..

AI.py

@@ -37,7 +37,7 @@ def get_postProcess_para(parfile):
     
     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）
@@ -100,21 +100,27 @@ def AI_process(im0s,model,segmodel,names,label_arraylist,rainbows,objectPar={ 'h
         imH,imW = im0s[0].shape[0:2]
         mmH,mmW  = seg_pred.shape[0:2]
         fx=mmW/imW;fy=mmH/imH
-
+        det_coords=[]
         
         det_coords_original=[]
         for box in p_result[2]:  
-            if int(box[0]) != 1: continue
             b_0 = box[1:5]
             b_0.insert(0,box[0]);b_0.append(box[5] )
             det_coords_original.append( b_0 )  
+            if int(box[0]) != 1: continue  
+            b=box[1:5]
+            b[0],b[2] = b[0]*fx,b[2]*fx
+            b[1],b[3] = b[1]*fy,b[3]*fy
+            b=[int(x) for x in b]
+            det_coords.append( (b[0],b[1])); det_coords.append(  (b[2],b[3])  )
 
         postPar['ZoomFactor']={'x':mmW/imW  ,'y':mmH/imH}
         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_p = trafficPostProcessingV2(postPar) 
+        
+        list8, image,time_infos = trafficPostProcessingV2(postPar) 
+        
         
         Accident_results = np.array(list8,dtype=object)  
         acc_det=[]
@@ -126,11 +132,13 @@ def AI_process(im0s,model,segmodel,names,label_arraylist,rainbows,objectPar={ 'h
                 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")
                 
-        p_result[2].extend( acc_det  )
-    else:
-        time_infos_p=''
-    time_info = 'letterbox:%.1f, seg:%.1f , infer:%.1f,%s, seginfo:%s, postjudge:%s '%( (time01-time0)*1000, (time1-time01)*1000 ,(time2-time1)*1000,timeOut , segstr.strip(),time_infos_p.strip() )
-    p_result.append(seg_pred_mulcls)
+        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 )
+    if mode=='highWay3.0':
+        p_result.append(seg_pred_mulcls)
 
     return p_result,time_info