add cityMangement2 model

demo.py

 sys.path.extend(['..','../AIlib2' ])
 
 from AI import AI_process,AI_process_forest,get_postProcess_para,get_postProcess_para_dic,ocr_process,AI_det_track,AI_det_track_batch
+from stdc import stdcModel
+
 import cv2,os,time
 from segutils.segmodel import SegModel
 
     if frame[2]:
         if len(p_result)==5:
             image_mask = p_result[4]
-            cv2.imwrite(os.path.join('images/results/',bname+'_mask.png' ) , (image_mask).astype(np.uint8))
+            if isinstance(image_mask,np.ndarray) and image_mask.shape[0]>0:
+
+                cv2.imwrite(os.path.join('images/results/',bname+'_mask.png' ) , (image_mask).astype(np.uint8))
     
     boxes=p_result[2]
     with open(  os.path.join('images/results/',bname+'.txt' ),'w'  ) as fp:
             'testOutPath':'images/results/',###输出测试图像位置
         }
 
+    if opt['business'] == 'cityMangement2':
+        from DMPR import DMPRModel
+        from DMPRUtils.jointUtil import  dmpr_yolo
+
+        par={
+            'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
+            'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%(opt['business']), ###检测类别对照表
+            'max_workers':1, ###并行线程数
+            'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
+            #'Detweights':"/mnt/thsw2/DSP2/weights/cityMangement2/weights/urbanManagement/yolo/best.pt",
+            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9]  ],###控制哪些检测类别显示、输出
+            'seg_nclass':4,###分割模型类别数目，默认2类
+            'segRegionCnt':2,###分割模型结果需要保留的等值线数目
+
+            'segPar':{ 'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.3, 'dmprimg_size':640,
+                      'mixFunction':{'function':dmpr_yolo,
+                        'pars':{'carCls':0 ,'illCls':3}
+                        }
+                    },
+                    
+            #'Segweights' : '/mnt/thsw2/DSP2/weights/cityMangement2/weights/urbanManagement/DMPR/dp_detector_499.engine',###分割模型权重位置
+            'Segweights':"../weights/%s/AIlib2/%s/dmpr_%s.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
+            'postFile': '../AIlib2/weights/conf/%s/para.json'%(opt['business'] ),###后处理参数文件
+            'txtFontSize':20,###文本字符的大小
+            'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'segLineShow':True,'waterLineWidth':2},###显示框、线设置
+            #'testImgPath':'/mnt/thsw2/DSP2/demoImages/illParking',###测试图像的位置
+            #'testImgPath':'/mnt/thsw2/DSP2/weights/cityMangement2/images/input',
+            'testImgPath':'/mnt/thsw2/DSP2/videos/hightWay2/',
+            'testOutPath':'images/results/',###输出测试图像位置
+        }
+
         
+    
     if  par['Segweights']:    
         par['trtFlag_seg']=True if par['Segweights'].endswith('.engine') else False
     else:
         par['trtFlag_seg']=False
      
     par['trtFlag_det']=True if par['Detweights'].endswith('.engine') else False
-            
-        
-    
+
     mode = par['mode'] if 'mode' in par.keys() else 'others'
     postPar = par['postPar'] if 'postPar' in par.keys() else None
     
         if half: model.half()
         
 
-    seg_nclass = par['seg_nclass']
+    par['segPar']['seg_nclass'] = par['seg_nclass']
     segPar=par['segPar']
     if par['Segweights']:
-        if trtFlag_seg:
-            Segweights = par['Segweights']
-            logger = trt.Logger(trt.Logger.ERROR)
-            with open(Segweights, "rb") as f, trt.Runtime(logger) as runtime:
-                segmodel=runtime.deserialize_cuda_engine(f.read())# 输入trt本地文件，返回ICudaEngine对象
-            print('############locad seg model trt success: ',Segweights)
+        if opt['business'] == 'cityMangement2':
+            segmodel = DMPRModel(weights=par['Segweights'], par = par['segPar'])
         else:
-            Segweights = par['Segweights']
-            print('####line293:',seg_nclass,  Segweights)
-            segmodel = SegModel(nclass=seg_nclass,weights=Segweights,device=device)
-            print('############locad seg model pth success:',Segweights,segmodel,seg_nclass )
+            segmodel = stdcModel(weights=par['Segweights'], par = par['segPar'])
     else:
         segmodel= None
         print('############None seg model is loaded###########:' )
     t1=time.time()     
     if max_workers==1:
         for i in range(len(imgpaths)):   
+            print('-'*20,imgpaths[i],'-'*20)
             t5=time.time()        
             process_v1(frames[i])
             t6=time.time()       
-            print('#######%s, ms:%.1f , accumetate time:%.1f, avage:%1.f '%(os.path.basename(imgpaths[i]), (t6-t5)*1000.0,(t6-t1)*1000.0, (t6-t1)*1000.0/(i+1)))
+            #print('#######%s, ms:%.1f , accumetate time:%.1f, avage:%1.f '%(os.path.basename(imgpaths[i]), (t6-t5)*1000.0,(t6-t1)*1000.0, (t6-t1)*1000.0/(i+1)))
     else:
         with ThreadPoolExecutor(max_workers=max_workers) as t:
             for result in t.map(process_v1, frames):
         
 
     
-    segRegionCnt=par['segRegionCnt']
+    #segRegionCnt=par['segRegionCnt']
     trtFlag_seg = par['trtFlag_seg'];segPar=par['segPar']
     ##使用森林，道路模型,business 控制['forest','road']
     ##预先设置的参数
 if __name__=="__main__":
 
     #jkm_demo()
-    businessAll=['river2','AnglerSwimmer',  'countryRoad','forest2',  'pedestrian' , 'smogfire' , 'vehicle','ship2',"highWay2","channelEmergency","cityMangement","drowning","noParking","illParking"]
+    businessAll=['river2','AnglerSwimmer',  'countryRoad','forest2',  'pedestrian' , 'smogfire' , 'vehicle','ship2',"highWay2","channelEmergency","cityMangement","drowning","noParking","illParking",'cityMangement2']
 
-    businessAll = ['noParking']
+    businessAll = ['highWay2']
     
     # forest 、 ocr2  、ocr_en 、 river 、 road 、 ship ,目前都没有在用
     
     for busi in businessAll:
         print('-'*40,'beg to test ',busi,'-'*40)
         opt={'gpu':'2080Ti','business':busi}
-        if opt['business'] in ['highWay2','river2','drowning','noParking','river',"illParking"]:
+        if opt['business'] in ['highWay2','river2','drowning','noParking','river',"illParking","cityMangement2"]:
             detSeg_demo(opt)
         elif  opt['business'] in ['ship2']:  
             OBB_demo(opt)    

demo3.0.py

 from AI import AI_process,AI_process_forest,get_postProcess_para,get_postProcess_para_dic,ocr_process,AI_det_track,AI_det_track_batch
 import cv2,os,time
 from segutils.segmodel import SegModel
-from segutils.segmodel import SegModel
+
+from stdc import stdcModel
 from segutils.trafficUtils import tracfficAccidentMixFunction
 from models.experimental import attempt_load
 from utils.torch_utils import select_device
             'segRegionCnt':2,###分割模型结果需要保留的等值线数目
             'segPar':{'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'predResize':True,'numpy':False, 'RGB_convert_first':True,###分割模型预处理参数
                       'mixFunction':{'function':mixNoParking_road_postprocess,
-                        'pars':
-                          #{ 'roundness': 0.3, 'cls': 9,  'laneArea': 10, 'laneAngleCha': 5 ,'RoadArea': 16000, }
-                            { 'roundness': 0.3, 'cls': 9,  'laneArea': 10, 'laneAngleCha': 5 ,'RoadArea': 16000}
-           
+                        'pars': { 'roundness': 0.3, 'cls': 9,  'laneArea': 10, 'laneAngleCha': 5 ,'RoadArea': 16000}
                         }    
                     },
                     
             'testOutPath':'images/results/',###输出测试图像位置
         }
         par['segPar']['mixFunction']['pars']['modelSize'] = par['segPar']['modelSize']
+    
+    if opt['business'] == 'cityMangement2':
+        from DMPR import DMPRModel
+        from DMPRUtils.jointUtil import  dmpr_yolo
+
+        par={
+            'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
+            'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%(opt['business']), ###检测类别对照表
+            'max_workers':1, ###并行线程数
+            'half':True,
+            'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
+            #'Detweights':"/mnt/thsw2/DSP2/weights/cityMangement2/weights/urbanManagement/yolo/best.pt",
+            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9]  ],###控制哪些检测类别显示、输出
+            'seg_nclass':4,###分割模型类别数目，默认2类
+            'segRegionCnt':2,###分割模型结果需要保留的等值线数目
+
+            'segPar':{ 'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.3, 'dmprimg_size':640,
+                      'mixFunction':{'function':dmpr_yolo,
+                        'pars':{'carCls':0 ,'illCls':3}
+                        }
+                    },
+            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},       
+            #'Segweights' : '/mnt/thsw2/DSP2/weights/cityMangement2/weights/urbanManagement/DMPR/dp_detector_499.engine',###分割模型权重位置
+            'Segweights':"../weights/%s/AIlib2/%s/dmpr_%s.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
+            'postFile': '../AIlib2/weights/conf/%s/para.json'%(opt['business'] ),###后处理参数文件
+            'txtFontSize':20,###文本字符的大小
+            'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'segLineShow':True,'waterLineWidth':2},###显示框、线设置
+            #'testImgPath':'/mnt/thsw2/DSP2/demoImages/illParking',###测试图像的位置
+            'testImgPath':'/mnt/thsw2/DSP2/weights/cityMangement2/images/input',
+            #'testImgPath':'images/cityMangement/',
+            'testOutPath':'images/results/',###输出测试图像位置
+        }
     if opt['business'] == 'drowning':
         par={
             'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
         
     ##使用森林，道路模型,business 控制['forest','road']
     ##预先设置的参数
-    gpuname=par['gpuname']#如果用trt就需要此参数，只能是"3090" "2080Ti"
+    #gpuname=par['gpuname']#如果用trt就需要此参数，只能是"3090" "2080Ti"
     device_=par['device']  ##选定模型，可选 cpu,'0','1'
     
     
     seg_nclass = par['seg_nclass']
     segPar=par['segPar']
     if par['Segweights']:
+        if opt['business'] == 'cityMangement2':
+            segmodel = DMPRModel(weights=par['Segweights'], par = par['segPar'])
+        else:
+            segmodel = stdcModel(weights=par['Segweights'], par = par['segPar'])
+        '''    
         if par['segPar']['trtFlag_seg']:
             Segweights = par['Segweights']
             logger = trt.Logger(trt.Logger.ERROR)
             Segweights = par['Segweights']
             segmodel = SegModel(nclass=seg_nclass,weights=Segweights,device=device)
             print('############locad seg model pth success:',Segweights)
+        '''    
     else:
         segmodel=None
         
 if __name__=="__main__":
 
     #jkm_demo()
-    #businessAll=['river', 'river2','highWay2','noParking','drowning','forest2','vehicle','pedestrian','smogfire' , 'AnglerSwimmer','channelEmergency', 'countryRoad','cityMangement','ship2']
-    businessAll = ['noParking']
+    businessAll=['river', 'river2','highWay2','noParking','drowning','forest2','vehicle','pedestrian','smogfire' , 'AnglerSwimmer','channelEmergency', 'countryRoad','cityMangement','ship2','cityMangement2']
+    businessAll = ['cityMangement2']
     
     for busi in businessAll:
         print('-'*40,'beg to test:',busi,'-'*40)