添加多种类干食切图

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

@@ -70,7 +70,7 @@ public class Convolution extends Frequency {
         List<ThreeChannelMatrix> threeChannelMatrixList = regionThreeChannelMatrix(threeMatrix, regionSize);
         for (ThreeChannelMatrix threeChannelMatrix : threeChannelMatrixList) {
             List<Double> feature = new ArrayList<>();
-            MeanClustering meanClustering = new MeanClustering(sqNub, templeConfig);
+            MeanClustering meanClustering = new MeanClustering(sqNub);
             Matrix matrixR = threeChannelMatrix.getMatrixR();
             Matrix matrixG = threeChannelMatrix.getMatrixG();
             Matrix matrixB = threeChannelMatrix.getMatrixB();
@@ -82,7 +82,7 @@ public class Convolution extends Frequency {
                     meanClustering.setColor(color);
                 }
             }
-            meanClustering.start(false);
+            meanClustering.start();
             List<RGBNorm> rgbNorms = meanClustering.getMatrices();
             Collections.sort(rgbNorms, rgbSort);
             for (RGBNorm rgbNorm : rgbNorms) {
@@ -96,48 +96,10 @@ public class Convolution extends Frequency {
         return features;
     }
 
-
-    public List<Double> getCenterColor(ThreeChannelMatrix threeChannelMatrix, TempleConfig templeConfig,
-                                       int sqNub) throws Exception {
-        Matrix matrixR = threeChannelMatrix.getMatrixR();
-        Matrix matrixG = threeChannelMatrix.getMatrixG();
-        Matrix matrixB = threeChannelMatrix.getMatrixB();
-        MeanClustering meanClustering = new MeanClustering(sqNub, templeConfig);
-        int maxX = matrixR.getX();
-        int maxY = matrixR.getY();
-        ColorFunction colorFunction = new ColorFunction(threeChannelMatrix);
-        int[] minBorder = new int[]{0, 0};
-        int[] maxBorder = new int[]{maxX - 1, maxY - 1};
-        //创建粒子群
-        PSO pso = new PSO(2, minBorder, maxBorder, 200, 200,
-                colorFunction, 0.2, 1, 0.5, true, 10, 1);
-        List<double[]> positions = pso.start();
-        for (int i = 0; i < positions.size(); i++) {
-            double[] parameter = positions.get(i);
-            //获取取样坐标
-            int x = (int) parameter[0];
-            int y = (int) parameter[1];
-            double[] rgb = new double[]{matrixR.getNumber(x, y), matrixG.getNumber(x, y),
-                    matrixB.getNumber(x, y)};
-            meanClustering.setColor(rgb);
-        }
-        meanClustering.start(true);
-        List<RGBNorm> rgbNorms = meanClustering.getMatrices();
-        List<Double> features = new ArrayList<>();
-        for (int i = 0; i < sqNub; i++) {
-            double[] rgb = rgbNorms.get(i).getRgb();
-            for (int j = 0; j < rgb.length; j++) {
-                features.add(rgb[j]);
-            }
-        }
-
-        return features;
-    }
-
     public List<Double> getCenterTexture(ThreeChannelMatrix threeChannelMatrix, int size, TempleConfig templeConfig
             , int sqNub) throws Exception {
-        //MeanClustering meanClustering = new MeanClustering(sqNub, templeConfig);
+        //MeanClustering meanClustering = new MeanClustering(sqNub);
-        GMClustering meanClustering = new GMClustering(sqNub, templeConfig);
+        GMClustering meanClustering = new GMClustering(sqNub);
         Matrix matrixR = threeChannelMatrix.getMatrixR();
         Matrix matrixG = threeChannelMatrix.getMatrixG();
         Matrix matrixB = threeChannelMatrix.getMatrixB();
@@ -158,40 +120,7 @@ public class Convolution extends Frequency {
                 }
             }
         }
-//        for (int i = 0; i <= xn - size; i += 2) {
+        meanClustering.start();
-//            for (int j = 0; j <= yn - size; j += 2) {
-//                Matrix sonR = matrixR.getSonOfMatrix(i, j, size, size);
-//                Matrix sonG = matrixG.getSonOfMatrix(i, j, size, size);
-//                Matrix sonB = matrixB.getSonOfMatrix(i, j, size, size);
-//                Matrix sonRGB = matrixRGB.getSonOfMatrix(i, j, size, size);
-//                double[] h = new double[nub];
-//                double[] rgb = new double[nub * 3];
-//                for (int t = 0; t < size; t++) {
-//                    for (int k = 0; k < size; k++) {
-//                        int index = t * size + k;
-//                        h[index] = sonRGB.getNumber(t, k);
-//                        rgb[index] = sonR.getNumber(t, k) / 255;
-//                        rgb[nub + index] = sonG.getNumber(t, k) / 255;
-//                        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);
-//                    }
-//                }
-//            }
-//        }
-        //List<double[]> list = meanClustering.start(true);//开始聚类
-        meanClustering.start(true);
-//        if (tag == 0) {//识别
-//            templeConfig.getFood().getkNerveManger().look(list);
-//        } else {//训练
-//            templeConfig.getFood().getkNerveManger().setFeature(tag, list);
-//        }
         List<RGBNorm> rgbNorms = meanClustering.getMatrices();
         List<Double> features = new ArrayList<>();
         for (int i = 0; i < sqNub; i++) {

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

@@ -38,7 +38,7 @@ public class CutFood {
                 mean.setColor(rgb);
             }
         }
-        mean.start(true);
+        mean.start();
     }
 
     private double getAvg(Matrix matrix) throws Exception {
@@ -89,7 +89,7 @@ public class CutFood {
             double regionSize = gmBody.getPixelNub() * s;
             int type = gmBody.getType();
             if (type != 1) {//背景直接过滤
-                int oneSize = meanMap.get(type).getRegionSize();
+                double oneSize = meanMap.get(type).getRegionSize();
                 if (regionSize > oneSize * 0.8) {
                     gmBodies2.add(gmBody);
                 }
@@ -98,7 +98,7 @@ public class CutFood {
         for (GMBody gmBody : gmBodies2) {
             int type = gmBody.getType();
             double regionSize = gmBody.getPixelNub() * s;
-            int oneSize = meanMap.get(type).getRegionSize();
+            double oneSize = meanMap.get(type).getRegionSize();
             double nub = regionSize / (double) oneSize;
             System.out.println("type==" + type + ",nub==" + nub + ",onSize==" + oneSize + ",gmNub=="
                     + gmBody.getPixelNub());
@@ -138,8 +138,8 @@ public class CutFood {
         Matrix matrixR = threeChannelMatrix.getMatrixR();
         int x = matrixR.getX();
         int y = matrixR.getY();
-        GMClustering mean = new GMClustering(templeConfig.getFeatureNub(), templeConfig);
+        GMClustering mean = new GMClustering(templeConfig.getFeatureNub());
-        mean.setRegionSize(x * y);
+        mean.setRegionSize(x * y * 0.8);
         meanMap.put(type, mean);
         mean(threeChannelMatrix, mean);
         //记录非背景的单物体面积

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

@@ -1,24 +0,0 @@
-package org.wlld.imageRecognition;
-
-import org.wlld.imageRecognition.border.DistBody;
-
-import java.util.Comparator;
-
-/**
- * @param
- * @DATA
- * @Author LiDaPeng
- * @Description
- */
-public class DistSort implements Comparator<DistBody> {
-    @Override
-    public int compare(DistBody o1, DistBody o2) {
-        if (o1.getDist() < o2.getDist()) {
-            return -1;
-        } else if (o1.getDist() > o2.getDist()) {
-            return 1;
-        } else {
-            return 0;
-        }
-    }
-}

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

@@ -9,24 +9,19 @@ public class MeanClustering {
     private int length;//向量长度(模型需要返回)
     protected int speciesQuantity;//种类数量(模型需要返回)
     protected List<RGBNorm> matrices = new ArrayList<>();//均值K模型(模型需要返回)
-    private TempleConfig templeConfig;
-    private int sensoryNerveNub;//神经元个数
-    private List<MeanClustering> kList = new ArrayList<>();
 
     public List<RGBNorm> getMatrices() {
         return matrices;
     }
 
-    public MeanClustering(int speciesQuantity, TempleConfig templeConfig) throws Exception {
+    public MeanClustering(int speciesQuantity) throws Exception {
         this.speciesQuantity = speciesQuantity;//聚类的数量
-        this.templeConfig = templeConfig;
     }
 
     public void setColor(double[] color) throws Exception {
         if (matrixList.size() == 0) {
             matrixList.add(color);
             length = color.length;
-            sensoryNerveNub = templeConfig.getFeatureNub() * length;
         } else {
             if (length == color.length) {
                 matrixList.add(color);
@@ -90,76 +85,7 @@ public class MeanClustering {
         return sigma;
     }
 
-    private List<double[]> listK(List<double[]> listOne, int nub) {
+    public void start() throws Exception {//开始聚类
-        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[]> 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++) {
-            double[] feature = new double[sensoryNerveNub];
-            for (int i = 0; i < lists.size(); i++) {
-                double[] data = lists.get(i).get(j);
-                int len = data.length;
-                for (int k = 0; k < len; k++) {
-                    feature[i * len + k] = data[k];
-                }
-            }
-            features.add(feature);
-        }
-        return features;
-    }
-
-    private List<double[]> startRegression() throws Exception {//开始聚类回归
-        for (int i = 0; i < matrices.size(); i++) {
-            List<double[]> list = matrices.get(i).getRgbs();
-            MeanClustering k = kList.get(i);
-            for (double[] rgb : list) {
-                k.setColor(rgb);
-            }
-            k.start(false);
-        }
-        //遍历子聚类
-        int times = 2000;
-        Random random = new Random();
-        List<double[]> features = new ArrayList<>();
-        for (int i = 0; i < times; i++) {
-            double[] feature = new double[sensoryNerveNub];
-            for (int k = 0; k < kList.size(); k++) {
-                MeanClustering mean = kList.get(k);
-                List<RGBNorm> rgbNorms = mean.getMatrices();
-                double[] rgb = rgbNorms.get(random.nextInt(rgbNorms.size())).getRgb();
-                int rgbLen = rgb.length;
-                for (int t = 0; t < rgbLen; t++) {
-                    int index = k * rgbLen + t;
-                    feature[index] = rgb[t];
-                }
-            }
-            //System.out.println(Arrays.toString(feature));
-            features.add(feature);
-        }
-        return features;
-    }
-
-    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
@@ -182,10 +108,6 @@ public class MeanClustering {
             }
             RGBSort rgbSort = new RGBSort();
             Collections.sort(matrices, rgbSort);
-//            for (RGBNorm rgbNorm : matrices) {
-//                rgbNorm.finish();
-//            }
-            // return startBp();
         } else {
             throw new Exception("matrixList number less than 2");
         }

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

@@ -1,33 +0,0 @@
-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/border/GMClustering.java

@@ -12,18 +12,18 @@ import org.wlld.imageRecognition.TempleConfig;
  * @Description
  */
 public class GMClustering extends MeanClustering {
-    private int regionSize;//单区域面积
+    private double regionSize;//单区域面积
 
-    public int getRegionSize() {
+    public double getRegionSize() {
         return regionSize;
     }
 
-    public void setRegionSize(int regionSize) {
+    public void setRegionSize(double regionSize) {
         this.regionSize = regionSize;
     }
 
-    public GMClustering(int speciesQuantity, TempleConfig templeConfig) throws Exception {
+    public GMClustering(int speciesQuantity) throws Exception {
-        super(speciesQuantity, templeConfig);
+        super(speciesQuantity);
     }
 
     public double getProbabilityDensity(double[] feature) throws Exception {//获取总概率密度
@@ -35,8 +35,8 @@ public class GMClustering extends MeanClustering {
     }
 
     @Override
-    public void start(boolean isRegression) throws Exception {
+    public void start() throws Exception {
-        super.start(isRegression);
+        super.start();
         for (RGBNorm rgbNorm : matrices) {//高斯系数初始化
             rgbNorm.gm();
         }

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

@@ -2,13 +2,9 @@ package org.wlld.imageRecognition.modelEntity;
 
 import org.wlld.MatrixTools.Matrix;
 import org.wlld.imageRecognition.TempleConfig;
-import org.wlld.imageRecognition.border.Knn;
 import org.wlld.imageRecognition.segmentation.DimensionMappingStudy;
 
 
-import java.util.List;
-import java.util.Set;
-
 /**
  * @param
  * @DATA
@@ -17,25 +13,14 @@ import java.util.Set;
  */
 public class DeepMappingBody {
     private DimensionMappingStudy dimensionAll;
-    private TempleConfig templeConfig;
-    private List<KeyMapping> mappingList;
 
     public DeepMappingBody(TempleConfig templeConfig) throws Exception {
-        this.templeConfig = templeConfig;
         dimensionAll = new DimensionMappingStudy(templeConfig, true);
-        mappingList = dimensionAll.start();
+        dimensionAll.start();
     }
 
     public int getType(Matrix feature) throws Exception {
         int type = dimensionAll.getType(feature);
-        for (KeyMapping keyMapping : mappingList) {
-            Set<Integer> region = keyMapping.getKeys();
-            if (region.contains(type)) {
-                DimensionMappingStudy mapping = keyMapping.getDimensionMapping();
-                type = mapping.getType(feature);
-                break;
-            }
-        }
         return type;
     }
 }

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

@@ -274,9 +274,8 @@ public class DimensionMappingStudy {
         myKnn.setFeatureMap(featureMapping(featureMap, mappingSigma));
     }
 
-    public List<KeyMapping> start() throws Exception {
+    public void start() throws Exception {
         mappingStart();
-        return selfTest(1);
     }
 
     private Map<Integer, List<Matrix>> featureMapping(Map<Integer, List<Matrix>> featureMap, double[] mapping) throws Exception {

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

@@ -1,112 +0,0 @@
-package org.wlld.imageRecognition.segmentation;
-
-import org.wlld.config.RZ;
-import org.wlld.function.Sigmod;
-import org.wlld.function.Tanh;
-import org.wlld.imageRecognition.modelEntity.RgbBack;
-import org.wlld.nerveCenter.NerveManager;
-import org.wlld.nerveEntity.SensoryNerve;
-
-import java.awt.image.Kernel;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
-import java.util.Set;
-
-/**
- * @param
- * @DATA
- * @Author LiDaPeng
- * @Description
- */
-public class KNerveManger {
-    private Map<Integer, List<double[]>> featureMap = new HashMap<>();
-    private int sensoryNerveNub;//输出神经元个数
-    private int speciesNub;//种类数
-    private NerveManager nerveManager;
-    private int times;
-    private RgbBack rgbBack = new RgbBack();
-
-    public KNerveManger(int sensoryNerveNub, int speciesNub, int times) throws Exception {
-        this.sensoryNerveNub = sensoryNerveNub;
-        this.speciesNub = speciesNub;
-        this.times = times;
-        nerveManager = new NerveManager(sensoryNerveNub, 24, speciesNub,
-                1, new Tanh(),//0.008  l1 0.02
-                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<>();
-        tagging.put(tag, 1.0);
-        return tagging;
-    }
-
-    public void look(List<double[]> data) throws Exception {
-        int size = data.size();
-        Map<Integer, Integer> map = new HashMap<>();
-        for (int i = 0; i < size; i++) {
-            rgbBack.clear();
-            post(data.get(i), null, false);
-            int type = rgbBack.getId();
-            if (map.containsKey(type)) {
-                map.put(type, map.get(type) + 1);
-            } else {
-                map.put(type, 1);
-            }
-        }
-        double max = 0;
-        int type = 0;
-        for (Map.Entry<Integer, Integer> entry : map.entrySet()) {
-            int nub = entry.getValue();
-            if (nub > max) {
-                max = nub;
-                type = entry.getKey();
-            }
-        }
-        double point = max / size;
-        System.out.println("类型是：" + type + ",总票数:" + size + ",得票率：" + point);
-        System.out.println("=================================完成");
-    }
-
-    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();
-                    System.out.println("=============================" + type);
-                    Map<Integer, Double> tag = createTag(type);//标注
-                    double[] feature = entry.getValue().get(i);//数据
-                    post(feature, tag, true);
-                }
-            }
-        }
-
-        for (Map.Entry<Integer, List<double[]>> entry : featureMap.entrySet()) {
-            int type = entry.getKey();
-            System.out.println("=============================" + type);
-            List<double[]> list = entry.getValue();
-            look(list);
-        }
-    }
-
-    private void post(double[] data, Map<Integer, Double> tagging, boolean isStudy) throws Exception {
-        List<SensoryNerve> sensoryNerveList = nerveManager.getSensoryNerves();
-        int size = sensoryNerveList.size();
-        for (int i = 0; i < size; i++) {
-            sensoryNerveList.get(i).postMessage(1, data[i], isStudy, tagging, rgbBack);
-        }
-    }
-
-    public void setFeature(int type, List<double[]> feature) {
-        if (type > 0) {
-            if (featureMap.containsKey(type)) {
-                featureMap.get(type).addAll(feature);
-            } else {
-                featureMap.put(type, feature);
-            }
-        }
-    }
-
-}

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

@@ -1,206 +0,0 @@
-package org.wlld.imageRecognition.segmentation;
-
-import org.wlld.MatrixTools.Matrix;
-import org.wlld.MatrixTools.MatrixOperation;
-import org.wlld.imageRecognition.DistSort;
-import org.wlld.imageRecognition.TempleConfig;
-import org.wlld.imageRecognition.border.DistBody;
-
-import java.util.*;
-
-/**
- * @param
- * @DATA
- * @Author LiDaPeng
- * @Description 修正映射
- */
-public class RegionMapping {
-    private Map<Integer, List<Matrix>> featureMap;
-    private double studyPoint = 0.01;
-    private Map<Integer, Matrix> powerMatrixMap = new HashMap<>();//映射层的权重矩阵
-    private int times = 200;
-
-    public RegionMapping(TempleConfig templeConfig) {
-        this.featureMap = templeConfig.getKnn().getFeatureMap();
-    }
-
-    public void detection() throws Exception {//对模型进行检查,先对当前模型最相近的三个模型进行检出
-        Map<Integer, List<DistBody>> map = new HashMap<>();
-        DistSort distSort = new DistSort();
-        for (Map.Entry<Integer, List<Matrix>> entry : featureMap.entrySet()) {
-            int key = entry.getKey();
-            List<Matrix> myFeature = entry.getValue();
-            //记录 哪个类别 每个类别距离这个类别的最小距离
-            List<DistBody> distBodies = new ArrayList<>();
-            map.put(key, distBodies);
-            for (Map.Entry<Integer, List<Matrix>> entrySon : featureMap.entrySet()) {
-                int type = entrySon.getKey();
-                if (key != type) {//当前其余类别
-                    DistBody distBody = new DistBody();
-                    List<Matrix> features = entrySon.getValue();//其余类别的所有特征
-                    double minError = -1;
-                    for (Matrix myMatrix : myFeature) {//待验证类别所有特征
-                        for (Matrix otherFeature : features) {
-                            double dist = MatrixOperation.getEDistByMatrix(myMatrix, otherFeature);
-                            if (minError < 0 || dist < minError) {
-                                minError = dist;
-                            }
-                        }
-                    }
-                    //当前类别遍历结束
-                    distBody.setId(type);
-                    distBody.setDist(minError);
-                    distBodies.add(distBody);
-                }
-            }
-        }
-        //进行排序
-        for (Map.Entry<Integer, List<DistBody>> entry : map.entrySet()) {
-            List<DistBody> list = entry.getValue();
-            Collections.sort(list, distSort);
-        }
-        adjustWeight(map, 3);
-        test(12, map, 3);
-//        int testType = 5;
-//        List<DistBody> testBody = map.get(testType);
-//        Matrix matrix = powerMatrixMap.get(testType);
-//        System.out.println(matrix.getString());
-    }
-
-    private Matrix mapping(Matrix feature, Matrix mapping) throws Exception {
-        int y = feature.getY();
-        if (y == mapping.getY()) {
-            Matrix matrix = new Matrix(1, y);
-            for (int i = 0; i < y; i++) {
-                double nub = feature.getNumber(0, i) * mapping.getNumber(0, i);
-                matrix.setNub(0, i, nub);
-            }
-            return matrix;
-        } else {
-            throw new Exception("matrix is not equals");
-        }
-    }
-
-    private void test(int key, Map<Integer, List<DistBody>> map, int nub) throws Exception {
-        Matrix matrixMapping = powerMatrixMap.get(key);//映射
-        List<Matrix> rightFeature = featureMap.get(key);//正确的特征
-        System.out.println("正确特征====================");
-        for (Matrix matrix : rightFeature) {
-            System.out.println(matrix.getString());
-        }
-        int y = rightFeature.get(0).getY();
-        Matrix featureMatrix = getFeatureAvg(rightFeature, y);//均值
-        List<DistBody> distBodies = map.get(key);
-        List<Matrix> wrongFeature = new ArrayList<>();
-        for (int i = 0; i < nub; i++) {
-            DistBody distBody = distBodies.get(i);
-            int id = distBody.getId();//相近的特征id
-            wrongFeature.addAll(featureMap.get(id));
-        }
-        System.out.println("错误特征=============");
-        for (Matrix matrix : wrongFeature) {
-            System.out.println(matrix.getString());
-        }
-        //做验证
-        double minOtherDist = -1;//其余特征最小距离
-        for (Matrix wrongMatrix : wrongFeature) {
-            Matrix wrongMapping = mapping(wrongMatrix, matrixMapping);
-            double dist = MatrixOperation.getEDistByMatrix(featureMatrix, wrongMapping);
-            System.out.println("异类距离：" + dist);
-            if (minOtherDist < 0 || dist < minOtherDist) {
-                minOtherDist = dist;
-            }
-        }
-        System.out.println("异类最小距离：" + minOtherDist);
-        for (Matrix rightMatrix : rightFeature) {
-            Matrix rightMapping = mapping(rightMatrix, matrixMapping);
-            double dist = MatrixOperation.getEDistByMatrix(featureMatrix, rightMapping);
-            System.out.println("同类距离：" + dist);
-        }
-    }
-
-    private void adjustWeight(Map<Integer, List<DistBody>> map, int nub) throws Exception {//进行权重调整
-        for (Map.Entry<Integer, List<DistBody>> entry : map.entrySet()) {
-            int key = entry.getKey();//当前类别的id
-            List<Matrix> rightFeature = featureMap.get(key);//正确的特征
-            List<DistBody> distBodies = entry.getValue();
-            List<Matrix> wrongFeature = new ArrayList<>();
-            for (int i = 0; i < nub; i++) {
-                DistBody distBody = distBodies.get(i);
-                int id = distBody.getId();//相近的特征id
-                wrongFeature.addAll(featureMap.get(id));
-            }
-            //对每个分类的特征图进行权重调整
-            updatePower(wrongFeature, rightFeature, key);
-        }
-    }
-
-    //调整权重矩阵
-    private void updatePower(List<Matrix> wrongFeature, List<Matrix> rightFeature, int key) throws Exception {
-        int size = wrongFeature.size();
-        int y = rightFeature.get(0).getY();
-        //特征均值向量
-        Matrix featureMatrix = getFeatureAvg(rightFeature, y);
-        Matrix powerMatrix = new Matrix(1, y);
-        powerMatrixMap.put(key, powerMatrix);
-        Random random = new Random();
-        for (int j = 0; j < times; j++) {
-            for (int i = 0; i < size; i++) {
-                Matrix feature = rightFeature.get(random.nextInt(rightFeature.size()));
-                Matrix noFeature = wrongFeature.get(i);
-                powerDeflection(feature, featureMatrix, true, powerMatrix);
-                powerDeflection(noFeature, featureMatrix, false, powerMatrix);
-            }
-        }
-        end();
-    }
-
-    private void end() throws Exception {
-        for (Map.Entry<Integer, Matrix> entry : powerMatrixMap.entrySet()) {
-            Matrix powerMatrix = entry.getValue();
-            int y = powerMatrix.getY();
-            double min = 0;
-            for (int i = 0; i < y; i++) {
-                double nub = powerMatrix.getNumber(0, i);
-                if (nub < min) {
-                    min = nub;
-                }
-            }
-            //获取最小值完毕
-            for (int i = 0; i < y; i++) {
-                double nub = powerMatrix.getNumber(0, i);
-                powerMatrix.setNub(0, i, nub - min);
-            }
-        }
-    }
-
-    private void powerDeflection(Matrix matrix1, Matrix matrix2, boolean polymerization,
-                                 Matrix powerMatrix) throws Exception {
-        int y = matrix1.getY();
-        for (int i = 0; i < y; i++) {
-            double sub = Math.abs(matrix1.getNumber(0, i) - matrix2.getNumber(0, i))
-                    * studyPoint;
-            double power = powerMatrix.getNumber(0, i);//当前矩阵中的权值
-            if (polymerization) {//同类聚合 聚合是减
-                power = power - sub;
-            } else {//异类离散
-                power = power + sub;
-            }
-            powerMatrix.setNub(0, i, power);
-        }
-    }
-
-    private Matrix getFeatureAvg(List<Matrix> rightFeature, int size) throws Exception {//求特征均值
-        Matrix feature = new Matrix(1, size);
-        int nub = rightFeature.size();
-        for (int i = 0; i < nub; i++) {
-            Matrix matrix = rightFeature.get(i);
-            for (int j = 0; j < size; j++) {
-                double sigma = matrix.getNumber(0, j) + feature.getNumber(0, j);
-                feature.setNub(0, j, sigma);
-            }
-        }
-        MatrixOperation.mathDiv(feature, nub);
-        return feature;
-    }
-}

src/main/java/org/wlld/param/Food.java

@@ -1,7 +1,6 @@
 package org.wlld.param;
 
 import org.wlld.imageRecognition.modelEntity.DeepMappingBody;
-import org.wlld.imageRecognition.segmentation.KNerveManger;
 import org.wlld.imageRecognition.segmentation.RgbRegression;
 
 import java.util.ArrayList;
@@ -24,7 +23,6 @@ public class Food {
     private int regionSize = 5;//纹理区域大小
     private int step = 1;//特征取样步长
     private int speciesNub = 24;//种类数
-    private KNerveManger kNerveManger;
     private DeepMappingBody deepMappingBody;//特征映射
 
     public DeepMappingBody getDeepMappingBody() {
@@ -35,14 +33,6 @@ public class Food {
         this.deepMappingBody = deepMappingBody;
     }
 
-    public KNerveManger getkNerveManger() {
-        return kNerveManger;
-    }
-
-    public void setkNerveManger(KNerveManger kNerveManger) {
-        this.kNerveManger = kNerveManger;
-    }
-
     public int getSpeciesNub() {
         return speciesNub;
     }

src/test/java/coverTest/FoodTest.java

@@ -71,8 +71,6 @@ public class FoodTest {
         //菜品识别实体类
         food.setShrink(0);//缩紧像素
         food.setRegionSize(2);
-        KNerveManger kNerveManger = new KNerveManger(12, 24, 6000);
-        food.setkNerveManger(kNerveManger);
         food.setRowMark(0.05);//0.12
         food.setColumnMark(0.05);//0.25
         food.setRegressionNub(50000);
@@ -128,12 +126,9 @@ public class FoodTest {
         ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix(c);
         study(threeChannelMatrix1, templeConfig, specificationsList, cutFood, 2);
         study(threeChannelMatrix2, templeConfig, specificationsList, cutFood, 3);
-        ///
+        //
-        ThreeChannelMatrix threeChannelMatrix3 = picture.getThreeMatrix(g);
+        ThreeChannelMatrix threeChannelMatrix3 = picture.getThreeMatrix(e);
-        long time1 = System.currentTimeMillis();
         look(threeChannelMatrix3, templeConfig, specificationsList, cutFood);
-        long end = System.currentTimeMillis() - time1;
-        System.out.println("time:" + end);
     }
 
     private static void look(ThreeChannelMatrix threeChannelMatrix, TempleConfig templeConfig, List<Specifications> specifications,