...

AI.py

     
     return parPost["conf_thres"],parPost["iou_thres"],parPost["classes"],parPost["rainbows"]  
 def AI_process(im0s,model,segmodel,names,label_arraylist,rainbows,objectPar={ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'slopeIndex':[5,6,7],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False }, font={ 'line_thickness':None, 'fontSize':None,'boxLine_thickness':None,'waterLineColor':(0,255,255),'waterLineWidth':3} ,segPar={'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'numpy':False, 'RGB_convert_first':True},mode='others',postPar=None):
-    print('########line40:',mode )
+
     #输入参数
     #   im0s---原始图像列表
     #   model---检测模型，segmodel---分割模型（如若没有用到，则为None）
     time2=time.time()
     datas = [[''], img, im0s, None,pred,seg_pred,10]        
     ObjectPar={ 'object_config':allowedList, 'slopeIndex':slopeIndex ,'segmodel':segFlag,'segRegionCnt':segRegionCnt }
+
     p_result,timeOut = post_process_(datas,conf_thres, iou_thres,names,label_arraylist,rainbows,10,ObjectPar=ObjectPar,font=font,padInfos=padInfos)
    
     if  mode=='highWay3.0':
         postPar['mask']=seg_pred;postPar['det']=deepcopy(det_coords_original)
         postPar['label_info']=label_info
         tlist = list(postPar.keys()); tlist.sort()
-        
-        list8, image,time_infos = trafficPostProcessingV2(postPar) 
-        
-        
-        Accident_results = np.array(list8,dtype=object)  
-        acc_det=[]
-        for bpoints in list8:
-            if bpoints[9]>conf_thres:
-                xyxy=bpoints[1:5];xyxy=[int(x) for x in xyxy]
-                cls=9;conf=bpoints[9];
-                box_acc = [cls,*xyxy,conf]
-                acc_det.append(box_acc)
-                p_result[1] = draw_painting_joint(xyxy,p_result[1],label_arraylist[int(cls)],score=conf,color=rainbows[int(cls)%20],font=font,socre_location="leftBottom")
-                
-        det_coords_original.extend(acc_det)   
-        p_result[2]=  deepcopy(det_coords_original)
+        if len(det_coords_original)> 0:
+           
+            list8, image,time_infos = trafficPostProcessingV2(postPar) 
+            
+            
+            Accident_results = np.array(list8,dtype=object)  
+            acc_det=[]
+            for bpoints in list8:
+                if bpoints[9]>conf_thres:
+                    xyxy=bpoints[1:5];xyxy=[int(x) for x in xyxy]
+                    cls=9;conf=bpoints[9];
+                    box_acc = [cls,*xyxy,conf]
+                    acc_det.append(box_acc)
+                    if cls  in allowedList:
+                        p_result[1] = draw_painting_joint(xyxy,p_result[1],label_arraylist[int(cls)],score=conf,color=rainbows[int(cls)%20],font=font,socre_location="leftBottom")
+                    
+            det_coords_original.extend(acc_det)   
+            p_result[2]=  deepcopy(det_coords_original)
         
         #print( '  time:',time_infos,'  results.shape:',Accident_results.shape, p_result[2])
     time_info = 'letterbox:%.1f, seg:%.1f , infer:%.1f,%s, seginfo:%s'%( (time01-time0)*1000, (time1-time01)*1000 ,(time2-time1)*1000,timeOut , segstr )

segutils/trafficUtils.py

         # print("line347", traffic_dict['det'])  # [[cls, x0, y0, x1, y1, score, 角度, 长宽比, 最小距离, [角度得分, 长宽比得分, 最小距离得分], 类别], ...]
         list23 = traffic_dict['det']
         for i in range(len(list23)):
-            list23[i][9] = max(list23[i][9])
+            if len(list23[i]) != 11:
+                list23[i].append(90)
+                list23[i].append(0.7)
+                list23[i].append(10)
+                list23[i].append(1)
+                list23[i].append(2)
+            else:
+                list23[i][9] = max(list23[i][9])
         # print("line351", list23)  # 目标对象, [[cls, x0, y0, x1, y1, score, 角度, 长宽比, 最小距离, max([角度得分, 长宽比得分, 最小距离得分]), 类别], ...]
     else:
         print("分割模型未检测到vehicle！")

utilsK/queRiver.py

         return image_array0,water
     max_ids = get_largest_contours(contours,river['segRegionCnt']);     
     for max_id in max_ids:    
-        cv2.fillPoly(water, [contours[max_id][:,0,:]], 1)     
+        cv2.fillPoly(water, [contours[max_id][:,0,:]], 1)   
         if river['segLineShow']:
             cv2.drawContours(image_array0,contours,max_id,river['color'],river['line_width'] )
     return     image_array0,water
     pad_image = cv2.copyMakeBorder(img_r, top, bottom, left, right, cv2.BORDER_CONSTANT,value=pad_value)
     return pad_image,(top, left,r)      
 
-def post_process_(datas,conf_thres, iou_thres,names,label_arraylist,rainbows,iframe,ObjectPar={ 'object_config':[0,1,2,3,4], 'slopeIndex':[5,6,7] ,'segmodel':True,'segRegionCnt':1 },font={ 'line_thickness':None, 'fontSize':None,'boxLine_thickness':None,'segLineShow':True,'waterLineColor':(0,255,255),'waterLineWidth':3},padInfos=None ):
+def post_process_(datas,conf_thres, iou_thres,names,label_arraylist,rainbows,iframe,ObjectPar={ 'object_config':[0,1,2,3,4], 'slopeIndex':[5,6,7] ,'segmodel':True,'segRegionCnt':1 },font={ 'line_thickness':None, 'fontSize':None,'boxLine_thickness':None,'waterLineColor':(0,255,255),'waterLineWidth':3},padInfos=None ):
     object_config,slopeIndex,segmodel,segRegionCnt=ObjectPar['object_config'],ObjectPar['slopeIndex'],ObjectPar['segmodel'],ObjectPar['segRegionCnt']
     ##输入dataset genereate 生成的数据，model预测的结果pred，nms参数
     ##主要操作NMS ---> 坐标转换 ---> 画图
     time1_3 = time.time()
     det_xywh=[];
     #im0_brg=cv2.cvtColor(im0,cv2.COLOR_RGB2BGR);
-    #if 'segLineShow' in font.keys():
-    if  segmodel:
+    if segmodel:
         if len(seg_pred)==2:
             im0,water = illBuildings(seg_pred,im0) 
         else:
             river={ 'color':font['waterLineColor'],'line_width':font['waterLineWidth'],'segRegionCnt':segRegionCnt,'segLineShow':font['segLineShow'] }
-            im0,water = drawWater(seg_pred,im0,river)     
-       
+            im0,water = drawWater(seg_pred,im0,river)      
     time2=time.time()
     #plt.imshow(im0);plt.show()
     if len(det)>0:
             det_c = det.clone(); det_c=det_c.cpu().numpy()  
             try:
                 area_factors = np.array([np.sum(water[int(x[1]):int(x[3]), int(x[0]):int(x[2])] )*1.0/(1.0*(x[2]-x[0])*(x[3]-x[1])+0.00001) for x in det_c]  )  
-            except Exception as e:
-                print('*****************************line143: error:',det_c, e)
+            except:
+                print('*****************************line143: error:',det_c)
             water_flag = np.array(area_factors>0.1)            
             det = det[water_flag|slope_flag]##如果是水上目标，则需要与水的iou超过0.1；如果是岸坡目标，则直接保留。
         #对检测匡绘图
         for *xyxy, conf, cls in reversed(det):
             xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
             cls_c = cls.cpu().numpy()
+           
+            
             conf_c = conf.cpu().numpy()
             tt=[ int(x.cpu()) for x in xyxy]
             line = [float(cls_c), *tt, float(conf_c)] # label format      
             det_xywh.append(line)
-
-            if int(cls_c) not in object_config: ###如果不是所需要的目标，则不显示;但依然输出到result。
-                continue
-
             label = f'{names[int(cls)]} {conf:.2f}'			    
-           
+            if int(cls_c) not in object_config: ###如果不是所需要的目标，则不显示
+                continue
             im0 = draw_painting_joint(xyxy,im0,label_arraylist[int(cls)],score=conf,color=rainbows[int(cls)%20],font=font)
     time3=time.time()
     strout='nms:%s drawWater:%s,copy:%s,toTensor:%s,detDraw:%s '%(get_ms(time0,time1),get_ms(time1,time2),get_ms(time1_1,time1_2),get_ms(time1_2,time1_3), get_ms(time2,time3)   )