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车牌及健康码权重文件路径优化

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th 2025-06-26 13:28:04 +08:00
parent 7f85fab1d8
commit 12a4b296e1
89 changed files with 2562 additions and 119 deletions
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concurrency/FileUploadThread.py
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@ -26,19 +26,34 @@ class FileUpload(Thread):
self._fb_queue, self._context, self._msg, self._image_queue, self._analyse_type,self._mqtt_list = args
self._storage_source = self._context['service']['storage_source']
self._algStatus = False # 默认关闭
# self._algStatus = True # 默认关闭
self._algSwitch = self._context['service']['algSwitch']
# for algswitch flag:
default_enabled = str(self._msg.get("defaultEnabled","True")).lower() == "true"
#0521:
default_enabled = str(self._msg.get("defaultEnabled", "True")).lower() == "true"
if default_enabled:
self._algSwitch= True
print("执行默认程序（defaultEnabled=True）")
self._algSwitch = True
# 这里放默认逻辑的代码
else:
self._algSwitch= False
print("执行替代程序（defaultEnabled=False）")
# 这里放非默认逻辑的代码
self._algSwitch = False
print("---line46 :FileUploadThread.py---",self._algSwitch)
#如果任务是在线、离线处理，则用此类
class ImageFileUpload(FileUpload):
__slots__ = ()

29
concurrency/PushVideoStreamProcess.py
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@ -36,17 +36,28 @@ class PushStreamProcess(Process):
# 传参
self._msg, self._push_queue, self._image_queue, self._push_ex_queue, self._hb_queue, self._context = args
self._algStatus = False # 默认关闭
self._algSwitch = self._context['service']['algSwitch']
# for algswitch flag:
default_enabled = str(self._msg.get("defaultEnabled","True")).lower() == "true"
self._algSwitch = self._context['service']['algSwitch']
#0521:
default_enabled = str(self._msg.get("defaultEnabled", "True")).lower() == "true"
if default_enabled:
self._algSwitch= True
print("执行默认程序（defaultEnabled=True）")
self._algSwitch = True
# 这里放默认逻辑的代码
else:
self._algSwitch= False
print("执行替代程序（defaultEnabled=False）")
# 这里放非默认逻辑的代码
self._algSwitch = False
print("---line53 :PushVideoStreamProcess.py---",self._algSwitch)
def build_logo_url(self):
logo = None

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7
config/minio/dsp_test_minio.yml
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@ -1,8 +1,7 @@
endpoint: 'minio.t-aaron.com'
endpoint: 'minio.t-aaron.com:9000'
domain: 'https://minio.t-aaron.com'
access_key: 'IKf3A0ZSXsR1m0oalMjV'
secret_key: 'yoC6qRo2hlyZu8Pdbt6eh9TVaTV4gD7KRudromrk'
secure: false
image_bucket: 'th-airport'
video_bucket: 'th-airport'
file_dir: 'testFile'
image_bucket: 'image'
video_bucket: 'video'

4
config/service/dsp_test_service.yml
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@ -31,9 +31,9 @@ service:
image:
limit: 20
#storage source,0--aliyun,1--minio
storage_source: 1
storage_source: 0
#是否启用mqtt，0--不用，1--启用
mqtt_flag: 0
#是否启用alg控制功能
algSwitch: false
algSwitch: False

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enums/ModelTypeEnum-jcq.py Normal file
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@ -0,0 +1,768 @@
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
from utilsK.drownUtils import mixDrowing_water_postprocess
from utilsK.noParkingUtils import mixNoParking_road_postprocess
from utilsK.illParkingUtils import illParking_postprocess
from stdc import stdcModel
from yolov5 import yolov5Model
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': "../AIlib2/weights/river/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/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': "../AIlib2/weights/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':"../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 } },
}
],
'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"],
'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),
#'modelSize': (1920,1080),
'RoadArea': 16000,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 9,
'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': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/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': "../AIlib2/weights/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': "../AIlib2/weights/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': "../AIlib2/weights/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': "../AIlib2/weights/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': "../AIlib2/weights/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': '../AIlib2/weights/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': "../AIlib2/weights/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
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/river2/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/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"],
'postProcess':{
'function':dmpr_yolo_stdc,
'pars':{'carCls':0 ,'illCls':3,'scaleRatio':0.5,'border':80,'rubCls': 1, 'Rubfilter': 150}
},
'models':[
{
#'weight':'../AIlib2/weights/conf/cityMangement3/yolov5.pt',
'weight':'../AIlib2/weights/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
'name':'yolov5',
'model':yolov5Model,
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.5,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.8,"1":0.4,"2":0.5,"3":0.5 } }
},
{
'weight':'../AIlib2/weights/conf/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':'../AIlib2/weights/conf/cityMangement3/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'
}
],
'postFile': {
"name": "post_process",
"conf_thres": 0.5,
"iou_thres": 0.5,
"classes": 5,
"rainbows": COLOR
},
'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
'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
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/drowning/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/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': "../AIlib2/weights/noParking/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/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': "../AIlib2/weights/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", "施工"],
'trtFlag_seg': False,
'trtFlag_det': True,
'slopeIndex': [],
'seg_nclass': 2,
'segRegionCnt': 0,
'segPar': None,
'postFile': {
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.5,
"classes": 5,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/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': "../AIlib2/weights/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","未封仓"], # 保持原来的标签顺序不变，方便后面业务端增加
'segRegionCnt': 0,
'postProcess':{'function':channel2_post_process,'name':'channel2','pars':{
'objs':[2],
'wRation':1/6.0,
'hRation':1/6.0,
'smallId':0, #旗帜
'bigId':3, #船只
'newId':4, #未挂国旗船只
'uncoverId':5, #未封仓标签
'recScale':1.2,
'target_cls':3.0, #目标种类
'filter_cls':4.0 #被过滤的种类
}},
'models':[
{
#'weight':'../AIlib2/weights/conf/channel2/yolov5.pt',
# 'weight':'../AIlib2/weights/channel2/yolov5_%s_fp16.engine'%(gpuName),
'weight':'/home/thsw2/jcq/test/AIlib2/weights/channel2/best.pt', # yolov5 原来模型基础上增加了未封仓
# 'weight':'../AIlib2/weights/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' : '../AIlib2/weights/ocr2/crnn_ch_4090_fp16_192X32.engine',
'weight' : '../AIlib2/weights/conf/ocr2/crnn_ch.pth',
'name':'ocr',
'model':ocrModel,
'par':{
'char_file':'../AIlib2/weights/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,
},
} ,
# {
# 'weight':'/home/thsw2/jcq/test/AIlib2/weights1/conf/channel2/yolov5_04.pt', # yolov5_04 添加了uncover 0 4 ；标签 yolov5_jcq
# 'name':'yolov5',
# 'model':yolov5Model,
# 'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.15,'iou_thres':0.25,'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} }
# }
],
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3]],
'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
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/riverT/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/riverT/stdc_360X640_%s_fp16.engine' % gpuName
})
FORESTCROWD_FARM_MODEL = ("2", "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':"../AIlib2/weights/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':0.5,'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),
#'modelSize': (1920,1080),
'RoadArea': 16000,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 9,
'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': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
})
@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

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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
from utilsK.drownUtils import mixDrowing_water_postprocess
from utilsK.noParkingUtils import mixNoParking_road_postprocess
from utilsK.illParkingUtils import illParking_postprocess
from stdc import stdcModel
from yolov5 import yolov5Model
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': "../AIlib2/weights/river/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/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': "../AIlib2/weights/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':"../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 } },
}
],
'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"],
'trtFlag_seg': True,
'trtFlag_det': True,
'seg_nclass': 3,
'segRegionCnt': 2,
'segPar': {
#'modelSize': (640, 360),
'modelSize': (1920, 1080),
'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),
'modelSize': (1920,1080),
'RoadArea': 16000,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 10,
'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': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/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': "../AIlib2/weights/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': "../AIlib2/weights/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': "../AIlib2/weights/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': "../AIlib2/weights/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': "../AIlib2/weights/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': '../AIlib2/weights/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': "../AIlib2/weights/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
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/river2/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/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", "裸土","占道经营","违停"],
'postProcess':{
'function':dmpr_yolo_stdc,
'pars':{'carCls':0 ,'illCls':5,'scaleRatio':0.5,'border':80}
},
'models':[
{
#'weight':'../AIlib2/weights/conf/cityMangement3/yolov5.pt',
'weight':'../AIlib2/weights/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],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.8,"1":0.4,"2":0.5,"3":0.5,"4":0.4,"5":0.5 } }
},
{
'weight':'../AIlib2/weights/conf/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':'../AIlib2/weights/conf/cityMangement3/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'
}
],
'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 [0,1,2,3,5,6,7,8,9] ],###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
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/drowning/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/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': "../AIlib2/weights/noParking/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/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': "../AIlib2/weights/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", "施工"],
'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': "../AIlib2/weights/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': "../AIlib2/weights/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"],
'segRegionCnt': 0,
'postProcess':{'function':channel2_post_process,'name':'channel2','pars':{
'objs':[2],
'wRation':1/6.0,
'hRation':1/6.0,
'smallId':0,
'bigId':3,
'newId':4,
'recScale':1.2}},
'models':[
{
#'weight':'../AIlib2/weights/conf/channel2/yolov5.pt',
'weight':'../AIlib2/weights/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' : '../AIlib2/weights/ocr2/crnn_ch_4090_fp16_192X32.engine',
'weight' : '../AIlib2/weights/conf/ocr2/crnn_ch.pth',
'name':'ocr',
'model':ocrModel,
'par':{
'char_file':'../AIlib2/weights/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]],
'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
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/riverT/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/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':"../AIlib2/weights/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),
#'modelSize': (1920,1080),
'RoadArea': 16000,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 9,
'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': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/highWay2/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':"../AIlib2/weights/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':"../AIlib2/weights/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':"../AIlib2/weights/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
},
})
@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

318
enums/ModelTypeEnum.py
View File

@ -13,6 +13,9 @@ from segutils.trafficUtils import tracfficAccidentMixFunction
from utilsK.drownUtils import mixDrowing_water_postprocess
from utilsK.noParkingUtils import mixNoParking_road_postprocess
from utilsK.illParkingUtils import illParking_postprocess
from utilsK.spillUtils import mixSpillage_postprocess
from utilsK.cthcUtils import mixCthc_postprocess
from utilsK.pannelpostUtils import pannel_post_process
from stdc import stdcModel
from yolov5 import yolov5Model
from DMPRUtils.jointUtil import dmpr_yolo_stdc
@ -61,8 +64,9 @@ class ModelType(Enum):
"classes": 5,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/river/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/river/stdc_360X640_%s_fp16.engine' % gpuName
'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: {
@ -71,7 +75,7 @@ class ModelType(Enum):
# 'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
# 'trtFlag_det': True,
# 'trtFlag_seg': False,
# 'Detweights': "../AIlib2/weights/forest2/yolov5_%s_fp16.engine" % gpuName,
# 'Detweights': "../weights/trt/AIlib2/forest2/yolov5_%s_fp16.engine" % gpuName,
# 'seg_nclass': 2,
# 'segRegionCnt': 0,
# 'slopeIndex': [],
@ -93,7 +97,7 @@ class ModelType(Enum):
'models':
[
{
'weight':"../AIlib2/weights/forest2/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'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 } },
@ -120,7 +124,8 @@ class ModelType(Enum):
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"],
'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,
@ -143,6 +148,8 @@ class ModelType(Enum):
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 10,
'CarId':1,
'CthcId':12,
'vehicleFactor': 0.1,
'confThres': 0.25,
'roadIou': 0.6,
@ -159,8 +166,9 @@ class ModelType(Enum):
"classes": 10,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
'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)
@ -176,7 +184,7 @@ class ModelType(Enum):
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/vehicle/yolov5_%s_fp16.engine" % gpuName,
'Detweights': "../weights/trt/AIlib2/vehicle/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
@ -196,7 +204,7 @@ class ModelType(Enum):
'segRegionCnt': 0,
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/pedestrian/yolov5_%s_fp16.engine" % gpuName,
'Detweights': "../weights/trt/AIlib2/pedestrian/yolov5_%s_fp16.engine" % gpuName,
'slopeIndex': [],
'segPar': None,
'postFile': {
@ -217,7 +225,7 @@ class ModelType(Enum):
'segRegionCnt': 0,
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/smogfire/yolov5_%s_fp16.engine" % gpuName,
'Detweights': "../weights/trt/AIlib2/smogfire/yolov5_%s_fp16.engine" % gpuName,
'slopeIndex': [],
'segPar': None,
'postFile': {
@ -239,7 +247,7 @@ class ModelType(Enum):
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/AnglerSwimmer/yolov5_%s_fp16.engine" % gpuName,
'Detweights': "../weights/trt/AIlib2/AnglerSwimmer/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
@ -260,7 +268,7 @@ class ModelType(Enum):
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/countryRoad/yolov5_%s_fp16.engine" % gpuName,
'Detweights': "../weights/trt/AIlib2/countryRoad/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
@ -279,7 +287,7 @@ class ModelType(Enum):
'device': 'cuda:%s' % device,
'down_ratio': 4,
'num_classes': 15,
'weights': '../AIlib2/weights/ship2/obb_608X608_%s_fp16.engine' % gpuName,
'weights': '../weights/trt/AIlib2/ship2/obb_608X608_%s_fp16.engine' % gpuName,
'dataset': 'dota',
'half': False,
'mean': (0.5, 0.5, 0.5),
@ -311,7 +319,7 @@ class ModelType(Enum):
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/channelEmergency/yolov5_%s_fp16.engine" % gpuName,
'Detweights': "../weights/trt/AIlib2/channelEmergency/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
@ -353,33 +361,33 @@ class ModelType(Enum):
"classes": 5,
"rainbows": COLOR
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/river2/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/river2/stdc_360X640_%s_fp16.engine' % gpuName
# "../weights/pth/AIlib2/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../weights/trt/AIlib2/river2/yolov5_%s_fp16.engine" % gpuName,
# '../weights/pth/AIlib2/%s/stdc_360X640.pth' % modeType.value[3]
'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" ],
'labelnames': [ "车辆", "垃圾", "商贩", "违停","占道经营","裸土","未覆盖裸土" ],
'postProcess':{
'function':dmpr_yolo_stdc,
'pars':{
'carCls':0 ,'illCls':5,'scaleRatio':0.5,'border':80,
'carCls':0 ,'illCls':6,'scaleRatio':0.5,'border':80,
#车辆","垃圾","商贩","裸土","占道经营","违停"--->
#"车辆","垃圾","商贩","违停","占道经营","裸土"
'classReindex':{ 0:0,1:1,2:2,3:5,4:4,5:3}
'classReindex':{ 0:0,1:1,2:2,3:6,4:4,5:5,6:3}
}
},
'models':[
{
#'weight':'../AIlib2/weights/conf/cityMangement3/yolov5.pt',
'weight':'../AIlib2/weights/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
'weight':'../weights/pth/AIlib2/cityMangement3/yolov5.pt',
#'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],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.8,"1":0.4,"2":0.5,"3":0.5 } }
'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":0.4,"2":0.5,"3":0.5 } }
},
{
'weight':'../AIlib2/weights/conf/cityMangement3/dmpr.pth',
'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'
@ -387,8 +395,8 @@ class ModelType(Enum):
'model':DMPRModel,
'name':'dmpr'
},
{
'weight':'../AIlib2/weights/conf/cityMangement3/stdc_360X640.pth',
{
'weight':'../weights/pth/AIlib2/cityMangement3/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,
@ -399,7 +407,7 @@ class ModelType(Enum):
"name": "post_process",
"conf_thres": 0.25,
"iou_thres": 0.45,
"classes": 5,
"classes": 6,
"rainbows": COLOR
},
'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
@ -435,10 +443,10 @@ class ModelType(Enum):
"classes": 9,
"rainbows": COLOR
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/drowning/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/drowning/stdc_360X640_%s_fp16.engine' % gpuName
# "../weights/pth/AIlib2/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../weights/trt/AIlib2/drowning/yolov5_%s_fp16.engine" % gpuName,
# '../weights/pth/AIlib2/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../weights/trt/AIlib2/drowning/stdc_360X640_%s_fp16.engine' % gpuName
})
NOPARKING_MODEL = (
@ -476,8 +484,8 @@ class ModelType(Enum):
"classes": 9,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/noParking/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/noParking/stdc_360X640_%s_fp16.engine' % gpuName
'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: {
@ -500,7 +508,7 @@ class ModelType(Enum):
"classes": 9,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/illParking/yolov5_%s_fp16.engine" % gpuName,
'Detweights': "../weights/trt/AIlib2/illParking/yolov5_%s_fp16.engine" % gpuName,
'Segweights': None
})
@ -520,7 +528,7 @@ class ModelType(Enum):
"classes": 5,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/cityRoad/yolov5_%s_fp16.engine" % gpuName,
'Detweights': "../weights/trt/AIlib2/cityRoad/yolov5_%s_fp16.engine" % gpuName,
'Segweights': None
})
@ -533,7 +541,7 @@ class ModelType(Enum):
'slopeIndex': [],
'trtFlag_det': True,
'trtFlag_seg': False,
'Detweights': "../AIlib2/weights/pothole/yolov5_%s_fp16.engine" % gpuName,
'Detweights': "../weights/trt/AIlib2/pothole/yolov5_%s_fp16.engine" % gpuName,
'segPar': None,
'postFile': {
"name": "post_process",
@ -548,31 +556,37 @@ class ModelType(Enum):
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"],
'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,
'smallId':0,
'bigId':3,
'newId':4,
'recScale':1.2}},
'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':'../AIlib2/weights/conf/channel2/yolov5.pt',
'weight':'../AIlib2/weights/channel2/yolov5_%s_fp16.engine'%(gpuName),
#'weight':'../weights/pth/AIlib2/channel2/yolov5.pt',
'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' : '../AIlib2/weights/ocr2/crnn_ch_4090_fp16_192X32.engine',
'weight' : '../AIlib2/weights/conf/ocr2/crnn_ch.pth',
# 'weight' : '../weights/trt/AIlib2/ocr2/crnn_ch_4090_fp16_192X32.engine',
'weight' : '../weights/pth/AIlib2/ocr2/crnn_ch.pth',
'name':'ocr',
'model':ocrModel,
'par':{
'char_file':'../AIlib2/weights/conf/ocr2/benchmark.txt',
'char_file':'../AIlib2/conf/ocr2/benchmark.txt',
'mode':'ch',
'nc':3,
'imgH':32,
@ -584,7 +598,7 @@ class ModelType(Enum):
},
}
],
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3]],
'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",
@ -626,10 +640,10 @@ class ModelType(Enum):
"classes": 5,
"rainbows": COLOR
},
# "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../AIlib2/weights/riverT/yolov5_%s_fp16.engine" % gpuName,
# '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../AIlib2/weights/riverT/stdc_360X640_%s_fp16.engine' % gpuName
# "../weights/pth/AIlib2/%s/yolov5.pt" % modeType.value[3]
'Detweights': "../weights/trt/AIlib2/riverT/yolov5_%s_fp16.engine" % gpuName,
# '../weights/pth/AIlib2/%s/stdc_360X640.pth' % modeType.value[3]
'Segweights': '../weights/trt/AIlib2/riverT/stdc_360X640_%s_fp16.engine' % gpuName
})
@ -640,7 +654,7 @@ class ModelType(Enum):
'models':
[
{
'weight':"../AIlib2/weights/forestCrowd/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'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 } },
@ -685,7 +699,7 @@ class ModelType(Enum):
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 9,
'cls': 10,
'vehicleFactor': 0.1,
'confThres': 0.25,
'roadIou': 0.6,
@ -702,8 +716,8 @@ class ModelType(Enum):
"classes": 10,
"rainbows": COLOR
},
'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
'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: {
@ -712,7 +726,7 @@ class ModelType(Enum):
'models':
[
{
'weight':"../AIlib2/weights/smartSite/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'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 } },
@ -732,7 +746,7 @@ class ModelType(Enum):
'models':
[
{
'weight':"../AIlib2/weights/rubbish/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'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 } },
@ -752,7 +766,7 @@ class ModelType(Enum):
'models':
[
{
'weight':"../AIlib2/weights/firework/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
'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 } },
@ -765,7 +779,191 @@ class ModelType(Enum):
},
})
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': 2,
'segRegionCnt': 2,
'segPar': {
#'modelSize': (640, 360),
'modelSize': (1920, 1080),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': mixSpillage_postprocess,
'pars': {
#'modelSize': (640, 360),
'modelSize': (1920,1080),
'RoadArea': 16000,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 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": 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': 2,
'segRegionCnt': 2,
'segPar': {
#'modelSize': (640, 360),
'modelSize': (1920, 1080),
'mean': (0.485, 0.456, 0.406),
'std': (0.229, 0.224, 0.225),
'predResize': True,
'numpy': False,
'RGB_convert_first': True,
'mixFunction': {
'function': mixCthc_postprocess,
'pars': {
#'modelSize': (640, 360),
'modelSize': (1920,1080),
'RoadArea': 16000,
'roadVehicleAngle': 15,
'speedRoadVehicleAngleMax': 75,
'roundness': 1.0,
'cls': 4,
'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": 1,
"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: {
'device': str(device),
'models':{
{
'weights': '../AIlib2/weights/conf/jkm/plate_yolov5s_v3.jit',
'conf_thres': 0.4,
'iou_thres': 0.45,
'nc':1,
},
{
'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
},
})
@staticmethod
def checkCode(code):

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4
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@ -6,7 +6,3 @@
(2)修改了minio情况下的，文件名命名方式。
3.2025.02.12
(1)增加了对alg算法开发的代码。可以通过配置文件config/service/dsp_test_service.yml中algSwitch: true，决定是否启用。
4.2025.04.26
(1)代码更新路径到gitadmin
5.updata-jcq

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# -*- coding: utf-8 -*-
import time,os
from os.path import join
from traceback import format_exc
import json
from cerberus import Validator
from common.Constant import ONLINE_START_SCHEMA, ONLINE_STOP_SCHEMA, OFFLINE_START_SCHEMA, OFFLINE_STOP_SCHEMA, \
IMAGE_SCHEMA, RECORDING_START_SCHEMA, RECORDING_STOP_SCHEMA, PULL2PUSH_START_SCHEMA, PULL2PUSH_STOP_SCHEMA
from common.YmlConstant import service_yml_path, kafka_yml_path
from concurrency.FeedbackThread import FeedbackThread
from concurrency.uploadGPU import uploadGPUinfos
from concurrency.IntelligentRecognitionProcess2 import OnlineIntelligentRecognitionProcess2, \
OfflineIntelligentRecognitionProcess2, PhotosIntelligentRecognitionProcess2
from concurrency.Pull2PushStreamProcess import PushStreamProcess
from entity.FeedBack import message_feedback, recording_feedback, pull_stream_feedback
from enums.AnalysisStatusEnum import AnalysisStatus
from enums.AnalysisTypeEnum import AnalysisType
from enums.ExceptionEnum import ExceptionType
from enums.ModelTypeEnum import ModelMethodTypeEnum, ModelType
from enums.RecordingStatusEnum import RecordingStatus
from enums.StatusEnum import PushStreamStatus, ExecuteStatus
from exception.CustomerException import ServiceException
from loguru import logger
from multiprocessing import Queue
from concurrency.IntelligentRecognitionProcess import OnlineIntelligentRecognitionProcess, \
OfflineIntelligentRecognitionProcess, PhotosIntelligentRecognitionProcess, ScreenRecordingProcess
from util.CpuUtils import print_cpu_ex_status
from util.FileUtils import create_dir_not_exist
from util.GPUtils import get_first_gpu_name, print_gpu_ex_status, check_cude_is_available,select_best_server
from util.KafkaUtils import CustomerKafkaConsumer
from util.QueUtil import put_queue
from util.RWUtils import getConfigs
from kafka import KafkaProducer, KafkaConsumer
'''
分发服务
'''
class DispatcherService:
__slots__ = ('__context', '__feedbackThread', '__listeningProcesses', '__fbQueue', '__topics','__taskType', '__task_type',
'__kafka_config', '__recordingProcesses', '__pull2PushProcesses','__topicsPort','__gpuTopic','__role','__uploadGPUThread','__gpuDics','__producer')
def __init__(self, base_dir, env):
# 检测cuda是否活动
check_cude_is_available()
# 获取全局上下文配置
self.__context = getConfigs(join(base_dir, service_yml_path % env))
# 创建任务执行, 视频保存路径
create_dir_not_exist(join(base_dir, self.__context["video"]["file_path"]))
# 将根路径和环境设置到上下文中
self.__context["base_dir"], self.__context["env"] = base_dir, env
# 问题反馈线程
self.__feedbackThread,self.__uploadGPUThread, self.__fbQueue = None,None, Queue()
# 实时、离线、图片任务进程字典
self.__listeningProcesses = {}
# 录屏任务进程字典
self.__recordingProcesses = {}
# 转推流任务进程字典
self.__pull2PushProcesses = {}
self.__kafka_config = getConfigs(join(base_dir, kafka_yml_path % env))
self.__producer = KafkaProducer(
bootstrap_servers=self.__kafka_config['bootstrap_servers'],#tencent yun
value_serializer=lambda v: v.encode('utf-8'))
self.__gpuDics = { }#用于存储gpu信息的字典
self.__role = self.__context["role"]
self.__topics = [
self.__kafka_config["topic"]["dsp-alg-online-tasks-topic"], # 实时监听topic
self.__kafka_config["topic"]["dsp-alg-offline-tasks-topic"], # 离线监听topic
self.__kafka_config["topic"]["dsp-alg-image-tasks-topic"], # 图片监听topic
self.__kafka_config["topic"]["dsp-recording-task-topic"], # 录屏监听topic
self.__kafka_config["topic"]["dsp-push-stream-task-topic"] # 推流监听topic
]
self.__topicsPort = [
self.__kafka_config["topicPort"]["dsp-alg-online-tasks-topic"], # 实时监听topic
self.__kafka_config["topicPort"]["dsp-alg-offline-tasks-topic"], # 离线监听topic
self.__kafka_config["topicPort"]["dsp-alg-image-tasks-topic"], # 图片监听topic
self.__kafka_config["topicPort"]["dsp-recording-task-topic"], # 录屏监听topic
self.__kafka_config["topicPort"]["dsp-push-stream-task-topic"] # 推流监听topic
]
self.__gpuTopic = [self.__kafka_config["topicGPU"]]
if self.__role==1:
self.__topics = self.__topics + self.__topicsPort + self.__gpuTopic
# 对应topic的各个lambda表达式
self.__task_type = {
self.__topics[0]: (AnalysisType.ONLINE.value, lambda x, y: self.online(x, y),
lambda x, y, z: self.identify_method(x, y, z)),
self.__topics[1]: (AnalysisType.OFFLINE.value, lambda x, y: self.offline(x, y),
lambda x, y, z: self.identify_method(x, y, z)),
self.__topics[2]: (AnalysisType.IMAGE.value, lambda x, y: self.image(x, y),
lambda x, y, z: self.identify_method(x, y, z)),
self.__topics[3]: (AnalysisType.RECORDING.value, lambda x, y: self.recording(x, y),
lambda x, y, z: self.recording_method(x, y, z)),
self.__topics[4]: (AnalysisType.PULLTOPUSH.value, lambda x, y: self.pullStream(x, y),
lambda x, y, z: self.push_stream_method(x, y, z))
}
self.__taskType={
self.__kafka_config["topic"]["dsp-alg-online-tasks-topic"]:0, # 实时监听topic
self.__kafka_config["topic"]["dsp-alg-offline-tasks-topic"]:1, # 离线监听topic
self.__kafka_config["topic"]["dsp-alg-image-tasks-topic"]:2, # 图片监听topic
self.__kafka_config["topic"]["dsp-recording-task-topic"]:3, # 录屏监听topic
self.__kafka_config["topic"]["dsp-push-stream-task-topic"]:4 # 推流监听topic
}
gpu_name_array = get_first_gpu_name()
gpu_array = [g for g in ('3090', '2080', '4090', 'A10') if g in gpu_name_array]
gpu_name = '2080Ti'
if len(gpu_array) > 0:
if gpu_array[0] != '2080':
gpu_name = gpu_array[0]
else:
raise Exception("GPU资源不在提供的模型所支持的范围内！请先提供对应的GPU模型！")
logger.info("当前服务环境为: {}, 服务器GPU使用型号: {}", env, gpu_name)
self.__context["gpu_name"] = gpu_name
self.start_service()
# 服务调用启动方法
def start_service(self):
# 初始化kafka监听者
customerKafkaConsumer = CustomerKafkaConsumer(self.__kafka_config, topics=self.__topics)
####增加一个线程，用于试试监控和发送gpu状态####
####
logger.info("(♥◠‿◠)ﾉﾞ DSP【算法调度服务】启动成功 服务器IP:{}".format(self.__kafka_config['bootstrap_servers'] ))
while True:
try:
# 检查任务进程运行情况，去除结束的任务
self.check_process_task()
# 启动反馈线程
self.start_feedback_thread()
self.start_uploadGPU_thread()
msg = customerKafkaConsumer.poll()
if msg is not None and len(msg) > 0:
for k, v in msg.items():
for m in v:
message = m.value
#如果收到的信息是gpu状态的话，收到信息后，更新自己的gpu服务器状态，下面不再执行
if m.topic in self.__gpuTopic:
customerKafkaConsumer.commit_offset(m,'x'*16,False)
#更新机器资源现状
ip = message['System']['Local IP Address']
self.__gpuDics[ip]=message
continue
#如果收到的信息是门户消息，收到信息后，要根据Gpu状态，转发到对应的机器。
elif m.topic in self.__topicsPort:
customerKafkaConsumer.commit_offset(m, 'y'*16)
#状态分析
#recondGpu={'hostname':'thsw2','IP':'192.168.10.66','gpuId':0}
recondGpu= select_best_server(self.__gpuDics)
if recondGpu is None:
print( 'recondGpu：',recondGpu, ' self.__gpuDics: ',self.__gpuDics,' topic:',m.topic, ' message:',message )
continue
#转发消息
message['transmit_topic'] = m.topic + '-' + recondGpu['IP']
transmitMsg={'transmit':message}
msg_json = json.dumps( message )
future = self.__producer.send( message['transmit_topic'] ,msg_json)
try:
future.get(timeout=2)
logger.info( "转发消息成功，消息topic:{},消息内容:{}",message['transmit_topic'],message )
except kafka_errors as e:
print('------transmitted error:',e)
logger.info("转发消息失败")
traceback.format_exc()
else:
requestId = message.get("request_id")
if requestId is None:
logger.error("请求参数格式错误, 请检查请求体格式是否正确！message:%s"%(message))
continue
customerKafkaConsumer.commit_offset(m, requestId)
logger.info("当前拉取到的消息, topic:{}, offset:{}, partition: {}, body: {}, requestId:{}",
m.topic, m.offset, m.partition, message, requestId)
message['taskType']=self.__taskType[m.topic]
topic_method = self.__task_type[m.topic]
topic_method[2](topic_method[1], message, topic_method[0])
else:
print_gpu_ex_status()
print_cpu_ex_status(self.__context["base_dir"])
time.sleep(1)
except Exception:
logger.error("主线程异常：{}", format_exc())
def identify_method(self, handle_method, message, analysisType):
try:
check_cude_is_available()
handle_method(message, analysisType)
except ServiceException as s:
logger.error("消息监听异常：{}, requestId: {}", s.msg, message["request_id"])
put_queue(self.__fbQueue, message_feedback(message["request_id"], AnalysisStatus.FAILED.value, analysisType,
s.code, s.msg), timeout=1)
except Exception:
logger.error("消息监听异常：{}, requestId: {}", format_exc(), message["request_id"])
put_queue(self.__fbQueue, message_feedback(message["request_id"], AnalysisStatus.FAILED.value, analysisType,
ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1]), timeout=1)
finally:
del message
def push_stream_method(self, handle_method, message, analysisType):
try:
check_cude_is_available()
handle_method(message, analysisType)
except ServiceException as s:
logger.error("消息监听异常：{}, requestId: {}", s.msg, message['request_id'])
videoInfo = [{"id": url.get("id"), "status": PushStreamStatus.FAILED.value[0]} for url in
message.get("video_urls", []) if url.get("id") is not None]
put_queue(self.__fbQueue, pull_stream_feedback(message['request_id'], ExecuteStatus.FAILED.value[0],
s.code, s.msg, videoInfo), timeout=1)
except Exception:
logger.error("消息监听异常：{}, requestId: {}", format_exc(), message['request_id'])
videoInfo = [{"id": url.get("id"), "status": PushStreamStatus.FAILED.value[0]} for url in
message.get("video_urls", []) if url.get("id") is not None]
put_queue(self.__fbQueue, pull_stream_feedback(message.get("request_id"), ExecuteStatus.FAILED.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1], videoInfo),
timeout=1)
finally:
del message
def recording_method(self, handle_method, message, analysisType):
try:
check_cude_is_available()
handle_method(message, analysisType)
except ServiceException as s:
logger.error("消息监听异常：{}, requestId: {}", s.msg, message["request_id"])
put_queue(self.__fbQueue,
recording_feedback(message["request_id"], RecordingStatus.RECORDING_FAILED.value[0],
error_code=s.code, error_msg=s.msg), timeout=1)
except Exception:
logger.error("消息监听异常：{}, requestId: {}", format_exc(), message["request_id"])
put_queue(self.__fbQueue,
recording_feedback(message["request_id"], RecordingStatus.RECORDING_FAILED.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
ExceptionType.SERVICE_INNER_EXCEPTION.value[1]), timeout=1)
finally:
del message
# 开启实时进程
def startOnlineProcess(self, msg, analysisType):
if self.__listeningProcesses.get(msg["request_id"]):
logger.warning("实时重复任务，请稍后再试！requestId:{}", msg["request_id"])
return
model_type = self.__context["service"]["model"]["model_type"]
codes = [model.get("code") for model in msg["models"] if model.get("code")]
if ModelMethodTypeEnum.NORMAL.value == model_type or ModelType.ILLPARKING_MODEL.value[1] in codes:
coir = OnlineIntelligentRecognitionProcess(self.__fbQueue, msg, analysisType, self.__context)
else:
coir = OnlineIntelligentRecognitionProcess2(self.__fbQueue, msg, analysisType, self.__context)
coir.start()
logger.info("开始实时进程！requestId:{},pid:{}, ppid:{}", msg["request_id"],os.getpid(),os.getppid())
self.__listeningProcesses[msg["request_id"]] = coir
# 结束实时进程
def stopOnlineProcess(self, msg):
ps = self.__listeningProcesses.get(msg["request_id"])
if ps is None:
logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg["request_id"])
return
ps.sendEvent({"command": "stop"})
# 新增该函数用于，向子任务发送命令（algStart，algStop）
def sendCmdToChildProcess(self, msg,cmd="algStart"):
ps = self.__listeningProcesses.get(msg["request_id"])
if ps is None:
logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg["request_id"])
return
ps.sendEvent({"command": cmd})
@staticmethod
def check_process(listeningProcess):
for requestId in list(listeningProcess.keys()):
if not listeningProcess[requestId].is_alive():
del listeningProcess[requestId]
def check_process_task(self):
self.check_process(self.__listeningProcesses)
self.check_process(self.__recordingProcesses)
self.check_process(self.__pull2PushProcesses)
# 开启离线进程
def startOfflineProcess(self, msg, analysisType):
if self.__listeningProcesses.get(msg["request_id"]):
logger.warning("离线重复任务，请稍后再试！requestId:{}", msg["request_id"])
return
model_type = self.__context["service"]["model"]["model_type"]
codes = [model.get("code") for model in msg["models"] if model.get("code")]
if ModelMethodTypeEnum.NORMAL.value == model_type:
first = OfflineIntelligentRecognitionProcess(self.__fbQueue, msg, analysisType, self.__context)
else:
first = OfflineIntelligentRecognitionProcess2(self.__fbQueue, msg, analysisType, self.__context)
first.start()
self.__listeningProcesses[msg["request_id"]] = first
# 结束离线进程
def stopOfflineProcess(self, msg):
ps = self.__listeningProcesses.get(msg["request_id"])
if ps is None:
logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg["request_id"])
return
ps.sendEvent({"command": "stop"})
# 开启图片分析进程
def startImageProcess(self, msg, analysisType):
pp = self.__listeningProcesses.get(msg["request_id"])
if pp is not None:
logger.warning("重复任务，请稍后再试！requestId:{}", msg["request_id"])
return
model_type = self.__context["service"]["model"]["model_type"]
codes = [model.get("code") for model in msg["models"] if model.get("code")]
if ModelMethodTypeEnum.NORMAL.value == model_type or ModelType.ILLPARKING_MODEL.value[1] in codes:
imaged = PhotosIntelligentRecognitionProcess(self.__fbQueue, msg, analysisType, self.__context)
else:
imaged = PhotosIntelligentRecognitionProcess2(self.__fbQueue, msg, analysisType, self.__context)
# 创建在线识别进程并启动
imaged.start()
self.__listeningProcesses[msg["request_id"]] = imaged
'''
校验kafka消息
'''
@staticmethod
def check_msg(msg, schema):
try:
v = Validator(schema, allow_unknown=True)
result = v.validate(msg)
if not result:
logger.error("参数校验异常: {}, requestId: {}", v.errors, msg["request_id"])
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
except ServiceException as s:
raise s
except Exception:
logger.error("参数校验异常: {}, requestId: {}", format_exc(), msg["request_id"])
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
'''
开启反馈线程，用于发送消息
'''
def start_feedback_thread(self):
if self.__feedbackThread is None:
self.__feedbackThread = FeedbackThread(self.__fbQueue, self.__kafka_config)
self.__feedbackThread.setDaemon(True)
self.__feedbackThread.start()
time.sleep(1)
if self.__feedbackThread and not self.__feedbackThread.is_alive():
logger.error("反馈线程异常停止, 开始重新启动反馈线程！！！！！")
self.__feedbackThread = FeedbackThread(self.__fbQueue, self.__kafka_config)
self.__feedbackThread.setDaemon(True)
self.__feedbackThread.start()
time.sleep(1)
def start_uploadGPU_thread(self):
if self.__uploadGPUThread is None:
self.__uploadGPUThread = uploadGPUinfos(self.__context, self.__kafka_config)
self.__uploadGPUThread.setDaemon(True)
self.__uploadGPUThread.start()
time.sleep(1)
if self.__uploadGPUThread and not self.__uploadGPUThread.is_alive():
logger.error("反馈线程异常停止, 开始重新启动反馈线程！！！！！")
self.__uploadGPUThread = uploadGPUinfos(self.__context, self.__kafka_config)
self.__uploadGPUThread.setDaemon(True)
self.__uploadGPUThread.start()
time.sleep(1)
'''
在线分析逻辑
'''
def online0(self, message, analysisType):
if "start" == message.get("command"):
self.check_msg(message, ONLINE_START_SCHEMA)
if len(self.__listeningProcesses) >= int(self.__context['service']["task"]["limit"]):
raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1])
self.startOnlineProcess(message, analysisType)
elif message.get("command") in ["algStart","algStop"]:
self.sendCmdToChildProcess(message,cmd=message.get("command"))
elif "stop" == message.get("command"):
self.check_msg(message, ONLINE_STOP_SCHEMA)
self.stopOnlineProcess(message)
else:
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
def online(self, message, analysisType):
if "start" == message.get("command"):
self.check_msg(message, ONLINE_START_SCHEMA)
if len(self.__listeningProcesses) >= int(self.__context['service']["task"]["limit"]):
raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1])
self.startOnlineProcess(message, analysisType)
elif message.get("command") in ["algStart","algStop"]:
if message.get("defaultEnabled",True):
self.sendCmdToChildProcess(message,cmd=message.get("command"))
elif "stop" == message.get("command"):
self.check_msg(message, ONLINE_STOP_SCHEMA)
self.stopOnlineProcess(message)
else:
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
def offline(self, message, analysisType):
if "start" == message.get("command"):
self.check_msg(message, OFFLINE_START_SCHEMA)
if len(self.__listeningProcesses) >= int(self.__context['service']["task"]["limit"]):
raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1])
self.startOfflineProcess(message, analysisType)
elif message.get("command") in ["algStart","algStop"]:
self.sendCmdToChildProcess( message,cmd=message.get("command"))
elif "stop" == message.get("command"):
self.check_msg(message, OFFLINE_STOP_SCHEMA)
self.stopOfflineProcess(message)
else:
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
def image(self, message, analysisType):
if "start" == message.get("command"):
self.check_msg(message, IMAGE_SCHEMA)
if len(self.__listeningProcesses) >= int(self.__context['service']["task"]["image"]["limit"]):
raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1])
self.startImageProcess(message, analysisType)
else:
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
def recording(self, message, analysisType):
if "start" == message.get("command"):
self.check_msg(message, RECORDING_START_SCHEMA)
if len(self.__recordingProcesses) >= int(self.__context['service']["task"]["limit"]):
raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1])
self.startRecordingProcess(message, analysisType)
elif "stop" == message.get("command"):
self.check_msg(message, RECORDING_STOP_SCHEMA)
self.stopRecordingProcess(message)
else:
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
# 开启录屏进程
def startRecordingProcess(self, msg, analysisType):
if self.__listeningProcesses.get(msg["request_id"]):
logger.warning("重复任务，请稍后再试！requestId:{}", msg["request_id"])
return
srp = ScreenRecordingProcess(self.__fbQueue, self.__context, msg, analysisType)
srp.start()
self.__recordingProcesses[msg["request_id"]] = srp
# 结束录屏进程
def stopRecordingProcess(self, msg):
rdp = self.__recordingProcesses.get(msg["request_id"])
if rdp is None:
logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg["request_id"])
return
rdp.sendEvent({"command": "stop"})
def pullStream(self, message, analysisType):
if "start" == message.get("command"):
self.check_msg(message, PULL2PUSH_START_SCHEMA)
if len(self.__pull2PushProcesses) >= int(self.__context['service']["task"]["limit"]):
raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1])
self.startPushStreamProcess(message, analysisType)
elif "stop" == message.get("command"):
self.check_msg(message, PULL2PUSH_STOP_SCHEMA)
self.stopPushStreamProcess(message)
else:
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
def startPushStreamProcess(self, msg, analysisType):
if self.__pull2PushProcesses.get(msg["request_id"]):
logger.warning("重复任务，请稍后再试！requestId:{}", msg["request_id"])
return
srp = PushStreamProcess(self.__fbQueue, self.__context, msg, analysisType)
srp.start()
self.__pull2PushProcesses[msg["request_id"]] = srp
# 结束录屏进程
def stopPushStreamProcess(self, msg):
srp = self.__pull2PushProcesses.get(msg["request_id"])
if srp is None:
logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg["request_id"])
return
srp.sendEvent({"command": "stop", "videoIds": msg.get("video_ids", [])})

133
service/Dispatcher.py
View File

@ -187,6 +187,9 @@ class DispatcherService:
except Exception:
logger.error("主线程异常：{}", format_exc())
def identify_method(self, handle_method, message, analysisType):
try:
check_cude_is_available()
@ -244,20 +247,30 @@ class DispatcherService:
# 开启实时进程
def startOnlineProcess(self, msg, analysisType):
# for algswitch flag:
default_enabled = str(msg.get("defaultEnabled","True")).lower() == "true"
if default_enabled:
self.__context['service']['algSwitch'] = True
else:
self.__context['service']['algSwitch'] = False
#0521:
default_enabled = str(msg.get("defaultEnabled", "True")).lower() == "true"
if default_enabled:
print("执行默认程序（defaultEnabled=True）")
self.__context['service']['algSwitch'] = True
# 这里放默认逻辑的代码
else:
print("执行替代程序（defaultEnabled=False）")
# 这里放非默认逻辑的代码
self.__context['service']['algSwitch'] = False
print("---line264-Dispatcher.py---",self.__context)
if self.__listeningProcesses.get(msg["request_id"]):
logger.warning("实时重复任务，请稍后再试！requestId:{}", msg["request_id"])
return
model_type = self.__context["service"]["model"]["model_type"]
codes = [model.get("code") for model in msg["models"] if model.get("code")]
if ModelMethodTypeEnum.NORMAL.value == model_type or ModelType.ILLPARKING_MODEL.value[1] in codes:
@ -337,8 +350,23 @@ class DispatcherService:
校验kafka消息
'''
@staticmethod
def check_msg(msg, schema):
# #0521
# # 检查 defaultEnabled 是否为 True（兼容字符串和布尔值）
# default_enabled = str(msg1.get("defaultEnabled", "True")).lower() == "true"
# # 如果不是 True，强制设置 command 为 'algStop'
# if not default_enabled and msg1["command"] == "algStart" :
# msg1["command"] = "algStop"
# msg = msg1
try:
v = Validator(schema, allow_unknown=True)
result = v.validate(msg)
@ -387,23 +415,104 @@ class DispatcherService:
在线分析逻辑
'''
#0520：主要是在线分析 -- "algStart","algStop" 外部多增加一层逻辑
# def online(self, message, analysisType):
# if "start" == message.get("command"):
# self.check_msg(message, ONLINE_START_SCHEMA)
# if len(self.__listeningProcesses) >= int(self.__context['service']["task"]["limit"]):
# raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
# ExceptionType.NO_RESOURCES.value[1])
# self.startOnlineProcess(message, analysisType)
# elif message.get("command") in ["algStart","algStop"]:
# self.sendCmdToChildProcess(message,cmd=message.get("command"))
# elif "stop" == message.get("command"):
# self.check_msg(message, ONLINE_STOP_SCHEMA)
# self.stopOnlineProcess(message)
# else:
# raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
# ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
def online(self, message, analysisType):
# #0521
# # 检查 defaultEnabled 是否为 True（兼容字符串和布尔值）
# #逻辑还是有问题 - 肯定是先判断是否为 true
# default_enabled = str(message1.get("defaultEnabled", "True")).lower() == "True"
# # 如果不是 True，强制设置 command 为 'algStop'
# if not default_enabled :
# message.get("command")
# message = message1
# message = message
# print("line429",message)
if "start" == message.get("command"):
self.check_msg(message, ONLINE_START_SCHEMA)
if len(self.__listeningProcesses) >= int(self.__context['service']["task"]["limit"]):
raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
ExceptionType.NO_RESOURCES.value[1])
self.startOnlineProcess(message, analysisType)
elif message.get("command") in ["algStart","algStop"]:
self.sendCmdToChildProcess(message,cmd=message.get("command"))
# elif (message.get("command") in ["algStart","algStop"] ) and (message.get("defaultEnabled",True)):
# self.sendCmdToChildProcess(message,cmd=message.get("command"))
elif (
message is not None # 防止 message 为 None
and isinstance(message, dict) # 确保 message 是字典
and (command := message.get("command")) in ["algStart", "algStop"] # Python 3.8+ 海象运算符
and message.get("defaultEnabled", True) is not False # 显式排除 False
):
self.sendCmdToChildProcess(message, cmd=command)
elif "stop" == message.get("command"):
self.check_msg(message, ONLINE_STOP_SCHEMA)
self.stopOnlineProcess(message)
else:
raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
def offline(self, message, analysisType):
# #0521
# # 检查 defaultEnabled 是否为 True（兼容字符串和布尔值）
# default_enabled = str(message.get("defaultEnabled", "True")).lower() == "true"
# # 如果不是 True，强制设置 command 为 'algStop'
# if not default_enabled and message["command"] == "algStart" :
# message["command"] = "algStop"
# message = message
# print("line429",message)
if "start" == message.get("command"):
self.check_msg(message, OFFLINE_START_SCHEMA)
if len(self.__listeningProcesses) >= int(self.__context['service']["task"]["limit"]):

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8
util/Cv2Utils.py
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@ -456,8 +456,8 @@ class Cv2Util:
# '-sc_threshold', '0',
'-pix_fmt', 'yuv420p',
# '-flvflags', 'no_duration_filesize',
# '-preset', 'fast', # 指定输出的视频质量，会影响文件的生成速度，有以下几个可用的值 ultrafast,
'-preset', 'p6', # 指定输出的视频质量，会影响文件的生成速度，有以下几个可用的值 ultrafast,
'-preset', 'fast', # 指定输出的视频质量，会影响文件的生成速度，有以下几个可用的值 ultrafast,
# '-preset', 'p6', # 指定输出的视频质量，会影响文件的生成速度，有以下几个可用的值 ultrafast,
'-tune', 'll',
'-f', 'flv',
self.pushUrl]
@ -876,8 +876,8 @@ def build_push_p(push_url, width, height, requestId):
# '-zerolatency', '1',
'-pix_fmt', 'yuv420p',
# '-flvflags', 'no_duration_filesize',
# '-preset', 'fast', # 指定输出的视频质量，会影响文件的生成速度，有以下几个可用的值 ultrafast,
'-preset', 'p6', # 指定输出的视频质量，会影响文件的生成速度，有以下几个可用的值 ultrafast,
'-preset', 'fast', # 指定输出的视频质量，会影响文件的生成速度，有以下几个可用的值 ultrafast,
# '-preset', 'p6', # 指定输出的视频质量，会影响文件的生成速度，有以下几个可用的值 ultrafast,
'-tune', 'll',
'-f', 'flv',
push_url]

4
util/MinioSdk.py
View File

@ -57,8 +57,8 @@ class MinioSdk:
self.create_bucknet(bucketName)
if '/' not in remotePath:
remoteUrl=join( self.__config["file_dir"] , request_id,remotePath )
else: remoteUrl = join(self.__config["file_dir"] , remotePath)
remoteUrl=join(request_id,remotePath )
else: remoteUrl = remotePath
max_retries = 3
retry_count = 0
while True:

54
util/ModelUtils.py
View File

@ -63,7 +63,8 @@ class OneModel:
'conf_thres': postFile["conf_thres"],
'ovlap_thres_crossCategory': postFile.get("ovlap_thres_crossCategory"),
'iou_thres': postFile["iou_thres"],
'allowedList': [],
# 对高速模型进行过滤
'allowedList': par['allowedList'] if modeType.value[0] == '3' else [],
'segRegionCnt': par['segRegionCnt'],
'trtFlag_det': par['trtFlag_det'],
'trtFlag_seg': par['trtFlag_seg']
@ -81,7 +82,7 @@ class OneModel:
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])
# 纯分类模型
class cityManagementModel:
__slots__ = "model_conf"
@ -104,7 +105,6 @@ class cityManagementModel:
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']
@ -119,8 +119,7 @@ def detSeg_demo2(args):
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]
@ -307,7 +306,6 @@ def obb_process(args):
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"
@ -321,8 +319,8 @@ class IMModel:
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},
'code': {'weights': '../weights/pth/AIlib2/jkm/health_yolov5s_v3.jit', 'img_type': 'code', 'nc': 10},
'plate': {'weights': '../weights/pth/AIlib2/jkm/plate_yolov5s_v3.jit', 'img_type': 'plate', 'nc': 1},
'conf_thres': 0.4,
'iou_thres': 0.45,
'device': 'cuda:%s' % device,
@ -331,7 +329,7 @@ class IMModel:
new_device = torch.device(par['device'])
model = torch.jit.load(par[img_type]['weights'])
logger.info("########################加载 ../AIlib2/weights/conf/jkm/plate_yolov5s_v3.jit 成功 ########################, requestId:{}",
logger.info("########################加载 jit 模型成功 成功 ########################, requestId:{}",
requestId)
self.model_conf = (modeType, allowedList, new_device, model, par, img_type)
except Exception:
@ -339,7 +337,6 @@ class IMModel:
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:
@ -639,6 +636,39 @@ MODEL_CONFIG = {
lambda x, y, z: one_label(x, y, z),
lambda x: detSeg_demo2(x)
),
# 加载高速公路抛撒物模型
ModelType.TRAFFIC_SPILL_MODEL.value[1]: (
lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.TRAFFIC_SPILL_MODEL, t, z, h),
ModelType.TRAFFIC_SPILL_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)
),
# 加载高速公路危化品模型
ModelType.TRAFFIC_CTHC_MODEL.value[1]: (
lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.TRAFFIC_CTHC_MODEL, t, z, h),
ModelType.TRAFFIC_CTHC_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: model_process(x)
),
# 加载光伏板异常检测模型
ModelType.TRAFFIC_PANNEL_MODEL.value[1]: (
lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.TRAFFIC_PANNEL_MODEL, t, z, h),
ModelType.TRAFFIC_PANNEL_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: detSeg_demo2(x)
),
# 加载红外行人检测模型
ModelType.CITY_INFRAREDPERSON_MODEL.value[1]: (
lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.CITY_INFRAREDPERSON_MODEL, t, z, h),
ModelType.CITY_INFRAREDPERSON_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: detSeg_demo2(x)
),
# 加载夜间烟火检测模型
ModelType.CITY_NIGHTFIRESMOKE_MODEL.value[1]: (
lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.CITY_NIGHTFIRESMOKE_MODEL, t, z, h),
ModelType.CITY_NIGHTFIRESMOKE_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: detSeg_demo2(x)
),
}

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vodsdk/__init__.py
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@ -0,0 +1,3 @@
__version__ = '1.3.1'

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