!42 清除冗余

Merge pull request !42 from 逐光/test

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

@@ -132,6 +132,49 @@ public class Convolution extends Frequency {
 
     }
 
+    private double[] compareDis(double[] rgbTest, List<RGBNorm> rgbNorms) {
+        double[] feature = null;
+        double minDis = -1;
+        for (int i = 0; i < 3; i++) {
+            double[] rgb = rgbNorms.get(i).getRgb();
+            double sigma = 0;
+            for (int j = 0; j < 3; j++) {
+                sigma = sigma + Math.pow(rgbTest[j] - rgb[j], 2);
+            }
+            if (sigma < minDis || minDis == -1) {
+                minDis = sigma;
+                feature = rgb;
+            }
+        }
+        return feature;
+    }
+
+    private void dispersed(Matrix matrixR, Matrix matrixG, Matrix matrixB, List<RGBNorm> rgbNorms) throws Exception {//图像离散化
+        ThreeChannelMatrix threeChannelMatrix = new ThreeChannelMatrix();
+        int x = matrixR.getX();
+        int y = matrixR.getY();
+        Matrix matrixRD = new Matrix(x, y);
+        Matrix matrixGD = new Matrix(x, y);
+        Matrix matrixBD = new Matrix(x, y);
+        threeChannelMatrix.setMatrixR(matrixRD);
+        threeChannelMatrix.setMatrixG(matrixGD);
+        threeChannelMatrix.setMatrixB(matrixBD);
+        for (int i = 0; i < x; i++) {
+            for (int j = 0; j < y; j++) {
+                double r = matrixR.getNumber(i, j);
+                double g = matrixG.getNumber(i, j);
+                double b = matrixB.getNumber(i, j);
+                double[] rgb = new double[]{r, g, b};
+                double[] rgbNow = compareDis(rgb, rgbNorms);
+                matrixRD.setNub(i, j, rgbNow[0]);
+                matrixGD.setNub(i, j, rgbNow[1]);
+                matrixBD.setNub(i, j, rgbNow[2]);
+            }
+        }
+        //输入结束进行卷积
+        //System.out.println(matrixBD.getString());
+    }
+
     public List<Double> getCenterColor(ThreeChannelMatrix threeChannelMatrix, int poolSize, int sqNub) throws Exception {
         Matrix matrixR = threeChannelMatrix.getMatrixR();
         Matrix matrixG = threeChannelMatrix.getMatrixG();
@@ -159,6 +202,8 @@ public class Convolution extends Frequency {
                 features.add(rgb[j]);
             }
         }
+        //测试卷积
+        //dispersed(matrixR, matrixG, matrixB, rgbNorms);
         //System.out.println("feature==" + feature);
         return features;
     }
@@ -252,6 +297,33 @@ public class Convolution extends Frequency {
         return frameBodies;
     }
 
+    private void convolutionByThree(ThreeChannelMatrix threeChannelMatrix, Matrix kernel) throws Exception {
+        Matrix matrixR = threeChannelMatrix.getMatrixR();
+        Matrix matrixG = threeChannelMatrix.getMatrixG();
+        Matrix matrixB = threeChannelMatrix.getMatrixB();
+        int x = matrixR.getX() - 2;//求导后矩阵的行数
+        int y = matrixR.getY() - 2;//求导后矩阵的列数
+        Matrix myMatrixR = new Matrix(x, y);
+        Matrix myMatrixG = new Matrix(x, y);
+        Matrix myMatrixB = new Matrix(x, y);
+        for (int i = 0; i < x; i++) {//遍历行
+            for (int j = 0; j < y; j++) {//遍历每行的列
+                double rm = MatrixOperation.convolution(matrixR, kernel, i, j, false);
+                double gm = MatrixOperation.convolution(matrixG, kernel, i, j, false);
+                double bm = MatrixOperation.convolution(matrixB, kernel, i, j, false);
+                myMatrixR.setNub(i, j, rm);
+                myMatrixG.setNub(i, j, gm);
+                myMatrixB.setNub(i, j, bm);
+            }
+        }
+        myMatrixR = late(myMatrixR, 2);
+        myMatrixG = late(myMatrixG, 2);
+        myMatrixB = late(myMatrixB, 2);
+        threeChannelMatrix.setMatrixR(myMatrixR);
+        threeChannelMatrix.setMatrixG(myMatrixG);
+        threeChannelMatrix.setMatrixB(myMatrixB);
+    }
+
     private Matrix convolution(Matrix matrix, Matrix kernel, boolean isFirst
             , Border border, boolean isOnce) throws Exception {
         int x = matrix.getX() - 2;//求导后矩阵的行数

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

@@ -90,15 +90,6 @@ public class MeanClustering {
                     break;
                 }
             }
-//            boolean isNext;
-//            do {
-//                averageMatrix();
-//                isNext = isNext();
-//                if (isNext) {
-//                    clear();
-//                }
-//            }
-//            while (isNext);
 
         } else {
             throw new Exception("matrixList number less than 2");

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

@@ -105,7 +105,7 @@ public class Operation {//进行计算
                 }
             }
         } else {
-            throw new Exception("Parameter exception");
+            throw new Exception("Parameter exception region size==" + regionBodies.size());
         }
     }
 

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

@@ -2,8 +2,6 @@ package org.wlld.imageRecognition;
 
 import org.wlld.tools.ArithUtil;
 
-import java.util.Arrays;
-
 public class RGBNorm {
     private double[] rgbAll = new double[3];
     private double norm;

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

@@ -68,105 +68,6 @@ public class Knn {//KNN分类器
         }
     }
 
-    public int getType2(Matrix vector) throws Exception {
-        Map<Integer, Matrix> map = new HashMap<>();
-        double[] sub = new double[vector.getY()];//记录最小差
-        for (int t = 0; t < sub.length; t++) {
-            sub[t] = -1;
-        }
-        int[] types = new int[vector.getY()];//记录每个位置的最小类别
-        for (Map.Entry<Integer, List<Matrix>> entry : featureMap.entrySet()) {
-            int type = entry.getKey();
-            List<Matrix> matrices = entry.getValue();
-            double minDist = -1;
-            Matrix minMatrix = null;
-            for (Matrix matrix : matrices) {
-                double dist = MatrixOperation.getEDist(matrix, vector);
-                if (minDist > dist || minDist == -1) {
-                    minDist = dist;
-                    minMatrix = matrix;
-                }
-            }
-            map.put(type, minMatrix);
-        }
-        //从所有最小的里面选取三名最小的
-        int[] ty = getTypes(vector);
-        System.out.println(Arrays.toString(ty));
-        //从所有最小的里面选取
-        for (Map.Entry<Integer, Matrix> entry : map.entrySet()) {
-            int type = entry.getKey();
-            if (isLive(ty, type)) {
-                Matrix matrix = entry.getValue();
-                setMin(matrix, types, sub, vector, type);
-            }
-        }
-        System.out.println("最小位置：" + Arrays.toString(types));
-        Map<Integer, Integer> typeMap = new HashMap<>();
-        for (int i = 0; i < types.length; i++) {
-            int type = types[i];
-            if (typeMap.containsKey(type)) {
-                typeMap.put(type, typeMap.get(type) + 1);
-            } else {
-                typeMap.put(type, 1);
-            }
-        }
-        int type = 0;
-        int max = 0;
-        for (Map.Entry<Integer, Integer> entry : typeMap.entrySet()) {
-            int key = entry.getKey();
-            int nub = entry.getValue();
-            if (nub > max) {
-                max = nub;
-                type = key;
-            }
-        }
-        return type;
-    }
-
-    private boolean isLive(int[] types, int type) {
-        boolean isLive = false;
-        for (int ty : types) {
-            if (ty == type) {
-                isLive = true;
-                break;
-            }
-        }
-        return isLive;
-    }
-
-    private void setMin(Matrix matrix, int[] types, double[] sub, Matrix vector, int type) throws Exception {
-        int y = vector.getY();
-        for (int j = 0; j < y; j++) {
-            double nub = vector.getNumber(0, j);
-            double myNub = matrix.getNumber(0, j);
-            double subs = sub[j];
-            double cha = Math.abs(nub - myNub);
-            if (subs > cha || subs == -1) {
-                sub[j] = cha;
-                types[j] = type;
-            }
-        }
-    }
-
-    public int[] getTypes(Matrix vector) throws Exception {
-        double[] dists = new double[nub];
-        // System.out.println("测试：" + vector.getString());
-        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.errorNub(vector, matrix, 0);
-                double dist = MatrixOperation.getEDist(matrix, vector);
-                compare(dists, types, dist, type);
-            }
-        }
-        return types;
-    }
-
     public int getType(Matrix vector) throws Exception {//识别分类
         int ty = 0;
         double[] dists = new double[nub];

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

@@ -59,7 +59,7 @@ public abstract class Frequency {//统计频数
         return var;
     }
 
-    public double varianceByAve(double[] m, double ave) {//计算方差 计算方差，依赖平均值
+    public double varianceByAve(double[] m, double ave) {// 计算方差，依赖平均值
         double allNub = 0;
         for (int i = 0; i < m.length; i++) {
             allNub = allNub + Math.pow(m[i] - ave, 2);

src/test/java/coverTest/FoodTest.java

@@ -51,8 +51,8 @@ public class FoodTest {
         specifications.setMaxHeight(750);
         specificationsList.add(specifications);
         Operation operation = new Operation(templeConfig);
-        for (int i = 1; i <= 28; i++) {
-            ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("D:\\share\\cai\\g/g" + i + ".jpg");
+        for (int i = 1; i <= 1; i++) {
+            ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("D:\\share\\cai\\g/g2.jpg");
             List<RegionBody> regionBody = operation.colorLook(threeChannelMatrix1, specificationsList);
             for (int j = 0; j < regionBody.size(); j++) {
                 RegionBody regionBody1 = regionBody.get(j);
@@ -80,7 +80,7 @@ public class FoodTest {
         //聚类
         templeConfig.setFeatureNub(3);//聚类特征数量
         //菜品识别实体类
-        food.setShrink(60);//缩紧像素
+        food.setShrink(50);//缩紧像素
         food.setTimes(2);//聚类数据增强
         templeConfig.setClassifier(Classifier.KNN);
         templeConfig.init(StudyPattern.Cover_Pattern, true, 400, 400, 3);
@@ -124,12 +124,7 @@ public class FoodTest {
             operation.colorStudy(threeChannelMatrix10, 10, specificationsList);
             System.out.println("=======================================" + i);
         }
-        ModelParameter modelParameter = templeConfig.getModel();
-        String model = JSON.toJSONString(modelParameter);
-        System.out.println(model);
-        ModelParameter modelParameter1 = JSONObject.parseObject(model, ModelParameter.class);
-        //templeConfig.finishStudy();
-        test2(getTemple(modelParameter1));
+        test2(templeConfig);
     }
 
     public static void study() throws Exception {