import sys
from enum import Enum, unique

from common.Constant import COLOR

sys.path.extend(['..', '../AIlib2'])
from DMPR import DMPRModel
from DMPRUtils.jointUtil import dmpr_yolo
from segutils.segmodel import SegModel
from utilsK.queRiver import riverDetSegMixProcess
from utilsK.crowdGather import  gather_post_process
from segutils.trafficUtils import tracfficAccidentMixFunction,mixTraffic_postprocess
from utilsK.drownUtils import mixDrowing_water_postprocess
from utilsK.noParkingUtils import mixNoParking_road_postprocess
from utilsK.illParkingUtils import illParking_postprocess
from utilsK.pannelpostUtils import pannel_post_process
from stdc import stdcModel
from yolov5 import yolov5Model
from p2pNet import p2NnetModel
from DMPRUtils.jointUtil import  dmpr_yolo_stdc
from AI import default_mix
from ocr import ocrModel
from utilsK.channel2postUtils import channel2_post_process

'''
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
5. 模型配置
6. 模型引用配置[Detweights文件, Segweights文件, 引用计数]
'''


@unique
class ModelType(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"],
        'seg_nclass': 2,
        'trtFlag_seg': True,
        'trtFlag_det': True,
        'segRegionCnt': 1,
        'segPar': {
            'modelSize': (640, 360),
            'mean': (0.485, 0.456, 0.406),
            'std': (0.229, 0.224, 0.225),
            'numpy': False,
            'RGB_convert_first': True,
            'mixFunction': {
                'function': riverDetSegMixProcess,
                'pars': {
                    'slopeIndex': [5, 6, 7],
                    'riverIou': 0.1
                }
            }
        },
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },

        'Detweights': "../weights/trt/AIlib2/river/yolov5_%s_fp16.engine" % gpuName,
        'Segweights': '../weights/trt/AIlib2/river/stdc_360X640_%s_fp16.engine' % gpuName
    })

    # FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
        # 'device': device,
        # 'gpu_name': gpuName,
        # 'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
        # 'trtFlag_det': True,
        # 'trtFlag_seg': False,
        # 'Detweights': "../weights/trt/AIlib2/forest2/yolov5_%s_fp16.engine" % gpuName,
        # 'seg_nclass': 2,
        # 'segRegionCnt': 0,
        # 'slopeIndex': [],
        # 'segPar': None,
        # 'postFile': {
            # "name": "post_process",
            # "conf_thres": 0.25,
            # "iou_thres": 0.45,
            # "classes": 6,
            # "rainbows": COLOR
        # },
        # 'Segweights': None
    # })


    FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
        'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
        'postProcess':{'function':default_mix,'pars':{}},
        'models':
                [
                    {
                        'weight':"../weights/trt/AIlib2/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  } },       
                    }


                ],

        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9]  ],###控制哪些检测类别显示、输出
        'segRegionCnt':2,###分割模型结果需要保留的等值线数目
        "pixScale": 1.2,


    })


    TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'highWay2', lambda device, gpuName: {
        'device': str(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", "影子",
                       "事故","抛撒物", "危化品车辆", "虚标线","其他标线","其他","桥梁外观","设施破损缺失","龙门架","防抛网","标识牌损坏","护栏损坏","钢筋裸露"],
        'trtFlag_seg': True,
        'trtFlag_det': True,
        'seg_nclass': 3,
        'segRegionCnt': 2,
        '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,
            'mixFunction': {
                'function': tracfficAccidentMixFunction,
                'pars': {
                    'modelSize': (640, 360),
                    'RoadArea': 16000,
                    'roadVehicleAngle': 15,
                    'speedRoadVehicleAngleMax': 75,
                    'roundness': 1.0,
                    'cls': 10,
                    'CarId':1,
                    'CthcId':12,
                    'vehicleFactor': 0.1,
                    'confThres': 0.25,
                    'roadIou': 0.6,
                    'radius': 50,
                    'vehicleFlag': False,
                    'distanceFlag': False
                }
            }
        },
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.25,
            "classes": 10,
            "rainbows": COLOR
        },
        'allowedList':[0,1,2,3,4,5,6,7,8,9,10,11,12,16,17,18,19,20,21,22],
        'Detweights': "../weights/trt/AIlib2/highWay2/yolov5_%s_fp16.engine" % gpuName,
        'Segweights': '../weights/trt/AIlib2/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
    })

    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,
        'gpu_name': gpuName,
        'labelnames': ["车辆"],
        'seg_nclass': 2,
        'segRegionCnt': 0,
        'slopeIndex': [],
        'trtFlag_det': True,
        'trtFlag_seg': False,
        'Detweights': "../weights/trt/AIlib2/vehicle/yolov5_%s_fp16.engine" % gpuName,
        'segPar': None,
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'Segweights': None
    })

    PEDESTRIAN_MODEL = ("7", "007", "行人模型", 'pedestrian', lambda device, gpuName: {
        'device': device,
        'gpu_name': gpuName,
        'labelnames': ["行人"],
        'seg_nclass': 2,
        'segRegionCnt': 0,
        'trtFlag_det': True,
        'trtFlag_seg': False,
        'Detweights': "../weights/trt/AIlib2/pedestrian/yolov5_%s_fp16.engine" % gpuName,
        'slopeIndex': [],
        'segPar': None,
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'Segweights': None
    })

    SMOGFIRE_MODEL = ("8", "008", "烟火模型", 'smogfire', lambda device, gpuName: {
        'device': device,
        'gpu_name': gpuName,
        'labelnames': ["火焰", "烟雾"],
        'seg_nclass': 2,  # 分割模型类别数目，默认2类
        'segRegionCnt': 0,
        'trtFlag_det': True,
        'trtFlag_seg': False,
        'Detweights': "../weights/trt/AIlib2/smogfire/yolov5_%s_fp16.engine" % gpuName,
        'slopeIndex': [],
        'segPar': None,
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'Segweights': None
    })

    ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer', lambda device, gpuName: {
        'device': device,
        'gpu_name': gpuName,
        'labelnames': ["钓鱼", "游泳"],
        'seg_nclass': 2,  # 分割模型类别数目，默认2类
        'segRegionCnt': 0,
        'slopeIndex': [],
        'trtFlag_det': True,
        'trtFlag_seg': False,
        'Detweights': "../weights/trt/AIlib2/AnglerSwimmer/yolov5_%s_fp16.engine" % gpuName,
        'segPar': None,
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'Segweights': None
    })

    COUNTRYROAD_MODEL = ("10", "010", "乡村模型", 'countryRoad', lambda device, gpuName: {
        'device': device,
        'gpu_name': gpuName,
        'labelnames': ["违法种植"],
        'seg_nclass': 2,  # 分割模型类别数目，默认2类
        'segRegionCnt': 0,
        'slopeIndex': [],
        'trtFlag_det': True,
        'trtFlag_seg': False,
        'Detweights': "../weights/trt/AIlib2/countryRoad/yolov5_%s_fp16.engine" % gpuName,
        'segPar': None,
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'Segweights': None
    })

    SHIP_MODEL = ("11", "011", "船只模型", 'ship2', lambda device, gpuName: {
        'model_size': (608, 608),
        'K': 100,
        'conf_thresh': 0.18,
        'device': 'cuda:%s' % device,
        'down_ratio': 4,
        'num_classes': 15,
        'weights': '../weights/trt/AIlib2/ship2/obb_608X608_%s_fp16.engine' % gpuName,
        'dataset': 'dota',
        'half': False,
        'mean': (0.5, 0.5, 0.5),
        'std': (1, 1, 1),
        'heads': {'hm': None, 'wh': 10, 'reg': 2, 'cls_theta': 1},
        'decoder': None,
        'test_flag': True,
        "rainbows": COLOR,
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'drawBox': False,
        'label_array': None,
        '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,
        'gpu_name': gpuName,
        'labelnames': ["人"],
        'seg_nclass': 2,  # 分割模型类别数目，默认2类
        'segRegionCnt': 0,
        'slopeIndex': [],
        'trtFlag_det': True,
        'trtFlag_seg': False,
        'Detweights': "../weights/trt/AIlib2/channelEmergency/yolov5_%s_fp16.engine" % gpuName,
        'segPar': None,
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'Segweights': None
    })

    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"],
        'trtFlag_seg': True,
        'trtFlag_det': True,
        'seg_nclass': 2,
        'segRegionCnt': 1,
        'segPar': {
            'modelSize': (640, 360),
            'mean': (0.485, 0.456, 0.406),
            'std': (0.229, 0.224, 0.225),
            'numpy': False,
            'RGB_convert_first': True,
            'mixFunction': {
                'function': riverDetSegMixProcess,
                'pars': {
                    'slopeIndex': [1, 3, 4, 7],
                    'riverIou': 0.1
                }
            }
        },
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.3,
            "ovlap_thres_crossCategory": 0.65,
            "classes": 5,
            "rainbows": COLOR
        },
        'Detweights': "../weights/trt/AIlib2/river2/yolov5_%s_fp16.engine" % gpuName,
        'Segweights': '../weights/trt/AIlib2/river2/stdc_360X640_%s_fp16.engine' % gpuName
    })

    CITY_MANGEMENT_MODEL = ("16", "016", "城管模型", 'cityMangement2', lambda device, gpuName: {
        'labelnames': [ "车辆", "垃圾", "商贩", "违停","占道经营","裸土","未覆盖裸土","违建" ],
        'postProcess':{
                    'function':dmpr_yolo_stdc,
                    'pars':{
                            'carCls':0 ,'illCls':7,'scaleRatio':0.5,'border':80,
                             #"车辆","垃圾","商贩","裸土","占道经营","未覆盖裸土","违建"
                             # key:实际训练index     value:展示index
                             'classReindex':{ 0:0,1:1,2:2,7:3,4:4,3:5,5:6,6:7}
                        }
                    },
        'models':[
            {
                'weight':'../weights/trt/AIlib2/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
                'name':'yolov5',
                'model':yolov5Model,
                'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3,4,5,6,7],'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':True, "score_byClass":{"0":0.8,"1":0.4,"2":0.5,"3":0.5  } }
            },
            {
                'weight':'../weights/pth/AIlib2/cityMangement3/dmpr.pth',
                'par':{
                    'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.1, 'dmprimg_size':640,
                    'name':'dmpr'
                        },
                'model':DMPRModel,
                'name':'dmpr'
            },
            {
                'weight':'../weights/trt/AIlib2/cityMangement3/stdc_360X640_%s_fp16.engine'%(gpuName),
                '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'
            }
        ],
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 8,
            "rainbows": COLOR
        },
        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9]  ],###控制哪些检测类别显示、输出
        'segRegionCnt':2,###分割模型结果需要保留的等值线数目
        "pixScale": 1.2,
    })

    DROWING_MODEL = ("17", "017", "人员落水模型", 'drowning', lambda device, gpuName: {
        'device': device,
        'labelnames': ["人头", "人", "船只"],
        'trtFlag_seg': True,
        'trtFlag_det': True,
        'seg_nclass': 2,
        'segRegionCnt': 2,
        '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,
            'mixFunction': {
                'function': mixDrowing_water_postprocess,
                'pars': {
                    'modelSize': (640, 360)
                }
            }
        },
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.25,
            "classes": 9,
            "rainbows": COLOR
        },
        'Detweights': "../weights/trt/AIlib2/drowning/yolov5_%s_fp16.engine" % gpuName,
        'Segweights': '../weights/trt/AIlib2/drowning/stdc_360X640_%s_fp16.engine' % gpuName
    })

    NOPARKING_MODEL = (
        "18", "018", "城市违章模型", 'noParking', lambda device, gpuName: {
            'device': device,
            'labelnames': ["车辆", "违停"],
            'trtFlag_seg': True,
            'trtFlag_det': True,
            'seg_nclass': 4,
            'segRegionCnt': 2,
            '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': {
                        'modelSize': (640, 360),
                        'roundness': 0.3,
                        'cls': 9,
                        'laneArea': 10,
                        'laneAngleCha': 5,
                        'RoadArea': 16000,
                        'fitOrder':2
                    }
                }
            },
            'postFile': {
                "name": "post_process",
                "conf_thres": 0.25,
                "iou_thres": 0.25,
                "classes": 9,
                "rainbows": COLOR
            },
            'Detweights': "../weights/trt/AIlib2/noParking/yolov5_%s_fp16.engine" % gpuName,
            'Segweights': '../weights/trt/AIlib2/noParking/stdc_360X640_%s_fp16.engine' % gpuName
        })

    ILLPARKING_MODEL = ("19", "019", "车辆违停模型", 'illParking', lambda device, gpuName: {
        'device': device,
        'labelnames': ["车", "T角点", "L角点", "违停"],
        'trtFlag_seg': False,
        'trtFlag_det': True,
        'seg_nclass': 4,
        'segRegionCnt': 2,
        'segPar': {
            'mixFunction': {
                'function': illParking_postprocess,
                'pars': {}
            }
        },
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.25,
            "classes": 9,
            "rainbows": COLOR
        },
        'Detweights': "../weights/trt/AIlib2/illParking/yolov5_%s_fp16.engine" % gpuName,
        'Segweights': None
    })

    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", "施工锥桶", "施工水马"],
        'trtFlag_seg': False,
        'trtFlag_det': True,
        'slopeIndex': [],
        'seg_nclass': 2,
        'segRegionCnt': 0,
        'segPar': None,
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.8,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'Detweights': "../weights/trt/AIlib2/cityRoad/yolov5_%s_fp16.engine" % gpuName,
        'Segweights': None
    })

    POTHOLE_MODEL = ("23", "023", "坑槽检测模型", 'pothole', lambda device, gpuName: {
        'device': device,
        'gpu_name': gpuName,
        'labelnames': ["坑槽"],
        'seg_nclass': 2,  # 分割模型类别数目，默认2类
        'segRegionCnt': 0,
        'slopeIndex': [],
        'trtFlag_det': True,
        'trtFlag_seg': False,
        'Detweights': "../weights/trt/AIlib2/pothole/yolov5_%s_fp16.engine" % gpuName,
        'segPar': None,
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'Segweights': None,
    })

    CHANNEL2_MODEL = ("24", "024", "船只综合检测模型", 'channel2', lambda device, gpuName: {
        'device': device,
        'gpu_name': gpuName,
        # 'labelnames': ["国旗", "浮标", "船名", "船只","未挂国旗船只"],
        'labelnames': ["国旗", "浮标", "船名", "船只", "未挂国旗船只","未封仓船只","未挂国旗且未封仓船只"],
        'segRegionCnt': 0,
        'postProcess':{'function':channel2_post_process,'name':'channel2','pars':{
            'objs':[2],
            'wRation':1/6.0,
            'hRation':1/6.0,
            'flagId':0,
            'boatId':3,
            'unflagId': 4,  # 未挂国旗船只
            'uncoverId': 5,  # 未封仓
            'unflagAndcoverId': 6,  # 未挂国旗且未封仓
            'recScale':1.2,
            'target_cls': 3,  # 船只目标种类
            'filter_cls': 4  # 被过滤的种类,模型文件中未封仓实际index
        }},
        'models':[
        {
            'weight':'../weights/trt/AIlib2/channel2/yolov5_%s_fp16.engine'%(gpuName),
            'name':'yolov5',
            'model':yolov5Model,
            'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.1,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.7,"1":0.7,"2":0.8,"3":0.6} }
        },
        {
            'weight' : '../weights/pth/AIlib2/ocr2/crnn_ch.pth',
            'name':'ocr',
            'model':ocrModel,
            'par':{
                'char_file':'../AIlib2/conf/ocr2/benchmark.txt',
                'mode':'ch',
                'nc':3,
                'imgH':32,
                'imgW':192,
                'hidden':256,
                'mean':[0.5,0.5,0.5],
                'std':[0.5,0.5,0.5],
                'dynamic':False,
                },
        }
        ],
        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]],
        'segPar': None,
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'Segweights': None,
    })

    RIVERT_MODEL = ("25", "025", "河道检测模型(T)", 'riverT', 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"],
        'trtFlag_seg': True,
        'trtFlag_det': True,
        'seg_nclass': 2,
        'segRegionCnt': 1,
        'segPar': {
            'modelSize': (640, 360),
            'mean': (0.485, 0.456, 0.406),
            'std': (0.229, 0.224, 0.225),
            'numpy': False,
            'RGB_convert_first': True,
            'mixFunction': {
                'function': riverDetSegMixProcess,
                'pars': {
                    'slopeIndex': [1, 3, 4, 7],
                    'riverIou': 0.1
                }
            }
        },
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.3,
            "ovlap_thres_crossCategory": 0.65,
            "classes": 5,
            "rainbows": COLOR
        },
        'Detweights': "../weights/trt/AIlib2/riverT/yolov5_%s_fp16.engine" % gpuName,
        'Segweights': '../weights/trt/AIlib2/riverT/stdc_360X640_%s_fp16.engine' % gpuName
    })



    FORESTCROWD_FARM_MODEL = ("26", "026", "森林人群模型", 'forestCrowd', lambda device, gpuName: {
        'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","人群"],
        'postProcess':{'function':gather_post_process,'pars':{'pedestrianId':2,'crowdThreshold':4,'gatherId':5,'distancePersonScale':2.0}},
        'models':
                [
                    {
                        'weight':"../weights/trt/AIlib2/forestCrowd/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":0.25,"2":0.6,"3":0.6,'4':0.6 ,'5':0.6   } },
                    }


                ],

        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.45,
            "classes": 5,
            "rainbows": COLOR
        },
        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9]  ],###控制哪些检测类别显示、输出
        'segRegionCnt':2,###分割模型结果需要保留的等值线数目
        "pixScale": 1.2,


    })
    TRAFFICFORDSJ_FARM_MODEL = ("27", "027", "交通模型-大数据局", 'highWay2T', lambda device, gpuName: {
        'device': str(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", "影子",
                       "事故", "桥梁外观","设施破损缺失","龙门架","防抛网","标识牌损坏","护栏损坏","钢筋裸露" ],
        'trtFlag_seg': True,
        'trtFlag_det': True,
        'seg_nclass': 3,
        'segRegionCnt': 2,
        '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,
            'mixFunction': {
                'function': tracfficAccidentMixFunction,
                'pars': {
                    'modelSize': (640, 360),
                    'RoadArea': 16000,
                    'roadVehicleAngle': 15,
                    'speedRoadVehicleAngleMax': 75,
                    'roundness': 1.0,
                    'cls': 10,
                    'CarId':1,
                    'CthcId':1,
                    'vehicleFactor': 0.1,
                    'confThres': 0.25,
                    'roadIou': 0.6,
                    'radius': 50,
                    'vehicleFlag': False,
                    'distanceFlag': False
                }
            }
        },
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.25,
            "classes": 10,
            "rainbows": COLOR
        },
        'Detweights': "../weights/trt/AIlib2/highWay2T/yolov5_%s_fp16.engine" % gpuName,
        'Segweights': '../weights/trt/AIlib2/highWay2T/stdc_360X640_%s_fp16.engine' % gpuName
    })

    SMARTSITE_MODEL = ("28", "028", "智慧工地模型", 'smartSite', lambda device, gpuName: {
        'labelnames': [  "工人","塔式起重机","悬臂","起重机","压路机","推土机","挖掘机","卡车","装载机","泵车","混凝土搅拌车","打桩","其他车辆" ],
        'postProcess':{'function':default_mix,'pars':{}},
        'models':
                [
                    {
                        'weight':"../weights/trt/AIlib2/smartSite/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':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },         
                    }


                ],
        'postFile': {
            "rainbows": COLOR
        },

    })

    RUBBISH_MODEL = ("29", "029", "垃圾模型", 'rubbish', lambda device, gpuName: {
        'labelnames': [ "建筑垃圾","白色垃圾","其他垃圾"],
        'postProcess':{'function':default_mix,'pars':{}},
        'models':
                [
                    {
                        'weight':"../weights/trt/AIlib2/rubbish/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':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },         
                    }


                ],
        'postFile': {
            "rainbows": COLOR
        },

    })

    FIREWORK_MODEL = ("30", "030", "烟花模型", 'firework', lambda device, gpuName: {
        'labelnames': [ "烟花"],
        'postProcess':{'function':default_mix,'pars':{}},
        'models':
                [
                    {
                        'weight':"../weights/trt/AIlib2/firework/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':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },         
                    }


                ],
        'postFile': {
            "rainbows": COLOR
        },

    })

    TRAFFIC_SPILL_MODEL = ("50", "501", "高速公路抛洒物模型", 'highWaySpill', lambda device, gpuName: {
        'device': str(device),
        'labelnames': ["抛洒物","车辆"],
        'trtFlag_seg': True,
        'trtFlag_det': True,
        'seg_nclass': 3,
        'segRegionCnt': 2,
        '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,
            'mixFunction': {
                'function': mixTraffic_postprocess,
                'pars': {
                    'modelSize': (640, 360),
                    'RoadArea': 16000,
                    'roadVehicleAngle': 15,
                    'speedRoadVehicleAngleMax': 75,
                    'roundness': 1.0,
                    'cls': 0,
                    'vehicleFactor': 0.1,
                    'confThres': 0.25,
                    'roadIou': 0.6,
                    'radius': 50,
                    'vehicleFlag': False,
                    'distanceFlag': False
                }
            }
        },
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.25,
            "classes": 2,
            "rainbows": COLOR
        },
        '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
        'Detweights': "../weights/trt/AIlib2/highWaySpill/yolov5_%s_fp16.engine" % gpuName,
        'Segweights': '../weights/trt/AIlib2/highWaySpill/stdc_360X640_%s_fp16.engine' % gpuName
    })

    TRAFFIC_CTHC_MODEL = ("50", "502", "高速公路危化品模型", 'highWayCthc', lambda device, gpuName: {
        'device': str(device),
        'labelnames': ["危化品","罐体","危险标识","普通车"],
        'trtFlag_seg': True,
        'trtFlag_det': True,
        'seg_nclass': 3,
        'segRegionCnt': 2,
        '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,
            'mixFunction': {
                'function': mixTraffic_postprocess,
                'pars': {
                    'modelSize': (640, 360),
                    'RoadArea': 16000,
                    'roadVehicleAngle': 15,
                    'speedRoadVehicleAngleMax': 75,
                    'roundness': 1.0,
                    'cls': 0,
                    'vehicleFactor': 0.1,
                    'confThres': 0.25,
                    'roadIou': 0.6,
                    'radius': 50,
                    'vehicleFlag': False,
                    'distanceFlag': False
                }
            }
        },
        'postFile': {
            "name": "post_process",
            "conf_thres": 0.25,
            "iou_thres": 0.25,
            "classes": 4,
            "rainbows": COLOR
        },
        '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
        'Detweights': "../weights/trt/AIlib2/highWayCthc/yolov5_%s_fp16.engine" % gpuName,
        'Segweights': '../weights/trt/AIlib2/highWayCthc/stdc_360X640_%s_fp16.engine' % gpuName
    })

    TRAFFIC_PANNEL_MODEL = ("50", "503", "光伏板模型", 'pannel', lambda device, gpuName: {
        'labelnames': ["光伏板","覆盖物","裂缝"],
        'postProcess': {'function': pannel_post_process, 'pars': {'objs': [0]}},
        'models':
            [
                {
                    'weight': "../weights/trt/AIlib2/pannel/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], 'segRegionCnt': 1, 'trtFlag_det': True,
                            'trtFlag_seg': False, "score_byClass": {"0": 0.25, "1": 0.3, "2": 0.3, "3": 0.3}},
                }

            ],
        'postFile': {
            "rainbows": COLOR
        },

    })

    CITY_CARPLATE_MODEL = ("30", "301", "自研车牌检测", 'carplate', lambda device, gpuName: {
        'labelnames': ["车牌"],
        'device': str(device),
        'rainbows': COLOR,
        'models': [
            {
                'trtFlag_det': False,
                'weight': '../weights/pth/AIlib2/carplate/plate_yolov5s_v3.jit',
                'name': 'yolov5',
                'model': yolov5Model,
                'par': {
                    'device': 'cuda:0',
                    'half': False,
                    'conf_thres': 0.4,
                    'iou_thres': 0.45,
                    'nc': 1,
                    'plate_dilate': (0.5, 0.1)
                },
            },
            {
                'trtFlag_ocr': False,
                'weight': '../weights/pth/AIlib2/ocr2/crnn_ch.pth',
                'name': 'ocr',
                'model': ocrModel,
                'par': {
                    'char_file': '../AIlib2/conf/ocr2/benchmark.txt',
                    'mode': 'ch',
                    'nc': 3,
                    'imgH': 32,
                    'imgW': 192,
                    'hidden': 256,
                    'mean': [0.5, 0.5, 0.5],
                    'std': [0.5, 0.5, 0.5],
                    'dynamic': False,
                }
            }],
    })

    CITY_INFRAREDPERSON_MODEL = ("30", "302", "红外行人模型", 'infraredPerson', lambda device, gpuName: {
        'labelnames': ["行人"],
        'postProcess': {'function': default_mix, 'pars': {}},
        'models':
            [
                {
                    'weight': "../weights/trt/AIlib2/infraredPerson/yolov5_%s_fp16.engine" % (gpuName),  ###检测模型路径
                    'name': 'yolov5',
                    'model': yolov5Model,
                    'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.50, 'iou_thres': 0.45,
                            'allowedList': list(range(20)), 'segRegionCnt': 1, 'trtFlag_det': True,
                            'trtFlag_seg': False, "score_byClass": {"0": 0.50, "1": 0.3, "2": 0.3, "3": 0.3}},
                }

            ],
        'postFile': {
            "rainbows": COLOR
        },

    })

    CITY_NIGHTFIRESMOKE_MODEL = ("30", "303", "夜间烟火模型", 'nightFireSmoke', lambda device, gpuName: {
        'labelnames': ["火","烟雾"],
        'postProcess': {'function': default_mix, 'pars': {}},
        'models':
            [
                {
                    'weight': "../weights/trt/AIlib2/nightFireSmoke/yolov5_%s_fp16.engine" % (gpuName),  ###检测模型路径
                    'name': 'yolov5',
                    'model': yolov5Model,
                    'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.50, 'iou_thres': 0.45,
                            'allowedList': list(range(20)), 'segRegionCnt': 1, 'trtFlag_det': True,
                            'trtFlag_seg': False, "score_byClass": {"0": 0.50, "1": 0.3, "2": 0.3, "3": 0.3}},
                }

            ],
        'postFile': {
            "rainbows": COLOR
        },

    })

    CITY_DENSECROWDCOUNT_MODEL = ("30", "304", "密集人群计数", 'DenseCrowdCount', lambda device, gpuName: {
        'labelnames': ["人群计数"],
        'device': str(device),
        'rainbows': COLOR,
        'models': [
            {
                'trtFlag_det': False,
                'weight': "../weights/pth/AIlib2/DenseCrowd/SHTechA.pth",  ###检测模型路径
                'vggweight': "../weights/pth/AIlib2/DenseCrowd/vgg16_bn-6c64b313.pth",  ###检测模型路径
                'name': 'p2pnet',
                'model': p2NnetModel,
                'par': {
                    'device': 'cuda:0',
                    'row': 2,
                    'line': 2,
                    'point_loss_coef': 0.45,
                    'conf': 0.25,
                    'gpu_id': 0,
                    'eos_coef': '0.5',
                    'set_cost_class': 1,
                    'set_cost_point': 0.05,
                    'backbone': 'vgg16_bn'
                },
            }],
    })

    CITY_DENSECROWDESTIMATION_MODEL = ("30", "305", "密集人群密度估计", 'DenseCrowdEstimation', lambda device, gpuName: {
        'labelnames': ["密度"],
        'models':
            [
                {
                    'weight': "../weights/pth/AIlib2/DenseCrowd/SHTechA.pth",  ###检测模型路径
                    'name': 'yolov5',
                    'model': yolov5Model,
                    'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.50, 'iou_thres': 0.45,
                            'allowedList': list(range(20)), 'segRegionCnt': 1, 'trtFlag_det': True,
                            'trtFlag_seg': False, "score_byClass": {"0": 0.50, "1": 0.3, "2": 0.3, "3": 0.3}},
                }

            ],
        'postFile': {
            "rainbows": COLOR
        },

    })

    @staticmethod
    def checkCode(code):
        for model in ModelType:
            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 BaiduModelTarget(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_CONFIG = {
    BaiduModelTarget.VEHICLE_DETECTION.value[1]: BaiduModelTarget.VEHICLE_DETECTION,
    BaiduModelTarget.HUMAN_DETECTION.value[1]: BaiduModelTarget.HUMAN_DETECTION,
    BaiduModelTarget.PEOPLE_COUNTING.value[1]: BaiduModelTarget.PEOPLE_COUNTING
}

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


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

    # 方式二: 追踪识别方式
    TRACE = 2