恢复

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

@@ -16,7 +16,6 @@ import org.wlld.nerveEntity.SensoryNerve;
 import org.wlld.tools.ArithUtil;
 import org.wlld.tools.IdCreator;
 
-import javax.swing.text.html.parser.Entity;
 import java.util.*;
 
 public class Operation {//进行计算
@@ -25,7 +24,7 @@ public class Operation {//进行计算
     private MatrixBack matrixBack = new MatrixBack();
     private ImageBack imageBack = new ImageBack();
     private OutBack outBack;
-    private double dif;
+    private int dif;
 
     public Operation(TempleConfig templeConfig) {
         this.templeConfig = templeConfig;
@@ -64,22 +63,13 @@ public class Operation {//进行计算
         List<RegionBody> regionBodies = watershed.rainfall();
         if (regionBodies.size() == 1) {
             RegionBody regionBody = regionBodies.get(0);
-            int integrate = templeConfig.getFood().getIntegrate();
-            int minX = regionBody.getMinX();
-            int minY = regionBody.getMinY();
-            int maxX = regionBody.getMaxX();
-            int maxY = regionBody.getMaxY();
+            int minX = regionBody.getMinX() + dif;
+            int minY = regionBody.getMinY() + dif;
+            int maxX = regionBody.getMaxX() - dif;
+            int maxY = regionBody.getMaxY() - dif;
             int xSize = maxX - minX;
             int ySize = maxY - minY;
-            for (int j = 1; j <= integrate; j++) {
-                double move = dif * j;
-                int moveX = (int) (xSize * move);
-                int moveY = (int) (ySize * move);
-                int x = minX + moveX;
-                int y = minY + moveY;
-                int xLength = xSize - moveX * 2;
-                int yLength = ySize - moveY * 2;
-                ThreeChannelMatrix threeChannelMatrix1 = convolution.getRegionMatrix(threeChannelMatrix, x, y, xLength, yLength);
+            ThreeChannelMatrix threeChannelMatrix1 = convolution.getRegionMatrix(threeChannelMatrix, minX, minY, xSize, ySize);
             //convolution.filtering(threeChannelMatrix1);//光照过滤
             int times = templeConfig.getFood().getTimes();
             for (int i = 0; i < times; i++) {
@@ -110,17 +100,17 @@ public class Operation {//进行计算
                         break;
                     case Classifier.KNN:
                         Matrix veck = MatrixOperation.listToRowVector(feature);
-                            knnStudy(tag, veck, templeConfig.getKnns()[j]);
+                        knnStudy(tag, veck);
                         break;
                 }
             }
-            }
         } else {
             throw new Exception("Parameter exception region size==" + regionBodies.size());
         }
     }
 
-    private void knnStudy(int tagging, Matrix vector, Knn knn) throws Exception {
+    private void knnStudy(int tagging, Matrix vector) throws Exception {
+        Knn knn = templeConfig.getKnn();
         knn.insertMatrix(vector, tagging);
     }
 
@@ -142,23 +132,13 @@ public class Operation {//进行计算
         List<RegionBody> regionList = watershed.rainfall();
         for (RegionBody regionBody : regionList) {
             MaxPoint maxPoint = new MaxPoint();
-            int integrate = templeConfig.getFood().getIntegrate();
-            int minX = regionBody.getMinX();
-            int minY = regionBody.getMinY();
-            int maxX = regionBody.getMaxX();
-            int maxY = regionBody.getMaxY();
+            int minX = regionBody.getMinX() + dif;
+            int minY = regionBody.getMinY() + dif;
+            int maxX = regionBody.getMaxX() - dif;
+            int maxY = regionBody.getMaxY() - dif;
             int xSize = maxX - minX;
             int ySize = maxY - minY;
-            Map<Integer, Integer> map = new HashMap<>();
-            for (int j = 1; j <= integrate; j++) {
-                double move = dif * j;
-                int moveX = (int) (xSize * move);
-                int moveY = (int) (ySize * move);
-                int x = minX + moveX;
-                int y = minY + moveY;
-                int xLength = xSize - moveX * 2;
-                int yLength = ySize - moveY * 2;
-                ThreeChannelMatrix threeChannelMatrix1 = convolution.getRegionMatrix(threeChannelMatrix, x, y, xLength, yLength);
+            ThreeChannelMatrix threeChannelMatrix1 = convolution.getRegionMatrix(threeChannelMatrix, minX, minY, xSize, ySize);
             //convolution.filtering(threeChannelMatrix1);//光照过滤
             List<Double> feature = convolution.getCenterColor(threeChannelMatrix1, templeConfig.getPoolSize(),
                     templeConfig.getFeatureNub());
@@ -186,26 +166,11 @@ public class Operation {//进行计算
                     break;
                 case Classifier.KNN:
                     Matrix myMatrix2 = MatrixOperation.listToRowVector(feature);
-                        Knn knn = templeConfig.getKnns()[j];
+                    Knn knn = templeConfig.getKnn();
                     id = knn.getType(myMatrix2);
                     break;
             }
-                //完成一个结果,统计所有结果
-                if (map.containsKey(id)) {
-                    map.put(id, map.get(id) + 1);
-                } else {
-                    map.put(id, 1);
-                }
-            }
-            int key = 0;
-            int max = 0;
-            for (Map.Entry<Integer, Integer> entry : map.entrySet()) {
-                int nub = entry.getValue();
-                if (nub > max) {
-                    key = entry.getKey();
-                }
-            }
-            regionBody.setType(key);
+            regionBody.setType(id);
             //System.out.println("类别" + id);
         }
         return regionList;

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

@@ -66,16 +66,11 @@ public class TempleConfig {
     private int featureNub = 4;//聚类特征数量
     private Food food = new Food();
     private Knn knn;//KNN分类器
-    private Knn[] knns;//集成knn分类
     private int knnNub = 7;//KNN投票人数
     private ThreeChannelMatrix backGround;//背景面板
     private double backGroundError;//背景误差偏移量，0-255
     private Cutting cutting = new Cutting();
 
-    public Knn[] getKnns() {
-        return knns;
-    }
-
     public Cutting getCutting() {
         return cutting;
     }
@@ -330,12 +325,7 @@ public class TempleConfig {
                         vectorK = new VectorK(featureNub * 3);
                         break;
                     case Classifier.KNN:
-                        int integrate = food.getIntegrate();//集成分类次数
-                        knns = new Knn[integrate];
-                        for (int i = 0; i < integrate; i++) {
-                            knns[i] = new Knn(knnNub);
-                        }
-                        //knn = new Knn(knnNub);
+                        knn = new Knn(knnNub);
                         break;
                 }
                 break;

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

@@ -7,26 +7,17 @@ package org.wlld.param;
  * @Description 菜品识别实体类
  */
 public class Food {
-    private double shrink = 0.03;//收缩参数
+    private int shrink = 60;//收缩参数
     private int times = 10;//聚类增强次数
-    private int integrate = 5;//聚类集成次数
 
-    public double getShrink() {
+    public int getShrink() {
         return shrink;
     }
 
-    public void setShrink(double shrink) {
+    public void setShrink(int shrink) {
         this.shrink = shrink;
     }
 
-    public int getIntegrate() {
-        return integrate;
-    }
-
-    public void setIntegrate(int integrate) {
-        this.integrate = integrate;
-    }
-
     public int getTimes() {
         return times;
     }

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 <= 1; i++) {
-            ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("D:\\share\\cai\\g/g2.jpg");
+        for (int i = 1; i <= 28; i++) {
+            ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix("D:\\share\\cai\\g/g" + i + ".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(50);//缩紧像素
+        food.setShrink(60);//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);