add crowdCounting demo

demo.py

             if isinstance(dataBack[key],list):
                 cv2.imwrite(  'images/results/%s_%s.jpg'%( imgp.replace('.jpg','').replace('.png',''),key),dataBack[key][0] )    ###返回值： dataBack
 
+def crowd_demo(opt):
+    if opt['business']=='crowdCounting':
+     
+        from crowd import crowdModel as Model
+        par={
+            'mean':[0.485, 0.456, 0.406], 'std':[0.229, 0.224, 0.225],'threshold':0.5, 
+            'input_profile_shapes':[(1,3,256,256),(1,3,1024,1024),(1,3,2048,2048)],
+            'modelPar':{'backbone':'vgg16_bn', 'gpu_id':0,'anchorFlag':False, 'width':None,'height':None ,'line':2, 'row':2},
+
+            'weights':"../weights/%s/AIlib2/%s/crowdCounting_%s_dynamic.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
+            'testImgPath':'images/%s'%(opt['business'] ),###测试图像的位置
+            'testOutPath':'images/results/',###输出测试图像位置
+            
+            }
+     
+    
+    #weights='weights/best_mae.pth'
+    cmodel = Model(par['weights'],par)
+
+    img_path = par['testImgPath']
+    File = os.listdir(img_path)
+    targetList = []
+    for file in File[0:]:
+        COORlist = []
+        imgPath = img_path + os.sep + file
+        
+        
+        img_raw = cv2.cvtColor(cv2.imread(imgPath),cv2.COLOR_BGR2RGB)
+        # cmodel.eval---
+        # 输入读取的RGB数组
+        # 输出:list,0--原图，1-人头坐标list,2-对接OBB的格式数据，其中4个坐标均相同，2-格式如下：
+        # [ [ [ (x0,y0),(x1,y1),(x2,y2),(x3,y3)  ],score， cls ],  [ [ (x0,y0),(x1,y1),(x2,y2),(x3,y3)  ],score ，cls ],........  ]
+        
+        prets, infos = cmodel.eval(img_raw)
+        
+        
+        print(file,infos,' 人数：',len(prets[1]))
+        
+ 
+        img_to_draw = cv2.cvtColor(np.array(img_raw), cv2.COLOR_RGB2BGR)
+        # 打印预测图像中人头的个数
+        for p in prets[1]:
+            img_to_draw = cv2.circle(img_to_draw, (int(p[0]), int(p[1])), 2, (0, 255, 0), -1)
+            COORlist.append((int(p[0]), int(p[1])))
+        # 将各测试图像中的人头坐标存储在targetList中, 格式：[[(x1, y1),(x2, y2),...], [(X1, Y1),(X2, Y2),..], ...]
+        targetList.append(COORlist)
+        #time.sleep(2)
+        # 保存预测图片
+        cv2.imwrite(os.path.join(par['testOutPath'], file), img_to_draw)
+    
 if __name__=="__main__":
 
     #jkm_demo()
-    businessAll=['river2','AnglerSwimmer',  'countryRoad','forest2',  'pedestrian' , 'smogfire' , 'vehicle','ship2',"highWay2","channelEmergency","cityMangement","drowning","noParking","illParking",'cityMangement2',"cityRoad"]
+    businessAll=['river2','AnglerSwimmer',  'countryRoad','forest2',  'pedestrian' , 'smogfire' , 'vehicle','ship2',"highWay2","channelEmergency","cityMangement","drowning","noParking","illParking",'cityMangement2',"cityRoad","crowdCounting"]
 
-    businessAll = ['cityMangement2']
+    businessAll = ['crowdCounting']
     
     # forest 、 ocr2  、ocr_en 、 river 、 road 、 ship ,目前都没有在用
     
         opt={'gpu':'2080Ti','business':busi}
         if opt['business'] in ['highWay2','river2','drowning','noParking','river',"illParking","cityMangement2"]:
             detSeg_demo(opt)
+        elif  opt['business'] in ['crowdCounting'] :
+            crowd_demo(opt)
         elif  opt['business'] in ['ship2']:  
             OBB_demo(opt)    
         elif opt['business'] in ['ocr']:  

demo3.0.py

                 
 def OBB_track_demo(opt):
     ###倾斜框（OBB）的ship目标检测
-    '''
-    par={
-        'model_size':(608,608), #width,height
-        'K':100, #Maximum of objects'
-        'conf_thresh':0.18,##Confidence threshold, 0.1 for general evaluation
-        'device':"cuda:0",
-        
-        'down_ratio':4,'num_classes':15,
-        #'weights':'../AIlib2/weights/conf/ship2/obb_608X608.engine',
-        'weights':'../weights/%s/AIlib2/%s/obb_608X608_%s_fp16.engine'%(opt['gpu'],opt['business'],opt['gpu']),
-        'dataset':'dota',
-        'test_dir': '/mnt/thsw2/DSP2/videos/obbShips',
-        'outpth': 'images/results',
-        'half': False,
-        'mean':(0.5, 0.5, 0.5),
-        'std':(1, 1, 1), 
-        'model_size':(608,608),##width,height
-        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
-        'heads': {'hm': None,'wh': 10,'reg': 2,'cls_theta': 1},
-        'decoder':None,
-        'test_flag':True,
-        'postFile': '../AIlib2/weights/conf/%s/para.json'%(opt['business'] ),###后处理参数文件
-        'drawBox':True,#####是否画框
-        'digitWordFont': { 'line_thickness':2,'boxLine_thickness':1,'wordSize':40, 'fontSize':1.0,'label_location':'leftTop'},  
-        'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%(opt['business'] ), ###检测类别对照表
-    }   
-    '''
     par={
      
         'obbModelPar':{
     for video_url in video_urls:
         process_video(video_url, parIn ,mode='obbTrack')
 
+def crowd_demo(opt):
+    if opt['business']=='crowdCounting':
+     
+        from crowd import crowdModel as Model
+        par={
+            'mean':[0.485, 0.456, 0.406], 'std':[0.229, 0.224, 0.225],'threshold':0.5, 
+            'input_profile_shapes':[(1,3,256,256),(1,3,1024,1024),(1,3,2048,2048)],
+            'modelPar':{'backbone':'vgg16_bn', 'gpu_id':0,'anchorFlag':False, 'width':None,'height':None ,'line':2, 'row':2},
+
+            'weights':"../weights/%s/AIlib2/%s/crowdCounting_%s_dynamic.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
+            'testImgPath':'images/%s'%(opt['business'] ),###测试图像的位置
+            'testOutPath':'images/results/',###输出测试图像位置
+            
+            }
+     
+    
+    #weights='weights/best_mae.pth'
+    cmodel = Model(par['weights'],par)
+
+    img_path = par['testImgPath']
+    File = os.listdir(img_path)
+    targetList = []
+    for file in File[0:]:
+        COORlist = []
+        imgPath = img_path + os.sep + file
+        
+        
+        img_raw = cv2.cvtColor(cv2.imread(imgPath),cv2.COLOR_BGR2RGB)
+        # cmodel.eval---
+        # 输入读取的RGB数组
+        # 输出:list,0--原图，1-人头坐标list,2-对接OBB的格式数据，其中4个坐标均相同，2-格式如下：
+        # [ [ [ (x0,y0),(x1,y1),(x2,y2),(x3,y3)  ],score， cls ],  [ [ (x0,y0),(x1,y1),(x2,y2),(x3,y3)  ],score ，cls ],........  ]
+        
+        prets, infos = cmodel.eval(img_raw)
+        
+        
+        print(file,infos,' 人数：',len(prets[1]))
+        
+ 
+        img_to_draw = cv2.cvtColor(np.array(img_raw), cv2.COLOR_RGB2BGR)
+        # 打印预测图像中人头的个数
+        for p in prets[1]:
+            img_to_draw = cv2.circle(img_to_draw, (int(p[0]), int(p[1])), 2, (0, 255, 0), -1)
+            COORlist.append((int(p[0]), int(p[1])))
+        # 将各测试图像中的人头坐标存储在targetList中, 格式：[[(x1, y1),(x2, y2),...], [(X1, Y1),(X2, Y2),..], ...]
+        targetList.append(COORlist)
+        #time.sleep(2)
+        # 保存预测图片
+        cv2.imwrite(os.path.join(par['testOutPath'], file), img_to_draw)
+ 
 if __name__=="__main__":
 
     #jkm_demo()
-    #businessAll=['river', 'river2','highWay2','noParking','drowning','forest2','vehicle','pedestrian','smogfire' , 'AnglerSwimmer','channelEmergency', 'countryRoad','cityMangement','ship2','cityMangement2','cityRoad','illParking']
-    businessAll = ['cityMangement2']
+    businessAll=['river', 'river2','highWay2','noParking','drowning','forest2','vehicle','pedestrian','smogfire' , 'AnglerSwimmer','channelEmergency', 'countryRoad','cityMangement','ship2','cityMangement2','cityRoad','illParking',"crowdCounting"]
+    businessAll = ['crowdCounting']
     
     for busi in businessAll:
         print('-'*40,'beg to test:',busi,'-'*40)
         opt={'gpu':'2080Ti','business':busi}
         if busi in ['ship2']:
             OBB_track_demo(opt)
+        elif  opt['business'] in ['crowdCounting'] :
+            crowd_demo(opt)    
         else:
         #if opt['business'] in ['river','highWay2','noParking','drowning','']:
             det_track_demo(opt ) 

images/results/DJI_0974_15.txt

+4.0,716.0,521.0,1189.0,0.7485809326171875,2.0
+1909.0,873.0,3017.0,1341.0,0.9105710983276367,0.0
+740.0,803.0,1798.0,1279.0,0.9193859100341797,0.0

images/results/DJI_0988_20.txt

+1494.0,1604.0,2538.0,2068.0,0.9015560150146484,0.0
+1579.0,486.0,2501.0,895.0,0.9098825454711914,0.0
+775.0,1700.0,1252.0,2159.0,0.9259564876556396,3.0
+1510.0,1192.0,2504.0,1622.0,0.9318015575408936,0.0