix(name + "w" + i + ".jpg");
-        ThreeChannelMatrix threeChannelMatrix24 = picture.getThreeMatrix(name + "x" + i + ".jpg");
+        // ThreeChannelMatrix threeChannelMatrix24 = picture.getThreeMatrix(name + "x" + i + ".jpg");
-        operation.colorLook(threeChannelMatrix1, specificationsList);
+//        operation.colorLook(threeChannelMatrix1, specificationsList);
-        operation.colorLook(threeChannelMatrix2, specificationsList);
+//        operation.colorLook(threeChannelMatrix2, specificationsList);
-        operation.colorLook(threeChannelMatrix3, specificationsList);
+//        operation.colorLook(threeChannelMatrix3, specificationsList);
-        operation.colorLook(threeChannelMatrix4, specificationsList);
+//        operation.colorLook(threeChannelMatrix4, specificationsList);
-        operation.colorLook(threeChannelMatrix5, specificationsList);
+//        operation.colorLook(threeChannelMatrix5, specificationsList);
-        operation.colorLook(threeChannelMatrix6, specificationsList);
+//        operation.colorLook(threeChannelMatrix6, specificationsList);
-        operation.colorLook(threeChannelMatrix7, specificationsList);
+//        operation.colorLook(threeChannelMatrix7, specificationsList);
-        operation.colorLook(threeChannelMatrix8, specificationsList);
+//        operation.colorLook(threeChannelMatrix8, specificationsList);
-        operation.colorLook(threeChannelMatrix9, specificationsList);
+//        operation.colorLook(threeChannelMatrix9, specificationsList);
-        operation.colorLook(threeChannelMatrix10, specificationsList);
+//        operation.colorLook(threeChannelMatrix10, specificationsList);
         operation.colorLook(threeChannelMatrix11, specificationsList);
-        operation.colorLook(threeChannelMatrix12, specificationsList);
+//        operation.colorLook(threeChannelMatrix12, specificationsList);
         operation.colorLook(threeChannelMatrix13, specificationsList);
-        operation.colorLook(threeChannelMatrix14, specificationsList);
+//        operation.colorLook(threeChannelMatrix14, specificationsList);
-        operation.colorLook(threeChannelMatrix15, specificationsList);
+//        operation.colorLook(threeChannelMatrix15, specificationsList);
-        operation.colorLook(threeChannelMatrix16, specificationsList);
+//        operation.colorLook(threeChannelMatrix16, specificationsList);
-        operation.colorLook(threeChannelMatrix17, specificationsList);
+//        operation.colorLook(threeChannelMatrix17, specificationsList);
-        operation.colorLook(threeChannelMatrix18, specificationsList);
+//        operation.colorLook(threeChannelMatrix18, specificationsList);
-        operation.colorLook(threeChannelMatrix19, specificationsList);
+//        operation.colorLook(threeChannelMatrix19, specificationsList);
-        operation.colorLook(threeChannelMatrix20, specificationsList);
+//        operation.colorLook(threeChannelMatrix20, specificationsList);
-        operation.colorLook(threeChannelMatrix21, specificationsList);
+//        operation.colorLook(threeChannelMatrix21, specificationsList);
-        operation.colorLook(threeChannelMatrix22, specificationsList);
+//        operation.colorLook(threeChannelMatrix22, specificationsList);
-        operation.colorLook(threeChannelMatrix23, specificationsList);
+//        operation.colorLook(threeChannelMatrix23, specificationsList);
-        operation.colorLook(threeChannelMatrix24, specificationsList);
+//        operation.colorLook(threeChannelMatrix24, specificationsList);
+//
+//        test3(threeChannelMatrix1, operation, specificationsList, 1);
+//        test3(threeChannelMatrix2, operation, specificationsList, 2);
+//        test3(threeChannelMatrix3, operation, specificationsList, 3);
+//        test3(threeChannelMatrix4, operation, specificationsList, 4);
+//        test3(threeChannelMatrix5, operation, specificationsList, 5);
+//        test3(threeChannelMatrix6, operation, specificationsList, 6);
+//        test3(threeChannelMatrix7, operation, specificationsList, 7);
+//        test3(threeChannelMatrix8, operation, specificationsList, 8);
+//        test3(threeChannelMatrix9, operation, specificationsList, 9);
+//        test3(threeChannelMatrix10, operation, specificationsList, 10);
+//        test3(threeChannelMatrix11, operation, specificationsList, 11);
+//        test3(threeChannelMatrix12, operation, specificationsList, 12);
+//        test3(threeChannelMatrix13, operation, specificationsList, 13);
+//        test3(threeChannelMatrix14, operation, specificationsList, 14);
+//        test3(threeChannelMatrix15, operation, specificationsList, 15);
+//        test3(threeChannelMatrix16, operation, specificationsList, 16);
+//        test3(threeChannelMatrix17, operation, specificationsList, 17);
+//        test3(threeChannelMatrix18, operation, specificationsList, 18);
+//        test3(threeChannelMatrix19, operation, specificationsList, 19);
+//        test3(threeChannelMatrix20, operation, specificationsList, 20);
+//        test3(threeChannelMatrix21, operation, specificationsList, 21);
+//        test3(threeChannelMatrix22, operation, specificationsList, 22);
+//        test3(threeChannelMatrix23, operation, specificationsList, 23);
+        //test3(threeChannelMatrix24, operation, specificationsList, 24);
 
     }
 
-    private static void test3(ThreeChannelMatrix threeChannelMatrix, Operation operation, List<Specifications> specifications) throws Exception {
+    private static void test3(ThreeChannelMatrix threeChannelMatrix, Operation operation, List<Specifications> specifications,
+                              int realType) throws Exception {
         int type = operation.colorLook(threeChannelMatrix, specifications).get(0).getType();
-        System.out.println(type);
+        System.out.println(type + ",realType==" + realType);
     }
 
     public static void study() throws Exception {

src/test/java/coverTest/regionCut/ColorFunction.java

@@ -0,0 +1,56 @@
+package coverTest.regionCut;
+
+import org.wlld.MatrixTools.Matrix;
+import org.wlld.i.PsoFunction;
+import org.wlld.imageRecognition.ThreeChannelMatrix;
+import org.wlld.tools.Frequency;
+
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description
+ */
+public class ColorFunction extends Frequency implements PsoFunction {
+    private Matrix matrixR;
+    private Matrix matrixG;
+    private Matrix matrixB;
+    private Map<Integer, double[]> pixels = new HashMap<>();
+
+    public ColorFunction(ThreeChannelMatrix threeChannelMatrix) {
+        matrixR = threeChannelMatrix.getMatrixR();
+        matrixG = threeChannelMatrix.getMatrixG();
+        matrixB = threeChannelMatrix.getMatrixB();
+    }
+
+    @Override
+    public double getResult(double[] parameter, int id) throws Exception {
+        int x = (int) parameter[0];
+        int y = (int) parameter[1];
+        double[] rgb = new double[]{matrixR.getNumber(x, y) / 255, matrixG.getNumber(x, y) / 255,
+                matrixB.getNumber(x, y) / 255};
+        pixels.put(id, rgb);
+        //计算当前方差
+        return getDist();
+    }
+
+    private double getDist() {//计算当前均方误差
+        double[] r = new double[pixels.size()];
+        double[] g = new double[pixels.size()];
+        double[] b = new double[pixels.size()];
+        for (Map.Entry<Integer, double[]> entry : pixels.entrySet()) {
+            double[] rgb = entry.getValue();
+            int key = entry.getKey();
+            r[key] = rgb[0];
+            g[key] = rgb[1];
+            b[key] = rgb[2];
+        }
+        return dc(r) + dc(g) + dc(b);
+    }
+
+}

src/test/java/coverTest/regionCut/RegionCutBody.java

@@ -1,8 +1,5 @@
 package coverTest.regionCut;
 
-import java.util.ArrayList;
-import java.util.List;
-
 /**
  * @param
  * @DATA
@@ -10,6 +7,11 @@ import java.util.List;
  * @Description
  */
 public class RegionCutBody {
-    private List<Integer> pixels = new ArrayList<>();
+    private double feature;//特征值
+    private int x;
+    private int y;
+    private int id;
+    private int minId;//最近id
+    private int channel;//渠道
 
 }

src/test/java/coverTest/regionCut/RegionFeature.java

@@ -17,9 +17,6 @@ import java.util.Arrays;
  */
 public class RegionFeature extends Frequency {
     private Matrix matrix;
-    private Matrix matrixR;
-    private Matrix matrixG;
-    private Matrix matrixB;
     private Matrix kernel = Kernel.Big;
     private int fatherX;
     private int fatherY;
@@ -29,9 +26,6 @@ public class RegionFeature extends Frequency {
         this.matrix = threeChannelMatrix.getMatrixRGB();
         this.fatherX = fatherX;
         this.fatherY = fatherY;
-        matrixR = threeChannelMatrix.getMatrixR();
-        matrixG = threeChannelMatrix.getMatrixG();
-        matrixB = threeChannelMatrix.getMatrixB();
     }
 
     private double getMatrixVar(Matrix matrix) throws Exception {