覆盖模式增加可选择分类器

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

@@ -71,7 +71,7 @@ public class Operation {//进行计算
         int ySize = maxY - minY;
         ThreeChannelMatrix threeChannelMatrix1 = convolution.getRegionMatrix(threeChannelMatrix, minX, minY, xSize, ySize);
         List<Double> feature = convolution.getCenterColor(threeChannelMatrix1, templeConfig.getPoolSize(),
-                templeConfig.getSensoryNerves().size());
+                templeConfig.getFeatureNub());
         if (templeConfig.isShowLog()) {
             System.out.println(feature);
         }
@@ -80,7 +80,11 @@ public class Operation {//进行计算
             case Classifier.DNN:
                 Map<Integer, Double> map = new HashMap<>();
                 map.put(tag, 1.0);
-                intoDnnNetwork(1, feature, templeConfig.getSensoryNerves(), true, map, null);
+                if (templeConfig.getSensoryNerves().size() == templeConfig.getFeatureNub()) {
+                    intoDnnNetwork(1, feature, templeConfig.getSensoryNerves(), true, map, null);
+                } else {
+                    throw new Exception("nerves number is not equal featureNub");
+                }
                 break;
             case Classifier.LVQ:
                 Matrix vector = MatrixOperation.listToRowVector(feature);
@@ -119,7 +123,7 @@ public class Operation {//进行计算
             int ySize = maxY - minY;
             ThreeChannelMatrix threeChannelMatrix1 = convolution.getRegionMatrix(threeChannelMatrix, minX, minY, xSize, ySize);
             List<Double> feature = convolution.getCenterColor(threeChannelMatrix1, templeConfig.getPoolSize(),
-                    templeConfig.getSensoryNerves().size());
+                    templeConfig.getFeatureNub());
             if (templeConfig.isShowLog()) {
                 System.out.println(feature);
             }
@@ -131,8 +135,12 @@ public class Operation {//进行计算
                     id = getIdByLVQ(myMatrix);
                     break;
                 case Classifier.DNN:
-                    intoDnnNetwork(IdCreator.get().nextId(), feature, templeConfig.getSensoryNerves(), false, null, maxPoint);
-                    id = maxPoint.getId();
+                    if (templeConfig.getSensoryNerves().size() == templeConfig.getFeatureNub()) {
+                        intoDnnNetwork(IdCreator.get().nextId(), feature, templeConfig.getSensoryNerves(), false, null, maxPoint);
+                        id = maxPoint.getId();
+                    } else {
+                        throw new Exception("nerves number is not equal featureNub");
+                    }
                     break;
                 case Classifier.VAvg:
                     Matrix myMatrix1 = MatrixOperation.listToRowVector(feature);

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

@@ -63,6 +63,15 @@ public class TempleConfig {
     private int regionNub = 100;//一张图行或列分多少个区块
     private double hTh = 0.88;//灰度阈值
     private double maxRain = 340;//不降雨RGB阈值
+    private int featureNub = 4;//聚类特征数量
+
+    public int getFeatureNub() {
+        return featureNub;
+    }
+
+    public void setFeatureNub(int featureNub) {
+        this.featureNub = featureNub;
+    }
 
     public double getMaxRain() {
         return maxRain;

src/test/java/coverTest/FoodTest.java

@@ -32,7 +32,7 @@ public class FoodTest {
         templeConfig.setStudyPoint(0.01);
         templeConfig.sethTh(0.86);
         templeConfig.setRegionNub(200);
-        templeConfig.setMaxRain(340);
+        templeConfig.setMaxRain(360);
         templeConfig.setSoftMax(true);
         List<Specifications> specificationsList = new ArrayList<>();
         Specifications specifications = new Specifications();