import sys
from enum import Enum, unique

from common.Constant import COLOR

sys.path.extend(['..', '../AIlib2'])
from segutils.segmodel import SegModel
from utilsK.queRiver import riverDetSegMixProcess_N
from segutils.trafficUtils import tracfficAccidentMixFunction_N
from utilsK.drownUtils import mixDrowing_water_postprocess_N
from utilsK.noParkingUtils import mixNoParking_road_postprocess_N
from utilsK.illParkingUtils import illParking_postprocess
from DMPR import DMPRModel
from DMPRUtils.jointUtil import dmpr_yolo
from yolov5 import yolov5Model
from stdc import stdcModel
from AI import default_mix
from DMPRUtils.jointUtil import  dmpr_yolo_stdc

'''
参数说明
1. 编号
2. 模型编号
3. 模型名称
4. 选用的模型名称
'''


@unique
class ModelType2(Enum):
    WATER_SURFACE_MODEL = ("1", "001", "河道模型", 'river', lambda device, gpuName: {
        'device': device,
        'labelnames': ["排口", "水生植被", "其它", "漂浮物", "污染排口", "菜地", "违建", "岸坡垃圾"],
        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7]  ],###控制哪些检测类别显示、输出
        'trackPar': {
            'sort_max_age': 2,  # 跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
            'sort_min_hits': 3,  # 每隔目标连续出现的次数，超过这个次数才认为是一个目标。
            'sort_iou_thresh': 0.2,  # 检测最小的置信度。
            'det_cnt': 10,  # 每隔几次做一个跟踪和检测，默认10。
            'windowsize': 29,  # 轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。一个目标在多个帧中出现，每一帧中都有一个位置，这些位置的连线交轨迹。
            'patchCnt': 100,  # 每次送入图像的数量，不宜少于100帧。
        },
        'postProcess':{'function':riverDetSegMixProcess_N,'pars':{'slopeIndex':[1,3,4,7], 'riverIou':0.1}}, #分割和检测混合处理的函数
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'txtFontSize': 80,
        'digitFont': {
            'line_thickness': 2,
            'boxLine_thickness': 1,
            'fontSize': 1.0,
            'segLineShow': False,
            'waterLineColor': (0, 255, 255),
            'waterLineWidth': 3
        },
        'models':
                [
                    {
                        'weight':"../AIlib2/weights/river/yolov5_%s_fp16.engine"% gpuName,###检测模型路径
                        'name':'yolov5',
                        'model':yolov5Model,
                        'par':{ 
                            'half':True,
                            'device':'cuda:0' ,
                            'conf_thres':0.25,
                            'iou_thres':0.45,
                            'allowedList':[0,1,2,3],
                            'segRegionCnt':1, 
                            'trtFlag_det':False,
                            'trtFlag_seg':False, 
                            "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                    },
                    {
                        'weight':'../AIlib2/weights/conf/river/stdc_360X640.pth',
                        'par':{
                            'modelSize':(640,360),
                            'mean':(0.485, 0.456, 0.406),
                            'std' :(0.229, 0.224, 0.225),
                            'numpy':False, 
                            'RGB_convert_first':True,
                            'seg_nclass':2},###分割模型预处理参数
                        'model':stdcModel,
                        'name':'stdc'        
                    }
                        
                ],
    })

    FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
        'device': device,
        'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾"],
        'models':
                [
                    {
                        'weight':"../AIlib2/weights/forest2/yolov5_%s_fp16.engine"% gpuName,###检测模型路径
                        'name':'yolov5',
                        'model':yolov5Model,
                        'par':{ 'half':True,
                               'device':'cuda:0' ,
                               'conf_thres':0.25,
                               'iou_thres':0.45,
                               'allowedList':[0,1,2,3],
                               'segRegionCnt':1, 
                               'trtFlag_det':False,
                               'trtFlag_seg':False, 
                               "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } 
                               },       
                    }                       
                ],
        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'postProcess':{'function':default_mix,'pars':{ }},
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'txtFontSize': 80,
        'digitFont': {
            'line_thickness': 2,
            'boxLine_thickness': 1,
            'fontSize': 1.0,
            'segLineShow': False,
            'waterLineColor': (0, 255, 255),
            'waterLineWidth': 3
        }
    })

    TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'highWay2', lambda device, gpuName: {
        'device': device,
        'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "事故"],
        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':5,'windowsize':29,'patchCnt':100},
        'postProcess':{
            'function':tracfficAccidentMixFunction_N,
            'pars':{ 
                'RoadArea': 16000, 
                'vehicleArea': 10, 
                'roadVehicleAngle': 15, 
                'speedRoadVehicleAngleMax': 75,
                'radius': 50 , 
                'roundness': 1.0, 
                'cls': 9, 
                'vehicleFactor': 0.1,
                'cls':9, 
                'confThres':0.25,
                'roadIou':0.6,
                'vehicleFlag':False,
                'distanceFlag': False,
                'modelSize':(640,360), 
            }
        },
        'models':
            [
                {
                    'weight':"../AIlib2/weights/highWay2/yolov5_%s_fp16.engine"% gpuName,###检测模型路径
                    'name':'yolov5',
                    'model':yolov5Model,
                    'par':{ 
                        'half':True,
                        'device':'cuda:0' ,
                        'conf_thres':0.25,
                        'iou_thres':0.45,
                        'allowedList':[0,1,2,3],
                        'segRegionCnt':1, 
                        'trtFlag_det':False,
                        'trtFlag_seg':False, 
                        "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                },
                {
                    'weight':'../AIlib2/weights/conf/highWay2/stdc_360X640.pth',
                    'par':{
                        '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,
                        'seg_nclass':3},###分割模型预处理参数
                    'model':stdcModel,
                    'name':'stdc'        
                }    
            ],
        '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
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.25,
            "classes": 9,
            "rainbows": COLOR
        },
        'txtFontSize': 20,
        'digitFont': {
            'line_thickness': 2,
            'boxLine_thickness': 1,
            'fontSize': 1.0,
            'waterLineColor': (0, 255, 255),
            'segLineShow': False,
            'waterLineWidth': 2
        }
    })

    EPIDEMIC_PREVENTION_MODEL = ("4", "004", "防疫模型", None, None)

    PLATE_MODEL = ("5", "005", "车牌模型", None, None)

    VEHICLE_MODEL = ("6", "006", "车辆模型", 'vehicle', lambda device, gpuName: {
        'device': device,
        'labelnames': ["车辆"],
        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
        'postProcess':{'function':default_mix,'pars':{ }},
        'models':
            [
                {
                    'weight':"../AIlib2/weights/vehicle/yolov5_%s_fp16.engine"% gpuName,###检测模型路径
                    'name':'yolov5',
                    'model':yolov5Model,
                    'par':{ 'half':True,
                           'device':'cuda:0' ,
                           'conf_thres':0.25,
                           'iou_thres':0.45,
                           'allowedList':[0,1,2,3],
                           'segRegionCnt':1, 
                           'trtFlag_det':False,
                           'trtFlag_seg':False, 
                           "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                }                    
            ], 
        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'txtFontSize': 40,
        'digitFont': {
            'line_thickness': 2,
            'boxLine_thickness': 1,
            'fontSize': 1.0,
            'waterLineColor': (0, 255, 255),
            'segLineShow': False,
            'waterLineWidth': 3
        }
    })

    PEDESTRIAN_MODEL = ("7", "007", "行人模型", 'pedestrian', lambda device, gpuName: {
        'device': device,
        'labelnames': ["行人"],
        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
        'postProcess':{'function':default_mix,'pars':{ }},
        'models':
            [
                {
                    'weight':"../AIlib2/weights/pedestrian/yolov5_%s_fp16.engine"% gpuName,###检测模型路径
                    'name':'yolov5',
                    'model':yolov5Model,
                    'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                }
            ], 
        
        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'digitFont': {
            'line_thickness': 2,
            'boxLine_thickness': 1,
            'fontSize': 1.0,
            'segLineShow': False,
            'waterLineColor': (0, 255, 255),
            'waterLineWidth': 3
        }
    })

    SMOGFIRE_MODEL = ("8", "008", "烟火模型", 'smogfire', lambda device, gpuName: {
        'device': device,
        'labelnames': ["烟雾", "火焰"],
        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'postProcess':{'function':default_mix,'pars':{ }},
            'models':
                [
                    {
                        'weight':"../AIlib2/weights/smogfire/yolov5_%s_fp16.engine"% gpuName,###检测模型路径
                        #'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
                        'name':'yolov5',
                        'model':yolov5Model,
                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                    }
                ],  
            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'txtFontSize': 40,
        'digitFont': {
            'line_thickness': 2,
            'boxLine_thickness': 1,
            'fontSize': 1.0,
            'segLineShow': False,
            'waterLineColor': (0, 255, 255),
            'waterLineWidth': 3
        }
    })

    ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer', lambda device, gpuName: {
        'device': device,
        'labelnames': ["钓鱼", "游泳"],
        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
        'postProcess':{'function':default_mix,'pars':{ }},
        'models':
            [
                {
                    'weight':"../AIlib2/weights/AnglerSwimmer/yolov5_%s_fp16.engine"% gpuName,###检测模型路径
                    'name':'yolov5',
                    'model':yolov5Model,
                    'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                }
            ],  
        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'txtFontSize': 40,
        'digitFont': {
            'line_thickness': 2,
            'boxLine_thickness': 1,
            'fontSize': 1.0,
            'segLineShow': False,
            'waterLineColor': (0, 255, 255),
            'waterLineWidth': 3
        },
    })

    COUNTRYROAD_MODEL = ("10", "010", "乡村模型", 'countryRoad', lambda device, gpuName: {
        'device': device,
        'labelnames': ["违法种植"],
        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
        'postProcess':{'function':default_mix,'pars':{ }},
        'models':
            [
                {
                    'weight':"../AIlib2/weights/countryRoad/yolov5_%s_fp16.engine"% gpuName,###检测模型路径
                    'name':'yolov5',
                    'model':yolov5Model,
                    'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                }
            ],  
        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'txtFontSize': 40,
        'digitFont': {
            'line_thickness': 2,
            'boxLine_thickness': 1,
            'fontSize': 1.0,
            'segLineShow': False,
            'waterLineColor': (0, 255, 255),
            'waterLineWidth': 3
        }
    })

    SHIP_MODEL = ("11", "011", "船只模型", 'ship2', lambda device, gpuName: {
        'obbModelPar': {
            'labelnames': ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "船只"],
            'model_size': (608, 608),
            'K': 100,
            'conf_thresh': 0.3,
            'down_ratio': 4,
            'num_classes': 15,
            'dataset': 'dota',
            'heads': {
                'hm': None,
                'wh': 10,
                'reg': 2,
                'cls_theta': 1
            },
            'mean': (0.5, 0.5, 0.5),
            'std': (1, 1, 1),
            'half': False,
            'test_flag': True,
            'decoder': None,
            'weights': '../AIlib2/weights/ship2/obb_608X608_%s_fp16.engine' % gpuName
        },
        'trackPar': {
            'sort_max_age': 2,  # 跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
            'sort_min_hits': 3,  # 每隔目标连续出现的次数，超过这个次数才认为是一个目标。
            'sort_iou_thresh': 0.2,  # 检测最小的置信度。
            'det_cnt': 10,  # 每隔几次做一个跟踪和检测，默认10。
            'windowsize': 29,  # 轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。一个目标在多个帧中出现，每一帧中都有一个位置，这些位置的连线交轨迹。
            'patchCnt': 100,  # 每次送入图像的数量，不宜少于100帧。
        },
        'device': "cuda:%s" % device,
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'drawBox': False,
        'drawPar': {
            "rainbows": COLOR,
            'digitWordFont': {
                'line_thickness': 2,
                'boxLine_thickness': 1,
                'wordSize': 40,
                'fontSize': 1.0,
                'label_location': 'leftTop'
            }
        },
        'labelnames': ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "船只"]
    })

    BAIDU_MODEL = ("12", "012", "百度AI图片识别模型", None, None)

    CHANNEL_EMERGENCY_MODEL = ("13", "013", "航道模型", 'channelEmergency', lambda device, gpuName: {
        'device': device,
        'labelnames': ["人"],
        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
        'postProcess':{'function':default_mix,'pars':{ }},
        'models':
            [
                {
                    'weight':"../AIlib2/weights/channelEmergency/yolov5_%s_fp16.engine"% gpuName,###检测模型路径
                    #'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
                    'name':'yolov5',
                    'model':yolov5Model,
                    'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                }
            ],  
        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in []  ],###控制哪些检测类别显示、输出
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'txtFontSize': 40,
        'digitFont': {
            'line_thickness': 2,
            'boxLine_thickness': 1,
            'fontSize': 1.0,
            'segLineShow': False,
            'waterLineColor': (0, 255, 255),
            'waterLineWidth': 3
        }
    })

    RIVER2_MODEL = ("15", "015", "河道检测模型", 'river2', lambda device, gpuName: {
        'device': device,
        'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
                       "蓝藻"],
        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
        'postProcess':{'function':riverDetSegMixProcess_N,'pars':{'slopeIndex':[1,3,4,7], 'riverIou':0.1}}, #分割和检测混合处理的函数         
        'models':
            [
                {
                    'weight':"../AIlib2/weights/river2/yolov5_%s_fp16.engine"% gpuName,###检测模型路径
                    'name':'yolov5',
                    'model':yolov5Model,
                    'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                },
                {
                    'weight':'../AIlib2/weights/conf/river2/stdc_360X640.pth',
                    'par':{
                        'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'numpy':False, 'RGB_convert_first':True,'seg_nclass':2},###分割模型预处理参数
                    'model':stdcModel,
                    'name':'stdc'        
                }      
            ],
        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.3,
            "ovlap_thres_crossCategory": 0.65,
            "classes": 5,
            "rainbows": COLOR
        },
        'txtFontSize': 80,
        'digitFont': {
            'line_thickness': 2,
            'boxLine_thickness': 1,
            'fontSize': 1.0,
            'segLineShow': False,
            'waterLineColor': (0, 255, 255),
            'waterLineWidth': 3
        }
    })

    CITY_MANGEMENT_MODEL = ("16", "016", "城管模型", 'cityMangement2', lambda device, gpuName: {
        'device': device,
        'labelnames': ["车辆", "垃圾", "商贩", "违停"],
        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':5,'windowsize':29,'patchCnt':100},       
        'postProcess':{
            'function':dmpr_yolo_stdc,
            'pars':{'carCls':0 ,'illCls':3,'scaleRatio':0.5,'border':80}
            }, 
        'models':[
                {
                    'weight':"../AIlib2/weights/cityMangement3/yolov5_%s_fp16.engine"% gpuName,###检测模型路径
                    'name':'yolov5',
                    'model':yolov5Model,
                    'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.8,"1":0.5,"2":0.5,"3":0.5  } }
                
                },
                {
                    'weight':"../AIlib2/weights/cityMangement3/dmpr_%s.engine"% gpuName,###DMPR模型路径
                    'par':{
                        'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.3, 'dmprimg_size':640,
                        'name':'dmpr'
                            },
                    'model':DMPRModel,     
                    'name':'dmpr'
                },
                {
                    'weight':"../AIlib2/weights/cityMangement3/stdc_360X640_%s_fp16.engine"% gpuName,###分割模型路径
                    'par':{
                        '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,'seg_nclass':2},###分割模型预处理参数
                    'model':stdcModel,
                    'name':'stdc'        
                    
                } 
            ],
        '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
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'txtFontSize': 20,
        'digitFont': {
            'line_thickness': 2,
            'boxLine_thickness': 1,
            'fontSize': 1.0,
            'segLineShow': False,
            'waterLineColor': (0, 255, 255),
            'waterLineWidth': 2
        }
    })

    DROWING_MODEL = ("17", "017", "人员落水模型", 'drowning', lambda device, gpuName: {
        'device': device,
        'labelnames': ["人头", "人", "船只"],
        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
        'postProcess':{'function':mixDrowing_water_postprocess_N,
                    'pars':{ }},
        'models':
            [
                {
                    'weight':"../AIlib2/weights/drowning/yolov5_%s_fp16.engine"% gpuName,###检测模型路径
                    'name':'yolov5',
                    'model':yolov5Model,
                    'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                },
                {
                    'weight':'../AIlib2/weights/conf/drowning/stdc_360X640.pth',
                    'par':{
                        '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,'seg_nclass':2},###分割模型预处理参数
                    'model':stdcModel,
                    'name':'stdc'        
                }      
            ],
        '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    
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.25,
            "classes": 9,
            "rainbows": COLOR
        },
        'txtFontSize': 20,
        'digitFont': {
            'line_thickness': 2,
            'boxLine_thickness': 1,
            'fontSize': 1.0,
            'waterLineColor': (0, 255, 255),
            'segLineShow': False,
            'waterLineWidth': 2
        }
    })

    NOPARKING_MODEL = (
        "18", "018", "城市违章模型", 'noParking', lambda device, gpuName: {
            'device': device,
            'labelnames': ["车辆", "违停"],
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'postProcess':{'function':mixNoParking_road_postprocess_N,
                        'pars': { 'roundness': 0.3, 'cls': 9,  'laneArea': 10, 'laneAngleCha': 5 ,'RoadArea': 16000,'fitOrder':2, 'modelSize':(640,360)}
                        } ,
            'models':
                [
                    {
                        'weight':"../AIlib2/weights/noParking/yolov5_%s_fp16.engine"% gpuName,###检测模型路径
                        'name':'yolov5',
                        'model':yolov5Model,
                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                    },
                    {
                        'weight':'../AIlib2/weights/conf/noParking/stdc_360X640.pth',
                        'par':{
                            '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,'seg_nclass':4},###分割模型预处理参数
                        'model':stdcModel,
                        'name':'stdc'        
                    }
                ],
            '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
            'postFile': {
                "name": "post_process",
                "conf_thres": 0.25,
                "iou_thres": 0.25,
                "classes": 9,
                "rainbows": COLOR
            },
            'txtFontSize': 20,
            'digitFont': {
                'line_thickness': 2,
                'boxLine_thickness': 1,
                'fontSize': 1.0,
                'waterLineColor': (0, 255, 255),
                'segLineShow': False,
                'waterLineWidth': 2
            }
        }
    )

    CITYROAD_MODEL = ("20", "020", "城市公路模型", 'cityRoad', lambda device, gpuName: {
        'device': device,
        'labelnames': ["护栏", "交通标志", "非交通标志", "施工", "施工"],
        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'postProcess':{'function':default_mix,'pars':{ }},
            'models':
                [
                    {
                        'weight':"../AIlib2/weights/cityRoad/yolov5_%s_fp16.engine"% gpuName,###检测模型路径
                        'name':'yolov5',
                        'model':yolov5Model,
                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.8,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                    }
                ],  
            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.8,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'txtFontSize': 40,
        'digitFont': {
            'line_thickness': 2,
            'boxLine_thickness': 1,
            'fontSize': 1.0,
            'segLineShow': False,
            'waterLineColor': (0, 255, 255),
            'waterLineWidth': 3
        }
    })

    POTHOLE_MODEL = ("23", "023", "坑槽检测模型", 'pothole', lambda device, gpuName: {   # 目前集成到另外的模型中去了 不单独使用
        'device': device,
        'labelnames': ["坑槽"],
        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':3,'windowsize':29,'patchCnt':100},
        'postProcess':{'function':default_mix,'pars':{ }},
        'models':
            [
                {
                    'weight':"../AIlib2/weights/pothole/yolov5_%s_fp16.engine"% gpuName,###检测模型路径
                    'name':'yolov5',
                    'model':yolov5Model,
                    'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3}},       
                }
            ],  
        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0]],###控制哪些检测类别显示、输出
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'txtFontSize': 40,
        'digitFont': {
            'line_thickness': 2,
            'boxLine_thickness': 1,
            'fontSize': 1.0,
            'segLineShow': False,
            'waterLineColor': (0, 255, 255),
            'waterLineWidth': 3
        },
    })


    @staticmethod
    def checkCode(code):
        for model in ModelType2:
            if model.value[1] == code:
                return True
        return False


'''
    参数1: 检测目标名称
    参数2: 检测目标
    参数3: 初始化百度检测客户端
'''


@unique
class BaiduModelTarget2(Enum):
    VEHICLE_DETECTION = (
        "车辆检测", 0, lambda client0, client1, url, request_id: client0.vehicleDetectUrl(url, request_id))

    HUMAN_DETECTION = (
        "人体检测与属性识别", 1, lambda client0, client1, url, request_id: client1.bodyAttr(url, request_id))

    PEOPLE_COUNTING = ("人流量统计", 2, lambda client0, client1, url, request_id: client1.bodyNum(url, request_id))


BAIDU_MODEL_TARGET_CONFIG2 = {
    BaiduModelTarget2.VEHICLE_DETECTION.value[1]: BaiduModelTarget2.VEHICLE_DETECTION,
    BaiduModelTarget2.HUMAN_DETECTION.value[1]: BaiduModelTarget2.HUMAN_DETECTION,
    BaiduModelTarget2.PEOPLE_COUNTING.value[1]: BaiduModelTarget2.PEOPLE_COUNTING
}

EPIDEMIC_PREVENTION_CONFIG = {1: "行程码", 2: "健康码"}


# 模型分析方式
@unique
class ModelMethodTypeEnum2(Enum):
    # 方式一: 正常识别方式
    NORMAL = 1

    # 方式二: 追踪识别方式
    TRACE = 2
