update cityMangement3 weights

AI.py

     elif   postProcess['name']=='channel2': 
         ocrInfo='no ocr'
         if len(_detRets0_obj)>0:
-            print('##line246:',detRets1[0][0] )
             res_real = detRets1[0][0]
             res_real="".join(  list(filter(lambda x:(ord(x) >19968 and ord(x)<63865 ) or (ord(x) >47 and ord(x)<58 ),res_real)))
             
     
         
 if __name__=="__main__":
-    main()
+    main()

ocr.py

             logger = trt.Logger(trt.Logger.ERROR)
             with open(weights, "rb") as f, trt.Runtime(logger) as runtime:
                 self.model=runtime.deserialize_cuda_engine(f.read())# 输入trt本地文件，返回ICudaEngine对象
-            #self.context = self.model.create_execution_context()     
+            self.context = self.model.create_execution_context()     
                 
         elif self.infer_type=='pth':
             if par['mode']=='ch':
             preds = self.model(image)
         else:
             preds,trtstr=OcrTrtForward(self.model,[image],False)
+            #preds,trtstr=OcrTrtForward(self.model,[image], self.context  )
             
         t2 = time.time()
         preds_size = torch.IntTensor([preds.size(0)]*1)
         print(res_real,os.path.basename(imgUrl),timeInfos )
     
         
-        
+        

readme.md

   2.调整crackMeasure模型中增加两个参数。
   3.增加channel2业务，采用检测模型识别"国旗","浮标","船名","船只"，并对"船名"调用ocr2。
     除返回格式和过去一样，"船名"对应目标的list末尾加上了ocr结果(原始是[x0,y0,x1,y1,score,cls]--->[x0,y0,x1,y1,score,cls,ocr)
+2024.1.26
+  1.增加“riverT”业务，和“river2”所有参数都相同，出去yolov5.pt是定制的。

weights/conf/riverT/labelnames.json

+{
+	"labelnames":[   "漂浮物","垃圾","排口","非法建筑","非法种植","水生植物","游泳人员","钓鱼人员","船只","蓝藻"]  ,
+	"labelIndexs":[  "SL04","SL05","SL06","SL07","SL08","SL09","SL10","SL11","SL12","SL13"   ],
+	"labelOrders":[0,1,2,3,4,5,6,7,8,9]
+}

weights/conf/riverT/para.json

+{
+
+
+	"post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.3,"ovlap_thres_crossCategory":0.65,"classes":5,"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,255],[255,255,0],[255,129,0],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255],  [0,127,0],[0,0,127],[0,255,255]]    }
+
+
+}