speed模式，识别+定位功能完成（未测试）

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

@@ -12,6 +12,7 @@ import org.wlld.nerveEntity.SensoryNerve;
 import org.wlld.tools.ArithUtil;
 
 import java.util.ArrayList;
+import java.util.HashMap;
 import java.util.List;
 import java.util.Map;
 
@@ -83,7 +84,7 @@ public class Operation {//进行计算
     }
 
     //边框检测+识别分类
-    public void lookWithPosition(Matrix matrix, long eventId) throws Exception {
+    public Map<Integer, List<FrameBody>> lookWithPosition(Matrix matrix, long eventId) throws Exception {
         Frame frame = templeConfig.getFrame();
         if (templeConfig.isHavePosition() && frame != null && frame.isReady()) {
             List<FrameBody> frameBodies = convolution.getRegion(matrix, frame);
@@ -104,21 +105,53 @@ public class Operation {//进行计算
                     //进入神经网络判断
                     intoNerve(eventId, list, templeConfig.getSensoryNerves(), false, null);
                 }
-                toPositon(frameBodies, frame.getWidth(), frame.getHeight());
+                return toPositon(frameBodies, frame.getWidth(), frame.getHeight());
             } else if (templeConfig.getStudyPattern() == StudyPattern.Accuracy_Pattern) {
 
+                return null;
+            } else {
+                throw new Exception("wrong model");
             }
         } else {
             throw new Exception("position not study or frame is not ready");
         }
     }
 
-    private void toPositon(List<FrameBody> frameBodies, int width, int height) throws Exception {//把分类都拿出来
+    private Map<Integer, List<FrameBody>> toPositon(List<FrameBody> frameBodies, int width, int height) throws Exception {//把分类都拿出来
         for (FrameBody frameBody : frameBodies) {
             if (frameBody.getPoint() > templeConfig.getTh()) {//存在一个识别分类
                 getBox(frameBody, width, height);
             }
         }
+        return result(frameBodies);
+    }
+
+    private Map<Integer, List<FrameBody>> result(List<FrameBody> frameBodies) {
+        Map<Integer, List<FrameBody>> map = new HashMap<>();
+        for (FrameBody frameBody : frameBodies) {
+            if (frameBody.getPoint() > templeConfig.getTh()) {//存在一个识别分类
+                int id = frameBody.getId();
+                if (map.containsKey(id)) {
+                    List<FrameBody> frameBodies1 = map.get(id);
+                    boolean isHere = false;
+                    for (FrameBody frameBody1 : frameBodies1) {
+                        double iou = getIou(frameBody1, frameBody);
+                        if (iou > templeConfig.getIouTh()) {
+                            isHere = true;
+                            break;
+                        }
+                    }
+                    if (!isHere) {
+                        frameBodies1.add(frameBody);
+                    }
+                } else {
+                    List<FrameBody> frameBodyList = new ArrayList<>();
+                    frameBodyList.add(frameBody);
+                    map.put(id, frameBodyList);
+                }
+            }
+        }
+        return map;
     }
 
     //获得预测边框

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

@@ -34,6 +34,15 @@ public class TempleConfig {
     private ImageBack imageBack = new ImageBack();//边框图像回调
     private double th = 0.6;//标准阈值
     private boolean boxReady = false;//边框已经学习完毕
+    private double iouTh = 0.5;//IOU阈值
+
+    public double getIouTh() {
+        return iouTh;
+    }
+
+    public void setIouTh(double iouTh) {
+        this.iouTh = iouTh;
+    }
 
     public boolean isBoxReady() {
         return boxReady;