# -*- coding: utf-8 -*-
import sys
from pickle import dumps, loads
from traceback import format_exc
import time

import cv2
from loguru import logger

from common.Constant import COLOR
from enums.BaiduSdkEnum import VehicleEnum
from enums.ExceptionEnum import ExceptionType
from enums.ModelTypeEnum import ModelType, BAIDU_MODEL_TARGET_CONFIG
from exception.CustomerException import ServiceException
from util.ImgBaiduSdk import AipBodyAnalysisClient, AipImageClassifyClient
from util.PlotsUtils import get_label_arrays, get_label_array_dict
from util.TorchUtils import select_device

sys.path.extend(['..', '../AIlib2'])
from AI import AI_process, AI_process_forest, get_postProcess_para, ocr_process, AI_process_N, AI_process_C
from stdc import stdcModel
from segutils.segmodel import SegModel
from models.experimental import attempt_load
from obbUtils.shipUtils import OBB_infer
from obbUtils.load_obb_model import load_model_decoder_OBB
import torch
import tensorrt as trt
from utilsK.jkmUtils import pre_process, post_process, get_return_data
from DMPR import DMPRModel
FONT_PATH = "../AIlib2/conf/platech.ttf"


# 河道模型、河道检测模型、交通模型、人员落水模型、城市违章公共模型
class OneModel:
    __slots__ = "model_conf"

    def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None, env=None):
        try:
            logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
                        requestId)
            par = modeType.value[4](str(device), gpu_name)
            mode, postPar, segPar = par.get('mode', 'others'), par.get('postPar'), par.get('segPar')
            names = par['labelnames']
            postFile = par['postFile']
            rainbows = postFile["rainbows"]
            new_device = select_device(par.get('device'))
            half = new_device.type != 'cpu'
            Detweights = par['Detweights']
            with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
                model = runtime.deserialize_cuda_engine(f.read())
            par['segPar']['seg_nclass'] = par['seg_nclass']
            Segweights = par['Segweights']
            if Segweights:
                if modeType.value[3] == 'cityMangement3':
                    segmodel = DMPRModel(weights=Segweights, par=par['segPar'])
                else:
                    segmodel = stdcModel(weights=Segweights, par=par['segPar'])
            else:
                segmodel = None
            objectPar = {
                'half': half,
                'device': new_device,
                'conf_thres': postFile["conf_thres"],
                'ovlap_thres_crossCategory': postFile.get("ovlap_thres_crossCategory"),
                'iou_thres': postFile["iou_thres"],
                'allowedList': [],
                'segRegionCnt': par['segRegionCnt'],
                'trtFlag_det': par['trtFlag_det'],
                'trtFlag_seg': par['trtFlag_seg']
            }
            model_param = {
                "model": model,
                "segmodel": segmodel,
                "objectPar": objectPar,
                "segPar": segPar,
                "mode": mode,
                "postPar": postPar
            }
            self.model_conf = (modeType, model_param, allowedList, names, rainbows)
        except Exception:
            logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
            raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
                                   ExceptionType.MODEL_LOADING_EXCEPTION.value[1])

class cityManagementModel:
    __slots__ = "model_conf"

    def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None, env=None):
        try:
            logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
                        requestId)
            par = modeType.value[4](str(device), gpu_name)
            postProcess = par['postProcess']
            names = par['labelnames']
            postFile = par['postFile']
            rainbows = postFile["rainbows"]
            modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par'])  for  modelPar in par['models'] ]
            model_param = {
                "modelList": modelList,
                "postProcess": postProcess,
            }
            self.model_conf = (modeType, model_param, allowedList, names, rainbows)
        except Exception:
            logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
            raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
                                   ExceptionType.MODEL_LOADING_EXCEPTION.value[1])

def detSeg_demo2(args):
    model_conf, frame, request_id = args
    modelList, postProcess = model_conf[1]['modelList'], model_conf[1]['postProcess']
    try:
        return AI_process_N([frame], modelList, postProcess)
    except ServiceException as s:
        raise s
    except Exception:
        # self.num += 1
        # cv2.imwrite('/home/th/tuo_heng/dev/img%s.jpg' % str(self.num), frame)
        logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
        raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
                               ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
     
        
def model_process(args):
    model_conf, frame, request_id = args
    model_param, names, rainbows = model_conf[1], model_conf[3], model_conf[4]
    # modeType, model_param, allowedList, names, rainbows = model_conf
    #   segmodel, names, label_arraylist, rainbows, objectPar, font, segPar, mode, postPar, requestId = args
    # model_param['digitFont'] = digitFont
    # model_param['label_arraylist'] = label_arraylist
    # model_param['font_config'] = font_config
    try:
        return AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
                          rainbows, objectPar=model_param['objectPar'], font=model_param['digitFont'],
                          segPar=loads(dumps(model_param['segPar'])), mode=model_param['mode'],
                          postPar=model_param['postPar'])
    except ServiceException as s:
        raise s
    except Exception:
        # self.num += 1
        # cv2.imwrite('/home/th/tuo_heng/dev/img%s.jpg' % str(self.num), frame)
        logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
        raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
                               ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])


# 森林模型、车辆模型、行人模型、烟火模型、 钓鱼模型、航道模型、乡村模型、城管模型公共模型
class TwoModel:
    __slots__ = "model_conf"

    def __init__(self, device1, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
                 env=None):
        s = time.time()
        try:
            logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
                        requestId)
            par = modeType.value[4](str(device1), gpu_name)
            device = select_device(par.get('device'))
            names = par['labelnames']
            half = device.type != 'cpu'
            Detweights = par['Detweights']
            with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
                model = runtime.deserialize_cuda_engine(f.read())
            segmodel = None
            postFile = par['postFile']
            conf_thres = postFile["conf_thres"]
            iou_thres = postFile["iou_thres"]
            rainbows = postFile["rainbows"]
            otc = postFile.get("ovlap_thres_crossCategory")
            model_param = {
                "model": model,
                "segmodel": segmodel,
                "half": half,
                "device": device,
                "conf_thres": conf_thres,
                "iou_thres": iou_thres,
                "trtFlag_det": par['trtFlag_det'],
                "otc": otc
            }
            self.model_conf = (modeType, model_param, allowedList, names, rainbows)
        except Exception:
            logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
            raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
                                   ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
        logger.info("模型初始化时间：{}, requestId:{}", time.time() - s, requestId)


def forest_process(args):
    model_conf, frame, request_id = args
    model_param, names, rainbows = model_conf[1], model_conf[3], model_conf[4]
    try:
        return AI_process_forest([frame], model_param['model'], model_param['segmodel'], names,
                                 model_param['label_arraylist'], rainbows, model_param['half'], model_param['device'],
                                 model_param['conf_thres'], model_param['iou_thres'], [], font=model_param['digitFont'],
                                 trtFlag_det=model_param['trtFlag_det'], SecNms=model_param['otc'])
    except ServiceException as s:
        raise s
    except Exception:
        # self.num += 1
        # cv2.imwrite('/home/th/tuo_heng/dev/img%s.jpg' % str(self.num), frame)
        logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
        raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
                               ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])

class MultiModel:
    __slots__ = "model_conf"

    def __init__(self, device1, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
                 env=None):
        s = time.time()
        try:
            logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
                        requestId)
            par = modeType.value[4](str(device1), gpu_name)
            postProcess = par['postProcess']
            names = par['labelnames']
            postFile = par['postFile']
            rainbows = postFile["rainbows"]
            modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par'])  for  modelPar in par['models'] ]
            model_param = {
                "modelList": modelList,
                "postProcess": postProcess,
            }
            self.model_conf = (modeType, model_param, allowedList, names, rainbows)
        except Exception:
            logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
            raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
                                   ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
        logger.info("模型初始化时间：{}, requestId:{}", time.time() - s, requestId)

def channel2_process(args):
    model_conf, frame, request_id = args
    modelList, postProcess = model_conf[1]['modelList'], model_conf[1]['postProcess']
    try:
        start = time.time()
        result = [[None, None, AI_process_C([frame], modelList, postProcess)[0]]]   # 为了让返回值适配统一的接口而写的shi
        # print("AI_process_C use time = {}".format(time.time()-start))
        return result
    except ServiceException as s:
        raise s
    except Exception:
        logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
        raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
                               ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
    
def get_label_arraylist(*args):
    width, height, names, rainbows = args
    # line = int(round(0.002 * (height + width) / 2) + 1)
    line = max(1, int(round(width / 1920 * 3)))
    label = ' 0.95'
    tf = max(line - 1, 1)
    fontScale = line * 0.33
    text_width, text_height = cv2.getTextSize(label, 0, fontScale=fontScale, thickness=tf)[0]
    # fontsize = int(width / 1920 * 40)
    numFontSize = float(format(width / 1920 * 1.1, '.1f'))
    digitFont = {'line_thickness': line,
                 'boxLine_thickness': line,
                 'fontSize': numFontSize,
                 'waterLineColor': (0, 255, 255),
                 'segLineShow': False,
                 'waterLineWidth': line,
                 'wordSize': text_height,
                 'label_location': 'leftTop'}
    label_arraylist = get_label_arrays(names, rainbows, fontSize=text_height, fontPath=FONT_PATH)
    return digitFont, label_arraylist, (line, text_width, text_height, fontScale, tf)


# 船只模型
class ShipModel:
    __slots__ = "model_conf"

    def __init__(self, device1, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
                 env=None):
        s = time.time()
        try:
            logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
                        requestId)
            par = modeType.value[4](str(device1), gpu_name)
            model, decoder2 = load_model_decoder_OBB(par)
            par['decoder'] = decoder2
            names = par['labelnames']
            rainbows = par['postFile']["rainbows"]
            model_param = {
                "model": model,
                "par": par
            }
            self.model_conf = (modeType, model_param, allowedList, names, rainbows)
        except Exception:
            logger.exception("模型加载异常：{}, requestId:{}", format_exc(), requestId)
            raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
                                   ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
        logger.info("模型初始化时间：{}, requestId:{}", time.time() - s, requestId)


def obb_process(args):
    model_conf, frame, request_id = args
    model_param = model_conf[1]
    # font_config, frame, names, label_arrays, rainbows, model, par, requestId = args
    try:
        return OBB_infer(model_param["model"], frame, model_param["par"])
    except ServiceException as s:
        raise s
    except Exception:
        # self.num += 1
        # cv2.imwrite('/home/th/tuo_heng/dev/img%s.jpg' % str(self.num), frame)
        logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
        raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
                               ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])


# 车牌分割模型、健康码、行程码分割模型
class IMModel:
    __slots__ = "model_conf"

    def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
                 env=None):
        try:
            logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
                        requestId)
            img_type = 'code'
            if ModelType.PLATE_MODEL == modeType:
                img_type = 'plate'
            par = {
                'code': {'weights': '../AIlib2/weights/conf/jkm/health_yolov5s_v3.jit', 'img_type': 'code', 'nc': 10},
                'plate': {'weights': '../AIlib2/weights/conf/jkm/plate_yolov5s_v3.jit', 'img_type': 'plate', 'nc': 1},
                'conf_thres': 0.4,
                'iou_thres': 0.45,
                'device': 'cuda:%s' % device,
                'plate_dilate': (0.5, 0.3)
            }

            new_device = torch.device(par['device'])
            model = torch.jit.load(par[img_type]['weights'])
            self.model_conf = (modeType, allowedList, new_device, model, par, img_type)
        except Exception:
            logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
            raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
                                   ExceptionType.MODEL_LOADING_EXCEPTION.value[1])


def im_process(args):
    frame, device, model, par, img_type, requestId = args
    try:
        img, padInfos = pre_process(frame, device)
        pred = model(img)
        boxes = post_process(pred, padInfos, device, conf_thres=par['conf_thres'],
                             iou_thres=par['iou_thres'], nc=par[img_type]['nc'])  # 后处理
        dataBack = get_return_data(frame, boxes, modelType=img_type, plate_dilate=par['plate_dilate'])
        return dataBack
    except ServiceException as s:
        raise s
    except Exception:
        logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), requestId)
        raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
                               ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])


# 百度AI图片识别模型
class BaiduAiImageModel:
    __slots__ = "model_conf"

    def __init__(self, device=None, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
                 env=None):
        try:
            logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
                        requestId)
            # 人体检测与属性识别、 人流量统计客户端
            aipBodyAnalysisClient = AipBodyAnalysisClient(base_dir, env)
            # 车辆检测检测客户端
            aipImageClassifyClient = AipImageClassifyClient(base_dir, env)
            rainbows = COLOR
            vehicle_names = [VehicleEnum.CAR.value[1], VehicleEnum.TRICYCLE.value[1], VehicleEnum.MOTORBIKE.value[1],
                             VehicleEnum.CARPLATE.value[1], VehicleEnum.TRUCK.value[1], VehicleEnum.BUS.value[1]]
            person_names = ['人']
            self.model_conf = (modeType, aipImageClassifyClient, aipBodyAnalysisClient, allowedList, rainbows,
                               vehicle_names, person_names, requestId)
        except Exception:
            logger.exception("模型加载异常：{}, requestId:{}", format_exc(), requestId)
            raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
                                   ExceptionType.MODEL_LOADING_EXCEPTION.value[1])


def get_baidu_label_arraylist(*args):
    width, height, vehicle_names, person_names, rainbows = args
    # line = int(round(0.002 * (height + width) / 2) + 1)
    line = max(1, int(round(width / 1920 * 3) + 1))
    label = ' 0.97'
    tf = max(line, 1)
    fontScale = line * 0.33
    text_width, text_height = cv2.getTextSize(label, 0, fontScale=fontScale, thickness=tf)[0]
    vehicle_label_arrays = get_label_arrays(vehicle_names, rainbows, fontSize=text_height, fontPath=FONT_PATH)
    person_label_arrays = get_label_arrays(person_names, rainbows, fontSize=text_height, fontPath=FONT_PATH)
    font_config = (line, text_width, text_height, fontScale, tf)
    return vehicle_label_arrays, person_label_arrays, font_config


def baidu_process(args):
    target, url, aipImageClassifyClient, aipBodyAnalysisClient, request_id = args
    try:
        # [target, url, aipImageClassifyClient, aipBodyAnalysisClient, requestId]
        baiduEnum = BAIDU_MODEL_TARGET_CONFIG.get(target)
        if baiduEnum is None:
            raise ServiceException(ExceptionType.DETECTION_TARGET_TYPES_ARE_NOT_SUPPORTED.value[0],
                                   ExceptionType.DETECTION_TARGET_TYPES_ARE_NOT_SUPPORTED.value[1]
                                   + " target: " + target)
        return baiduEnum.value[2](aipImageClassifyClient, aipBodyAnalysisClient, url, request_id)
    except ServiceException as s:
        raise s
    except Exception:
        logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
        raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
                               ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])


def one_label(width, height, model_conf):
    # modeType, model_param, allowedList, names, rainbows = model_conf
    names = model_conf[3]
    rainbows = model_conf[4]
    model_param = model_conf[1]
    digitFont, label_arraylist, font_config = get_label_arraylist(width, height, names, rainbows)
    model_param['digitFont'] = digitFont
    model_param['label_arraylist'] = label_arraylist
    model_param['font_config'] = font_config

def dynamics_label(width, height, model_conf):
    # modeType, model_param, allowedList, names, rainbows = model_conf
    names = model_conf[3]
    rainbows = model_conf[4]
    model_param = model_conf[1]
    digitFont, label_arraylist, font_config = get_label_arraylist(width, height, names, rainbows)
    line = max(1, int(round(width / 1920 * 3)))
    label = ' 0.95'
    tf = max(line - 1, 1)
    fontScale = line * 0.33
    _, text_height = cv2.getTextSize(label, 0, fontScale=fontScale, thickness=tf)[0]
    label_dict = get_label_array_dict(rainbows, fontSize=text_height, fontPath=FONT_PATH)
    model_param['digitFont'] = digitFont
    model_param['label_arraylist'] = label_arraylist
    model_param['font_config'] = font_config
    model_param['label_dict'] = label_dict
def baidu_label(width, height, model_conf):
    # modeType, aipImageClassifyClient, aipBodyAnalysisClient, allowedList, rainbows,
    # vehicle_names, person_names, requestId
    vehicle_names = model_conf[5]
    person_names = model_conf[6]
    rainbows = model_conf[4]
    vehicle_label_arrays, person_label_arrays, font_config = get_baidu_label_arraylist(width, height, vehicle_names,
                                                                                       person_names, rainbows)
    return vehicle_label_arrays, person_label_arrays, font_config


MODEL_CONFIG = {
    # 加载河道模型
    ModelType.WATER_SURFACE_MODEL.value[1]: (
        lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.WATER_SURFACE_MODEL, t, z, h),
        ModelType.WATER_SURFACE_MODEL,
        lambda x, y, z: one_label(x, y, z),
        lambda x: model_process(x)
    ),
    # 加载森林模型
    ModelType.FOREST_FARM_MODEL.value[1]: (
        lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.FOREST_FARM_MODEL, t, z, h),
        ModelType.FOREST_FARM_MODEL,
        lambda x, y, z: one_label(x, y, z),
        lambda x: forest_process(x)
    ),
    # 加载交通模型
    ModelType.TRAFFIC_FARM_MODEL.value[1]: (
        lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.TRAFFIC_FARM_MODEL, t, z, h),
        ModelType.TRAFFIC_FARM_MODEL,
        lambda x, y, z: one_label(x, y, z),
        lambda x: model_process(x)
    ),
    # 加载防疫模型
    ModelType.EPIDEMIC_PREVENTION_MODEL.value[1]: (
        lambda x, y, r, t, z, h: IMModel(x, y, r, ModelType.EPIDEMIC_PREVENTION_MODEL, t, z, h),
        ModelType.EPIDEMIC_PREVENTION_MODEL,
        None,
        lambda x: im_process(x)),
    # 加载车牌模型
    ModelType.PLATE_MODEL.value[1]: (
        lambda x, y, r, t, z, h: IMModel(x, y, r, ModelType.PLATE_MODEL, t, z, h),
        ModelType.PLATE_MODEL,
        None,
        lambda x: im_process(x)),
    # 加载车辆模型
    ModelType.VEHICLE_MODEL.value[1]: (
        lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.VEHICLE_MODEL, t, z, h),
        ModelType.VEHICLE_MODEL,
        lambda x, y, z: one_label(x, y, z),
        lambda x: forest_process(x)
    ),
    # 加载行人模型
    ModelType.PEDESTRIAN_MODEL.value[1]: (
        lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.PEDESTRIAN_MODEL, t, z, h),
        ModelType.PEDESTRIAN_MODEL,
        lambda x, y, z: one_label(x, y, z),
        lambda x: forest_process(x)),
    # 加载烟火模型
    ModelType.SMOGFIRE_MODEL.value[1]: (
        lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.SMOGFIRE_MODEL, t, z, h),
        ModelType.SMOGFIRE_MODEL,
        lambda x, y, z: one_label(x, y, z),
        lambda x: forest_process(x)),
    # 加载钓鱼游泳模型
    ModelType.ANGLERSWIMMER_MODEL.value[1]: (
        lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.ANGLERSWIMMER_MODEL, t, z, h),
        ModelType.ANGLERSWIMMER_MODEL,
        lambda x, y, z: one_label(x, y, z),
        lambda x: forest_process(x)),
    # 加载乡村模型
    ModelType.COUNTRYROAD_MODEL.value[1]: (
        lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.COUNTRYROAD_MODEL, t, z, h),
        ModelType.COUNTRYROAD_MODEL,
        lambda x, y, z: one_label(x, y, z),
        lambda x: forest_process(x)),
    # 加载船只模型
    ModelType.SHIP_MODEL.value[1]: (
        lambda x, y, r, t, z, h: ShipModel(x, y, r, ModelType.SHIP_MODEL, t, z, h),
        ModelType.SHIP_MODEL,
        lambda x, y, z: one_label(x, y, z),
        lambda x: obb_process(x)),
    # 百度AI图片识别模型
    ModelType.BAIDU_MODEL.value[1]: (
        lambda x, y, r, t, z, h: BaiduAiImageModel(x, y, r, ModelType.BAIDU_MODEL, t, z, h),
        ModelType.BAIDU_MODEL,
        lambda x, y, z: baidu_label(x, y, z),
        lambda x: baidu_process(x)),
    # 航道模型
    ModelType.CHANNEL_EMERGENCY_MODEL.value[1]: (
        lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.CHANNEL_EMERGENCY_MODEL, t, z, h),
        ModelType.CHANNEL_EMERGENCY_MODEL,
        lambda x, y, z: one_label(x, y, z),
        lambda x: forest_process(x)),
    # 河道检测模型
    ModelType.RIVER2_MODEL.value[1]: (
        lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.RIVER2_MODEL, t, z, h),
        ModelType.RIVER2_MODEL,
        lambda x, y, z: one_label(x, y, z),
        lambda x: model_process(x)),
    # 城管模型
    ModelType.CITY_MANGEMENT_MODEL.value[1]: (
        lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.CITY_MANGEMENT_MODEL, t, z, h),
        ModelType.CITY_MANGEMENT_MODEL,
        lambda x, y, z: one_label(x, y, z),
        lambda x: detSeg_demo2(x)
    ),
    # 人员落水模型
    ModelType.DROWING_MODEL.value[1]: (
        lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.DROWING_MODEL, t, z, h),
        ModelType.DROWING_MODEL,
        lambda x, y, z: one_label(x, y, z),
        lambda x: model_process(x)
    ),
    # 城市违章模型
    ModelType.NOPARKING_MODEL.value[1]: (
        lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.NOPARKING_MODEL, t, z, h),
        ModelType.NOPARKING_MODEL,
        lambda x, y, z: one_label(x, y, z),
        lambda x: model_process(x)
    ),
    # 车辆违停模型
    ModelType.ILLPARKING_MODEL.value[1]: (
        lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.ILLPARKING_MODEL, t, z, h),
        ModelType.ILLPARKING_MODEL,
        lambda x, y, z: one_label(x, y, z),
        lambda x: model_process(x)
    ),
    # 城市公路模型
    ModelType.CITYROAD_MODEL.value[1]: (
        lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.CITYROAD_MODEL, t, z, h),
        ModelType.CITYROAD_MODEL,
        lambda x, y, z: one_label(x, y, z),
        lambda x: forest_process(x)),
    # 加载坑槽模型
    ModelType.POTHOLE_MODEL.value[1]: (
        lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.POTHOLE_MODEL, t, z, h),
        ModelType.POTHOLE_MODEL,
        lambda x, y, z: one_label(x, y, z),
        lambda x: forest_process(x)
    ),
    # 加载船只综合检测模型
    ModelType.CHANNEL2_MODEL.value[1]: (
        lambda x, y, r, t, z, h: MultiModel(x, y, r, ModelType.CHANNEL2_MODEL, t, z, h),
        ModelType.CHANNEL2_MODEL,
        lambda x, y, z: dynamics_label(x, y, z),
        lambda x: channel2_process(x)
    ),
    # 河道检测模型
    ModelType.RIVERT_MODEL.value[1]: (
        lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.RIVERT_MODEL, t, z, h),
        ModelType.RIVERT_MODEL,
        lambda x, y, z: one_label(x, y, z),
        lambda x: model_process(x)),
}