添加多种类干食切图

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

@@ -136,7 +136,9 @@ public class Convolution extends Frequency {
             if (isFood) {//干食学习
                 food.setFoodType(insertFoodTypes(food.getFoodType(), tag));
                 meanMap = food.getFoodMeanMap();
-                foods.put(tag, xn * yn * food.getFoodFilterTh());
+                double size = xn * yn * food.getFoodFilterTh();
+                meanClustering.setRegionSize(size);
+                foods.put(tag, size);
             } else {//非干食学习
                 meanMap = food.getNotFoodMeanMap();
             }

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

@@ -18,16 +18,13 @@ import java.util.Map;
  */
 public class CutFood {
     private TempleConfig templeConfig;
-    private Map<Integer, GMClustering> meanMap = new HashMap<>();//干食混高模型
+    private Map<Integer, GMClustering> meanMap;//干食混高模型
     private Matrix regionMap;
     private double foodFilterTh;
 
-    public CutFood(TempleConfig templeConfig) {
+    public CutFood(TempleConfig templeConfig, Map<Integer, GMClustering> meanMap) {
         this.templeConfig = templeConfig;
         foodFilterTh = templeConfig.getFood().getFoodFilterTh();
-    }
-
-    public void setMeanMap(Map<Integer, GMClustering> meanMap) {
         this.meanMap = meanMap;
     }
 

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

@@ -167,7 +167,7 @@ public class Operation {//进行计算
                     }
                 }
             }
-            CutFood cutFood = food.getCutFood();
+            CutFood cutFood = new CutFood(templeConfig, food.getFoodMeanMap());
             if (isFood) {//一次判定就属于干食，则无需进行二次判定
                 regionBody.setTypeNub(cutFood.getTypeNub(threeChannelMatrix1, null));
             } else {//一次判定属于非干食，则进行二次判定

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

@@ -1,20 +1,16 @@
 package org.wlld.imageRecognition;
 
-import com.sun.tools.javac.comp.Enter;
 import org.wlld.MatrixTools.Matrix;
 import org.wlld.MatrixTools.MatrixOperation;
 import org.wlld.config.Classifier;
-import org.wlld.config.Kernel;
 import org.wlld.config.RZ;
 import org.wlld.config.StudyPattern;
 import org.wlld.function.ReLu;
-import org.wlld.function.Sigmod;
 import org.wlld.function.Tanh;
 import org.wlld.i.ActiveFunction;
 import org.wlld.imageRecognition.border.*;
 import org.wlld.imageRecognition.modelEntity.*;
 import org.wlld.imageRecognition.segmentation.DimensionMappingStudy;
-import org.wlld.imageRecognition.segmentation.RgbRegression;
 import org.wlld.nerveCenter.NerveManager;
 import org.wlld.nerveCenter.Normalization;
 import org.wlld.nerveEntity.BodyList;
@@ -705,6 +701,7 @@ public class TempleConfig {
                             GMClustering gmClustering = new GMClustering(featureNub);
                             gmClustering.insertParameter(matrix);
                             if (set.contains(key)) {//干食
+                                gmClustering.setRegionSize(foods.get(key));
                                 food.getFoodMeanMap().put(key, gmClustering);
                             } else {
                                 food.getNotFoodMeanMap().put(key, gmClustering);
@@ -714,9 +711,6 @@ public class TempleConfig {
                         DimensionMappingStudy dimensionAll = new DimensionMappingStudy(this);
                         dimensionAll.start();//生成映射层，并将已经保存的knn特征进行映射
                         food.setDimensionMappingStudy(dimensionAll);
-                        CutFood cutFood = new CutFood(this);
-                        cutFood.setMeanMap(food.getFoodMeanMap());
-                        food.setCutFood(cutFood);
                     }
                     break;
             }

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

@@ -29,15 +29,6 @@ public class Food {
     private Map<Integer, GMClustering> foodMeanMap = new HashMap<>();//干食类别混高模型 需激活注入
     private Map<Integer, GMClustering> notFoodMeanMap = new HashMap<>();//干食类别混高模型 需激活注入
     private DimensionMappingStudy dimensionMappingStudy;//需激活注入
-    private CutFood cutFood;//需激活时注入
-
-    public CutFood getCutFood() {
-        return cutFood;
-    }
-
-    public void setCutFood(CutFood cutFood) {
-        this.cutFood = cutFood;
-    }
 
     public DimensionMappingStudy getDimensionMappingStudy() {
         return dimensionMappingStudy;

src/test/java/coverTest/FoodTest.java

@@ -59,35 +59,6 @@ public class FoodTest {
         operation.setTray(threeChannelMatrix);
     }
 
-    public static void setting2() throws Exception {//前置设定第二步，设定餐盘
-        Picture picture = new Picture();
-        Operation operation = getTemple();
-        CutFood cutFood = new CutFood(operation.getTempleConfig());
-        ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix("/Users/lidapeng/Desktop/myDocument/pan1.jpg");
-        cutFood.study(1, threeChannelMatrix);
-    }
-
-    public static void study1() throws Exception {//进行学习
-        List<Specifications> specificationsList = new ArrayList<>();
-        Specifications specifications = new Specifications();
-        specifications.setMinWidth(60);//150
-        specifications.setMinHeight(60);//150
-        specifications.setMaxWidth(600);
-        specifications.setMaxHeight(600);
-        specificationsList.add(specifications);
-        Picture picture = new Picture();
-        String a = "/Users/lidapeng/Desktop/test/testOne/a.jpg";
-        Operation operation = getTemple();
-        ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix(a);
-        //进行第一步学习目的是将特征生成混高模型
-        operation.colorStudy(threeChannelMatrix1, 2, specificationsList, "url", true);
-
-
-        CutFood cutFood = new CutFood(operation.getTempleConfig());
-        TempleConfig templeConfig = operation.getTempleConfig();
-        study(threeChannelMatrix1, templeConfig, specificationsList, cutFood, 2);
-
-    }
 
     public static void test() throws Exception {
         Picture picture = new Picture();
@@ -100,41 +71,16 @@ public class FoodTest {
         specifications.setMaxHeight(600);
         specificationsList.add(specifications);
         TempleConfig templeConfig = operation.getTempleConfig();
-        CutFood cutFood = new CutFood(templeConfig);
-        //KNerveManger kNerveManger = templeConfig.getFood().getkNerveManger();
         ThreeChannelMatrix threeChannelMatrixB = picture.getThreeMatrix("/Users/lidapeng/Desktop/myDocument/d.jpg");
         //背景也是盘子
-        ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix("/Users/lidapeng/Desktop/myDocument/pan1.jpg");
+        ThreeChannelMatrix threeChannelMatrix = picture.getThreeMatrix("/Users/lidapeng/Desktop/myDocument/pan.jpeg");
         //设定背景回归
         operation.setTray(threeChannelMatrixB);
-        //学习背景
+        String a = "/Users/lidapeng/Desktop/myDocument/man.jpeg";
-        cutFood.study(1, threeChannelMatrix);
-        //训练数据，单张馒头
-        String a = "/Users/lidapeng/Desktop/test/testOne/a.jpg";
-        //训练数据，一个鸡蛋
-        String c = "/Users/lidapeng/Desktop/test/testOne/c.jpg";
-        //测试数据，两个鸡蛋
-        String d = "/Users/lidapeng/Desktop/test/testOne/d.jpg";
-        //测试数据，两个馒头
-        String b = "/Users/lidapeng/Desktop/test/testOne/b.jpg";
-        String f = "/Users/lidapeng/Desktop/test/testOne/f.jpeg";
-
-        //测试数据，一个鸡蛋和一个馒头
-        String e = "/Users/lidapeng/Desktop/test/testOne/e.jpg";
-        //测试数据，两个鸡蛋 一个馒头
-        String g = "/Users/lidapeng/Desktop/test/testOne/f.jpg";
-        //测试数据，两个鸡蛋两个馒头
-        String h = "/Users/lidapeng/Desktop/test/testOne/g.jpg";
-
-        //训练馒头
         ThreeChannelMatrix threeChannelMatrix1 = picture.getThreeMatrix(a);
-        //训练鸡蛋
+        Watershed watershed = new Watershed(threeChannelMatrix1, specificationsList, templeConfig);
-        ThreeChannelMatrix threeChannelMatrix2 = picture.getThreeMatrix(c);
+        List<RegionBody> regionList = watershed.rainfall();
-        study(threeChannelMatrix1, templeConfig, specificationsList, cutFood, 2);
+        System.out.println("==");
-        study(threeChannelMatrix2, templeConfig, specificationsList, cutFood, 3);
-        //
-        ThreeChannelMatrix threeChannelMatrix3 = picture.getThreeMatrix(g);
-        look(threeChannelMatrix3, templeConfig, specificationsList, cutFood);
     }
 
     private static void look(ThreeChannelMatrix threeChannelMatrix, TempleConfig templeConfig, List<Specifications> specifications,
@@ -155,34 +101,6 @@ public class FoodTest {
         }
     }
 
-    private static void study(ThreeChannelMatrix threeChannelMatrix, TempleConfig templeConfig, List<Specifications> specifications,
-                              CutFood cutFood, int type) throws Exception {
-        Convolution convolution = new Convolution();
-        Watershed watershed = new Watershed(threeChannelMatrix, specifications, templeConfig);
-        List<RegionBody> regionList = watershed.rainfall();
-        if (regionList.size() == 1) {
-            RegionBody regionBody = regionList.get(0);
-            int minX = regionBody.getMinX();
-            int minY = regionBody.getMinY();
-            int maxX = regionBody.getMaxX();
-            int maxY = regionBody.getMaxY();
-            int xSize = maxX - minX;
-            int ySize = maxY - minY;
-            System.out.println("正常：minX==" + minX + ",minY==" + minY + ",maxX==" + maxX + ",maxY==" + maxY);
-            ThreeChannelMatrix threeChannelMatrix1 = convolution.getRegionMatrix(threeChannelMatrix, minX, minY, xSize, ySize);
-            cutFood.study(type, threeChannelMatrix1);
-        } else {
-            for (RegionBody regionBody : regionList) {
-                int minX = regionBody.getMinX();
-                int minY = regionBody.getMinY();
-                int maxX = regionBody.getMaxX();
-                int maxY = regionBody.getMaxY();
-                System.out.println("异常：minX==" + minX + ",minY==" + minY + ",maxX==" + maxX + ",maxY==" + maxY);
-            }
-            throw new Exception("Parameter exception region size:" + regionList.size());
-        }
-
-    }
 
     public static void study() throws Exception {
         TempleConfig templeConfig = new TempleConfig();

src/test/java/coverTest/ForestTest.java

@@ -83,7 +83,6 @@ public class ForestTest {
         //聚类
         templeConfig.setFeatureNub(3);//聚类特征数量
         //菜品识别实体类
-        food.setShrink(5);//缩紧像素
         food.setRowMark(0.15);//0.12
         food.setColumnMark(0.15);//0.25
         food.setRegressionNub(20000);