diff --git a/DrGraph/Bussiness/Bussiness.py b/DrGraph/Bussiness/Bussiness.py
new file mode 100644
index 0000000..ea8c168
--- /dev/null
+++ b/DrGraph/Bussiness/Bussiness.py
@@ -0,0 +1,285 @@
+from loguru import logger
+import cv2,os,time, json, glob
+import numpy as np
+from collections import namedtuple
+from concurrent.futures import ThreadPoolExecutor
+import tensorrt as trt
+from models.experimental import attempt_load
+from DrGraph.util.masterUtils import get_needed_objectsIndex
+from DrGraph.util.stdc import stdcModel
+
+from DrGraph.appIOs.conf.ModelTypeEnum import *
+from DrGraph.appIOs.conf.ModelUtils import *
+from DrGraph.util import aiHelper
+from DrGraph.util.drHelper import *
+
+AnalysisFrameType = namedtuple('AnalysisFrameData', ['images', 'model', 'seg_model', 'names', 'label_arrays',
+ 'rainbows', 'object_params', 'font', 'image_name', 'seg_params', 'mode', 'post_params' ])
+
+class BussinessBase:
+ @staticmethod
+ def createModel(opt):
+ business = opt['business']
+ if business == 'illParking':
+ from .Bussiness_IllParking import Bussiness_IllParking
+ return Bussiness_IllParking(opt)
+
+ def __init__(self, opt):
+ self.bussiness = opt['business']
+ from DrGraph.appIOs.conf.ModelUtils import MODEL_CONFIG
+ self.code = '019'
+ model_method = MODEL_CONFIG[self.code]
+ self.modelClass = model_method[0]
+ self.modelProcessFun = model_method[3]
+
+ self.param = {
+ 'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
+ # 'labelnames':"../AIlib2/DrGraph/weights/conf/%s/labelnames.json" % (self.bussiness), ###检测类别对照表
+ 'labelnames':"../weights/conf/%s/labelnames.json" % (self.bussiness), ###检测类别对照表
+ 'max_workers':1, ###并行线程数
+ 'Detweights':"../weights/%s/yolov5_%s_fp16.engine"%(self.bussiness ,opt['gpu'] ),###检测模型路径
+ '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
+ 'seg_nclass':4,###分割模型类别数目，默认2类
+ 'segRegionCnt':2,###分割模型结果需要保留的等值线数目
+ 'Segweights' : "../weights/%s/stdc_360X640_3090_fp16.engine" % (self.bussiness), ###分割模型权重位置
+ 'postFile': '../weights/conf/%s/para.json'%(self.bussiness),###后处理参数文件
+ 'txtFontSize':20,###文本字符的大小
+ 'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'segLineShow':True,'waterLineWidth':2},###显示框、线设置
+ 'testImgPath':'./DrGraph/appIOs/samples/%s/' % (self.bussiness),###测试图像的位置
+ 'testOutPath':'./DrGraph/appIOs/results/%s/' % (self.bussiness),###输出测试图像位置
+ 'segPar': {'mixFunction':{'function':self.modelProcessFun, 'pars':{}}}
+ }
+ self.extraConfig(opt)
+
+ logger.warning(f"""[{self.bussiness}] 业务配置 - {[key for key in self.param if self.param[key] is not None]} - 重点配置：
+ 检测类别(labelnames):{self.param['labelnames']} >>>>>> {ioHelper.get_labelnames(self.param['labelnames'])}
+ 检测模型路径(Detweights): {self.param['Detweights']}
+ 分割模型权重文件(Segweights): {self.param['Segweights']}
+ 后处理参数文件(postFile): {self.param['postFile']}
+ 测试图像路径(testImgPath): {self.param['testImgPath']}
+ 输出图像位置(testOutPath): {self.param['testOutPath']}
+ 输出图像路径: {self.param['testOutPath']}""")
+ ioHelper.checkFile(self.param['labelnames'], '检测类别')
+ ioHelper.checkFile(self.param['Detweights'], '检测模型路径')
+ ioHelper.checkFile(self.param['postFile'], '后处理参数文件')
+ ioHelper.checkFile(self.param['Segweights'], '分割模型权重文件')
+ ioHelper.checkFile(self.param['testImgPath'], '测试图像路径')
+ if ioHelper.checkFile(self.param['testOutPath'], '输出图像路径') is False:
+ os.makedirs(self.param['testOutPath'], exist_ok=True)
+ ioHelper.checkFile(self.param['testOutPath'], '创建后再检查输出图像路径')
+
+ def extraConfig(self, opt):
+ pass
+
+ def setParam(self, key, value):
+ self.param[key] = value
+
+ def addParams(self, params):
+ for key, value in params.items():
+ self.param[key] = value
+
+ def getTestParam_Model(self):
+ device = torchHelper.select_device(self.param['device']) # 1 device
+ half = device.type != 'cpu'
+ trtFlag_det=self.param['trtFlag_det']
+ if trtFlag_det:
+ Detweights = self.param['Detweights'] ##升级后的检测模型
+ trt_logger = trt.Logger(trt.Logger.ERROR)
+ with open(Detweights, "rb") as f, trt.Runtime(trt_logger) as runtime:
+ model = runtime.deserialize_cuda_engine(f.read())# 输入trt本地文件，返回ICudaEngine对象
+ logger.info(f"step {step}: 情况 1 - 成功载入 det model trt [{Detweights}]"); step += 1
+ else:
+ Detweights = self.param['Detweights']
+ model = attempt_load(Detweights, map_location=device) # load FP32 model
+ logger.info(f'step {step}: 情况 2 - 成功载入 det model pth [{Detweights}]'); step += 1
+ if half:
+ model.half() # 启用半精度推理
+ return model
+
+ def getTestParam_SegModel(self):
+ segmodel= None
+ if self.param['Segweights']:
+ if self.bussiness == 'cityMangement2':
+ from DMPR import DMPRModel
+ segmodel = DMPRModel(weights=self.param['Segweights'], par = self.param['segPar'])
+ else:
+ segmodel = stdcModel(weights=self.param['Segweights'], par = self.param['segPar'])
+ else:
+ logger.warning('############None seg model is loaded###########:' )
+ return segmodel
+
+ def getTestParm_ObjectPar(self):
+ from DrGraph.util import torchHelper
+ device = torchHelper.select_device(self.param['device']) # 1 device
+ half = device.type != 'cpu' # 2 half
+ postFile= self.param['postFile']
+ # 3 allowedList
+ allowedList,allowedList_string=get_needed_objectsIndex(self.param['detModelpara'])
+ # 4 segRegionCnt
+ segRegionCnt=self.param['segRegionCnt']
+
+ if self.param['Segweights']:
+ self.param['trtFlag_seg']=True if self.param['Segweights'].endswith('.engine') else False
+ else:
+ self.param['trtFlag_seg']=False
+ self.param['trtFlag_det']=True if self.param['Detweights'].endswith('.engine') else False
+
+ trtFlag_det=self.param['trtFlag_det'] # 5 trtFlag_det
+ trtFlag_seg=self.param['trtFlag_seg'] # 6 trtFlag_seg
+
+ detPostPar = ioHelper.get_postProcess_para_dic(postFile)
+ # 7 conf_thres 8 iou_thres
+ conf_thres,iou_thres,classes,rainbows = detPostPar["conf_thres"],detPostPar["iou_thres"],detPostPar["classes"],detPostPar["rainbows"]
+ # 9 ovlap_thres_crossCategory
+ if 'ovlap_thres_crossCategory' in detPostPar.keys():
+ ovlap_thres_crossCategory=detPostPar['ovlap_thres_crossCategory']
+ else:
+ ovlap_thres_crossCategory = None
+ # 10 score_byClass
+ if 'score_byClass' in detPostPar.keys(): score_byClass=detPostPar['score_byClass']
+ else: score_byClass = None
+
+ objectPar={
+ 'half':half,
+ 'device':device,
+ 'conf_thres':conf_thres,
+ 'ovlap_thres_crossCategory':ovlap_thres_crossCategory,
+ 'iou_thres':iou_thres,
+ 'allowedList':allowedList,
+ 'segRegionCnt':segRegionCnt,
+ 'trtFlag_det':trtFlag_det,
+ 'trtFlag_seg':trtFlag_seg ,
+ 'score_byClass':score_byClass}
+ return objectPar
+ def run(self):
+ postFile= self.param['postFile']
+ digitFont= self.param['digitFont']
+ detPostPar = ioHelper.get_postProcess_para_dic(postFile)
+ rainbows = detPostPar["rainbows"]
+
+ mode_paras=self.param['detModelpara']
+ allowedList,allowedList_string=get_needed_objectsIndex(mode_paras)
+ requestId = '1234'
+ gpu_name = '3090'
+ base_dir = None
+ env = None
+ from GPUtil import getAvailable, getGPUs
+ gpu_ids = getAvailable(maxLoad=0.80, maxMemory=0.80)
+ modelObject = self.modelClass(gpu_ids[0], allowedList, requestId, gpu_name, base_dir, env)
+ model_conf = modelObject.model_conf
+ model_param = model_conf[1]
+ if 'model' not in model_param:
+ model_param['model'] = self.getTestParam_Model()
+ logger.error(f"[{self.bussiness}] 业务配置 - 缺少模型参数 model - 置为测试配置量")
+ if 'segmodel' not in model_param:
+ model_param['segmodel'] = self.getTestParam_SegModel()
+ logger.error(f"[{self.bussiness}] 业务配置 - 缺少模型参数 segmodel - {model_param}")
+ if 'objectPar' not in model_param:
+ model_param['objectPar'] = self.getTestParm_ObjectPar()
+ logger.error(f"[{self.bussiness}] 业务配置 - 缺少模型参数 objectPar - {model_param}")
+ if 'segPar' not in model_param:
+ self.param['segPar']['seg_nclass'] = self.param['seg_nclass']
+ model_param['segPar']=self.param['segPar']
+ logger.error(f"[{self.bussiness}] 业务配置 - 缺少模型参数 segPar - {model_param}")
+ if 'mode' not in model_param:
+ model_param['mode'] = self.param['mode'] if 'mode' in self.param.keys() else 'others'
+ logger.error(f"[{self.bussiness}] 业务配置 - 缺少模型参数 mode - 置为测试配置量{model_param['mode']}")
+ if 'postPar' not in model_param:
+ model_param['postPar'] = self.param['postPar'] if 'postPar' in self.param.keys() else None
+ logger.error(f"[{self.bussiness}] 业务配置 - 缺少模型参数 postPar - 置为测试配置量{model_param['postPar']}")
+
+ labelnames = self.param['labelnames']
+ names = ioHelper.get_labelnames(labelnames)
+ label_arraylist = imgHelper.get_label_arrays(names,rainbows,outfontsize=self.param['txtFontSize'],fontpath="./DrGraph/appIOs/conf/platech.ttf")
+
+ max_workers=self.param['max_workers']
+
+ # 获取测试图像和视频路径
+ impth = self.param['testImgPath']
+ outpth = self.param['testOutPath']
+ imgpaths=[]###获取文件里所有的图像
+ for postfix in ['.jpg','.JPG','.PNG','.png']:
+ imgpaths.extend(glob.glob('%s/*%s'%(impth,postfix )) )
+ videopaths=[]###获取文件里所有的视频
+ for postfix in ['.MP4','.mp4','.avi']:
+ videopaths.extend(glob.glob('%s/*%s'%(impth,postfix )) )
+
+ # 构造图像帧处理对象列表
+ frames=[]
+ for imgpath in imgpaths:
+ im0s=[cv2.imread(imgpath)]
+ analysisFrameData = AnalysisFrameType(
+ im0s,
+ model_param['model'], # model
+ model_param['segmodel'], # segmodel,
+ names,
+ label_arraylist,
+ rainbows,
+ model_param['objectPar'], # objectPar,
+ digitFont,
+ os.path.basename(imgpath),
+ model_param['segPar'], # segPar,
+ model_param['mode'], # mode,
+ model_param['postPar'] # postPar
+ )
+ # im0s,model,segmodel,names,label_arraylist,rainbows,objectPar,digitFont,os.path.basename(imgpath),segPar,mode,postPar)
+ frames.append(analysisFrameData)
+ logger.info(f'共读入 %d 张图片待处理' % len(imgpaths));
+ t1=time.time()
+ # 多线程或单线程处理图像
+ if max_workers==1:
+ for index, img in enumerate(frames):
+ logger.warning(f'-'*20 + ' 处理图片 ' + imgpaths[index] + '-'*20);
+ t5=time.time()
+ self.doAnalysis(img)
+ t6=time.time()
+ else:
+ with ThreadPoolExecutor(max_workers=max_workers) as t:
+ for result in t.map(self.doAnalysis, frames):
+ t=result
+
+ t2=time.time()
+ if len(imgpaths)>0:
+ logger.info('%d 张图片共耗时:%.1f ms ,依次为:%.1f ms, 占用 %d 线程'%(len(imgpaths),(t2-t1)*1000, (t2-t1)*1000.0/len(imgpaths) , max_workers) );
+
+ def doAnalysis(self, frameData: AnalysisFrameType):
+ time00 = time.time()
+ H,W,C = frameData[0][0].shape
+ #frmess---- (im0s,model,segmodel,names,label_arraylist,rainbows,objectPar,digitFont,os.path.basename(imgpath),segPar,mode,postPar)
+ #p_result[1] = draw_painting_joint(xyxy,p_result[1],label_arraylist[int(cls)],score=conf,color=rainbows[int(cls)%20],font=font,socre_location="leftBottom")
+
+ with TimeDebugger('业务分析') as td:
+ p_result, timeOut = aiHelper.AI_process(frameData.images, frameData.model, frameData.seg_model,
+ frameData.names, frameData.label_arrays, frameData.rainbows,
+ objectPar=frameData.object_params, font=frameData.font,
+ segPar=frameData.seg_params, mode=frameData.mode, postPar=frameData.post_params)
+ td.addStep('AI_Process')
+ p_result[1] = drawHelper.drawAllBox(p_result[2],p_result[1],frameData[4],frameData[5],frameData[7])
+ td.addStep('drawAllBox')
+ # time11 = time.time()
+ image_array = p_result[1]
+
+ cv2.imwrite(os.path.join(self.param['testOutPath'], frameData[8] ) ,image_array)
+ bname = frameData[8].split('.')[0]
+ if frameData[2]:
+ if len(p_result)==5:
+ image_mask = p_result[4]
+ if isinstance(image_mask,np.ndarray) and image_mask.shape[0]>0:
+ cv2.imwrite(os.path.join(self.param['testOutPath'],bname+'_mask.png' ) , (image_mask).astype(np.uint8))
+ td.addStep('testOutPath')
+ boxes=p_result[2]
+ with open(os.path.join(self.param['testOutPath'], bname+'.txt' ),'w' ) as fp:
+ for box in boxes:
+ box_str=[str(x) for x in box]
+ out_str=','.join(box_str)+'\n'
+ fp.write(out_str)
+ td.addStep('fp')
+ # time22 = time.time()
+ logger.info(td.getReportInfo())
+ # logger.info('''耗时记录分析：
+ # 原始图像：%s,%d*%d
+ # AI-process: %.1f，其中：
+ # image save:%.1f %s'''%(frameData[8],H,W, \
+ # (time11 - time00) * 1000.0, (time22-time11)*1000.0,timeOut))
+ return 'success'
+
diff --git a/DrGraph/Bussiness/Bussiness_IllParking.py b/DrGraph/Bussiness/Bussiness_IllParking.py
new file mode 100644
index 0000000..562d57d
--- /dev/null
+++ b/DrGraph/Bussiness/Bussiness_IllParking.py
@@ -0,0 +1,119 @@
+from loguru import logger
+import cv2, time
+import numpy as np
+
+from DrGraph.util.drHelper import *
+from .Bussiness import BussinessBase
+
+class Bussiness_IllParking(BussinessBase):
+ def __init__(self, opt):
+ logger.info("create AlAlg_IllParking")
+ super().__init__(opt)
+
+ @staticmethod
+ def postProcess(pred, cvMask, pars):
+ #pred:直接预测结果，不要原图。预测结果[0,1,2,...],不是[车、T角点，L角点]
+ #mask_cv:分割结果图，numpy格式(H,W),结果是int，[0,1,2,...]
+ #pars: 其它参数,dict格式
+ '''三个标签：车、T角点，L角点'''
+ '''输入：落水人员的结果（类别+坐标）、原图
+
+ 过程：将车辆识别框外扩，并按contours形成区域。
+ T角点与L角点的坐标合并为列表。
+ 判断每个车辆contours区域内有几个角点，少于2个则判断违停。
+ 返回：最终违停车辆标记结果图、违停车辆信息（坐标、类别、置信度）。
+ '''
+ #输入的是[cls,x0,y0,x1,y1,score]---> [x0,y0,x1,y1,cls,score]
+ #输出的也是[cls,x0,y0,x1,y1,score]
+ #pred = [ [ int(x[4]) ,*x[1:5], x[5] ] for x in pred]
+
+ #pred = [[ *x[1:5],x[0], x[5] ] for x in pred]
+ pred = [[ *x[0:4],x[5], x[4] ] for x in pred]
+
+ ##统一格式
+ imgSize=pars['imgSize']
+ '''1、pred中车辆识别框形成列表，T角点与L角点形成列表'''
+ tW1=time.time()
+ init_vehicle=[]
+ init_corner = []
+
+ for i in range(len(pred)):
+ #if pred[i][4]=='TCorner' or pred[i][4]=='LCorner': #vehicle、TCorner、LCorner
+ if pred[i][4]==1 or pred[i][4]==2: #vehicle、TCorner、LCorner
+ init_corner.append(pred[i])
+ else:
+ init_vehicle.append(pred[i])
+
+ '''2、init_corner中心点坐标计算，并形成列表。'''
+ tW2 = time.time()
+ center_corner=[]
+ for i in range(len(init_corner)):
+ center_corner.append(mathHelper.center_coordinate(init_corner[i]))
+
+
+ '''3、遍历每个车辆识别框，扩充矩形区域，将矩形区域形成contours，判断扩充区域内的。'''
+ tW3 = time.time()
+ final_weiting=[] #违停车辆列表
+ '''遍历车辆列表，扩大矩形框形成contours'''
+ for i in range(len(init_vehicle)):
+ boundbxs1=[init_vehicle[i][0],init_vehicle[i][1],init_vehicle[i][2],init_vehicle[i][3]]
+ width_boundingbox=init_vehicle[i][2]-init_vehicle[i][0] #框宽度
+ height_boundingbox=init_vehicle[i][2] - init_vehicle[i][0] #框长度
+ #当框长大于宽，则是水平方向车辆；否则认为是竖向车辆
+ if width_boundingbox>=height_boundingbox:
+ ex_width=0.4*(init_vehicle[i][2]-init_vehicle[i][0]) #矩形扩充宽度，取车宽0.4倍 #膨胀系数小一些。角点设成1个。
+ ex_height=0.2*(init_vehicle[i][2]-init_vehicle[i][0]) #矩形扩充宽度，取车长0.2倍
+ boundbxs1 = imgHelper.expand_rectangle(boundbxs1, imgSize, ex_width, ex_height) # 扩充后矩形对角坐标
+ else:
+ ex_width=0.2*(init_vehicle[i][2]-init_vehicle[i][0]) #竖向，不需要改变变量名称，将系数对换下就行。（坐标点顺序还是1234不变）
+ ex_height=0.4*(init_vehicle[i][2]-init_vehicle[i][0]) #
+ boundbxs1 = imgHelper.expand_rectangle(boundbxs1, imgSize, ex_width, ex_height) # 扩充后矩形对角坐标
+ contour_temp = mathHelper.fourcorner_coordinate(boundbxs1) #得到扩充后矩形框的contour
+ contour_temp_=np.array(contour_temp)#contour转为array
+ contour_temp_=np.float32(contour_temp_)
+
+ '''遍历角点识别框中心坐标是否在contours内，在则计1'''
+ zzz=0
+ for j in range(len(center_corner)):
+ flag = cv2.pointPolygonTest(contour_temp_, (center_corner[j][0], center_corner[j][1]), False) #若为False，会找点是否在内，外，或轮廓上(相应返回+1, -1, 0)。
+ if flag==+1:
+ zzz+=1
+ '''contours框内小于等于1个角点，认为不在停车位内'''
+ # if zzz<=1:
+ if zzz<1:
+ final_weiting.append(init_vehicle[i])
+ #print('t7-t6',t7-t6)
+ #print('final_weiting',final_weiting)
+
+ '''4、绘制保存检违停车辆图像'''
+
+ tW4=time.time()
+ '''
+ colors = Colors()
+ if final_weiting is not None:
+ for i in range(len(final_weiting)):
+ lbl='illegal park'
+ xyxy=[final_weiting[i][0],final_weiting[i][1],final_weiting[i][2],final_weiting[i][3]]
+ c = int(5)
+ plot_one_box(xyxy, _img_cv, label=lbl, color=colors(c, True), line_thickness=3)
+ final_img=_img_cv
+ '''
+ tW5=time.time()
+ # cv2.imwrite('final_result.png', _img_cv)
+
+
+ timeStr = ' step1:%s step2:%s step3:%s save:%s'%(\
+ timeHelper.deltaTimeString_MS(tW2,tW1), \
+ timeHelper.deltaTimeString_MS(tW3,tW2), \
+ timeHelper.deltaTimeString_MS(tW4,tW3), \
+ timeHelper.deltaTimeString_MS(tW5,tW4) )
+
+ #final_weiting-----[x0,y0,x1,y1,cls,score]
+ #输出的也是outRe----[cls,x0,y0,x1,y1,score]
+
+ #outRes = [ [ 3 ,*x[0:4], x[5] ] for x in final_weiting]###违停用3表示
+
+ outRes = [ [ *x[0:4], x[5],3 ] for x in final_weiting]###违停用3表示
+
+ return outRes,timeStr #返回最终绘制的结果图、违停车辆（坐标、类别、置信度）
+
\ No newline at end of file
diff --git a/DrGraph/Bussiness/Models.py b/DrGraph/Bussiness/Models.py
new file mode 100644
index 0000000..e743f54
--- /dev/null
+++ b/DrGraph/Bussiness/Models.py
@@ -0,0 +1,74 @@
+from loguru import logger
+import time
+import tensorrt as trt
+from DMPR import DMPRModel
+from traceback import format_exc
+from models.experimental import attempt_load
+
+from DrGraph.util.drHelper import *
+from DrGraph.util.Constant import *
+from DrGraph.enums.ExceptionEnum import ExceptionType
+from DrGraph.util.stdc import stdcModel
+
+# 河道模型、河道检测模型、交通模型、人员落水模型、城市违章公共模型
+class Model1:
+ __slots__ = "model_conf"
+ # 3090
+ def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None, env=None):
+ try:
+ start = time.time()
+ logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
+ requestId)
+ logger.info('__init__(device={}, allowedList={}, requestId={}, modeType={}, gpu_name={}, base_dir={}, env={})', \
+ device, allowedList, requestId, modeType, gpu_name, base_dir, env)
+ par = modeType.value[4](str(device), gpu_name)
+ mode, postPar, segPar = par.get('mode', 'others'), par.get('postPar'), par.get('segPar')
+ names = par['labelnames']
+ postFile = par['postFile']
+ rainbows = postFile["rainbows"]
+ new_device = torchHelper.select_device(par.get('device'))
+ half = new_device.type != 'cpu'
+ Detweights = par['Detweights']
+ if par['trtFlag_det']:
+ with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
+ model = runtime.deserialize_cuda_engine(f.read())
+ else:
+ model = attempt_load(Detweights, map_location=new_device) # load FP32 model
+ if half: model.half()
+ par['segPar']['seg_nclass'] = par['seg_nclass']
+ Segweights = par['Segweights']
+ if Segweights:
+ if modeType.value[3] == 'cityMangement3':
+ segmodel = DMPRModel(weights=Segweights, par=par['segPar'])
+ else:
+ segmodel = stdcModel(weights=Segweights, par=par['segPar'])
+ else:
+ segmodel = None
+ objectPar = {
+ 'half': half,
+ 'device': new_device,
+ 'conf_thres': postFile["conf_thres"],
+ 'ovlap_thres_crossCategory': postFile.get("ovlap_thres_crossCategory"),
+ 'iou_thres': postFile["iou_thres"],
+ # 对高速模型进行过滤
+ 'segRegionCnt': par['segRegionCnt'],
+ 'trtFlag_det': par['trtFlag_det'],
+ 'trtFlag_seg': par['trtFlag_seg'],
+ 'score_byClass':par['score_byClass'] if 'score_byClass' in par.keys() else None,
+ 'fiterList': par['fiterList'] if 'fiterList' in par.keys() else []
+ }
+ model_param = {
+ "model": model,
+ "segmodel": segmodel,
+ "objectPar": objectPar,
+ "segPar": segPar,
+ "mode": mode,
+ "postPar": postPar
+ }
+ self.model_conf = (modeType, model_param, allowedList, names, rainbows)
+ except Exception:
+ logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
+ raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
+ ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
+ logger.info("模型初始化时间：{}, requestId:{}", time.time() - start, requestId)
+
\ No newline at end of file
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diff --git a/DrGraph/appIOs/conf/ModelTypeEnum.py b/DrGraph/appIOs/conf/ModelTypeEnum.py
new file mode 100644
index 0000000..1003162
--- /dev/null
+++ b/DrGraph/appIOs/conf/ModelTypeEnum.py
@@ -0,0 +1,1141 @@
+import sys
+from enum import Enum, unique
+
+from DrGraph.util.Constant import COLOR
+
+sys.path.extend(['..', '../AIlib2'])
+from utilsK.illParkingUtils import illParking_postprocess
+from DrGraph.Bussiness.Bussiness_IllParking import Bussiness_IllParking
+# 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 util.segutils.trafficUtils import tracfficAccidentMixFunction,mixTraffic_postprocess
+# from utilsK.drownUtils import mixDrowing_water_postprocess
+# from utilsK.noParkingUtils import mixNoParking_road_postprocess
+# from utilsK.pannelpostUtils import pannel_post_process
+# from utilsK.securitypostUtils import security_post_process
+# from stdc import stdcModel
+# from yolov5 import yolov5Model
+# from p2pNet import p2NnetModel
+# from DMPRUtils.jointUtil import dmpr_yolo_stdc
+# from AI import default_mix
+# from ocr import ocrModel
+# from utilsK.channel2postUtils import channel2_post_process
+
+'''
+参数说明
+1. 编号
+2. 模型编号
+3. 模型名称
+4. 选用的模型名称
+5. 模型配置
+6. 模型引用配置[Detweights文件, Segweights文件, 引用计数]
+'''
+
+
+@unique
+class ModelType(Enum):
+ 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': Bussiness_IllParking.postProcess,
+ 'pars': {}
+ }
+ },
+ 'postFile': {
+ "name": "post_process",
+ "conf_thres": 0.25,
+ "iou_thres": 0.25,
+ "classes": 9,
+ "rainbows": COLOR
+ },
+ 'Detweights': "../weights/illParking/yolov5_%s_fp16.engine" % gpuName,
+ 'Segweights': None
+ })
+
+ # 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
+ # },
+ # 'fiterList':[2],
+ # 'Detweights': "../weights/trt/AIlib2/river/yolov5_%s_fp16.engine" % gpuName,
+ # 'Segweights': '../weights/trt/AIlib2/river/stdc_360X640_%s_fp16.engine' % gpuName
+ # })
+
+ # # FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
+ # # 'device': device,
+ # # 'gpu_name': gpuName,
+ # # 'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
+ # # 'trtFlag_det': True,
+ # # 'trtFlag_seg': False,
+ # # 'Detweights': "../weights/trt/AIlib2/forest2/yolov5_%s_fp16.engine" % gpuName,
+ # # 'seg_nclass': 2,
+ # # 'segRegionCnt': 0,
+ # # 'slopeIndex': [],
+ # # 'segPar': None,
+ # # 'postFile': {
+ # # "name": "post_process",
+ # # "conf_thres": 0.25,
+ # # "iou_thres": 0.45,
+ # # "classes": 6,
+ # # "rainbows": COLOR
+ # # },
+ # # 'Segweights': None
+ # # })
+
+
+ # FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
+ # 'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
+ # 'postProcess':{'function':default_mix,'pars':{}},
+ # 'models':
+ # [
+ # {
+ # 'weight':"../weights/trt/AIlib2/forest2/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
+ # 'name':'yolov5',
+ # 'model':yolov5Model,
+ # 'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False},
+ # }
+ # ],
+
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.25,
+ # "iou_thres": 0.45,
+ # "classes": 5,
+ # "rainbows": COLOR
+ # },
+ # "score_byClass": {0: 0.25, 1: 0.3, 2: 0.3, 3: 0.3},
+ # 'fiterList': [5],
+ # 'segRegionCnt':2,###分割模型结果需要保留的等值线数目
+ # "pixScale": 1.2,
+ # })
+
+
+ # TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'highWay2', lambda device, gpuName: {
+ # 'device': str(device),
+ # 'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子",
+ # "事故","抛撒物", "危化品车辆", "虚标线","其他标线","其他","桥梁外观","设施破损缺失","龙门架","防抛网","标识牌损坏","护栏损坏","钢筋裸露"],
+ # 'trtFlag_seg': True,
+ # 'trtFlag_det': True,
+ # 'seg_nclass': 3,
+ # 'segRegionCnt': 2,
+ # 'segPar': {
+ # 'modelSize': (640, 360),
+ # 'mean': (0.485, 0.456, 0.406),
+ # 'std': (0.229, 0.224, 0.225),
+ # 'predResize': True,
+ # 'numpy': False,
+ # 'RGB_convert_first': True,
+ # 'mixFunction': {
+ # 'function': tracfficAccidentMixFunction,
+ # 'pars': {
+ # 'modelSize': (640, 360),
+ # 'RoadArea': 16000,
+ # 'roadVehicleAngle': 15,
+ # 'speedRoadVehicleAngleMax': 75,
+ # 'roundness': 1.0,
+ # 'cls': 10,
+ # 'CarId':1,
+ # 'CthcId':12,
+ # 'vehicleFactor': 0.1,
+ # 'confThres': 0.25,
+ # 'roadIou': 0.6,
+ # 'radius': 50,
+ # 'vehicleFlag': False,
+ # 'distanceFlag': False
+ # }
+ # }
+ # },
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.25,
+ # "iou_thres": 0.25,
+ # "classes": 10,
+ # "rainbows": COLOR
+ # },
+ # 'score_byClass':{11:0.75,12:0.75},
+ # 'fiterList': [13,14,15,16,17,18,19,20,21,22],
+ # 'Detweights': "../weights/trt/AIlib2/highWay2/yolov5_%s_fp16.engine" % gpuName,
+ # 'Segweights': '../weights/trt/AIlib2/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
+ # })
+
+ # EPIDEMIC_PREVENTION_MODEL = ("4", "004", "防疫模型", None, None)
+
+ # PLATE_MODEL = ("5", "005", "车牌模型", None, None)
+
+ # VEHICLE_MODEL = ("6", "006", "车辆模型", 'vehicle', lambda device, gpuName: {
+ # 'device': device,
+ # 'gpu_name': gpuName,
+ # 'labelnames': ["车辆"],
+ # 'seg_nclass': 2,
+ # 'segRegionCnt': 0,
+ # 'slopeIndex': [],
+ # 'trtFlag_det': True,
+ # 'trtFlag_seg': False,
+ # 'Detweights': "../weights/trt/AIlib2/vehicle/yolov5_%s_fp16.engine" % gpuName,
+ # 'segPar': None,
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.25,
+ # "iou_thres": 0.45,
+ # "classes": 5,
+ # "rainbows": COLOR
+ # },
+ # 'Segweights': None
+ # })
+
+ # PEDESTRIAN_MODEL = ("7", "007", "行人模型", 'pedestrian', lambda device, gpuName: {
+ # 'device': device,
+ # 'gpu_name': gpuName,
+ # 'labelnames': ["行人"],
+ # 'seg_nclass': 2,
+ # 'segRegionCnt': 0,
+ # 'trtFlag_det': True,
+ # 'trtFlag_seg': False,
+ # 'Detweights': "../weights/trt/AIlib2/pedestrian/yolov5_%s_fp16.engine" % gpuName,
+ # 'slopeIndex': [],
+ # 'segPar': None,
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.25,
+ # "iou_thres": 0.45,
+ # "classes": 5,
+ # "rainbows": COLOR
+ # },
+ # 'Segweights': None
+ # })
+
+ # SMOGFIRE_MODEL = ("8", "008", "烟火模型", 'smogfire', lambda device, gpuName: {
+ # 'device': device,
+ # 'gpu_name': gpuName,
+ # 'labelnames': ["火焰", "烟雾"],
+ # 'seg_nclass': 2, # 分割模型类别数目，默认2类
+ # 'segRegionCnt': 0,
+ # 'trtFlag_det': True,
+ # 'trtFlag_seg': False,
+ # 'Detweights': "../weights/trt/AIlib2/smogfire/yolov5_%s_fp16.engine" % gpuName,
+ # 'slopeIndex': [],
+ # 'segPar': None,
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.25,
+ # "iou_thres": 0.45,
+ # "classes": 5,
+ # "rainbows": COLOR
+ # },
+ # 'Segweights': None,
+ # })
+
+ # ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer', lambda device, gpuName: {
+ # 'device': device,
+ # 'gpu_name': gpuName,
+ # 'labelnames': ["钓鱼", "游泳"],
+ # 'seg_nclass': 2, # 分割模型类别数目，默认2类
+ # 'segRegionCnt': 0,
+ # 'slopeIndex': [],
+ # 'trtFlag_det': True,
+ # 'trtFlag_seg': False,
+ # 'Detweights': "../weights/trt/AIlib2/AnglerSwimmer/yolov5_%s_fp16.engine" % gpuName,
+ # 'segPar': None,
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.25,
+ # "iou_thres": 0.45,
+ # "classes": 5,
+ # "rainbows": COLOR
+ # },
+ # 'Segweights': None
+ # })
+
+ # COUNTRYROAD_MODEL = ("10", "010", "乡村模型", 'countryRoad', lambda device, gpuName: {
+ # 'device': device,
+ # 'gpu_name': gpuName,
+ # 'labelnames': ["违法种植"],
+ # 'seg_nclass': 2, # 分割模型类别数目，默认2类
+ # 'segRegionCnt': 0,
+ # 'slopeIndex': [],
+ # 'trtFlag_det': True,
+ # 'trtFlag_seg': False,
+ # 'Detweights': "../weights/trt/AIlib2/countryRoad/yolov5_%s_fp16.engine" % gpuName,
+ # 'segPar': None,
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.25,
+ # "iou_thres": 0.45,
+ # "classes": 5,
+ # "rainbows": COLOR
+ # },
+ # 'Segweights': None
+ # })
+
+ # SHIP_MODEL = ("11", "011", "船只模型", 'ship2', lambda device, gpuName: {
+ # 'model_size': (608, 608),
+ # 'K': 100,
+ # 'conf_thresh': 0.18,
+ # 'device': 'cuda:%s' % device,
+ # 'down_ratio': 4,
+ # 'num_classes': 15,
+ # 'weights': '../weights/trt/AIlib2/ship2/obb_608X608_%s_fp16.engine' % gpuName,
+ # 'dataset': 'dota',
+ # 'half': False,
+ # 'mean': (0.5, 0.5, 0.5),
+ # 'std': (1, 1, 1),
+ # 'heads': {'hm': None, 'wh': 10, 'reg': 2, 'cls_theta': 1},
+ # 'decoder': None,
+ # 'test_flag': True,
+ # "rainbows": COLOR,
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.25,
+ # "iou_thres": 0.45,
+ # "classes": 5,
+ # "rainbows": COLOR
+ # },
+ # 'drawBox': False,
+ # 'label_array': None,
+ # 'labelnames': ("0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "船只"),
+ # })
+
+ # BAIDU_MODEL = ("12", "012", "百度AI图片识别模型", None, None)
+
+ # CHANNEL_EMERGENCY_MODEL = ("13", "013", "航道模型", 'channelEmergency', lambda device, gpuName: {
+ # 'device': device,
+ # 'gpu_name': gpuName,
+ # 'labelnames': ["人"],
+ # 'seg_nclass': 2, # 分割模型类别数目，默认2类
+ # 'segRegionCnt': 0,
+ # 'slopeIndex': [],
+ # 'trtFlag_det': True,
+ # 'trtFlag_seg': False,
+ # 'Detweights': "../weights/trt/AIlib2/channelEmergency/yolov5_%s_fp16.engine" % gpuName,
+ # 'segPar': None,
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.25,
+ # "iou_thres": 0.45,
+ # "classes": 5,
+ # "rainbows": COLOR
+ # },
+ # 'Segweights': None
+ # })
+
+ # RIVER2_MODEL = ("15", "015", "河道检测模型", 'river2', lambda device, gpuName: {
+ # 'device': device,
+ # 'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
+ # "蓝藻"],
+ # 'trtFlag_seg': True,
+ # 'trtFlag_det': True,
+ # 'seg_nclass': 2,
+ # 'segRegionCnt': 1,
+ # 'segPar': {
+ # 'modelSize': (640, 360),
+ # 'mean': (0.485, 0.456, 0.406),
+ # 'std': (0.229, 0.224, 0.225),
+ # 'numpy': False,
+ # 'RGB_convert_first': True,
+ # 'mixFunction': {
+ # 'function': riverDetSegMixProcess,
+ # 'pars': {
+ # 'slopeIndex': [1, 3, 4, 7],
+ # 'riverIou': 0.1,
+ # 'scale': 0.25
+ # }
+ # }
+ # },
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.25,
+ # "iou_thres": 0.3,
+ # "ovlap_thres_crossCategory": 0.65,
+ # "classes": 5,
+ # "rainbows": COLOR
+ # },
+ # 'Detweights': "../weights/trt/AIlib2/river2/yolov5_%s_fp16.engine" % gpuName,
+ # 'Segweights': '../weights/trt/AIlib2/river2/stdc_360X640_%s_fp16.engine' % gpuName
+ # })
+
+ # CITY_MANGEMENT_MODEL = ("16", "016", "城管模型", 'cityMangement2', lambda device, gpuName: {
+ # 'labelnames': [ "车辆", "垃圾", "商贩", "违停","占道经营","裸土","未覆盖裸土","违建" ],
+ # 'postProcess':{
+ # 'function':dmpr_yolo_stdc,
+ # 'pars':{
+ # 'carCls':0 ,'illCls':7,'scaleRatio':0.5,'border':80,
+ # #"车辆","垃圾","商贩","裸土","占道经营","未覆盖裸土","违建"
+ # # key:实际训练index value:展示index
+ # 'classReindex':{ 0:0,1:1,2:2,7:3,4:4,3:5,5:6,6:7}
+ # }
+ # },
+ # 'models':[
+ # {
+ # 'weight':'../weights/trt/AIlib2/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
+ # 'name':'yolov5',
+ # 'model':yolov5Model,
+ # 'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':True}
+ # },
+ # {
+ # 'weight':'../weights/trt/AIlib2/cityMangement3/dmpr_3090.engine',
+ # #'weight':'../weights/pth/AIlib2/cityMangement3/dmpr.pth',
+ # 'par':{
+ # 'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.1, 'dmprimg_size':640,
+ # 'name':'dmpr'
+ # },
+ # 'model':DMPRModel,
+ # 'name':'dmpr'
+ # },
+ # {
+ # 'weight':'../weights/trt/AIlib2/cityMangement3/stdc_360X640_%s_fp16.engine'%(gpuName),
+ # 'par':{
+ # 'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'predResize':True,'numpy':False, 'RGB_convert_first':True,'seg_nclass':3},###分割模型预处理参数
+ # 'model':stdcModel,
+ # 'name':'stdc'
+ # }
+ # ],
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.25,
+ # "iou_thres": 0.45,
+ # "classes": 8,
+ # "rainbows": COLOR
+ # },
+ # "score_byClass":{0:0.8, 1:0.4, 2:0.5, 3:0.5},
+ # 'segRegionCnt':2,###分割模型结果需要保留的等值线数目
+ # "pixScale": 1.2,
+ # })
+
+ # DROWING_MODEL = ("17", "017", "人员落水模型", 'drowning', lambda device, gpuName: {
+ # 'device': device,
+ # 'labelnames': ["人头", "人", "船只"],
+ # 'trtFlag_seg': True,
+ # 'trtFlag_det': True,
+ # 'seg_nclass': 2,
+ # 'segRegionCnt': 2,
+ # 'segPar': {
+ # 'modelSize': (640, 360),
+ # 'mean': (0.485, 0.456, 0.406),
+ # 'std': (0.229, 0.224, 0.225),
+ # 'predResize': True,
+ # 'numpy': False,
+ # 'RGB_convert_first': True,
+ # 'mixFunction': {
+ # 'function': mixDrowing_water_postprocess,
+ # 'pars': {
+ # 'modelSize': (640, 360)
+ # }
+ # }
+ # },
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.25,
+ # "iou_thres": 0.25,
+ # "classes": 9,
+ # "rainbows": COLOR
+ # },
+ # 'Detweights': "../weights/trt/AIlib2/drowning/yolov5_%s_fp16.engine" % gpuName,
+ # 'Segweights': '../weights/trt/AIlib2/drowning/stdc_360X640_%s_fp16.engine' % gpuName
+ # })
+
+ # NOPARKING_MODEL = (
+ # "18", "018", "城市违章模型", 'noParking', lambda device, gpuName: {
+ # 'device': device,
+ # 'labelnames': ["车辆", "违停"],
+ # 'trtFlag_seg': True,
+ # 'trtFlag_det': True,
+ # 'seg_nclass': 4,
+ # 'segRegionCnt': 2,
+ # 'segPar': {
+ # 'modelSize': (640, 360),
+ # 'mean': (0.485, 0.456, 0.406),
+ # 'std': (0.229, 0.224, 0.225),
+ # 'predResize': True,
+ # 'numpy': False,
+ # 'RGB_convert_first': True, ###分割模型预处理参数
+ # 'mixFunction': {
+ # 'function': mixNoParking_road_postprocess,
+ # 'pars': {
+ # 'modelSize': (640, 360),
+ # 'roundness': 0.3,
+ # 'cls': 9,
+ # 'laneArea': 10,
+ # 'laneAngleCha': 5,
+ # 'RoadArea': 16000,
+ # 'fitOrder':2
+ # }
+ # }
+ # },
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.25,
+ # "iou_thres": 0.25,
+ # "classes": 9,
+ # "rainbows": COLOR
+ # },
+ # 'Detweights': "../weights/trt/AIlib2/noParking/yolov5_%s_fp16.engine" % gpuName,
+ # 'Segweights': '../weights/trt/AIlib2/noParking/stdc_360X640_%s_fp16.engine' % gpuName
+ # })
+
+
+ # CITYROAD_MODEL = ("20", "020", "城市公路模型", 'cityRoad', lambda device, gpuName: {
+ # 'device': device,
+ # 'labelnames': ["护栏", "交通标志", "非交通标志", "施工锥桶", "施工水马"],
+ # 'trtFlag_seg': False,
+ # 'trtFlag_det': True,
+ # 'slopeIndex': [],
+ # 'seg_nclass': 2,
+ # 'segRegionCnt': 0,
+ # 'segPar': None,
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.8,
+ # "iou_thres": 0.45,
+ # "classes": 5,
+ # "rainbows": COLOR
+ # },
+ # 'Detweights': "../weights/trt/AIlib2/cityRoad/yolov5_%s_fp16.engine" % gpuName,
+ # 'Segweights': None
+ # })
+
+ # POTHOLE_MODEL = ("23", "023", "坑槽检测模型", 'pothole', lambda device, gpuName: {
+ # 'device': device,
+ # 'gpu_name': gpuName,
+ # 'labelnames': ["坑槽"],
+ # 'seg_nclass': 2, # 分割模型类别数目，默认2类
+ # 'segRegionCnt': 0,
+ # 'slopeIndex': [],
+ # 'trtFlag_det': True,
+ # 'trtFlag_seg': False,
+ # 'Detweights': "../weights/trt/AIlib2/pothole/yolov5_%s_fp16.engine" % gpuName,
+ # 'segPar': None,
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.25,
+ # "iou_thres": 0.45,
+ # "classes": 5,
+ # "rainbows": COLOR
+ # },
+ # 'Segweights': None,
+ # })
+
+ # CHANNEL2_MODEL = ("24", "024", "船只综合检测模型", 'channel2', lambda device, gpuName: {
+ # 'device': device,
+ # 'gpu_name': gpuName,
+ # # 'labelnames': ["国旗", "浮标", "船名", "船只","未挂国旗船只"],
+ # 'labelnames': ["国旗", "浮标", "船名", "船只", "未挂国旗船只","未封仓船只","未挂国旗且未封仓船只"],
+ # 'segRegionCnt': 0,
+ # 'postProcess':{'function':channel2_post_process,'name':'channel2','pars':{
+ # 'objs':[2],
+ # 'wRation':1/6.0,
+ # 'hRation':1/6.0,
+ # 'flagId':0,
+ # 'boatId':3,
+ # 'unflagId': 4, # 未挂国旗船只
+ # 'uncoverId': 5, # 未封仓
+ # 'unflagAndcoverId': 6, # 未挂国旗且未封仓
+ # 'recScale':1.2,
+ # 'target_cls': 3, # 船只目标种类
+ # 'filter_cls': 4 # 被过滤的种类,模型文件中未封仓实际index
+ # }},
+ # 'models':[
+ # {
+ # 'weight':'../weights/trt/AIlib2/channel2/yolov5_%s_fp16.engine'%(gpuName),
+ # 'name':'yolov5',
+ # 'model':yolov5Model,
+ # 'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.1,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False}
+ # },
+ # {
+ # 'weight' : '../weights/trt/AIlib2/ocr2/crnn_ch_%s_fp16_192X32.engine'%(gpuName),
+ # '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,
+ # },
+ # }
+ # ],
+ # 'segPar': None,
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.25,
+ # "iou_thres": 0.45,
+ # "classes": 5,
+ # "rainbows": COLOR
+ # },
+ # 'Segweights': None,
+ # "score_byClass": {0: 0.7, 1: 0.7, 2: 0.8, 3: 0.6}
+
+ # })
+
+ # RIVERT_MODEL = ("25", "025", "河道检测模型(T)", 'riverT', lambda device, gpuName: {
+ # 'device': device,
+ # 'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
+ # "蓝藻"],
+ # 'trtFlag_seg': True,
+ # 'trtFlag_det': True,
+ # 'seg_nclass': 2,
+ # 'segRegionCnt': 1,
+ # 'segPar': {
+ # 'modelSize': (640, 360),
+ # 'mean': (0.485, 0.456, 0.406),
+ # 'std': (0.229, 0.224, 0.225),
+ # 'numpy': False,
+ # 'RGB_convert_first': True,
+ # 'mixFunction': {
+ # 'function': riverDetSegMixProcess,
+ # 'pars': {
+ # 'slopeIndex': [1, 3, 4, 7],
+ # 'riverIou': 0.1
+ # }
+ # }
+ # },
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.25,
+ # "iou_thres": 0.3,
+ # "ovlap_thres_crossCategory": 0.65,
+ # "classes": 5,
+ # "rainbows": COLOR
+ # },
+ # 'Detweights': "../weights/trt/AIlib2/riverT/yolov5_%s_fp16.engine" % gpuName,
+ # 'Segweights': '../weights/trt/AIlib2/riverT/stdc_360X640_%s_fp16.engine' % gpuName
+ # })
+
+ # FORESTCROWD_FARM_MODEL = ("26", "026", "森林人群模型", 'forestCrowd', lambda device, gpuName: {
+ # 'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","人群"],
+ # 'postProcess':{'function':gather_post_process,'pars':{'pedestrianId':2,'crowdThreshold':4,'gatherId':5,'distancePersonScale':2.0}},
+ # 'models':
+ # [
+ # {
+ # 'weight':"../weights/trt/AIlib2/forestCrowd/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
+ # 'name':'yolov5',
+ # 'model':yolov5Model,
+ # 'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False},
+ # }
+
+
+ # ],
+
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.25,
+ # "iou_thres": 0.45,
+ # "classes": 5,
+ # "rainbows": COLOR
+ # },
+ # "score_byClass":{0:0.25,1:0.25,2:0.6,3:0.6,4:0.6 ,5:0.6},
+ # 'segRegionCnt':2,###分割模型结果需要保留的等值线数目
+ # "pixScale": 1.2,
+
+
+ # })
+ # TRAFFICFORDSJ_FARM_MODEL = ("27", "027", "交通模型-大数据局", 'highWay2T', lambda device, gpuName: {
+ # 'device': str(device),
+ # 'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子",
+ # "事故", "桥梁外观","设施破损缺失","龙门架","防抛网","标识牌损坏","护栏损坏","钢筋裸露" ],
+ # 'trtFlag_seg': True,
+ # 'trtFlag_det': True,
+ # 'seg_nclass': 3,
+ # 'segRegionCnt': 2,
+ # 'segPar': {
+ # 'modelSize': (640, 360),
+ # 'mean': (0.485, 0.456, 0.406),
+ # 'std': (0.229, 0.224, 0.225),
+ # 'predResize': True,
+ # 'numpy': False,
+ # 'RGB_convert_first': True,
+ # 'mixFunction': {
+ # 'function': tracfficAccidentMixFunction,
+ # 'pars': {
+ # 'modelSize': (640, 360),
+ # 'RoadArea': 16000,
+ # 'roadVehicleAngle': 15,
+ # 'speedRoadVehicleAngleMax': 75,
+ # 'roundness': 1.0,
+ # 'cls': 10,
+ # 'CarId':1,
+ # 'CthcId':1,
+ # 'vehicleFactor': 0.1,
+ # 'confThres': 0.25,
+ # 'roadIou': 0.6,
+ # 'radius': 50,
+ # 'vehicleFlag': False,
+ # 'distanceFlag': False
+ # }
+ # }
+ # },
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.25,
+ # "iou_thres": 0.25,
+ # "classes": 10,
+ # "rainbows": COLOR
+ # },
+ # 'fiterltList': [11,12,13,14,15,16,17],
+ # 'Detweights': "../weights/trt/AIlib2/highWay2T/yolov5_%s_fp16.engine" % gpuName,
+ # 'Segweights': '../weights/trt/AIlib2/highWay2T/stdc_360X640_%s_fp16.engine' % gpuName
+ # })
+
+ # SMARTSITE_MODEL = ("28", "028", "智慧工地模型", 'smartSite', lambda device, gpuName: {
+ # 'labelnames': [ "工人","塔式起重机","悬臂","起重机","压路机","推土机","挖掘机","卡车","装载机","泵车","混凝土搅拌车","打桩","其他车辆" ],
+ # 'postProcess':{'function':default_mix,'pars':{}},
+ # 'models':
+ # [
+ # {
+ # 'weight':"../weights/trt/AIlib2/smartSite/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
+ # 'name':'yolov5',
+ # 'model':yolov5Model,
+ # 'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False},
+ # }
+
+
+ # ],
+ # 'postFile': {
+ # "rainbows": COLOR
+ # },
+ # "score_byClass": {0: 0.25, 1: 0.3, 2: 0.3, 3: 0.3}
+
+ # })
+
+ # RUBBISH_MODEL = ("29", "029", "垃圾模型", 'rubbish', lambda device, gpuName: {
+ # 'labelnames': [ "建筑垃圾","白色垃圾","其他垃圾"],
+ # 'postProcess':{'function':default_mix,'pars':{}},
+ # 'models':
+ # [
+ # {
+ # 'weight':"../weights/trt/AIlib2/rubbish/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
+ # 'name':'yolov5',
+ # 'model':yolov5Model,
+ # 'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False},
+ # }
+
+
+ # ],
+ # 'postFile': {
+ # "rainbows": COLOR
+ # },
+ # "score_byClass": {0: 0.25, 1: 0.3, 2: 0.3, 3: 0.3}
+
+ # })
+
+ # FIREWORK_MODEL = ("30", "030", "烟花模型", 'firework', lambda device, gpuName: {
+ # 'labelnames': [ "烟花"],
+ # 'postProcess':{'function':default_mix,'pars':{}},
+ # 'models':
+ # [
+ # {
+ # 'weight':"../weights/trt/AIlib2/firework/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
+ # 'name':'yolov5',
+ # 'model':yolov5Model,
+ # 'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False },
+ # }
+
+
+ # ],
+ # 'postFile': {
+ # "rainbows": COLOR
+ # },
+ # })
+
+ # TRAFFIC_SPILL_MODEL = ("50", "501", "高速公路抛洒物模型", 'highWaySpill', lambda device, gpuName: {
+ # 'device': str(device),
+ # 'labelnames': ["抛洒物","车辆"],
+ # 'trtFlag_seg': True,
+ # 'trtFlag_det': True,
+ # 'seg_nclass': 3,
+ # 'segRegionCnt': 2,
+ # 'segPar': {
+ # 'modelSize': (640, 360),
+ # 'mean': (0.485, 0.456, 0.406),
+ # 'std': (0.229, 0.224, 0.225),
+ # 'predResize': True,
+ # 'numpy': False,
+ # 'RGB_convert_first': True,
+ # 'mixFunction': {
+ # 'function': mixTraffic_postprocess,
+ # 'pars': {
+ # 'modelSize': (640, 360),
+ # 'RoadArea': 16000,
+ # 'roadVehicleAngle': 15,
+ # 'speedRoadVehicleAngleMax': 75,
+ # 'roundness': 1.0,
+ # 'cls': 0,
+ # 'vehicleFactor': 0.1,
+ # 'confThres': 0.25,
+ # 'roadIou': 0.6,
+ # 'radius': 50,
+ # 'vehicleFlag': False,
+ # 'distanceFlag': False
+ # }
+ # }
+ # },
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.25,
+ # "iou_thres": 0.25,
+ # "classes": 2,
+ # "rainbows": COLOR
+ # },
+ # 'fiterList': [1],
+ # ###控制哪些检测类别显示、输出
+ # 'Detweights': "../weights/trt/AIlib2/highWaySpill/yolov5_%s_fp16.engine" % gpuName,
+ # 'Segweights': '../weights/trt/AIlib2/highWaySpill/stdc_360X640_%s_fp16.engine' % gpuName
+ # })
+
+ # TRAFFIC_CTHC_MODEL = ("50", "502", "高速公路危化品模型", 'highWayCthc', lambda device, gpuName: {
+ # 'device': str(device),
+ # 'labelnames': ["危化品","罐体","危险标识","普通车"],
+ # 'trtFlag_seg': True,
+ # 'trtFlag_det': True,
+ # 'seg_nclass': 3,
+ # 'segRegionCnt': 2,
+ # 'segPar': {
+ # 'modelSize': (640, 360),
+ # 'mean': (0.485, 0.456, 0.406),
+ # 'std': (0.229, 0.224, 0.225),
+ # 'predResize': True,
+ # 'numpy': False,
+ # 'RGB_convert_first': True,
+ # 'mixFunction': {
+ # 'function': mixTraffic_postprocess,
+ # 'pars': {
+ # 'modelSize': (640, 360),
+ # 'RoadArea': 16000,
+ # 'roadVehicleAngle': 15,
+ # 'speedRoadVehicleAngleMax': 75,
+ # 'roundness': 1.0,
+ # 'cls': 0,
+ # 'vehicleFactor': 0.1,
+ # 'confThres': 0.25,
+ # 'roadIou': 0.6,
+ # 'radius': 50,
+ # 'vehicleFlag': False,
+ # 'distanceFlag': False
+ # }
+ # }
+ # },
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.25,
+ # "iou_thres": 0.25,
+ # "classes": 4,
+ # "rainbows": COLOR
+ # },
+ # 'fiterList':[1,2,3],
+ # ###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,
+ # 'segRegionCnt': 1, 'trtFlag_det': True,
+ # 'trtFlag_seg': False},
+ # }
+
+ # ],
+ # 'postFile': {
+ # "rainbows": COLOR
+ # },
+ # 'fiterList':[0]
+
+ # })
+
+ # CITY_CARPLATE_MODEL = ("30", "301", "自研车牌检测", 'carplate', lambda device, gpuName: {
+ # 'labelnames': ["车牌"],
+ # 'device': str(device),
+ # 'rainbows': COLOR,
+ # 'models': [
+ # {
+ # #'weight': '../weights/pth/AIlib2/carplate/plate_yolov5s_v3.jit',
+ # 'weight': '../weights/trt/AIlib2/carplate/yolov5_%s_fp16.engine' % (gpuName),
+ # 'name': 'yolov5',
+ # 'model': yolov5Model,
+ # 'par': {
+ # 'trtFlag_det': True,
+ # 'device': 'cuda:0',
+ # 'half': True,
+ # 'conf_thres': 0.4,
+ # 'iou_thres': 0.45,
+ # 'nc': 1,
+ # 'plate':8,
+ # 'plate_dilate': (0.5, 0.1)
+ # },
+ # },
+ # {
+ # 'weight' : '../weights/trt/AIlib2/ocr2/crnn_ch_%s_fp16_192X32.engine'%(gpuName),
+ # 'name': 'ocr',
+ # 'model': ocrModel,
+ # 'par': {
+ # 'trtFlag_ocr': True,
+ # '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,
+ # 'segRegionCnt': 1, 'trtFlag_det': True,'trtFlag_seg': False},
+ # }
+ # ],
+ # '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,
+ # 'segRegionCnt': 1, 'trtFlag_det': True, 'trtFlag_seg': False},
+ # }
+
+ # ],
+ # 'postFile': {
+ # "rainbows": COLOR
+ # },
+
+ # })
+
+ # CITY_DENSECROWDCOUNT_MODEL = ("30", "304", "密集人群计数", 'DenseCrowdCount', lambda device, gpuName: {
+ # 'labelnames': ["人群计数"],
+ # 'device': str(device),
+ # 'rainbows': COLOR,
+ # 'models': [
+ # {
+ # 'trtFlag_det': True,
+ # 'weight': "../weights/pth/AIlib2/DenseCrowd/SHTechA.pth", ###检测模型路径
+ # 'vggweight': "../weights/pth/AIlib2/DenseCrowd/vgg16_bn-6c64b313.pth", ###检测模型路径
+ # #'weight': "../weights/trt/AIlib2/DenseCrowd/SHTechA_%s.engine" %(gpuName), ###检测模型路径
+ # #'vggweight': "../weights/trt/AIlib2/DenseCrowd/vgg16_bn-6c64b313_%s.engine" %(gpuName), ###检测模型路径
+ # 'name': 'p2pnet',
+ # 'model': p2NnetModel,
+ # 'par': {
+ # 'device': 'cuda:0',
+ # 'row': 2,
+ # 'line': 2,
+ # 'point_loss_coef': 0.45,
+ # 'conf': 0.65,
+ # 'gpu_id': 0,
+ # 'eos_coef': '0.5',
+ # 'set_cost_class': 1,
+ # 'set_cost_point': 0.05,
+ # 'backbone': 'vgg16_bn',
+ # 'expend': 10,
+ # 'psize': 2,
+ # },
+ # }],
+ # })
+
+ # CITY_DENSECROWDESTIMATION_MODEL = ("30", "305", "密集人群密度估计", 'DenseCrowdEstimation', lambda device, gpuName: {
+ # 'labelnames': ["密度"],
+ # 'models':
+ # [
+ # {
+ # 'weight': "../weights/pth/AIlib2/DenseCrowd/SHTechA.pth", ###检测模型路径
+ # 'name': 'yolov5',
+ # 'model': yolov5Model,
+ # 'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.50, 'iou_thres': 0.45,
+ # 'segRegionCnt': 1, 'trtFlag_det': True, 'trtFlag_seg': False},
+ # }
+
+ # ],
+ # 'postFile': {
+ # "rainbows": COLOR
+ # },
+
+ # })
+
+ # CITY_UNDERBUILDCOUNT_MODEL = ("30", "306", "建筑物下人群计数", 'perUnderBuild', lambda device, gpuName: {
+ # 'labelnames': ["建筑物下人群"],
+ # 'device': str(device),
+ # 'rainbows': COLOR,
+ # 'models': [
+ # {
+ # 'weight': "../weights/trt/AIlib2/perUnderBuild/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,
+ # 'segRegionCnt': 1, 'trtFlag_det': True, 'trtFlag_seg': False},
+ # },
+ # {
+ # 'trtFlag_det': False,
+ # 'weight': "../weights/pth/AIlib2/DenseCrowd/SHTechA.pth", ###检测模型路径
+ # 'vggweight': "../weights/pth/AIlib2/DenseCrowd/vgg16_bn-6c64b313.pth", ###检测模型路径
+ # 'name': 'p2pnet',
+ # 'model': p2NnetModel,
+ # 'par': {
+ # 'device': 'cuda:0',
+ # 'row': 2,
+ # 'line': 2,
+ # 'point_loss_coef': 0.45,
+ # 'conf': 0.50,
+ # 'gpu_id': 0,
+ # 'eos_coef': '0.5',
+ # 'set_cost_class': 1,
+ # 'set_cost_point': 0.05,
+ # 'backbone': 'vgg16_bn',
+ # 'expend': 10,
+ # 'psize': 5
+ # },
+ # }],
+ # })
+
+ # CITY_FIREAREA_MODEL = ("30", "307", "火焰面积模型", 'FireArea', lambda device, gpuName: {
+ # 'device': device,
+ # 'gpu_name': gpuName,
+ # 'labelnames': ["火焰"],
+ # 'seg_nclass': 2, # 分割模型类别数目，默认2类
+ # 'segRegionCnt': 0,
+ # 'trtFlag_det': True,
+ # 'trtFlag_seg': False,
+ # 'Detweights': "../weights/trt/AIlib2/smogfire/yolov5_%s_fp16.engine" % gpuName, # 0：fire 1：smoke
+ # 'Samweights': "../weights/pth/AIlib2/firearea/sam_vit_b_01ec64.pth", #分割模型
+ # 'ksize':(7,7),
+ # 'sam_type':'vit_b',
+ # 'slopeIndex': [],
+ # 'segPar': None,
+ # 'postFile': {
+ # "name": "post_process",
+ # "conf_thres": 0.25,
+ # "iou_thres": 0.45,
+ # "classes": 5,
+ # "rainbows": COLOR
+ # },
+ # 'Segweights': None,
+ # 'fiterList':[1],
+ # "score_byClass": {0: 0.1}
+
+ # })
+
+ # CITY_SECURITY_MODEL = ("30", "308", "安防模型", 'SECURITY', lambda device, gpuName: {
+ # 'labelnames': ["带安全帽","安全帽","攀爬","斗殴","未戴安全帽"],
+ # 'postProcess': {'function': security_post_process, 'pars': {'objs': [0,1],'iou':0.25,'unhelmet':4}},
+ # 'models':
+ # [
+ # {
+ # 'weight': "../weights/trt/AIlib2/security/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,
+ # 'segRegionCnt': 1, 'trtFlag_det': True, 'trtFlag_seg': False},
+ # }
+
+ # ],
+ # 'postFile': {
+ # "rainbows": COLOR
+ # },
+ # 'fiterList': [0,1],
+ # "score_byClass": {"0": 0.50}
+ # })
+
+ @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
diff --git a/DrGraph/appIOs/conf/ModelUtils.py b/DrGraph/appIOs/conf/ModelUtils.py
new file mode 100644
index 0000000..15169dc
--- /dev/null
+++ b/DrGraph/appIOs/conf/ModelUtils.py
@@ -0,0 +1,785 @@
+# -*- coding: utf-8 -*-
+import sys
+from pickle import dumps, loads
+from traceback import format_exc
+import time
+
+import cv2
+import torch
+import tensorrt as trt
+from loguru import logger
+
+from DrGraph.util.drHelper import *
+from DrGraph.util import aiHelper
+from DrGraph.util.Constant import *
+from DrGraph.enums.ExceptionEnum import ExceptionType
+
+from .ModelTypeEnum import ModelType
+from DrGraph.util.PlotsUtils import get_label_arrays
+
+sys.path.extend(['..', '../AIlib2'])
+FONT_PATH = "./DrGraph/appIOs/conf/platech.ttf"
+
+from DrGraph.Bussiness.Models import *
+
+MODEL_CONFIG = {
+ # 车辆违停模型
+ ModelType.ILLPARKING_MODEL.value[1]: (
+ lambda x, y, r, t, z, h: Model1(x, y, r, ModelType.ILLPARKING_MODEL, t, z, h),
+ ModelType.ILLPARKING_MODEL,
+ lambda x, y, z: one_label(x, y, z), # MODEL_CONFIG[code][2]
+ lambda x: model_process(x)
+ ),
+}
+
+# 河道模型、河道检测模型、交通模型、人员落水模型、城市违章公共模型
+class OneModel:
+ __slots__ = "model_conf"
+
+ # 3090
+ def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None, env=None):
+ try:
+ start = time.time()
+ logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
+ requestId)
+ logger.info('__init__(device={}, allowedList={}, requestId={}, modeType={}, gpu_name={}, base_dir={}, env={})', \
+ device, allowedList, requestId, modeType, gpu_name, base_dir, env)
+ par = modeType.value[4](str(device), gpu_name)
+ mode, postPar, segPar = par.get('mode', 'others'), par.get('postPar'), par.get('segPar')
+ names = par['labelnames']
+ postFile = par['postFile']
+ rainbows = postFile["rainbows"]
+ new_device = torchHelper.select_device(par.get('device'))
+ half = new_device.type != 'cpu'
+ Detweights = par['Detweights']
+ if par['trtFlag_det']:
+ with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
+ model = runtime.deserialize_cuda_engine(f.read())
+ else:
+ model = attempt_load(Detweights, map_location=new_device) # load FP32 model
+ if half: model.half()
+ par['segPar']['seg_nclass'] = par['seg_nclass']
+ Segweights = par['Segweights']
+ if Segweights:
+ if modeType.value[3] == 'cityMangement3':
+ segmodel = DMPRModel(weights=Segweights, par=par['segPar'])
+ else:
+ segmodel = stdcModel(weights=Segweights, par=par['segPar'])
+ else:
+ segmodel = None
+ objectPar = {
+ 'half': half,
+ 'device': new_device,
+ 'conf_thres': postFile["conf_thres"],
+ 'ovlap_thres_crossCategory': postFile.get("ovlap_thres_crossCategory"),
+ 'iou_thres': postFile["iou_thres"],
+ # 对高速模型进行过滤
+ 'segRegionCnt': par['segRegionCnt'],
+ 'trtFlag_det': par['trtFlag_det'],
+ 'trtFlag_seg': par['trtFlag_seg'],
+ 'score_byClass':par['score_byClass'] if 'score_byClass' in par.keys() else None,
+ 'fiterList': par['fiterList'] if 'fiterList' in par.keys() else []
+ }
+ model_param = {
+ "model": model,
+ "segmodel": segmodel,
+ "objectPar": objectPar,
+ "segPar": segPar,
+ "mode": mode,
+ "postPar": postPar
+ }
+ self.model_conf = (modeType, model_param, allowedList, names, rainbows)
+ except Exception:
+ logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
+ raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
+ ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
+ logger.info("模型初始化时间：{}, requestId:{}", time.time() - start, requestId)
+
+# 纯分类模型
+class cityManagementModel:
+ __slots__ = "model_conf"
+
+ def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None, env=None):
+ try:
+ logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
+ requestId)
+ par = modeType.value[4](str(device), gpu_name)
+ postProcess = par['postProcess']
+ names = par['labelnames']
+ postFile = par['postFile']
+ rainbows = postFile["rainbows"]
+ modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par']) for modelPar in par['models'] ]
+ model_param = {
+ "modelList": modelList,
+ "postProcess": postProcess,
+ "score_byClass":par['score_byClass'] if 'score_byClass' in par.keys() else None,
+ "fiterList":par['fiterList'] if 'fiterList' in par.keys() else [],
+ }
+ self.model_conf = (modeType, model_param, allowedList, names, rainbows)
+ except Exception:
+ logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
+ raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
+ ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
+def detSeg_demo2(args):
+ model_conf, frame, request_id = args
+ modelList, postProcess,score_byClass,fiterList = (
+ model_conf[1]['modelList'], model_conf[1]['postProcess'],model_conf[1]['score_byClass'], model_conf[1]['fiterList'])
+ try:
+ result = [[ None, None, AI_process_N([frame], modelList, postProcess,score_byClass,fiterList)[0] ] ] # 为了让返回值适配统一的接口而写的shi
+ return result
+ except ServiceException as s:
+ raise s
+ except Exception:
+ logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
+ raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
+ ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
+
+def model_process(args):
+ model_conf, frame, request_id = args
+ model_param, names, rainbows = model_conf[1], model_conf[3], model_conf[4]
+ try:
+ return aiHelper.AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
+ rainbows, objectPar=model_param['objectPar'], font=model_param['digitFont'],
+ segPar=loads(dumps(model_param['segPar'])), mode=model_param['mode'],
+ postPar=model_param['postPar'])
+ except ServiceException as s:
+ raise s
+ except Exception:
+ # self.num += 1
+ # cv2.imwrite('/home/th/tuo_heng/dev/img%s.jpg' % str(self.num), frame)
+ logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
+ raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
+ ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
+
+
+# 森林模型、车辆模型、行人模型、烟火模型、 钓鱼模型、航道模型、乡村模型、城管模型公共模型
+class TwoModel:
+ __slots__ = "model_conf"
+
+ def __init__(self, device1, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
+ env=None):
+ s = time.time()
+ try:
+ logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
+ requestId)
+ par = modeType.value[4](str(device1), gpu_name)
+ device = select_device(par.get('device'))
+ names = par['labelnames']
+ half = device.type != 'cpu'
+ Detweights = par['Detweights']
+ with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
+ model = runtime.deserialize_cuda_engine(f.read())
+ if modeType == ModelType.CITY_FIREAREA_MODEL:
+ sam = sam_model_registry[par['sam_type']](checkpoint=par['Samweights'])
+ sam.to(device=device)
+ segmodel = SamPredictor(sam)
+ else:
+ segmodel = None
+
+ postFile = par['postFile']
+ conf_thres = postFile["conf_thres"]
+ iou_thres = postFile["iou_thres"]
+ rainbows = postFile["rainbows"]
+ otc = postFile.get("ovlap_thres_crossCategory")
+ model_param = {
+ "model": model,
+ "segmodel": segmodel,
+ "half": half,
+ "device": device,
+ "conf_thres": conf_thres,
+ "iou_thres": iou_thres,
+ "trtFlag_det": par['trtFlag_det'],
+ "otc": otc,
+ "ksize":par['ksize'] if 'ksize' in par.keys() else None,
+ "score_byClass": par['score_byClass'] if 'score_byClass' in par.keys() else None,
+ "fiterList": par['fiterList'] if 'fiterList' in par.keys() else []
+ }
+ self.model_conf = (modeType, model_param, allowedList, names, rainbows)
+ except Exception:
+ logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
+ raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
+ ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
+ logger.info("模型初始化时间：{}, requestId:{}", time.time() - s, requestId)
+def forest_process(args):
+ model_conf, frame, request_id = args
+ model_param, names, rainbows = model_conf[1], model_conf[3], model_conf[4]
+ try:
+ return AI_process_forest([frame], model_param['model'], model_param['segmodel'], names,
+ model_param['label_arraylist'], rainbows, model_param['half'], model_param['device'],
+ model_param['conf_thres'], model_param['iou_thres'],font=model_param['digitFont'],
+ trtFlag_det=model_param['trtFlag_det'], SecNms=model_param['otc'],ksize = model_param['ksize'],
+ score_byClass=model_param['score_byClass'],fiterList=model_param['fiterList'])
+ except ServiceException as s:
+ raise s
+ except Exception:
+ # self.num += 1
+ # cv2.imwrite('/home/th/tuo_heng/dev/img%s.jpg' % str(self.num), frame)
+ logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
+ raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
+ ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
+class MultiModel:
+ __slots__ = "model_conf"
+
+ def __init__(self, device1, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
+ env=None):
+ s = time.time()
+ try:
+ logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
+ requestId)
+ par = modeType.value[4](str(device1), gpu_name)
+ postProcess = par['postProcess']
+ names = par['labelnames']
+ postFile = par['postFile']
+ rainbows = postFile["rainbows"]
+ modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par']) for modelPar in par['models'] ]
+ model_param = {
+ "modelList": modelList,
+ "postProcess": postProcess,
+ "score_byClass": par['score_byClass'] if 'score_byClass' in par.keys() else None,
+ "fiterList": par['fiterList'] if 'fiterList' in par.keys() else []
+ }
+ self.model_conf = (modeType, model_param, allowedList, names, rainbows)
+ except Exception:
+ logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
+ raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
+ ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
+ logger.info("模型初始化时间：{}, requestId:{}", time.time() - s, requestId)
+def channel2_process(args):
+ model_conf, frame, request_id = args
+ modelList, postProcess,score_byClass,fiterList = (
+ model_conf[1]['modelList'], model_conf[1]['postProcess'],model_conf[1]['score_byClass'], model_conf[1]['fiterList'])
+ try:
+ start = time.time()
+ result = [[None, None, AI_process_C([frame], modelList, postProcess,score_byClass,fiterList)[0]]] # 为了让返回值适配统一的接口而写的shi
+ # print("AI_process_C use time = {}".format(time.time()-start))
+ return result
+ except ServiceException as s:
+ raise s
+ except Exception:
+ logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
+ raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
+ ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
+def get_label_arraylist(*args):
+ width, height, names, rainbows = args
+ # line = int(round(0.002 * (height + width) / 2) + 1)
+ line = max(1, int(round(width / 1920 * 3)))
+ label = ' 0.95'
+ tf = max(line - 1, 1)
+ fontScale = line * 0.33
+ text_width, text_height = cv2.getTextSize(label, 0, fontScale=fontScale, thickness=tf)[0]
+ # fontsize = int(width / 1920 * 40)
+ numFontSize = float(format(width / 1920 * 1.1, '.1f'))
+ digitFont = {'line_thickness': line,
+ 'boxLine_thickness': line,
+ 'fontSize': numFontSize,
+ 'waterLineColor': (0, 255, 255),
+ 'segLineShow': False,
+ 'waterLineWidth': line,
+ 'wordSize': text_height,
+ 'label_location': 'leftTop'}
+ label_arraylist = get_label_arrays(names, rainbows, fontSize=text_height, fontPath=FONT_PATH)
+ return digitFont, label_arraylist, (line, text_width, text_height, fontScale, tf)
+# 船只模型
+class ShipModel:
+ __slots__ = "model_conf"
+
+ def __init__(self, device1, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
+ env=None):
+ s = time.time()
+ try:
+ logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
+ requestId)
+ par = modeType.value[4](str(device1), gpu_name)
+ model, decoder2 = load_model_decoder_OBB(par)
+ par['decoder'] = decoder2
+ names = par['labelnames']
+ rainbows = par['postFile']["rainbows"]
+ model_param = {
+ "model": model,
+ "par": par
+ }
+ self.model_conf = (modeType, model_param, allowedList, names, rainbows)
+ except Exception:
+ logger.exception("模型加载异常：{}, requestId:{}", format_exc(), requestId)
+ raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
+ ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
+ logger.info("模型初始化时间：{}, requestId:{}", time.time() - s, requestId)
+def obb_process(args):
+ model_conf, frame, request_id = args
+ model_param = model_conf[1]
+ # font_config, frame, names, label_arrays, rainbows, model, par, requestId = args
+ try:
+ return OBB_infer(model_param["model"], frame, model_param["par"])
+ except ServiceException as s:
+ raise s
+ except Exception:
+ # self.num += 1
+ # cv2.imwrite('/home/th/tuo_heng/dev/img%s.jpg' % str(self.num), frame)
+ logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
+ raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
+ ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
+# 车牌分割模型、健康码、行程码分割模型
+class IMModel:
+ __slots__ = "model_conf"
+
+ def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
+ env=None):
+ try:
+ logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
+ requestId)
+ img_type = 'code'
+ if ModelType.PLATE_MODEL == modeType:
+ img_type = 'plate'
+ par = {
+ 'code': {'weights': '../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,
+ 'plate_dilate': (0.5, 0.3)
+ }
+
+ new_device = torch.device(par['device'])
+ model = torch.jit.load(par[img_type]['weights'])
+ 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:
+ logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
+ raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
+ ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
+
+def im_process(args):
+ frame, device, model, par, img_type, requestId = args
+ try:
+ img, padInfos = pre_process(frame, device)
+ pred = model(img)
+ boxes = post_process(pred, padInfos, device, conf_thres=par['conf_thres'],
+ iou_thres=par['iou_thres'], nc=par[img_type]['nc']) # 后处理
+ dataBack = get_return_data(frame, boxes, modelType=img_type, plate_dilate=par['plate_dilate'])
+ print('-------line351----:',dataBack)
+ return dataBack
+ except ServiceException as s:
+ raise s
+ except Exception:
+ logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), requestId)
+ raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
+ ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
+
+def immulti_process(args):
+ model_conf, frame, requestId = args
+ device, modelList, detpar = model_conf[1], model_conf[2], model_conf[3]
+ try:
+ # new_device = torch.device(device)
+ # img, padInfos = pre_process(frame, new_device)
+ # pred = model(img)
+ # boxes = post_process(pred, padInfos, device, conf_thres=pardet['conf_thres'],
+ # iou_thres=pardet['iou_thres'], nc=pardet['nc']) # 后处理
+ return AI_process_Ocr([frame], modelList, device, detpar)
+ except ServiceException as s:
+ raise s
+ except Exception:
+ logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), requestId)
+ raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
+ ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
+
+class CARPLATEModel:
+ __slots__ = "model_conf"
+ def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
+ env=None):
+ try:
+ logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
+ requestId)
+ par = modeType.value[4](str(device), gpu_name)
+ modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par']) for modelPar in par['models'] ]
+ detpar = par['models'][0]['par']
+ # new_device = torch.device(par['device'])
+ # modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par']) for modelPar in par['models'] ]
+ self.model_conf = (modeType, device, modelList, detpar, par['rainbows'])
+ except Exception:
+ logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
+ raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
+ ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
+
+class DENSECROWDCOUNTModel:
+ __slots__ = "model_conf"
+
+ def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None, env=None):
+ try:
+ logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
+ requestId)
+ par = modeType.value[4](str(device), gpu_name)
+ rainbows = par["rainbows"]
+ models=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par']) for modelPar in par['models'] ]
+ postPar = [pp['par'] for pp in par['models']]
+ self.model_conf = (modeType, device, models, postPar, rainbows)
+ except Exception:
+ logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
+ raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
+ ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
+
+def cc_process(args):
+ model_conf, frame, requestId = args
+ device, model, postPar = model_conf[1], model_conf[2], model_conf[3]
+ try:
+ return AI_process_Crowd([frame], model, device, postPar)
+ except ServiceException as s:
+ raise s
+ except Exception:
+ logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), requestId)
+ raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
+ ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
+
+
+# # 百度AI图片识别模型
+# class BaiduAiImageModel:
+# __slots__ = "model_conf"
+
+# def __init__(self, device=None, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
+# env=None):
+# try:
+# logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
+# requestId)
+# # 人体检测与属性识别、 人流量统计客户端
+# aipBodyAnalysisClient = AipBodyAnalysisClient(base_dir, env)
+# # 车辆检测检测客户端
+# aipImageClassifyClient = AipImageClassifyClient(base_dir, env)
+# rainbows = COLOR
+# vehicle_names = [VehicleEnum.CAR.value[1], VehicleEnum.TRICYCLE.value[1], VehicleEnum.MOTORBIKE.value[1],
+# VehicleEnum.CARPLATE.value[1], VehicleEnum.TRUCK.value[1], VehicleEnum.BUS.value[1]]
+# person_names = ['人']
+# self.model_conf = (modeType, aipImageClassifyClient, aipBodyAnalysisClient, allowedList, rainbows,
+# vehicle_names, person_names, requestId)
+# except Exception:
+# logger.exception("模型加载异常：{}, requestId:{}", format_exc(), requestId)
+# raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
+# ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
+
+
+# def get_baidu_label_arraylist(*args):
+# width, height, vehicle_names, person_names, rainbows = args
+# # line = int(round(0.002 * (height + width) / 2) + 1)
+# line = max(1, int(round(width / 1920 * 3) + 1))
+# label = ' 0.97'
+# tf = max(line, 1)
+# fontScale = line * 0.33
+# text_width, text_height = cv2.getTextSize(label, 0, fontScale=fontScale, thickness=tf)[0]
+# vehicle_label_arrays = get_label_arrays(vehicle_names, rainbows, fontSize=text_height, fontPath=FONT_PATH)
+# person_label_arrays = get_label_arrays(person_names, rainbows, fontSize=text_height, fontPath=FONT_PATH)
+# font_config = (line, text_width, text_height, fontScale, tf)
+# return vehicle_label_arrays, person_label_arrays, font_config
+
+
+# def baidu_process(args):
+# target, url, aipImageClassifyClient, aipBodyAnalysisClient, request_id = args
+# try:
+# # [target, url, aipImageClassifyClient, aipBodyAnalysisClient, requestId]
+# baiduEnum = BAIDU_MODEL_TARGET_CONFIG.get(target)
+# if baiduEnum is None:
+# raise ServiceException(ExceptionType.DETECTION_TARGET_TYPES_ARE_NOT_SUPPORTED.value[0],
+# ExceptionType.DETECTION_TARGET_TYPES_ARE_NOT_SUPPORTED.value[1]
+# + " target: " + target)
+# return baiduEnum.value[2](aipImageClassifyClient, aipBodyAnalysisClient, url, request_id)
+# except ServiceException as s:
+# raise s
+# except Exception:
+# logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
+# raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
+# ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
+
+
+def one_label(width, height, model_conf):
+ # modeType, model_param, allowedList, names, rainbows = model_conf
+ names = model_conf[3]
+ rainbows = model_conf[4]
+ model_param = model_conf[1]
+ digitFont, label_arraylist, font_config = get_label_arraylist(width, height, names, rainbows)
+ model_param['digitFont'] = digitFont
+ model_param['label_arraylist'] = label_arraylist
+ model_param['font_config'] = font_config
+
+# def dynamics_label(width, height, model_conf):
+# # modeType, model_param, allowedList, names, rainbows = model_conf
+# names = model_conf[3]
+# rainbows = model_conf[4]
+# model_param = model_conf[1]
+# digitFont, label_arraylist, font_config = get_label_arraylist(width, height, names, rainbows)
+# line = max(1, int(round(width / 1920 * 3)))
+# label = ' 0.95'
+# tf = max(line - 1, 1)
+# fontScale = line * 0.33
+# _, text_height = cv2.getTextSize(label, 0, fontScale=fontScale, thickness=tf)[0]
+# label_dict = get_label_array_dict(rainbows, fontSize=text_height, fontPath=FONT_PATH)
+# model_param['digitFont'] = digitFont
+# model_param['label_arraylist'] = label_arraylist
+# model_param['font_config'] = font_config
+# model_param['label_dict'] = label_dict
+# def baidu_label(width, height, model_conf):
+# # modeType, aipImageClassifyClient, aipBodyAnalysisClient, allowedList, rainbows,
+# # vehicle_names, person_names, requestId
+# vehicle_names = model_conf[5]
+# person_names = model_conf[6]
+# rainbows = model_conf[4]
+# vehicle_label_arrays, person_label_arrays, font_config = get_baidu_label_arraylist(width, height, vehicle_names,
+# person_names, rainbows)
+# return vehicle_label_arrays, person_label_arrays, font_config
+
+
+# MODEL_CONFIG = {
+# # 车辆违停模型
+# ModelType.ILLPARKING_MODEL.value[1]: (
+# lambda x, y, r, t, z, h: Model1(x, y, r, ModelType.ILLPARKING_MODEL, t, z, h),
+# ModelType.ILLPARKING_MODEL,
+# lambda x, y, z: one_label(x, y, z), # MODEL_CONFIG[code][2]
+# lambda x: model_process(x)
+# ),
+# # # 加载河道模型
+# # ModelType.WATER_SURFACE_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.WATER_SURFACE_MODEL, t, z, h),
+# # ModelType.WATER_SURFACE_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: model_process(x)
+# # ),
+# # # 加载森林模型
+# # # ModelType.FOREST_FARM_MODEL.value[1]: (
+# # # lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.FOREST_FARM_MODEL, t, z, h),
+# # # ModelType.FOREST_FARM_MODEL,
+# # # lambda x, y, z: one_label(x, y, z),
+# # # lambda x: forest_process(x)
+# # # ),
+# # ModelType.FOREST_FARM_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.FOREST_FARM_MODEL, t, z, h),
+# # ModelType.FOREST_FARM_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: detSeg_demo2(x)
+# # ),
+
+# # # 加载交通模型
+# # ModelType.TRAFFIC_FARM_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.TRAFFIC_FARM_MODEL, t, z, h),
+# # ModelType.TRAFFIC_FARM_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: model_process(x)
+# # ),
+# # # 加载防疫模型
+# # ModelType.EPIDEMIC_PREVENTION_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: IMModel(x, y, r, ModelType.EPIDEMIC_PREVENTION_MODEL, t, z, h),
+# # ModelType.EPIDEMIC_PREVENTION_MODEL,
+# # None,
+# # lambda x: im_process(x)),
+# # # 加载车牌模型
+# # ModelType.PLATE_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: IMModel(x, y, r, ModelType.PLATE_MODEL, t, z, h),
+# # ModelType.PLATE_MODEL,
+# # None,
+# # lambda x: im_process(x)),
+# # # 加载车辆模型
+# # ModelType.VEHICLE_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.VEHICLE_MODEL, t, z, h),
+# # ModelType.VEHICLE_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: forest_process(x)
+# # ),
+# # # 加载行人模型
+# # ModelType.PEDESTRIAN_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.PEDESTRIAN_MODEL, t, z, h),
+# # ModelType.PEDESTRIAN_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: forest_process(x)),
+# # # 加载烟火模型
+# # ModelType.SMOGFIRE_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.SMOGFIRE_MODEL, t, z, h),
+# # ModelType.SMOGFIRE_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: forest_process(x)),
+# # # 加载钓鱼游泳模型
+# # ModelType.ANGLERSWIMMER_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.ANGLERSWIMMER_MODEL, t, z, h),
+# # ModelType.ANGLERSWIMMER_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: forest_process(x)),
+# # # 加载乡村模型
+# # ModelType.COUNTRYROAD_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.COUNTRYROAD_MODEL, t, z, h),
+# # ModelType.COUNTRYROAD_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: forest_process(x)),
+# # # 加载船只模型
+# # ModelType.SHIP_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: ShipModel(x, y, r, ModelType.SHIP_MODEL, t, z, h),
+# # ModelType.SHIP_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: obb_process(x)),
+# # # 百度AI图片识别模型
+# # ModelType.BAIDU_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: BaiduAiImageModel(x, y, r, ModelType.BAIDU_MODEL, t, z, h),
+# # ModelType.BAIDU_MODEL,
+# # lambda x, y, z: baidu_label(x, y, z),
+# # lambda x: baidu_process(x)),
+# # # 航道模型
+# # ModelType.CHANNEL_EMERGENCY_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.CHANNEL_EMERGENCY_MODEL, t, z, h),
+# # ModelType.CHANNEL_EMERGENCY_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: forest_process(x)),
+# # # 河道检测模型
+# # ModelType.RIVER2_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.RIVER2_MODEL, t, z, h),
+# # ModelType.RIVER2_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: model_process(x)),
+# # # 城管模型
+# # ModelType.CITY_MANGEMENT_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.CITY_MANGEMENT_MODEL, t, z, h),
+# # ModelType.CITY_MANGEMENT_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: detSeg_demo2(x)
+# # ),
+# # # 人员落水模型
+# # ModelType.DROWING_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.DROWING_MODEL, t, z, h),
+# # ModelType.DROWING_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: model_process(x)
+# # ),
+# # # 城市违章模型
+# # ModelType.NOPARKING_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.NOPARKING_MODEL, t, z, h),
+# # ModelType.NOPARKING_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: model_process(x)
+# # ),
+# # # 城市公路模型
+# # ModelType.CITYROAD_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.CITYROAD_MODEL, t, z, h),
+# # ModelType.CITYROAD_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: forest_process(x)),
+# # # 加载坑槽模型
+# # ModelType.POTHOLE_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.POTHOLE_MODEL, t, z, h),
+# # ModelType.POTHOLE_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: forest_process(x)
+# # ),
+# # # 加载船只综合检测模型
+# # ModelType.CHANNEL2_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: MultiModel(x, y, r, ModelType.CHANNEL2_MODEL, t, z, h),
+# # ModelType.CHANNEL2_MODEL,
+# # lambda x, y, z: dynamics_label(x, y, z),
+# # lambda x: channel2_process(x)
+# # ),
+# # # 河道检测模型
+# # ModelType.RIVERT_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.RIVERT_MODEL, t, z, h),
+# # ModelType.RIVERT_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: model_process(x)),
+# # # 加载森林人群模型
+# # ModelType.FORESTCROWD_FARM_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.FORESTCROWD_FARM_MODEL, t, z, h),
+# # ModelType.FORESTCROWD_FARM_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: detSeg_demo2(x)
+# # ),
+# # # 加载交通模型
+# # ModelType.TRAFFICFORDSJ_FARM_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.TRAFFICFORDSJ_FARM_MODEL, t, z, h),
+# # ModelType.TRAFFICFORDSJ_FARM_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: model_process(x)
+# # ),
+# # # 加载智慧工地模型
+# # ModelType.SMARTSITE_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.SMARTSITE_MODEL, t, z, h),
+# # ModelType.SMARTSITE_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: detSeg_demo2(x)
+# # ),
+
+# # # 加载垃圾模型
+# # ModelType.RUBBISH_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.RUBBISH_MODEL, t, z, h),
+# # ModelType.RUBBISH_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: detSeg_demo2(x)
+# # ),
+
+# # # 加载烟花模型
+# # ModelType.FIREWORK_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.FIREWORK_MODEL, t, z, h),
+# # ModelType.FIREWORK_MODEL,
+# # 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)
+# # ),
+# # # 加载自研车牌检测模型
+# # ModelType.CITY_CARPLATE_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: CARPLATEModel(x, y, r, ModelType.CITY_CARPLATE_MODEL, t, z, h),
+# # ModelType.CITY_CARPLATE_MODEL,
+# # None,
+# # lambda x: immulti_process(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)
+# # ),
+# # # 加载密集人群计数检测模型
+# # ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: DENSECROWDCOUNTModel(x, y, r, ModelType.CITY_DENSECROWDCOUNT_MODEL, t, z, h),
+# # ModelType.CITY_DENSECROWDCOUNT_MODEL,
+# # None,
+# # lambda x: cc_process(x)
+# # ),
+# # # 加载建筑物下行人检测模型
+# # ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: DENSECROWDCOUNTModel(x, y, r, ModelType.CITY_UNDERBUILDCOUNT_MODEL, t, z, h),
+# # ModelType.CITY_UNDERBUILDCOUNT_MODEL,
+# # None,
+# # lambda x: cc_process(x)
+# # ),
+# # # 加载火焰面积模型
+# # ModelType.CITY_FIREAREA_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.CITY_FIREAREA_MODEL, t, z, h),
+# # ModelType.CITY_FIREAREA_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: forest_process(x)
+# # ),
+# # # 加载安防模型
+# # ModelType.CITY_SECURITY_MODEL.value[1]: (
+# # lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.CITY_SECURITY_MODEL, t, z, h),
+# # ModelType.CITY_SECURITY_MODEL,
+# # lambda x, y, z: one_label(x, y, z),
+# # lambda x: detSeg_demo2(x)
+# # ),
+# }
diff --git a/DrGraph/appIOs/conf/__pycache__/ModelTypeEnum.cpython-310.pyc b/DrGraph/appIOs/conf/__pycache__/ModelTypeEnum.cpython-310.pyc
new file mode 100644
index 0000000..6f55d41
Binary files /dev/null and b/DrGraph/appIOs/conf/__pycache__/ModelTypeEnum.cpython-310.pyc differ
diff --git a/DrGraph/appIOs/conf/__pycache__/ModelUtils.cpython-310.pyc b/DrGraph/appIOs/conf/__pycache__/ModelUtils.cpython-310.pyc
new file mode 100644
index 0000000..e630b10
Binary files /dev/null and b/DrGraph/appIOs/conf/__pycache__/ModelUtils.cpython-310.pyc differ
diff --git a/DrGraph/appIOs/conf/logger/algDev_logger.yml b/DrGraph/appIOs/conf/logger/algDev_logger.yml
new file mode 100644
index 0000000..11584bf
--- /dev/null
+++ b/DrGraph/appIOs/conf/logger/algDev_logger.yml
@@ -0,0 +1,9 @@
+enable_file_log: true
+enable_stderr: true
+base_path: "./appIOs/logs"
+log_name: "drgraph_aialg.log"
+log_fmt: "{time: HH:mm:ss.SSS} [{level}] - {message} @ {file}:{line} in {function}"
+level: "INFO"
+rotation: "00:00"
+retention: "1 days"
+encoding: "utf8"
\ No newline at end of file
diff --git a/DrGraph/appIOs/conf/para.json b/DrGraph/appIOs/conf/para.json
new file mode 100644
index 0000000..7808956
--- /dev/null
+++ b/DrGraph/appIOs/conf/para.json
@@ -0,0 +1,7 @@
+{
+
+
+ "post_process":{ "name":"post_process","conf_thres":0.25,"iou_thres":0.45,"classes":5,"rainbows":[ [0,0,255],[0,255,0],[255,0,0],[255,0,255],[255,255,0],[255,129,0],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255], [0,127,0],[0,0,127],[0,255,255]] }
+
+
+}
diff --git a/DrGraph/appIOs/conf/platech.ttf b/DrGraph/appIOs/conf/platech.ttf
new file mode 100644
index 0000000..d66a970
Binary files /dev/null and b/DrGraph/appIOs/conf/platech.ttf differ
diff --git a/DrGraph/appIOs/logs/drgraph_aialg.2025-09-19_08-54-50_467830.log b/DrGraph/appIOs/logs/drgraph_aialg.2025-09-19_08-54-50_467830.log
new file mode 100644
index 0000000..e428150
--- /dev/null
+++ b/DrGraph/appIOs/logs/drgraph_aialg.2025-09-19_08-54-50_467830.log
@@ -0,0 +1,1822 @@
+ 08:54:50.464 [INFO] - 待测试业务名称: @ main.py:15 in
+ 08:54:50.464 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 08:54:50.464 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 08:54:50.659 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 08:54:50.659 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 08:54:50.660 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:195 in checkFile
+ 08:54:50.660 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:195 in checkFile
+ 08:54:50.660 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:195 in checkFile
+ 08:54:50.660 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:195 in checkFile
+ 08:54:50.661 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:195 in checkFile
+ 08:54:50.661 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:195 in checkFile
+ 08:54:50.661 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 08:54:50.688 [INFO] - select_device YOLOv5 🚀 2025-9-17 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:106 in select_device
+ 08:54:50.689 [INFO] - step 2: 取得参数配置 @ Bussiness_Seg.py:73 in run
+ 08:54:50.848 [INFO] - step 3: 情况 1 - 成功载入 det model trt [./weights/illParking/yolov5_3090_fp16.engine] @ Bussiness_Seg.py:83 in run
+ 08:54:50.874 [INFO] - 加载 stdcModel 模型: ./weights/illParking/stdc_360X640_3090_fp16.engine 类型: trt @ stdc.py:53 in __init__
+ 08:54:50.883 [INFO] - step 4: 共读入 1 张图片待处理 @ Bussiness_Seg.py:170 in run
+ 08:54:50.883 [WARNING] - step 5-------------------- 处理图片 ./appIOs/samples/illParking/a2fe274345f77fb8985d2bc90aaaae7.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 08:54:51.339 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 08:54:51.341 [WARNING] - mean not in par, use default mean(0.485, 0.456, 0.406) @ stdc.py:75 in preprocess_image
+ 08:54:51.341 [WARNING] - std not in par, use default std(0.229, 0.224, 0.225) @ stdc.py:78 in preprocess_image
+ 08:54:51.429 [INFO] - [业务分析]业务 总共耗时 545.5 毫秒，其中：
+ AI_Process: 537.7 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 537.7 毫秒，其中：
+ img_pad: 3.1 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.1 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 450.9 毫秒 aiHelper.py:165 in AI_process
+ infer: 12.3 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 65.3 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 5.7 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 5.7 毫秒，其中：
+ NMS: 2.4 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.3 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 7.0 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 08:54:51.430 [INFO] - step 6: 1 张图片共耗时:546.4 ms ,依次为:546.4 ms, 占用 1 线程 @ Bussiness_Seg.py:187 in run
+ 08:55:45.440 [INFO] - 待测试业务名称: @ main.py:15 in
+ 08:55:45.440 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 08:55:45.440 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 08:55:45.636 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 08:55:45.637 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 08:55:45.638 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:195 in checkFile
+ 08:55:45.638 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:195 in checkFile
+ 08:55:45.638 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:195 in checkFile
+ 08:55:45.638 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:195 in checkFile
+ 08:55:45.639 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:195 in checkFile
+ 08:55:45.639 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:195 in checkFile
+ 08:55:45.639 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 08:55:45.666 [INFO] - select_device YOLOv5 🚀 2025-9-17 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:106 in select_device
+ 08:55:45.667 [INFO] - step 2: 取得参数配置 @ Bussiness_Seg.py:73 in run
+ 08:55:45.840 [INFO] - step 3: 情况 1 - 成功载入 det model trt [./weights/illParking/yolov5_3090_fp16.engine] @ Bussiness_Seg.py:83 in run
+ 08:55:45.864 [INFO] - 加载 stdcModel 模型: ./weights/illParking/stdc_360X640_3090_fp16.engine 类型: trt @ stdc.py:53 in __init__
+ 08:55:45.873 [INFO] - step 4: 共读入 1 张图片待处理 @ Bussiness_Seg.py:170 in run
+ 08:55:45.874 [WARNING] - step 5-------------------- 处理图片 ./appIOs/samples/illParking/a2fe274345f77fb8985d2bc90aaaae7.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 08:55:45.904 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 08:55:45.907 [WARNING] - mean not in par, use default mean(0.485, 0.456, 0.406) @ stdc.py:75 in preprocess_image
+ 08:55:45.907 [WARNING] - std not in par, use default std(0.229, 0.224, 0.225) @ stdc.py:78 in preprocess_image
+ 08:56:03.016 [INFO] - 待测试业务名称: @ main.py:15 in
+ 08:56:03.016 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 08:56:03.017 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 08:56:03.207 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 08:56:03.207 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 08:56:03.208 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:195 in checkFile
+ 08:56:03.208 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:195 in checkFile
+ 08:56:03.208 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:195 in checkFile
+ 08:56:03.209 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:195 in checkFile
+ 08:56:03.209 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:195 in checkFile
+ 08:56:03.210 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:195 in checkFile
+ 08:56:03.210 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 08:56:03.237 [INFO] - select_device YOLOv5 🚀 2025-9-17 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:106 in select_device
+ 08:56:03.237 [INFO] - step 2: 取得参数配置 @ Bussiness_Seg.py:73 in run
+ 08:56:03.407 [INFO] - step 3: 情况 1 - 成功载入 det model trt [./weights/illParking/yolov5_3090_fp16.engine] @ Bussiness_Seg.py:83 in run
+ 08:56:03.433 [INFO] - 加载 stdcModel 模型: ./weights/illParking/stdc_360X640_3090_fp16.engine 类型: trt @ stdc.py:53 in __init__
+ 08:56:03.443 [INFO] - step 4: 共读入 1 张图片待处理 @ Bussiness_Seg.py:170 in run
+ 08:56:03.443 [WARNING] - step 5-------------------- 处理图片 ./appIOs/samples/illParking/a2fe274345f77fb8985d2bc90aaaae7.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 08:56:03.897 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 08:56:03.899 [WARNING] - mean not in par, use default mean(0.485, 0.456, 0.406) @ stdc.py:75 in preprocess_image
+ 08:56:03.900 [WARNING] - std not in par, use default std(0.229, 0.224, 0.225) @ stdc.py:78 in preprocess_image
+ 08:56:03.989 [INFO] - [业务分析]业务 总共耗时 545.6 毫秒，其中：
+ AI_Process: 537.9 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 537.9 毫秒，其中：
+ img_pad: 1.6 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.1 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 450.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 13.5 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 65.5 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 6.5 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 6.5 毫秒，其中：
+ NMS: 3.2 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.2 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.9 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 08:56:03.990 [INFO] - step 6: 1 张图片共耗时:546.4 ms ,依次为:546.4 ms, 占用 1 线程 @ Bussiness_Seg.py:187 in run
+ 08:56:54.624 [INFO] - 待测试业务名称: @ main.py:15 in
+ 08:56:54.624 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 08:56:54.624 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 08:56:54.815 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 08:56:54.816 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 08:56:54.816 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:195 in checkFile
+ 08:56:54.816 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:195 in checkFile
+ 08:56:54.817 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:195 in checkFile
+ 08:56:54.817 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:195 in checkFile
+ 08:56:54.818 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:195 in checkFile
+ 08:56:54.818 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:195 in checkFile
+ 08:56:54.819 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 08:56:54.845 [INFO] - select_device YOLOv5 🚀 2025-9-17 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:106 in select_device
+ 08:56:54.845 [INFO] - step 2: 取得参数配置 @ Bussiness_Seg.py:73 in run
+ 08:56:55.009 [INFO] - step 3: 情况 1 - 成功载入 det model trt [./weights/illParking/yolov5_3090_fp16.engine] @ Bussiness_Seg.py:83 in run
+ 08:56:55.033 [INFO] - 加载 stdcModel 模型: ./weights/illParking/stdc_360X640_3090_fp16.engine 类型: trt @ stdc.py:53 in __init__
+ 08:56:55.042 [INFO] - step 4: 共读入 1 张图片待处理 @ Bussiness_Seg.py:170 in run
+ 08:56:55.043 [WARNING] - step 5-------------------- 处理图片 ./appIOs/samples/illParking/a2fe274345f77fb8985d2bc90aaaae7.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 08:56:55.047 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 08:56:55.049 [WARNING] - mean not in par, use default mean(0.485, 0.456, 0.406) @ stdc.py:75 in preprocess_image
+ 08:56:55.050 [WARNING] - std not in par, use default std(0.229, 0.224, 0.225) @ stdc.py:78 in preprocess_image
+ 08:57:56.058 [INFO] - 待测试业务名称: @ main.py:15 in
+ 08:57:56.058 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 08:57:56.058 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 08:57:56.254 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 08:57:56.254 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 08:57:56.255 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:195 in checkFile
+ 08:57:56.255 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:195 in checkFile
+ 08:57:56.255 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:195 in checkFile
+ 08:57:56.256 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:195 in checkFile
+ 08:57:56.256 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:195 in checkFile
+ 08:57:56.256 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:195 in checkFile
+ 08:57:56.256 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 08:57:56.283 [INFO] - select_device YOLOv5 🚀 2025-9-17 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:106 in select_device
+ 08:57:56.283 [INFO] - step 2: 取得参数配置 @ Bussiness_Seg.py:73 in run
+ 08:57:56.453 [INFO] - step 3: 情况 1 - 成功载入 det model trt [./weights/illParking/yolov5_3090_fp16.engine] @ Bussiness_Seg.py:83 in run
+ 08:57:56.477 [INFO] - 加载 stdcModel 模型: ./weights/illParking/stdc_360X640_3090_fp16.engine 类型: trt @ stdc.py:53 in __init__
+ 08:57:56.486 [INFO] - step 4: 共读入 1 张图片待处理 @ Bussiness_Seg.py:170 in run
+ 08:57:56.487 [WARNING] - step 5-------------------- 处理图片 ./appIOs/samples/illParking/a2fe274345f77fb8985d2bc90aaaae7.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 08:57:56.935 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 08:57:56.937 [WARNING] - mean not in par, use default mean(0.485, 0.456, 0.406) @ stdc.py:75 in preprocess_image
+ 08:57:56.937 [WARNING] - std not in par, use default std(0.229, 0.224, 0.225) @ stdc.py:78 in preprocess_image
+ 08:57:57.123 [INFO] - [业务分析]业务 总共耗时 635.6 毫秒，其中：
+ AI_Process: 531.9 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 531.9 毫秒，其中：
+ img_pad: 1.8 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.1 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 443.2 毫秒 aiHelper.py:166 in AI_process
+ infer: 15.9 毫秒 aiHelper.py:178 in AI_process
+ yolov5Trtforward: 64.6 毫秒 aiHelper.py:185 in AI_process
+ 后处理: 6.0 毫秒 aiHelper.py:192 in AI_process -> [预测结果后处理]业务 总共耗时 6.0 毫秒，其中：
+ NMS: 2.6 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.4 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 102.8 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.2 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 08:57:57.124 [INFO] - step 6: 1 张图片共耗时:636.8 ms ,依次为:636.8 ms, 占用 1 线程 @ Bussiness_Seg.py:187 in run
+ 11:46:19.955 [INFO] - 待测试业务名称: @ main.py:15 in
+ 11:46:19.955 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 11:46:19.955 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 11:46:20.149 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 11:46:20.150 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 11:46:20.150 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:195 in checkFile
+ 11:46:20.151 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:195 in checkFile
+ 11:46:20.151 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:195 in checkFile
+ 11:46:20.151 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:195 in checkFile
+ 11:46:20.152 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:195 in checkFile
+ 11:46:20.152 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:195 in checkFile
+ 11:46:20.152 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 11:46:20.179 [INFO] - select_device YOLOv5 🚀 2025-9-17 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:106 in select_device
+ 11:46:20.180 [INFO] - step 2: 取得参数配置 @ Bussiness_Seg.py:73 in run
+ 11:46:20.344 [INFO] - step 3: 情况 1 - 成功载入 det model trt [./weights/illParking/yolov5_3090_fp16.engine] @ Bussiness_Seg.py:83 in run
+ 11:46:20.368 [INFO] - 加载 stdcModel 模型: ./weights/illParking/stdc_360X640_3090_fp16.engine 类型: trt @ stdc.py:53 in __init__
+ 11:46:20.391 [INFO] - step 4: 共读入 5 张图片待处理 @ Bussiness_Seg.py:170 in run
+ 11:46:20.391 [WARNING] - step 5-------------------- 处理图片 ./appIOs/samples/illParking/4.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 11:46:20.842 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 11:46:20.844 [WARNING] - mean not in par, use default mean(0.485, 0.456, 0.406) @ stdc.py:75 in preprocess_image
+ 11:46:20.844 [WARNING] - std not in par, use default std(0.229, 0.224, 0.225) @ stdc.py:78 in preprocess_image
+ 11:46:20.935 [INFO] - [业务分析]业务 总共耗时 543.1 毫秒，其中：
+ AI_Process: 535.4 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 535.4 毫秒，其中：
+ img_pad: 1.7 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.1 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 446.9 毫秒 aiHelper.py:165 in AI_process
+ infer: 14.3 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 66.4 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 5.7 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 5.7 毫秒，其中：
+ NMS: 2.4 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.3 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.8 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 11:46:20.935 [WARNING] - step 6-------------------- 处理图片 ./appIOs/samples/illParking/3.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 11:46:20.938 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 11:46:20.958 [INFO] - [业务分析]业务 总共耗时 22.7 毫秒，其中：
+ AI_Process: 14.7 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 14.7 毫秒，其中：
+ img_pad: 1.9 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.1 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.0 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.1 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.0 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.1 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 7.2 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 11:46:20.959 [WARNING] - step 7-------------------- 处理图片 ./appIOs/samples/illParking/2.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 11:46:20.961 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 11:46:20.981 [INFO] - [业务分析]业务 总共耗时 21.8 毫秒，其中：
+ AI_Process: 14.0 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 14.0 毫秒，其中：
+ img_pad: 1.1 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 5.7 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.0 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.5 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.5 毫秒，其中：
+ NMS: 1.3 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.2 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.9 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 11:46:20.981 [WARNING] - step 8-------------------- 处理图片 ./appIOs/samples/illParking/1.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 11:46:20.983 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 11:46:21.003 [INFO] - [业务分析]业务 总共耗时 21.8 毫秒，其中：
+ AI_Process: 14.3 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 14.3 毫秒，其中：
+ img_pad: 0.9 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.3 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.1 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.3 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.2 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.2 毫秒，其中：
+ NMS: 1.1 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.1 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.7 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 11:46:21.003 [WARNING] - step 9-------------------- 处理图片 ./appIOs/samples/illParking/5.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 11:46:21.005 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 11:46:21.026 [INFO] - [业务分析]业务 总共耗时 22.5 毫秒，其中：
+ AI_Process: 14.8 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 14.8 毫秒，其中：
+ img_pad: 1.0 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.3 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.7 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.1 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.3 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.2 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.2 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.9 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 11:46:21.027 [INFO] - step 10: 5 张图片共耗时:635.3 ms ,依次为:127.1 ms, 占用 1 线程 @ Bussiness_Seg.py:187 in run
+ 11:48:30.299 [INFO] - 待测试业务名称: @ main.py:15 in
+ 11:48:30.299 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 11:48:30.300 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 11:48:30.498 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 11:48:30.499 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 11:48:30.500 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:195 in checkFile
+ 11:48:30.500 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:195 in checkFile
+ 11:48:30.500 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:195 in checkFile
+ 11:48:30.500 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:195 in checkFile
+ 11:48:30.501 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:195 in checkFile
+ 11:48:30.501 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:195 in checkFile
+ 11:48:30.501 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 11:48:30.527 [INFO] - select_device YOLOv5 🚀 2025-9-17 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:106 in select_device
+ 11:48:30.528 [INFO] - step 2: 取得参数配置 @ Bussiness_Seg.py:73 in run
+ 11:48:30.691 [INFO] - step 3: 情况 1 - 成功载入 det model trt [./weights/illParking/yolov5_3090_fp16.engine] @ Bussiness_Seg.py:83 in run
+ 11:48:30.716 [INFO] - 加载 stdcModel 模型: ./weights/illParking/stdc_360X640_3090_fp16.engine 类型: trt @ stdc.py:53 in __init__
+ 11:48:30.740 [INFO] - step 4: 共读入 5 张图片待处理 @ Bussiness_Seg.py:170 in run
+ 11:48:30.741 [WARNING] - step 5-------------------- 处理图片 ./appIOs/samples/illParking/4.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 11:48:31.206 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 11:48:31.208 [WARNING] - mean not in par, use default mean(0.485, 0.456, 0.406) @ stdc.py:75 in preprocess_image
+ 11:48:31.209 [WARNING] - std not in par, use default std(0.229, 0.224, 0.225) @ stdc.py:78 in preprocess_image
+ 11:48:31.299 [INFO] - [业务分析]业务 总共耗时 557.9 毫秒，其中：
+ AI_Process: 549.9 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 549.9 毫秒，其中：
+ img_pad: 1.7 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.1 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 461.5 毫秒 aiHelper.py:165 in AI_process
+ infer: 14.2 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 66.0 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 6.1 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 6.1 毫秒，其中：
+ NMS: 2.7 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.4 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 7.1 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 11:48:31.299 [WARNING] - step 6-------------------- 处理图片 ./appIOs/samples/illParking/3.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 11:48:31.302 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 11:48:31.322 [INFO] - [业务分析]业务 总共耗时 22.1 毫秒，其中：
+ AI_Process: 14.2 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 14.2 毫秒，其中：
+ img_pad: 1.7 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 5.7 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.0 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.1 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.1 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.0 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 7.1 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 11:48:31.322 [WARNING] - step 7-------------------- 处理图片 ./appIOs/samples/illParking/2.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 11:48:31.324 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 11:48:31.345 [INFO] - [业务分析]业务 总共耗时 22.8 毫秒，其中：
+ AI_Process: 15.1 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 15.0 毫秒，其中：
+ img_pad: 1.0 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.8 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.1 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.4 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.3 毫秒，其中：
+ NMS: 1.1 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.3 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.9 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 11:48:31.346 [WARNING] - step 8-------------------- 处理图片 ./appIOs/samples/illParking/1.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 11:48:31.348 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 11:48:31.374 [INFO] - [业务分析]业务 总共耗时 28.3 毫秒，其中：
+ AI_Process: 18.7 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 18.7 毫秒，其中：
+ img_pad: 1.4 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 7.2 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.1 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 6.8 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 6.8 毫秒，其中：
+ NMS: 1.1 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 5.6 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.1 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 9.3 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 11:48:31.375 [WARNING] - step 9-------------------- 处理图片 ./appIOs/samples/illParking/5.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 11:48:31.377 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 11:48:31.398 [INFO] - [业务分析]业务 总共耗时 23.0 毫秒，其中：
+ AI_Process: 15.2 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 15.1 毫秒，其中：
+ img_pad: 1.1 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.9 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.0 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.3 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.3 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.3 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 7.0 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 11:48:31.398 [INFO] - step 10: 5 张图片共耗时:657.7 ms ,依次为:131.5 ms, 占用 1 线程 @ Bussiness_Seg.py:187 in run
+ 11:49:48.090 [INFO] - 待测试业务名称: @ main.py:15 in
+ 11:49:48.090 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 11:49:48.091 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 11:49:48.285 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 11:49:48.286 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 11:49:48.286 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:195 in checkFile
+ 11:49:48.286 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:195 in checkFile
+ 11:49:48.286 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:195 in checkFile
+ 11:49:48.287 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:195 in checkFile
+ 11:49:48.287 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:195 in checkFile
+ 11:49:48.287 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:195 in checkFile
+ 11:49:48.288 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 11:49:48.315 [INFO] - select_device YOLOv5 🚀 2025-9-17 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:106 in select_device
+ 11:49:48.316 [INFO] - step 2: 取得参数配置 @ Bussiness_Seg.py:73 in run
+ 11:49:48.481 [INFO] - step 3: 情况 1 - 成功载入 det model trt [./weights/illParking/yolov5_3090_fp16.engine] @ Bussiness_Seg.py:83 in run
+ 11:49:48.506 [INFO] - 加载 stdcModel 模型: ./weights/illParking/stdc_360X640_3090_fp16.engine 类型: trt @ stdc.py:53 in __init__
+ 11:49:48.530 [INFO] - step 4: 共读入 5 张图片待处理 @ Bussiness_Seg.py:170 in run
+ 11:49:48.531 [WARNING] - step 5-------------------- 处理图片 ./appIOs/samples/illParking/4.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 11:49:48.979 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 11:49:48.981 [WARNING] - mean not in par, use default mean(0.485, 0.456, 0.406) @ stdc.py:75 in preprocess_image
+ 11:49:48.981 [WARNING] - std not in par, use default std(0.229, 0.224, 0.225) @ stdc.py:78 in preprocess_image
+ 11:49:49.069 [INFO] - [业务分析]业务 总共耗时 538.4 毫秒，其中：
+ AI_Process: 530.7 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 530.7 毫秒，其中：
+ img_pad: 1.8 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.1 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 444.7 毫秒 aiHelper.py:165 in AI_process
+ infer: 13.3 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 64.3 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 6.3 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 6.2 毫秒，其中：
+ NMS: 2.9 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.3 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.9 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 11:49:49.070 [WARNING] - step 6-------------------- 处理图片 ./appIOs/samples/illParking/3.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 11:49:49.073 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 11:49:49.093 [INFO] - [业务分析]业务 总共耗时 22.9 毫秒，其中：
+ AI_Process: 15.3 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 15.3 毫秒，其中：
+ img_pad: 1.8 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.6 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 1.9 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.1 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.1 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.2 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.8 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 11:49:49.094 [WARNING] - step 7-------------------- 处理图片 ./appIOs/samples/illParking/2.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 11:49:49.096 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 11:49:49.117 [INFO] - [业务分析]业务 总共耗时 22.5 毫秒，其中：
+ AI_Process: 14.9 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 14.9 毫秒，其中：
+ img_pad: 1.2 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.5 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.1 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.3 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.3 毫秒，其中：
+ NMS: 1.1 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.2 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.8 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 11:49:49.117 [WARNING] - step 8-------------------- 处理图片 ./appIOs/samples/illParking/1.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 11:49:49.120 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 11:49:49.145 [INFO] - [业务分析]业务 总共耗时 27.4 毫秒，其中：
+ AI_Process: 18.2 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 18.2 毫秒，其中：
+ img_pad: 1.4 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.8 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.0 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 6.9 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 6.9 毫秒，其中：
+ NMS: 1.1 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 5.8 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.1 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 9.0 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 11:49:49.145 [WARNING] - step 9-------------------- 处理图片 ./appIOs/samples/illParking/5.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 11:49:49.147 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 11:49:49.168 [INFO] - [业务分析]业务 总共耗时 23.0 毫秒，其中：
+ AI_Process: 15.4 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 15.4 毫秒，其中：
+ img_pad: 1.1 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.3 毫秒 aiHelper.py:165 in AI_process
+ infer: 7.0 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.4 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.3 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.3 毫秒，其中：
+ NMS: 1.2 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.1 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.7 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 11:49:49.169 [INFO] - step 10: 5 张图片共耗时:638.2 ms ,依次为:127.6 ms, 占用 1 线程 @ Bussiness_Seg.py:187 in run
+ 13:32:54.857 [INFO] - 待测试业务名称: @ main.py:15 in
+ 13:32:54.858 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 13:32:54.858 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 13:32:55.048 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 13:32:55.048 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 13:32:55.049 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:195 in checkFile
+ 13:32:55.049 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:195 in checkFile
+ 13:32:55.050 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:195 in checkFile
+ 13:32:55.050 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:195 in checkFile
+ 13:32:55.050 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:195 in checkFile
+ 13:32:55.051 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:195 in checkFile
+ 13:32:55.051 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 13:32:55.077 [INFO] - select_device YOLOv5 🚀 2025-9-17 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:106 in select_device
+ 13:32:55.078 [INFO] - step 2: 取得参数配置 @ Bussiness_Seg.py:73 in run
+ 13:32:55.243 [INFO] - step 3: 情况 1 - 成功载入 det model trt [./weights/illParking/yolov5_3090_fp16.engine] @ Bussiness_Seg.py:83 in run
+ 13:32:55.267 [INFO] - 加载 stdcModel 模型: ./weights/illParking/stdc_360X640_3090_fp16.engine 类型: trt @ stdc.py:53 in __init__
+ 13:32:55.290 [INFO] - step 4: 共读入 5 张图片待处理 @ Bussiness_Seg.py:170 in run
+ 13:32:55.290 [WARNING] - step 5-------------------- 处理图片 ./appIOs/samples/illParking/4.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 13:32:55.737 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 13:32:55.739 [WARNING] - mean not in par, use default mean(0.485, 0.456, 0.406) @ stdc.py:75 in preprocess_image
+ 13:32:55.739 [WARNING] - std not in par, use default std(0.229, 0.224, 0.225) @ stdc.py:78 in preprocess_image
+ 13:32:55.827 [INFO] - [业务分析]业务 总共耗时 536.1 毫秒，其中：
+ AI_Process: 528.4 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 528.4 毫秒，其中：
+ img_pad: 1.6 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.1 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 442.9 毫秒 aiHelper.py:165 in AI_process
+ infer: 13.0 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 64.6 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 5.9 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 5.8 毫秒，其中：
+ NMS: 2.5 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.3 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.9 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 13:32:55.827 [WARNING] - step 6-------------------- 处理图片 ./appIOs/samples/illParking/3.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 13:32:55.830 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 13:32:55.851 [INFO] - [业务分析]业务 总共耗时 22.8 毫秒，其中：
+ AI_Process: 15.1 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 15.1 毫秒，其中：
+ img_pad: 1.8 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.4 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.0 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.2 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.1 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.1 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.9 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 13:32:55.851 [WARNING] - step 7-------------------- 处理图片 ./appIOs/samples/illParking/2.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 13:32:55.854 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 13:32:55.874 [INFO] - [业务分析]业务 总共耗时 22.2 毫秒，其中：
+ AI_Process: 14.5 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 14.4 毫秒，其中：
+ img_pad: 0.8 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.6 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.5 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.1 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.0 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.0 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.0 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.9 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 13:32:55.874 [WARNING] - step 8-------------------- 处理图片 ./appIOs/samples/illParking/1.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 13:32:55.878 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 13:32:55.903 [INFO] - [业务分析]业务 总共耗时 28.1 毫秒，其中：
+ AI_Process: 18.8 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 18.8 毫秒，其中：
+ img_pad: 1.8 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.6 毫秒 aiHelper.py:165 in AI_process
+ infer: 7.2 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 1.9 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 6.6 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 6.6 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 5.5 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.1 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 9.1 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 13:32:55.903 [WARNING] - step 9-------------------- 处理图片 ./appIOs/samples/illParking/5.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 13:32:55.905 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 13:32:55.927 [INFO] - [业务分析]业务 总共耗时 23.4 毫秒，其中：
+ AI_Process: 15.6 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 15.5 毫秒，其中：
+ img_pad: 1.3 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 7.2 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.2 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.2 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.1 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.1 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 7.0 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 13:32:55.927 [INFO] - step 10: 5 张图片共耗时:636.5 ms ,依次为:127.3 ms, 占用 1 线程 @ Bussiness_Seg.py:187 in run
+ 14:02:22.400 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:02:22.400 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:02:22.401 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:02:22.595 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:02:22.595 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:02:22.596 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:195 in checkFile
+ 14:02:22.596 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:195 in checkFile
+ 14:02:22.597 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:195 in checkFile
+ 14:02:22.597 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:195 in checkFile
+ 14:02:22.597 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:195 in checkFile
+ 14:02:22.598 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:195 in checkFile
+ 14:02:22.598 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:03:33.347 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:03:33.348 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:03:33.348 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:03:33.541 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:03:33.542 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:03:33.543 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:196 in checkFile
+ 14:03:33.543 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:196 in checkFile
+ 14:03:33.543 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:196 in checkFile
+ 14:03:33.544 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:196 in checkFile
+ 14:03:33.544 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:196 in checkFile
+ 14:03:33.545 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:196 in checkFile
+ 14:03:33.545 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:04:10.003 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:04:10.003 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:04:10.003 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:04:10.198 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:04:10.198 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:04:10.199 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:196 in checkFile
+ 14:04:10.199 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:196 in checkFile
+ 14:04:10.199 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:196 in checkFile
+ 14:04:10.200 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:196 in checkFile
+ 14:04:10.200 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:196 in checkFile
+ 14:04:10.200 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:196 in checkFile
+ 14:04:10.201 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:04:38.411 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:04:38.412 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:04:38.412 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:04:38.603 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:04:38.603 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:04:38.604 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:196 in checkFile
+ 14:04:38.604 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:196 in checkFile
+ 14:04:38.605 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:196 in checkFile
+ 14:04:38.605 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:196 in checkFile
+ 14:04:38.605 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:196 in checkFile
+ 14:04:38.605 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:196 in checkFile
+ 14:04:38.606 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:05:50.927 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:05:50.928 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:05:50.928 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:05:51.127 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:05:51.128 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:05:51.128 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:05:51.128 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:05:51.129 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:05:51.129 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:05:51.129 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:05:51.130 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:05:51.130 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:06:25.266 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:06:25.266 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:06:25.266 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:06:25.461 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:06:25.462 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:06:25.462 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:06:25.462 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:06:25.463 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:06:25.463 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:06:25.463 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:06:25.464 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:06:25.464 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:06:59.331 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:06:59.331 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:06:59.332 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:06:59.522 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:06:59.523 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:06:59.523 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:06:59.524 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:06:59.524 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:06:59.524 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:06:59.525 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:06:59.525 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:06:59.525 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:07:23.751 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:07:23.752 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:07:23.752 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:07:23.945 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:07:23.946 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:07:23.946 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:07:23.947 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:07:23.947 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:07:23.947 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:07:23.947 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:07:23.948 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:07:23.948 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:07:23.978 [INFO] - select_device YOLOv5 🚀 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:74 in select_device
+ 14:07:23.979 [INFO] - step 2: 取得参数配置 @ Bussiness_Seg.py:73 in run
+ 14:07:24.137 [INFO] - step 3: 情况 1 - 成功载入 det model trt [./weights/illParking/yolov5_3090_fp16.engine] @ Bussiness_Seg.py:83 in run
+ 14:07:24.161 [INFO] - 加载 stdcModel 模型: ./weights/illParking/stdc_360X640_3090_fp16.engine 类型: trt @ stdc.py:53 in __init__
+ 14:07:24.184 [INFO] - step 4: 共读入 5 张图片待处理 @ Bussiness_Seg.py:170 in run
+ 14:07:24.185 [WARNING] - step 5-------------------- 处理图片 ./appIOs/samples/illParking/4.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:07:24.618 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:07:24.620 [WARNING] - mean not in par, use default mean(0.485, 0.456, 0.406) @ stdc.py:75 in preprocess_image
+ 14:07:24.620 [WARNING] - std not in par, use default std(0.229, 0.224, 0.225) @ stdc.py:78 in preprocess_image
+ 14:07:24.705 [INFO] - [业务分析]业务 总共耗时 519.9 毫秒，其中：
+ AI_Process: 512.1 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 512.1 毫秒，其中：
+ img_pad: 1.8 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.1 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 429.5 毫秒 aiHelper.py:165 in AI_process
+ infer: 13.2 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 61.1 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 6.0 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 6.0 毫秒，其中：
+ NMS: 2.5 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.4 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.9 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:07:24.705 [WARNING] - step 6-------------------- 处理图片 ./appIOs/samples/illParking/3.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:07:24.708 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:07:24.729 [INFO] - [业务分析]业务 总共耗时 23.9 毫秒，其中：
+ AI_Process: 16.1 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 16.1 毫秒，其中：
+ img_pad: 2.0 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.5 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.7 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.1 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.4 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.3 毫秒，其中：
+ NMS: 1.2 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.2 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 7.0 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:07:24.730 [WARNING] - step 7-------------------- 处理图片 ./appIOs/samples/illParking/2.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:07:24.732 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:07:24.753 [INFO] - [业务分析]业务 总共耗时 22.3 毫秒，其中：
+ AI_Process: 14.5 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 14.5 毫秒，其中：
+ img_pad: 0.9 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.6 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.0 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.3 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.3 毫秒，其中：
+ NMS: 1.1 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.2 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 7.0 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:07:24.753 [WARNING] - step 8-------------------- 处理图片 ./appIOs/samples/illParking/1.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:07:24.756 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:07:24.782 [INFO] - [业务分析]业务 总共耗时 28.8 毫秒，其中：
+ AI_Process: 18.9 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 18.9 毫秒，其中：
+ img_pad: 1.6 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 7.4 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.0 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 6.8 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 6.8 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 5.8 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.2 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 9.6 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:07:24.783 [WARNING] - step 9-------------------- 处理图片 ./appIOs/samples/illParking/5.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:07:24.785 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:07:24.807 [INFO] - [业务分析]业务 总共耗时 24.0 毫秒，其中：
+ AI_Process: 16.2 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 16.2 毫秒，其中：
+ img_pad: 1.3 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 7.6 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.2 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.3 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.2 毫秒，其中：
+ NMS: 1.1 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.2 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 7.0 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:07:24.807 [INFO] - step 10: 5 张图片共耗时:623.0 ms ,依次为:124.6 ms, 占用 1 线程 @ Bussiness_Seg.py:187 in run
+ 14:07:31.613 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:07:31.614 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:07:31.614 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:07:31.809 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:07:31.810 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:07:31.810 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:07:31.810 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:07:31.811 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:07:31.811 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:07:31.812 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:07:31.812 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:07:31.812 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:39:36.115 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:39:36.116 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:39:36.116 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:39:36.308 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:39:36.309 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:39:36.309 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:39:36.310 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:39:36.310 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:39:36.310 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:39:36.311 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:39:36.311 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:39:36.312 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:40:00.446 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:40:00.446 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:40:00.446 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:40:00.642 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:40:00.643 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:40:00.643 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:40:00.643 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:40:00.644 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:40:00.644 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:40:00.644 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:40:00.645 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:40:00.645 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:41:19.176 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:41:19.177 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:41:19.177 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:41:19.369 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:41:19.370 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:41:19.370 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:41:19.370 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:41:19.371 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:41:19.371 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:41:19.372 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:41:19.372 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:41:19.372 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:41:44.892 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:41:44.893 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:41:44.893 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:41:45.087 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:41:45.087 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:41:45.088 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:41:45.088 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:41:45.089 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:41:45.089 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:41:45.090 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:41:45.090 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:41:45.090 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:42:23.564 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:42:23.564 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:42:23.565 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:42:23.757 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:42:23.758 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:42:23.758 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:42:23.759 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:42:23.759 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:42:23.759 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:42:23.760 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:42:23.760 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:42:23.760 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:42:40.698 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:42:40.698 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:42:40.699 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:42:40.893 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:42:40.894 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:42:40.894 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:42:40.895 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:42:40.895 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:42:40.895 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:42:40.896 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:42:40.896 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:42:40.896 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:43:54.433 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:43:54.434 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:43:54.434 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:43:54.629 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:43:54.630 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:43:54.630 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:43:54.630 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:43:54.631 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:43:54.631 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:43:54.631 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:43:54.632 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:43:54.632 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:44:38.939 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:44:38.940 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:44:38.940 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:44:39.133 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:44:39.134 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:44:39.134 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:44:39.135 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:44:39.135 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:44:39.136 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:44:39.136 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:44:39.137 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:44:39.137 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:45:07.678 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:45:07.679 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:45:07.679 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:45:07.873 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:45:07.874 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:45:07.874 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:45:07.875 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:45:07.875 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:45:07.875 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:45:07.875 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:45:07.876 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:45:07.876 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:45:21.917 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:45:21.918 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:45:21.918 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:45:22.109 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:45:22.110 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:45:22.111 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:45:22.111 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:45:22.111 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:45:22.112 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:45:22.112 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:45:22.112 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:45:22.113 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:45:36.433 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:45:36.433 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:45:36.433 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:45:36.626 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:45:36.627 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:45:36.627 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:45:36.627 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:45:36.628 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:45:36.628 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:45:36.629 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:45:36.629 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:45:36.629 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:45:36.657 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:78 in select_device
+ 14:45:36.658 [INFO] - step 2: 取得参数配置 @ Bussiness_Seg.py:73 in run
+ 14:45:36.813 [INFO] - step 3: 情况 1 - 成功载入 det model trt [./weights/illParking/yolov5_3090_fp16.engine] @ Bussiness_Seg.py:83 in run
+ 14:45:36.837 [INFO] - 加载 stdcModel 模型: ./weights/illParking/stdc_360X640_3090_fp16.engine 类型: trt @ stdc.py:53 in __init__
+ 14:45:36.860 [INFO] - step 4: 共读入 5 张图片待处理 @ Bussiness_Seg.py:170 in run
+ 14:45:36.860 [WARNING] - step 5-------------------- 处理图片 ./appIOs/samples/illParking/4.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:45:37.285 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:45:37.287 [WARNING] - mean not in par, use default mean(0.485, 0.456, 0.406) @ stdc.py:75 in preprocess_image
+ 14:45:37.288 [WARNING] - std not in par, use default std(0.229, 0.224, 0.225) @ stdc.py:78 in preprocess_image
+ 14:45:37.386 [INFO] - [业务分析]业务 总共耗时 525.0 毫秒，其中：
+ AI_Process: 505.4 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 505.4 毫秒，其中：
+ img_pad: 1.8 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.1 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 420.6 毫秒 aiHelper.py:165 in AI_process
+ infer: 14.9 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 62.0 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 5.7 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 5.7 毫秒，其中：
+ NMS: 2.4 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.3 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 18.8 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:45:37.386 [WARNING] - step 6-------------------- 处理图片 ./appIOs/samples/illParking/3.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:45:37.389 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:45:37.410 [INFO] - [业务分析]业务 总共耗时 23.6 毫秒，其中：
+ AI_Process: 16.0 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 16.0 毫秒，其中：
+ img_pad: 1.9 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.6 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.7 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.1 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.3 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.3 毫秒，其中：
+ NMS: 1.2 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.1 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.8 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:45:37.411 [WARNING] - step 7-------------------- 处理图片 ./appIOs/samples/illParking/2.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:45:37.413 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:45:37.435 [INFO] - [业务分析]业务 总共耗时 23.8 毫秒，其中：
+ AI_Process: 14.4 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 14.3 毫秒，其中：
+ img_pad: 1.0 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.7 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.0 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.0 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.0 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.0 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 8.6 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:45:37.435 [WARNING] - step 8-------------------- 处理图片 ./appIOs/samples/illParking/1.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:45:37.438 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:45:37.463 [INFO] - [业务分析]业务 总共耗时 27.0 毫秒，其中：
+ AI_Process: 17.5 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 17.5 毫秒，其中：
+ img_pad: 1.4 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.3 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.5 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.0 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 6.6 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 6.6 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 5.6 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.1 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 9.2 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:45:37.463 [WARNING] - step 9-------------------- 处理图片 ./appIOs/samples/illParking/5.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:45:37.465 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:45:37.486 [INFO] - [业务分析]业务 总共耗时 22.4 毫秒，其中：
+ AI_Process: 14.8 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 14.8 毫秒，其中：
+ img_pad: 1.0 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.6 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.3 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.2 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.2 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.2 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.8 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:45:37.486 [INFO] - step 10: 5 张图片共耗时:626.0 ms ,依次为:125.2 ms, 占用 1 线程 @ Bussiness_Seg.py:187 in run
+ 14:45:54.396 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:45:54.396 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:45:54.397 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:45:54.590 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:45:54.590 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:45:54.591 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:45:54.591 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:45:54.591 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:45:54.592 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:45:54.592 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:45:54.592 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:45:54.593 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:45:54.618 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:77 in select_device
+ 14:45:54.619 [INFO] - step 2: 取得参数配置 @ Bussiness_Seg.py:73 in run
+ 14:45:54.773 [INFO] - step 3: 情况 1 - 成功载入 det model trt [./weights/illParking/yolov5_3090_fp16.engine] @ Bussiness_Seg.py:83 in run
+ 14:45:54.796 [INFO] - 加载 stdcModel 模型: ./weights/illParking/stdc_360X640_3090_fp16.engine 类型: trt @ stdc.py:53 in __init__
+ 14:45:54.820 [INFO] - step 4: 共读入 5 张图片待处理 @ Bussiness_Seg.py:170 in run
+ 14:45:54.820 [WARNING] - step 5-------------------- 处理图片 ./appIOs/samples/illParking/4.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:45:55.251 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:45:55.253 [WARNING] - mean not in par, use default mean(0.485, 0.456, 0.406) @ stdc.py:75 in preprocess_image
+ 14:45:55.254 [WARNING] - std not in par, use default std(0.229, 0.224, 0.225) @ stdc.py:78 in preprocess_image
+ 14:45:55.338 [INFO] - [业务分析]业务 总共耗时 517.7 毫秒，其中：
+ AI_Process: 509.9 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 509.8 毫秒，其中：
+ img_pad: 1.7 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.1 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 427.5 毫秒 aiHelper.py:165 in AI_process
+ infer: 13.3 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 60.8 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 6.1 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 6.1 毫秒，其中：
+ NMS: 2.7 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.4 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 7.0 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:45:55.339 [WARNING] - step 6-------------------- 处理图片 ./appIOs/samples/illParking/3.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:45:55.342 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:45:55.362 [INFO] - [业务分析]业务 总共耗时 23.1 毫秒，其中：
+ AI_Process: 15.4 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 15.4 毫秒，其中：
+ img_pad: 1.7 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.3 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.2 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.4 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.4 毫秒，其中：
+ NMS: 1.1 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.3 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.9 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:45:55.362 [WARNING] - step 7-------------------- 处理图片 ./appIOs/samples/illParking/2.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:45:55.364 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:45:55.385 [INFO] - [业务分析]业务 总共耗时 22.1 毫秒，其中：
+ AI_Process: 14.5 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 14.5 毫秒，其中：
+ img_pad: 0.9 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.3 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.2 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.2 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.4 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.4 毫秒，其中：
+ NMS: 1.1 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.3 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.8 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:45:55.385 [WARNING] - step 8-------------------- 处理图片 ./appIOs/samples/illParking/1.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:45:55.389 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:45:55.415 [INFO] - [业务分析]业务 总共耗时 28.9 毫秒，其中：
+ AI_Process: 19.5 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 19.5 毫秒，其中：
+ img_pad: 1.7 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.5 毫秒 aiHelper.py:165 in AI_process
+ infer: 7.6 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.1 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 6.8 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 6.8 毫秒，其中：
+ NMS: 1.1 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 5.7 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.1 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 9.1 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:45:55.415 [WARNING] - step 9-------------------- 处理图片 ./appIOs/samples/illParking/5.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:45:55.417 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:45:55.438 [INFO] - [业务分析]业务 总共耗时 23.0 毫秒，其中：
+ AI_Process: 14.9 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 14.9 毫秒，其中：
+ img_pad: 0.9 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.5 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.8 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.1 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.2 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.2 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.2 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 7.3 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:45:55.439 [INFO] - step 10: 5 张图片共耗时:618.7 ms ,依次为:123.7 ms, 占用 1 线程 @ Bussiness_Seg.py:187 in run
+ 14:46:02.358 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:46:02.358 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:46:02.358 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:46:02.549 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:46:02.550 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:46:02.550 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:46:02.551 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:46:02.551 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:46:02.551 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:46:02.552 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:46:02.552 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:46:02.552 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:57 in run
+ 14:46:32.124 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:46:32.125 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:46:32.125 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:46:32.321 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:306 in __init__
+ 14:46:32.322 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:46:32.322 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:46:32.323 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:46:32.323 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:46:32.323 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:46:32.324 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:46:32.324 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:46:32.324 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:56 in run
+ 14:47:11.982 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:47:11.982 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:47:11.982 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:47:12.180 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:306 in __init__
+ 14:47:12.181 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:47:12.181 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:47:12.181 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:47:12.182 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:47:12.182 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:47:12.182 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:47:12.183 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:47:12.183 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:56 in run
+ 14:47:12.209 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:77 in select_device
+ 14:47:12.211 [INFO] - step 2: 取得参数配置 @ Bussiness_Seg.py:72 in run
+ 14:47:12.369 [INFO] - step 3: 情况 1 - 成功载入 det model trt [./weights/illParking/yolov5_3090_fp16.engine] @ Bussiness_Seg.py:82 in run
+ 14:47:12.395 [INFO] - 加载 stdcModel 模型: ./weights/illParking/stdc_360X640_3090_fp16.engine 类型: trt @ stdc.py:53 in __init__
+ 14:47:12.418 [INFO] - step 4: 共读入 5 张图片待处理 @ Bussiness_Seg.py:169 in run
+ 14:47:12.419 [WARNING] - step 5-------------------- 处理图片 ./appIOs/samples/illParking/4.jpg-------------------- @ Bussiness_Seg.py:174 in run
+ 14:47:12.848 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:47:12.850 [WARNING] - mean not in par, use default mean(0.485, 0.456, 0.406) @ stdc.py:75 in preprocess_image
+ 14:47:12.850 [WARNING] - std not in par, use default std(0.229, 0.224, 0.225) @ stdc.py:78 in preprocess_image
+ 14:47:12.935 [INFO] - [业务分析]业务 总共耗时 515.7 毫秒，其中：
+ AI_Process: 507.8 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 507.8 毫秒，其中：
+ img_pad: 1.6 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.1 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 425.1 毫秒 aiHelper.py:165 in AI_process
+ infer: 14.2 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 60.9 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 5.7 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 5.7 毫秒，其中：
+ NMS: 2.3 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.3 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 7.0 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:47:12.935 [WARNING] - step 6-------------------- 处理图片 ./appIOs/samples/illParking/3.jpg-------------------- @ Bussiness_Seg.py:174 in run
+ 14:47:12.938 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:47:12.958 [INFO] - [业务分析]业务 总共耗时 22.4 毫秒，其中：
+ AI_Process: 14.7 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 14.7 毫秒，其中：
+ img_pad: 1.6 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.3 毫秒 aiHelper.py:165 in AI_process
+ infer: 5.8 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.2 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.3 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.3 毫秒，其中：
+ NMS: 1.1 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.2 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 7.0 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:47:12.959 [WARNING] - step 7-------------------- 处理图片 ./appIOs/samples/illParking/2.jpg-------------------- @ Bussiness_Seg.py:174 in run
+ 14:47:12.961 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:47:12.982 [INFO] - [业务分析]业务 总共耗时 22.8 毫秒，其中：
+ AI_Process: 15.0 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 15.0 毫秒，其中：
+ img_pad: 1.3 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.2 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.2 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.5 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.5 毫秒，其中：
+ NMS: 1.2 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.3 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.9 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:47:12.982 [WARNING] - step 8-------------------- 处理图片 ./appIOs/samples/illParking/1.jpg-------------------- @ Bussiness_Seg.py:174 in run
+ 14:47:12.984 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:47:13.009 [INFO] - [业务分析]业务 总共耗时 27.0 毫秒，其中：
+ AI_Process: 17.6 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 17.6 毫秒，其中：
+ img_pad: 1.4 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.3 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.4 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.2 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 6.7 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 6.7 毫秒，其中：
+ NMS: 1.1 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 5.5 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.2 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 9.1 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:47:13.010 [WARNING] - step 9-------------------- 处理图片 ./appIOs/samples/illParking/5.jpg-------------------- @ Bussiness_Seg.py:174 in run
+ 14:47:13.012 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:47:13.034 [INFO] - [业务分析]业务 总共耗时 23.3 毫秒，其中：
+ AI_Process: 15.5 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 15.5 毫秒，其中：
+ img_pad: 1.0 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 7.6 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 1.9 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.2 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.2 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.2 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 7.0 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:47:13.034 [INFO] - step 10: 5 张图片共耗时:615.0 ms ,依次为:123.0 ms, 占用 1 线程 @ Bussiness_Seg.py:186 in run
+ 14:49:09.918 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:49:09.918 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:49:09.918 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:49:10.110 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:306 in __init__
+ 14:49:10.111 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:49:10.111 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:49:10.112 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:49:10.112 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:49:10.112 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:49:10.113 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:49:10.113 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:49:10.113 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:56 in run
+ 14:49:10.141 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:75 in select_device
+ 14:49:10.142 [INFO] - step 2: 取得参数配置 @ Bussiness_Seg.py:72 in run
+ 14:49:10.299 [INFO] - step 3: 情况 1 - 成功载入 det model trt [./weights/illParking/yolov5_3090_fp16.engine] @ Bussiness_Seg.py:82 in run
+ 14:49:10.323 [INFO] - 加载 stdcModel 模型: ./weights/illParking/stdc_360X640_3090_fp16.engine 类型: trt @ stdc.py:53 in __init__
+ 14:49:10.347 [INFO] - step 4: 共读入 5 张图片待处理 @ Bussiness_Seg.py:169 in run
+ 14:49:10.347 [WARNING] - step 5-------------------- 处理图片 ./appIOs/samples/illParking/4.jpg-------------------- @ Bussiness_Seg.py:174 in run
+ 14:49:10.773 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:49:10.775 [WARNING] - mean not in par, use default mean(0.485, 0.456, 0.406) @ stdc.py:75 in preprocess_image
+ 14:49:10.776 [WARNING] - std not in par, use default std(0.229, 0.224, 0.225) @ stdc.py:78 in preprocess_image
+ 14:49:10.902 [INFO] - [业务分析]业务 总共耗时 554.5 毫秒，其中：
+ AI_Process: 505.7 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 505.7 毫秒，其中：
+ img_pad: 1.6 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.1 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 422.1 毫秒 aiHelper.py:165 in AI_process
+ infer: 14.1 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 61.2 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 6.3 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 6.2 毫秒，其中：
+ NMS: 2.9 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.3 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.8 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 47.6 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.4 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:49:10.903 [WARNING] - step 6-------------------- 处理图片 ./appIOs/samples/illParking/3.jpg-------------------- @ Bussiness_Seg.py:174 in run
+ 14:49:10.917 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:49:10.969 [INFO] - [业务分析]业务 总共耗时 58.2 毫秒，其中：
+ AI_Process: 40.6 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 40.5 毫秒，其中：
+ img_pad: 5.0 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.1 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.6 毫秒 aiHelper.py:165 in AI_process
+ infer: 15.9 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 4.9 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 13.1 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 13.0 毫秒，其中：
+ NMS: 4.1 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 8.9 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 2.0 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 15.2 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.3 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:49:10.970 [WARNING] - step 7-------------------- 处理图片 ./appIOs/samples/illParking/2.jpg-------------------- @ Bussiness_Seg.py:174 in run
+ 14:49:10.973 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:49:11.003 [INFO] - [业务分析]业务 总共耗时 32.4 毫秒，其中：
+ AI_Process: 20.9 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 20.9 毫秒，其中：
+ img_pad: 1.5 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 9.8 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.5 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 6.2 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 6.2 毫秒，其中：
+ NMS: 1.5 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 4.6 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 1.0 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 10.3 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.2 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:49:11.003 [WARNING] - step 8-------------------- 处理图片 ./appIOs/samples/illParking/1.jpg-------------------- @ Bussiness_Seg.py:174 in run
+ 14:49:11.007 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:49:11.043 [INFO] - [业务分析]业务 总共耗时 39.0 毫秒，其中：
+ AI_Process: 24.8 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 24.7 毫秒，其中：
+ img_pad: 2.1 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 9.3 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.5 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 9.5 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 9.5 毫秒，其中：
+ NMS: 1.5 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 8.0 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.3 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 13.8 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:49:11.044 [WARNING] - step 9-------------------- 处理图片 ./appIOs/samples/illParking/5.jpg-------------------- @ Bussiness_Seg.py:174 in run
+ 14:49:11.046 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:49:11.069 [INFO] - [业务分析]业务 总共耗时 24.8 毫秒，其中：
+ AI_Process: 16.7 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 16.6 毫秒，其中：
+ img_pad: 1.5 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 7.3 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.4 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.7 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.7 毫秒，其中：
+ NMS: 1.2 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.5 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.8 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 7.2 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:49:11.069 [INFO] - step 10: 5 张图片共耗时:722.1 ms ,依次为:144.4 ms, 占用 1 线程 @ Bussiness_Seg.py:186 in run
+ 14:50:12.079 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:50:12.079 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:50:12.080 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:50:12.273 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:50:12.274 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:50:12.274 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:50:12.274 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:50:12.275 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:50:12.275 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:50:12.275 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:50:12.276 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:50:12.276 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:56 in run
+ 14:50:58.328 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:50:58.328 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:50:58.329 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:50:58.523 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:50:58.524 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:50:58.524 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:50:58.525 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:50:58.525 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:50:58.525 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:50:58.526 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:50:58.526 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:50:58.526 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:56 in run
+ 14:50:58.552 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:74 in select_device
+ 14:50:58.553 [INFO] - step 2: 取得参数配置 @ Bussiness_Seg.py:73 in run
+ 14:50:58.714 [INFO] - step 3: 情况 1 - 成功载入 det model trt [./weights/illParking/yolov5_3090_fp16.engine] @ Bussiness_Seg.py:83 in run
+ 14:50:58.738 [INFO] - 加载 stdcModel 模型: ./weights/illParking/stdc_360X640_3090_fp16.engine 类型: trt @ stdc.py:53 in __init__
+ 14:50:58.761 [INFO] - step 4: 共读入 5 张图片待处理 @ Bussiness_Seg.py:170 in run
+ 14:50:58.761 [WARNING] - step 5-------------------- 处理图片 ./appIOs/samples/illParking/4.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:50:59.194 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:50:59.196 [WARNING] - mean not in par, use default mean(0.485, 0.456, 0.406) @ stdc.py:75 in preprocess_image
+ 14:50:59.196 [WARNING] - std not in par, use default std(0.229, 0.224, 0.225) @ stdc.py:78 in preprocess_image
+ 14:50:59.281 [INFO] - [业务分析]业务 总共耗时 519.3 毫秒，其中：
+ AI_Process: 511.5 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 511.5 毫秒，其中：
+ img_pad: 1.5 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.1 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 428.7 毫秒 aiHelper.py:165 in AI_process
+ infer: 13.7 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 60.9 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 6.2 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 6.2 毫秒，其中：
+ NMS: 2.8 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.3 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.9 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:50:59.282 [WARNING] - step 6-------------------- 处理图片 ./appIOs/samples/illParking/3.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:50:59.284 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:50:59.305 [INFO] - [业务分析]业务 总共耗时 23.2 毫秒，其中：
+ AI_Process: 15.5 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 15.5 毫秒，其中：
+ img_pad: 1.7 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.6 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.1 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.3 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.3 毫秒，其中：
+ NMS: 1.1 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.2 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.9 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:50:59.306 [WARNING] - step 7-------------------- 处理图片 ./appIOs/samples/illParking/2.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:50:59.308 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:50:59.329 [INFO] - [业务分析]业务 总共耗时 22.7 毫秒，其中：
+ AI_Process: 15.0 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 14.9 毫秒，其中：
+ img_pad: 0.9 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.8 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.2 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.3 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.3 毫秒，其中：
+ NMS: 1.1 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.2 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.9 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:50:59.329 [WARNING] - step 8-------------------- 处理图片 ./appIOs/samples/illParking/1.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:50:59.332 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:50:59.359 [INFO] - [业务分析]业务 总共耗时 29.0 毫秒，其中：
+ AI_Process: 19.5 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 19.5 毫秒，其中：
+ img_pad: 1.7 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 7.8 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.0 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 6.8 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 6.7 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 5.7 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.2 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 9.2 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:50:59.359 [WARNING] - step 9-------------------- 处理图片 ./appIOs/samples/illParking/5.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:50:59.361 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:50:59.382 [INFO] - [业务分析]业务 总共耗时 22.8 毫秒，其中：
+ AI_Process: 15.1 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 15.1 毫秒，其中：
+ img_pad: 1.0 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.8 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.3 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.2 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.2 毫秒，其中：
+ NMS: 1.1 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.1 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.9 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:50:59.383 [INFO] - step 10: 5 张图片共耗时:621.1 ms ,依次为:124.2 ms, 占用 1 线程 @ Bussiness_Seg.py:187 in run
+ 14:51:38.374 [INFO] - 待测试业务名称: @ main.py:15 in
+ 14:51:38.375 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 14:51:38.375 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 14:51:38.570 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 14:51:38.571 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 14:51:38.571 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 14:51:38.571 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:51:38.572 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 14:51:38.572 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 14:51:38.573 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:51:38.573 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 14:51:38.573 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:56 in run
+ 14:51:38.618 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 14:51:38.619 [INFO] - step 2: 取得参数配置 @ Bussiness_Seg.py:73 in run
+ 14:51:38.784 [INFO] - step 3: 情况 1 - 成功载入 det model trt [./weights/illParking/yolov5_3090_fp16.engine] @ Bussiness_Seg.py:83 in run
+ 14:51:38.808 [INFO] - 加载 stdcModel 模型: ./weights/illParking/stdc_360X640_3090_fp16.engine 类型: trt @ stdc.py:53 in __init__
+ 14:51:38.832 [INFO] - step 4: 共读入 5 张图片待处理 @ Bussiness_Seg.py:170 in run
+ 14:51:38.833 [WARNING] - step 5-------------------- 处理图片 ./appIOs/samples/illParking/4.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:51:39.263 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:51:39.264 [WARNING] - mean not in par, use default mean(0.485, 0.456, 0.406) @ stdc.py:75 in preprocess_image
+ 14:51:39.265 [WARNING] - std not in par, use default std(0.229, 0.224, 0.225) @ stdc.py:78 in preprocess_image
+ 14:51:39.351 [INFO] - [业务分析]业务 总共耗时 517.7 毫秒，其中：
+ AI_Process: 509.9 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 509.8 毫秒，其中：
+ img_pad: 1.6 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.1 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 426.6 毫秒 aiHelper.py:165 in AI_process
+ infer: 13.1 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 62.3 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 5.9 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 5.8 毫秒，其中：
+ NMS: 2.4 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.4 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 7.0 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:51:39.351 [WARNING] - step 6-------------------- 处理图片 ./appIOs/samples/illParking/3.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:51:39.354 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:51:39.374 [INFO] - [业务分析]业务 总共耗时 22.7 毫秒，其中：
+ AI_Process: 14.8 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 14.8 毫秒，其中：
+ img_pad: 1.7 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 5.9 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.2 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.2 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.2 毫秒，其中：
+ NMS: 1.1 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.1 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 7.0 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:51:39.375 [WARNING] - step 7-------------------- 处理图片 ./appIOs/samples/illParking/2.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:51:39.377 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:51:39.398 [INFO] - [业务分析]业务 总共耗时 22.8 毫秒，其中：
+ AI_Process: 14.8 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 14.8 毫秒，其中：
+ img_pad: 1.1 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.5 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.0 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.4 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.4 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.4 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 7.2 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:51:39.398 [WARNING] - step 8-------------------- 处理图片 ./appIOs/samples/illParking/1.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:51:39.401 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:51:39.425 [INFO] - [业务分析]业务 总共耗时 27.0 毫秒，其中：
+ AI_Process: 17.6 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 17.6 毫秒，其中：
+ img_pad: 1.4 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.3 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.1 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.1 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 7.0 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 7.0 毫秒，其中：
+ NMS: 1.3 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 5.7 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.2 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 9.0 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:51:39.426 [WARNING] - step 9-------------------- 处理图片 ./appIOs/samples/illParking/5.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 14:51:39.428 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 14:51:39.449 [INFO] - [业务分析]业务 总共耗时 22.8 毫秒，其中：
+ AI_Process: 15.1 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 15.0 毫秒，其中：
+ img_pad: 1.1 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.7 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.2 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.3 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.2 毫秒，其中：
+ NMS: 1.1 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.2 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.9 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 14:51:39.450 [INFO] - step 10: 5 张图片共耗时:617.0 ms ,依次为:123.4 ms, 占用 1 线程 @ Bussiness_Seg.py:187 in run
+ 16:26:04.590 [INFO] - 待测试业务名称: @ main.py:15 in
+ 16:26:04.590 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 16:26:04.590 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 16:26:04.980 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 16:26:04.982 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 16:26:04.982 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 16:26:04.982 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 16:26:04.983 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 16:26:04.983 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 16:26:04.983 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 16:26:04.984 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 16:26:04.984 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:56 in run
+ 16:26:05.020 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 16:26:05.021 [INFO] - step 2: 取得参数配置 @ Bussiness_Seg.py:73 in run
+ 16:26:05.235 [INFO] - step 3: 情况 1 - 成功载入 det model trt [./weights/illParking/yolov5_3090_fp16.engine] @ Bussiness_Seg.py:83 in run
+ 16:26:05.261 [INFO] - 加载 stdcModel 模型: ./weights/illParking/stdc_360X640_3090_fp16.engine 类型: trt @ stdc.py:53 in __init__
+ 16:26:05.308 [INFO] - step 4: 共读入 5 张图片待处理 @ Bussiness_Seg.py:170 in run
+ 16:26:05.309 [WARNING] - step 5-------------------- 处理图片 ./appIOs/samples/illParking/4.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 16:26:05.754 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 16:26:05.757 [WARNING] - mean not in par, use default mean(0.485, 0.456, 0.406) @ stdc.py:75 in preprocess_image
+ 16:26:05.757 [WARNING] - std not in par, use default std(0.229, 0.224, 0.225) @ stdc.py:78 in preprocess_image
+ 16:26:05.849 [INFO] - [业务分析]业务 总共耗时 539.3 毫秒，其中：
+ AI_Process: 531.6 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 531.5 毫秒，其中：
+ img_pad: 1.6 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.1 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 441.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 14.7 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 67.6 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 5.8 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 5.8 毫秒，其中：
+ NMS: 2.6 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.2 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.9 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 16:26:05.849 [WARNING] - step 6-------------------- 处理图片 ./appIOs/samples/illParking/3.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 16:26:05.853 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 16:26:05.873 [INFO] - [业务分析]业务 总共耗时 23.4 毫秒，其中：
+ AI_Process: 15.5 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 15.5 毫秒，其中：
+ img_pad: 2.2 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.5 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.0 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.0 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.0 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.0 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 7.0 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.2 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 16:26:05.874 [WARNING] - step 7-------------------- 处理图片 ./appIOs/samples/illParking/2.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 16:26:05.877 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 16:26:05.897 [INFO] - [业务分析]业务 总共耗时 22.2 毫秒，其中：
+ AI_Process: 14.4 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 14.4 毫秒，其中：
+ img_pad: 1.2 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.1 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.0 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.3 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.2 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.2 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 7.0 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 16:26:05.897 [WARNING] - step 8-------------------- 处理图片 ./appIOs/samples/illParking/1.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 16:26:05.900 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 16:26:05.925 [INFO] - [业务分析]业务 总共耗时 27.4 毫秒，其中：
+ AI_Process: 18.1 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 18.0 毫秒，其中：
+ img_pad: 1.4 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.3 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.7 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.2 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 6.7 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 6.6 毫秒，其中：
+ NMS: 1.1 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 5.5 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.1 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 9.1 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 16:26:05.925 [WARNING] - step 9-------------------- 处理图片 ./appIOs/samples/illParking/5.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 16:26:05.927 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 16:26:05.948 [INFO] - [业务分析]业务 总共耗时 22.4 毫秒，其中：
+ AI_Process: 14.9 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 14.8 毫秒，其中：
+ img_pad: 1.0 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.3 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.6 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.0 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.5 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.5 毫秒，其中：
+ NMS: 1.2 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.2 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.8 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 16:26:05.948 [INFO] - step 10: 5 张图片共耗时:639.5 ms ,依次为:127.9 ms, 占用 1 线程 @ Bussiness_Seg.py:187 in run
diff --git a/DrGraph/appIOs/logs/drgraph_aialg.log b/DrGraph/appIOs/logs/drgraph_aialg.log
new file mode 100644
index 0000000..4d559af
--- /dev/null
+++ b/DrGraph/appIOs/logs/drgraph_aialg.log
@@ -0,0 +1,6204 @@
+ 09:31:45.393 [INFO] - 待测试业务名称: @ main.py:15 in
+ 09:31:45.394 [INFO] - -------------------- 开始业务 [illParking] -------------------- @ main.py:19 in
+ 09:31:45.394 [INFO] - bussiness: illParking @ Bussiness.py:16 in createModel
+ 09:31:45.587 [INFO] - create AlAlg_IllParking @ Bussiness_Seg.py:307 in __init__
+ 09:31:45.588 [WARNING] - [illParking] 业务配置 - ['device', 'labelnames', 'max_workers', 'Detweights', 'detModelpara', 'seg_nclass', 'segRegionCnt', 'Segweights', 'postFile', 'txtFontSize', 'digitFont', 'testImgPath', 'testOutPath', 'segPar'] - 重点配置：
+ 检测类别(labelnames):./weights/conf/illParking/labelnames.json >>>>>> ['车', 'T角点', 'L角点', '违停']
+ 检测模型路径(Detweights): ./weights/illParking/yolov5_3090_fp16.engine
+ 分割模型权重文件(Segweights): ./weights/illParking/stdc_360X640_3090_fp16.engine
+ 后处理参数文件(postFile): ./weights/conf/illParking/para.json
+ 测试图像路径(testImgPath): ./appIOs/samples/illParking/
+ 输出图像位置(testOutPath): ./appIOs/results/illParking/
+ 输出图像路径: ./appIOs/results/illParking/ @ Bussiness.py:39 in __init__
+ 09:31:45.588 [INFO] - 检测类别 - ./weights/conf/illParking/labelnames.json 存在 @ drHelper.py:197 in checkFile
+ 09:31:45.589 [INFO] - 检测模型路径 - ./weights/illParking/yolov5_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 09:31:45.589 [INFO] - 后处理参数文件 - ./weights/conf/illParking/para.json 存在 @ drHelper.py:197 in checkFile
+ 09:31:45.590 [INFO] - 分割模型权重文件 - ./weights/illParking/stdc_360X640_3090_fp16.engine 存在 @ drHelper.py:197 in checkFile
+ 09:31:45.590 [INFO] - 测试图像路径 - ./appIOs/samples/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 09:31:45.590 [INFO] - 输出图像路径 - ./appIOs/results/illParking/ 存在 @ drHelper.py:197 in checkFile
+ 09:31:45.591 [INFO] - step 1: 业务分析配置 - trtFlag_seg: True, trtFlag_det: True @ Bussiness_Seg.py:56 in run
+ 09:31:45.639 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 09:31:45.639 [INFO] - step 2: 取得参数配置 @ Bussiness_Seg.py:73 in run
+ 09:31:45.802 [INFO] - step 3: 情况 1 - 成功载入 det model trt [./weights/illParking/yolov5_3090_fp16.engine] @ Bussiness_Seg.py:83 in run
+ 09:31:45.827 [INFO] - 加载 stdcModel 模型: ./weights/illParking/stdc_360X640_3090_fp16.engine 类型: trt @ stdc.py:53 in __init__
+ 09:31:45.850 [INFO] - step 4: 共读入 5 张图片待处理 @ Bussiness_Seg.py:170 in run
+ 09:31:45.851 [WARNING] - step 5-------------------- 处理图片 ./appIOs/samples/illParking/4.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 09:31:46.295 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 09:31:46.297 [WARNING] - mean not in par, use default mean(0.485, 0.456, 0.406) @ stdc.py:75 in preprocess_image
+ 09:31:46.298 [WARNING] - std not in par, use default std(0.229, 0.224, 0.225) @ stdc.py:78 in preprocess_image
+ 09:31:46.388 [INFO] - [业务分析]业务 总共耗时 536.3 毫秒，其中：
+ AI_Process: 528.6 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 528.5 毫秒，其中：
+ img_pad: 1.9 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.1 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 440.2 毫秒 aiHelper.py:165 in AI_process
+ infer: 13.5 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 66.7 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 5.7 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 5.7 毫秒，其中：
+ NMS: 2.5 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.2 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.9 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.2 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 09:31:46.388 [WARNING] - step 6-------------------- 处理图片 ./appIOs/samples/illParking/3.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 09:31:46.392 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 09:31:46.412 [INFO] - [业务分析]业务 总共耗时 22.9 毫秒，其中：
+ AI_Process: 15.2 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 15.2 毫秒，其中：
+ img_pad: 2.2 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.2 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 1.9 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.0 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.0 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.0 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.9 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 09:31:46.412 [WARNING] - step 7-------------------- 处理图片 ./appIOs/samples/illParking/2.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 09:31:46.414 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 09:31:46.435 [INFO] - [业务分析]业务 总共耗时 22.7 毫秒，其中：
+ AI_Process: 14.3 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 14.3 毫秒，其中：
+ img_pad: 1.0 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.3 毫秒 aiHelper.py:165 in AI_process
+ infer: 6.2 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.2 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.1 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.1 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.1 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 7.6 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.2 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 09:31:46.436 [WARNING] - step 8-------------------- 处理图片 ./appIOs/samples/illParking/1.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 09:31:46.439 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 09:31:46.464 [INFO] - [业务分析]业务 总共耗时 27.8 毫秒，其中：
+ AI_Process: 18.4 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 18.3 毫秒，其中：
+ img_pad: 1.6 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 7.0 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.1 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 6.6 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 6.6 毫秒，其中：
+ NMS: 1.1 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 5.5 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.1 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 9.2 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 09:31:46.465 [WARNING] - step 9-------------------- 处理图片 ./appIOs/samples/illParking/5.jpg-------------------- @ Bussiness_Seg.py:175 in run
+ 09:31:46.467 [WARNING] - modelSize not in par, use default size(640, 360) @ stdc.py:66 in preprocess_image
+ 09:31:46.488 [INFO] - [业务分析]业务 总共耗时 23.1 毫秒，其中：
+ AI_Process: 15.4 毫秒 Bussiness.py:80 in doAnalysis -> [AI_process]业务 总共耗时 15.3 毫秒，其中：
+ img_pad: 1.0 毫秒 aiHelper.py:159 in AI_process
+ from_numpy(640 x 640): 0.0 毫秒 aiHelper.py:163 in AI_process
+ to GPU(640 x 640): 0.4 毫秒 aiHelper.py:165 in AI_process
+ infer: 7.1 毫秒 aiHelper.py:177 in AI_process
+ yolov5Trtforward: 2.3 毫秒 aiHelper.py:184 in AI_process
+ 后处理: 4.2 毫秒 aiHelper.py:191 in AI_process -> [预测结果后处理]业务 总共耗时 4.2 毫秒，其中：
+ NMS: 1.0 毫秒 aiHelper.py:40 in getDetectionsFromPreds
+ ScaleBack: 3.2 毫秒 aiHelper.py:65 in getDetectionsFromPreds
+ drawAllBox: 0.7 毫秒 Bussiness.py:82 in doAnalysis
+ testOutPath: 6.9 毫秒 Bussiness.py:93 in doAnalysis
+ fp: 0.1 毫秒 Bussiness.py:100 in doAnalysis @ Bussiness.py:102 in doAnalysis
+ 09:31:46.488 [INFO] - step 10: 5 张图片共耗时:637.5 ms ,依次为:127.5 ms, 占用 1 线程 @ Bussiness_Seg.py:187 in run
+ 10:21:56.234 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 10:21:56.255 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:54 in __init__
+ 10:21:56.282 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 10:21:56.283 [ERROR] - 模型加载异常：Traceback (most recent call last):
+ File "/home/thsw/chenbw/DrGraph/appIOs/conf/ModelUtils.py", line 65, in __init__
+ with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
+FileNotFoundError: [Errno 2] No such file or directory: '../weights/trt/AIlib2/illParking/yolov5_3090_fp16.engine'
+, requestId:1234 @ ModelUtils.py:102 in __init__
+ 10:21:56.284 [ERROR] - 异常编码：SP017, 异常描述：模型加载异常! @ ModelUtils.py:43 in __str__
+ 10:22:58.022 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 10:22:58.044 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:54 in __init__
+ 10:22:58.074 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 10:22:58.074 [INFO] - 加载模型：../weights/trt/AIlib2/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:64 in __init__
+ 10:22:58.076 [ERROR] - 模型加载异常：Traceback (most recent call last):
+ File "/home/thsw/chenbw/DrGraph/appIOs/conf/ModelUtils.py", line 66, in __init__
+ with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
+FileNotFoundError: [Errno 2] No such file or directory: '../weights/trt/AIlib2/illParking/yolov5_3090_fp16.engine'
+, requestId:1234 @ ModelUtils.py:103 in __init__
+ 10:22:58.077 [ERROR] - 异常编码：SP017, 异常描述：模型加载异常! @ ModelUtils.py:43 in __str__
+ 10:24:14.077 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 10:24:14.097 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:54 in __init__
+ 10:24:14.123 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 10:24:14.123 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:64 in __init__
+ 10:24:14.289 [INFO] - 模型初始化时间：0.19141888618469238, requestId:1234 @ ModelUtils.py:106 in __init__
+ 10:24:14.289 [INFO] - [(( at 0x7f773658e8c0>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])), '019')] @ main.py:45 in
+ 10:26:33.698 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 10:26:33.718 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:54 in __init__
+ 10:26:33.744 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 10:26:33.744 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:64 in __init__
+ 10:26:33.903 [INFO] - 模型初始化时间：0.18465828895568848, requestId:1234 @ ModelUtils.py:106 in __init__
+ 10:26:33.903 [INFO] - [(( at 0x7fea16c5e680>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])), '019')] @ main.py:45 in
+ 10:27:19.013 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 10:27:19.033 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:54 in __init__
+ 10:27:19.058 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 10:27:19.058 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:64 in __init__
+ 10:27:19.217 [INFO] - 模型初始化时间：0.1833491325378418, requestId:1234 @ ModelUtils.py:106 in __init__
+ 10:27:19.217 [INFO] - [(( at 0x7f7dc4ffe710>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])), '019')] @ main.py:45 in
+ 10:27:19.217 [INFO] - 模型编号: 019, 模型参数: ( at 0x7f7dc4ffe710>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:49 in
+ 10:27:32.595 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 10:27:32.615 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:54 in __init__
+ 10:27:32.640 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 10:27:32.640 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:64 in __init__
+ 10:27:32.794 [INFO] - 模型初始化时间：0.17897987365722656, requestId:1234 @ ModelUtils.py:106 in __init__
+ 10:27:32.795 [INFO] - 模型编号: 019, 模型参数: ( at 0x7f1beb6de710>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:49 in
+ 10:33:25.238 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 10:33:25.262 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:54 in __init__
+ 10:33:25.295 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 10:33:25.296 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:64 in __init__
+ 10:33:25.469 [INFO] - 模型初始化时间：0.20696687698364258, requestId:1234 @ ModelUtils.py:106 in __init__
+ 10:33:28.409 [INFO] - 模型编号: 019, 模型参数: ( at 0x7f4021667d00>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:49 in
+ 10:35:21.011 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 10:35:21.035 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:54 in __init__
+ 10:35:21.061 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 10:35:21.062 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:64 in __init__
+ 10:35:21.228 [INFO] - 模型初始化时间：0.19243597984313965, requestId:1234 @ ModelUtils.py:106 in __init__
+ 10:35:21.228 [INFO] - 模型编号: 019, 模型参数: ( at 0x7f6cf5126710>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:49 in
+ 10:37:38.672 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 10:37:38.697 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:54 in __init__
+ 10:37:38.722 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 10:37:38.722 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:64 in __init__
+ 10:37:38.884 [INFO] - 模型初始化时间：0.1872081756591797, requestId:1234 @ ModelUtils.py:106 in __init__
+ 10:37:38.884 [INFO] - 模型编号: 019, 模型参数: ( at 0x7fd290b0a680>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:49 in
+ 10:41:09.625 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 10:41:09.645 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:54 in __init__
+ 10:41:09.671 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 10:41:09.672 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:64 in __init__
+ 10:41:09.834 [INFO] - 模型初始化时间：0.18894720077514648, requestId:1234 @ ModelUtils.py:106 in __init__
+ 10:41:09.835 [INFO] - 模型编号: 019, 模型参数: ( at 0x7fa55e8ba680>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:49 in
+ 10:41:37.225 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 10:41:37.246 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:54 in __init__
+ 10:41:37.271 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 10:41:37.271 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:64 in __init__
+ 10:41:37.447 [INFO] - 模型初始化时间：0.2016286849975586, requestId:1234 @ ModelUtils.py:106 in __init__
+ 10:41:37.448 [INFO] - 模型编号: 019, 模型参数: ( at 0x7f727d2be680>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:49 in
+ 10:42:04.389 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 10:42:04.409 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:54 in __init__
+ 10:42:04.438 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 10:42:04.438 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:64 in __init__
+ 10:42:04.600 [INFO] - 模型初始化时间：0.1905205249786377, requestId:1234 @ ModelUtils.py:106 in __init__
+ 10:42:04.600 [INFO] - 模型编号: 019, 模型参数: ( at 0x7f443b34a680>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:49 in
+ 10:42:50.264 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 10:42:50.284 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:54 in __init__
+ 10:42:50.311 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 10:42:50.312 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:64 in __init__
+ 10:42:50.465 [INFO] - 模型初始化时间：0.18108057975769043, requestId:1234 @ ModelUtils.py:106 in __init__
+ 10:42:50.466 [INFO] - 模型编号: 019, 模型参数: ( at 0x7fed332f6680>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:49 in
+ 10:43:18.095 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 10:43:18.120 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:54 in __init__
+ 10:43:18.145 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 10:43:18.146 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:64 in __init__
+ 10:43:18.303 [INFO] - 模型初始化时间：0.18239474296569824, requestId:1234 @ ModelUtils.py:106 in __init__
+ 10:43:18.303 [INFO] - 模型编号: 019, 模型参数: ( at 0x7f20812ba680>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:49 in
+ 10:44:18.655 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 10:44:18.685 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:54 in __init__
+ 10:44:18.739 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 10:44:18.740 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:64 in __init__
+ 10:44:18.948 [INFO] - 模型初始化时间：0.26304006576538086, requestId:1234 @ ModelUtils.py:106 in __init__
+ 10:44:18.949 [INFO] - 模型编号: 019, 模型参数: ( at 0x7fc9aeb3a9e0>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:49 in
+ 10:44:18.949 [INFO] - fontPath:../AIlib2/conf/platech.ttf @ ModelUtils.py:289 in get_label_arraylist
+ 10:45:10.803 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 10:45:10.826 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:54 in __init__
+ 10:45:10.852 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 10:45:10.853 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:64 in __init__
+ 10:45:11.020 [INFO] - 模型初始化时间：0.19400358200073242, requestId:1234 @ ModelUtils.py:106 in __init__
+ 10:45:11.020 [INFO] - 模型编号: 019, 模型参数: ( at 0x7f2d70ffe9e0>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:49 in
+ 10:45:11.020 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:289 in get_label_arraylist
+ 10:45:57.975 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 10:45:58.001 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:54 in __init__
+ 10:45:58.033 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 10:45:58.033 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:64 in __init__
+ 10:45:58.202 [INFO] - 模型初始化时间：0.20077276229858398, requestId:1234 @ ModelUtils.py:106 in __init__
+ 10:46:03.081 [INFO] - 模型编号: 019, 模型参数: ( at 0x7f3dd5947c70>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:49 in
+ 10:47:47.258 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:289 in get_label_arraylist
+ 11:00:31.842 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 11:00:31.863 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:54 in __init__
+ 11:00:31.889 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 11:00:31.890 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:64 in __init__
+ 11:00:32.047 [INFO] - 模型初始化时间：0.18349575996398926, requestId:1234 @ ModelUtils.py:106 in __init__
+ 11:00:32.048 [INFO] - 模型编号: 019, 模型参数: ( at 0x7fc930b0a9e0>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:52 in
+ 11:00:32.049 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:289 in get_label_arraylist
+ 11:02:36.952 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 11:02:36.977 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:54 in __init__
+ 11:02:37.004 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 11:02:37.005 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:64 in __init__
+ 11:02:37.183 [INFO] - 模型初始化时间：0.20563292503356934, requestId:1234 @ ModelUtils.py:106 in __init__
+ 11:02:37.183 [INFO] - 模型编号: 019, 模型参数: ( at 0x7f78f85ce7a0>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:52 in
+ 11:02:37.184 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:289 in get_label_arraylist
+ 11:02:37.187 [ERROR] - 算法模型分析异常：Traceback (most recent call last):
+ File "/home/thsw/chenbw/DrGraph/appIOs/conf/ModelUtils.py", line 150, in model_process
+ return AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
+NameError: name 'AI_process' is not defined
+, requestId:1234 @ ModelUtils.py:159 in model_process
+ 11:02:37.188 [ERROR] - 异常编码：SP018, 异常描述：算法模型分析异常! @ ModelUtils.py:43 in __str__
+ 11:20:25.465 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 11:20:25.487 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:54 in __init__
+ 11:20:25.523 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 11:20:25.523 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:64 in __init__
+ 11:20:25.686 [INFO] - 模型初始化时间：0.19849014282226562, requestId:1234 @ ModelUtils.py:106 in __init__
+ 11:20:25.686 [INFO] - 模型编号: 019, 模型参数: ( at 0x7f605fbc67a0>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:52 in
+ 11:20:25.687 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:289 in get_label_arraylist
+ 11:20:25.691 [ERROR] - 算法模型分析异常：Traceback (most recent call last):
+ File "/home/thsw/chenbw/DrGraph/appIOs/conf/ModelUtils.py", line 150, in model_process
+ return AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
+NameError: name 'AI_process' is not defined
+, requestId:1234 @ ModelUtils.py:159 in model_process
+ 11:20:25.691 [ERROR] - 异常编码：SP018, 异常描述：算法模型分析异常! @ ModelUtils.py:43 in __str__
+ 11:31:05.760 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 11:31:05.782 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:55 in __init__
+ 11:31:05.808 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 11:31:05.809 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:65 in __init__
+ 11:31:05.977 [INFO] - 模型初始化时间：0.19477510452270508, requestId:1234 @ ModelUtils.py:107 in __init__
+ 11:31:05.977 [INFO] - 模型编号: 019, 模型参数: ( at 0x7f7e6b58ac20>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:52 in
+ 11:31:05.977 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:290 in get_label_arraylist
+ 11:31:05.981 [ERROR] - 算法模型分析异常：Traceback (most recent call last):
+ File "/home/thsw/chenbw/DrGraph/appIOs/conf/ModelUtils.py", line 151, in model_process
+ return aiHelper.AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
+ File "/home/thsw/chenbw/DrGraph/DrUtils/aiHelper.py", line 135, in AI_process
+ half,device,conf_thres,iou_thres,allowedList = objectPar['half'],objectPar['device'],objectPar['conf_thres'],objectPar['iou_thres'],objectPar['allowedList']
+KeyError: 'allowedList'
+, requestId:1234 @ ModelUtils.py:160 in model_process
+ 11:31:05.982 [ERROR] - 异常编码：SP018, 异常描述：算法模型分析异常! @ ModelUtils.py:44 in __str__
+ 11:32:14.535 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 11:32:14.557 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:55 in __init__
+ 11:32:14.583 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 11:32:14.584 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:65 in __init__
+ 11:32:14.751 [INFO] - 模型初始化时间：0.19419503211975098, requestId:1234 @ ModelUtils.py:107 in __init__
+ 11:32:14.751 [INFO] - 模型编号: 019, 模型参数: ( at 0x7fab9f70ac20>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:52 in
+ 11:32:14.754 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:291 in get_label_arraylist
+ 11:32:14.757 [INFO] - model_process([( at 0x7fab9f70ac20>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None, 'digitFont': {'line_thickness': 1, 'boxLine_thickness': 1, 'fontSize': 0.4, 'waterLineColor': (0, 255, 255), 'segLineShow': False, 'waterLineWidth': 1, 'wordSize': 8, 'label_location': 'leftTop'}, 'label_arraylist': [array([[[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 15, 15],
+ [255, 48, 48],
+ [255, 61, 61],
+ [255, 33, 33],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 34, 34],
+ [255, 50, 50],
+ [255, 83, 83],
+ [255, 129, 129],
+ [255, 143, 143],
+ [255, 104, 104],
+ [255, 61, 61],
+ [255, 61, 61],
+ [255, 61, 61],
+ [255, 48, 48],
+ [255, 29, 29]],
+
+ [[255, 104, 104],
+ [255, 154, 154],
+ [255, 203, 203],
+ [255, 232, 232],
+ [255, 222, 222],
+ [255, 205, 205],
+ [255, 189, 189],
+ [255, 186, 186],
+ [255, 186, 186],
+ [255, 145, 145],
+ [255, 87, 87]],
+
+ [[255, 48, 48],
+ [255, 104, 104],
+ [255, 168, 168],
+ [255, 217, 217],
+ [255, 141, 141],
+ [255, 134, 134],
+ [255, 137, 137],
+ [255, 90, 90],
+ [255, 87, 87],
+ [255, 68, 68],
+ [255, 40, 40]],
+
+ [[255, 16, 16],
+ [255, 106, 106],
+ [255, 170, 170],
+ [255, 168, 168],
+ [255, 112, 112],
+ [255, 136, 136],
+ [255, 149, 149],
+ [255, 56, 56],
+ [255, 50, 50],
+ [255, 35, 35],
+ [255, 14, 14]],
+
+ [[255, 3, 3],
+ [255, 124, 124],
+ [255, 188, 188],
+ [255, 143, 143],
+ [255, 132, 132],
+ [255, 176, 176],
+ [255, 190, 190],
+ [255, 87, 87],
+ [255, 80, 80],
+ [255, 49, 49],
+ [255, 4, 4]],
+
+ [[255, 9, 9],
+ [255, 124, 124],
+ [255, 203, 203],
+ [255, 201, 201],
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+ [255, 228, 228],
+ [255, 234, 234],
+ [255, 199, 199],
+ [255, 196, 196],
+ [255, 120, 120],
+ [255, 9, 9]],
+
+ [[255, 54, 54],
+ [255, 99, 99],
+ [255, 130, 130],
+ [255, 132, 132],
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+ [255, 199, 199],
+ [255, 209, 209],
+ [255, 135, 135],
+ [255, 130, 130],
+ [255, 99, 99],
+ [255, 54, 54]],
+
+ [[255, 93, 93],
+ [255, 108, 108],
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+ [255, 120, 120],
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+ [255, 108, 108],
+ [255, 93, 93]],
+
+ [[255, 102, 102],
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+ [255, 131, 131],
+ [255, 133, 133],
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+ [255, 102, 102]],
+
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+ [255, 16, 16]],
+
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+ [255, 73, 73],
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+
+ [[255, 0, 0],
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+ [255, 0, 0],
+ [255, 0, 0]]], dtype=uint8), array([[[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[212, 6, 150],
+ [212, 7, 151],
+ [212, 7, 151],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[224, 77, 180],
+ [225, 84, 183],
+ [226, 89, 185],
+ ...,
+ [225, 80, 182],
+ [223, 70, 177],
+ [221, 59, 173]],
+
+ ...,
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [218, 40, 165],
+ [219, 49, 168],
+ [221, 61, 173]],
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+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]]], dtype=uint8), array([[[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 2, 128, 2],
+ [ 5, 129, 5],
+ [ 4, 129, 4],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 26, 140, 26],
+ [ 63, 159, 63],
+ [ 60, 157, 60],
+ ...,
+ [ 79, 167, 79],
+ [ 64, 159, 64],
+ [ 59, 157, 59]],
+
+ ...,
+
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+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 38, 146, 38],
+ [ 55, 155, 55],
+ [ 61, 157, 61]],
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+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
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+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
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+ [184, 203, 255],
+ [160, 185, 255]],
+
+ [[ 17, 81, 255],
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+ [123, 159, 255],
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+ [197, 213, 255],
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+ [ 75, 124, 255]],
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+ [[ 93, 137, 255],
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+ [ 36, 95, 255],
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+ [176, 197, 255],
+ [176, 197, 255],
+ [204, 217, 255],
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+
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+ [176, 197, 255],
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+ [214, 225, 255],
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+ [199, 215, 255],
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+ [204, 218, 255],
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+ [ 78, 126, 255]],
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+
+ [[ 12, 78, 255],
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+ [145, 175, 255],
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+
+ [[ 44, 101, 255],
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+
+ [[109, 148, 255],
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+ [ 31, 92, 255],
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+ [125, 160, 255],
+ [165, 189, 255],
+ [200, 215, 255],
+ [119, 155, 255],
+ [ 60, 113, 255],
+ [ 32, 92, 255]],
+
+ [[144, 173, 255],
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+ [163, 187, 255],
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+ [205, 218, 255],
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+ [210, 222, 255],
+ [212, 223, 255],
+ [186, 204, 255],
+ [129, 163, 255],
+ [ 40, 99, 255],
+ [142, 173, 255],
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+ [ 9, 76, 255],
+ [ 91, 135, 255],
+ [173, 195, 255],
+ [209, 221, 255],
+ [145, 175, 255],
+ [ 53, 108, 255],
+ [ 9, 76, 255],
+ [ 5, 73, 255]],
+
+ [[ 62, 114, 255],
+ [ 47, 103, 255],
+ [ 47, 103, 255],
+ [ 70, 120, 255],
+ [ 84, 130, 255],
+ [ 91, 135, 255],
+ [ 93, 137, 255],
+ [ 93, 137, 255],
+ [ 93, 137, 255],
+ [ 82, 129, 255],
+ [ 59, 112, 255],
+ [ 18, 82, 255],
+ [ 60, 113, 255],
+ [ 65, 116, 255],
+ [ 2, 71, 255],
+ [ 38, 97, 255],
+ [ 74, 123, 255],
+ [ 89, 134, 255],
+ [ 55, 109, 255],
+ [ 17, 81, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 1, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 3, 71, 255],
+ [ 3, 71, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]]], dtype=uint8)], 'font_config': (1, 29, 8, 0.33, 1)}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])), None, '1234']) @ ModelUtils.py:148 in model_process
+ 11:32:14.762 [ERROR] - 算法模型分析异常：Traceback (most recent call last):
+ File "/home/thsw/chenbw/DrGraph/appIOs/conf/ModelUtils.py", line 152, in model_process
+ return aiHelper.AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
+ File "/home/thsw/chenbw/DrGraph/DrUtils/aiHelper.py", line 135, in AI_process
+ half,device,conf_thres,iou_thres,allowedList = objectPar['half'],objectPar['device'],objectPar['conf_thres'],objectPar['iou_thres'],objectPar['allowedList']
+KeyError: 'allowedList'
+, requestId:1234 @ ModelUtils.py:161 in model_process
+ 11:32:14.763 [ERROR] - 异常编码：SP018, 异常描述：算法模型分析异常! @ ModelUtils.py:44 in __str__
+ 11:39:25.958 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 11:39:25.978 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:55 in __init__
+ 11:39:26.003 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 11:39:26.004 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:65 in __init__
+ 11:39:26.168 [INFO] - 模型初始化时间：0.18923592567443848, requestId:1234 @ ModelUtils.py:107 in __init__
+ 11:39:26.168 [INFO] - 模型编号: 019, 模型参数: ( at 0x7f3e9b436c20>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:52 in
+ 11:39:26.169 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:291 in get_label_arraylist
+ 11:39:26.172 [INFO] - model_process(( at 0x7f3e9b436c20>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None, 'digitFont': {'line_thickness': 1, 'boxLine_thickness': 1, 'fontSize': 0.4, 'waterLineColor': (0, 255, 255), 'segLineShow': False, 'waterLineWidth': 1, 'wordSize': 8, 'label_location': 'leftTop'}, 'label_arraylist': [array([[[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 15, 15],
+ [255, 48, 48],
+ [255, 61, 61],
+ [255, 33, 33],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 34, 34],
+ [255, 50, 50],
+ [255, 83, 83],
+ [255, 129, 129],
+ [255, 143, 143],
+ [255, 104, 104],
+ [255, 61, 61],
+ [255, 61, 61],
+ [255, 61, 61],
+ [255, 48, 48],
+ [255, 29, 29]],
+
+ [[255, 104, 104],
+ [255, 154, 154],
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+ [255, 232, 232],
+ [255, 222, 222],
+ [255, 205, 205],
+ [255, 189, 189],
+ [255, 186, 186],
+ [255, 186, 186],
+ [255, 145, 145],
+ [255, 87, 87]],
+
+ [[255, 48, 48],
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+ [255, 217, 217],
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+ [255, 134, 134],
+ [255, 137, 137],
+ [255, 90, 90],
+ [255, 87, 87],
+ [255, 68, 68],
+ [255, 40, 40]],
+
+ [[255, 16, 16],
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+ [255, 112, 112],
+ [255, 136, 136],
+ [255, 149, 149],
+ [255, 56, 56],
+ [255, 50, 50],
+ [255, 35, 35],
+ [255, 14, 14]],
+
+ [[255, 3, 3],
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+ [255, 190, 190],
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+ [255, 80, 80],
+ [255, 49, 49],
+ [255, 4, 4]],
+
+ [[255, 9, 9],
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+ [255, 228, 228],
+ [255, 234, 234],
+ [255, 199, 199],
+ [255, 196, 196],
+ [255, 120, 120],
+ [255, 9, 9]],
+
+ [[255, 54, 54],
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+ [255, 130, 130],
+ [255, 99, 99],
+ [255, 54, 54]],
+
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+ [255, 205, 205],
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+ [255, 119, 119],
+ [255, 108, 108],
+ [255, 93, 93]],
+
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+ [255, 133, 133],
+ [255, 158, 158],
+ [255, 199, 199],
+ [255, 209, 209],
+ [255, 136, 136],
+ [255, 131, 131],
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+ [255, 102, 102]],
+
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+ [255, 21, 21],
+ [255, 19, 19],
+ [255, 16, 16]],
+
+ [[255, 0, 0],
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+ [255, 63, 63],
+ [255, 73, 73],
+ [255, 4, 4],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
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+ [255, 3, 3],
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+ [255, 8, 8],
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+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
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+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]]], dtype=uint8), array([[[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[212, 6, 150],
+ [212, 7, 151],
+ [212, 7, 151],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[224, 77, 180],
+ [225, 84, 183],
+ [226, 89, 185],
+ ...,
+ [225, 80, 182],
+ [223, 70, 177],
+ [221, 59, 173]],
+
+ ...,
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [218, 40, 165],
+ [219, 49, 168],
+ [221, 61, 173]],
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]]], dtype=uint8), array([[[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 2, 128, 2],
+ [ 5, 129, 5],
+ [ 4, 129, 4],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 26, 140, 26],
+ [ 63, 159, 63],
+ [ 60, 157, 60],
+ ...,
+ [ 79, 167, 79],
+ [ 64, 159, 64],
+ [ 59, 157, 59]],
+
+ ...,
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 38, 146, 38],
+ [ 55, 155, 55],
+ [ 61, 157, 61]],
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
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+ [ 0, 127, 0]]], dtype=uint8), array([[[ 0, 69, 255],
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+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
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+
+ [[ 17, 81, 255],
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+ [[ 93, 137, 255],
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+ [ 50, 105, 255]],
+
+ [[150, 178, 255],
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+ [ 78, 126, 255]],
+
+ [[ 37, 96, 255],
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+ [234, 239, 255],
+ [207, 219, 255],
+ [169, 192, 255],
+ [170, 193, 255],
+ [202, 216, 255],
+ [207, 220, 255],
+ [173, 195, 255],
+ [194, 211, 255],
+ [200, 215, 255],
+ [199, 214, 255],
+ [199, 214, 255],
+ [200, 215, 255],
+ [188, 206, 255],
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+
+ [[ 12, 78, 255],
+ [156, 183, 255],
+ [190, 208, 255],
+ [ 53, 108, 255],
+ [ 69, 119, 255],
+ [141, 171, 255],
+ [196, 212, 255],
+ [157, 184, 255],
+ [189, 207, 255],
+ [187, 206, 255],
+ [150, 179, 255],
+ [ 87, 133, 255],
+ [163, 188, 255],
+ [201, 215, 255],
+ [152, 180, 255],
+ [150, 179, 255],
+ [145, 175, 255],
+ [140, 171, 255],
+ [140, 171, 255],
+ [147, 176, 255],
+ [151, 179, 255],
+ [153, 180, 255]],
+
+ [[ 44, 101, 255],
+ [169, 192, 255],
+ [188, 206, 255],
+ [ 45, 102, 255],
+ [ 23, 85, 255],
+ [ 97, 140, 255],
+ [162, 187, 255],
+ [111, 150, 255],
+ [143, 174, 255],
+ [140, 171, 255],
+ [100, 142, 255],
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+ [144, 174, 255],
+ [183, 202, 255],
+ [ 97, 140, 255],
+ [108, 148, 255],
+ [118, 155, 255],
+ [130, 164, 255],
+ [152, 180, 255],
+ [126, 161, 255],
+ [107, 147, 255],
+ [ 99, 141, 255]],
+
+ [[109, 148, 255],
+ [182, 202, 255],
+ [197, 212, 255],
+ [118, 155, 255],
+ [ 76, 124, 255],
+ [117, 155, 255],
+ [159, 185, 255],
+ [117, 154, 255],
+ [127, 162, 255],
+ [104, 145, 255],
+ [ 57, 111, 255],
+ [ 29, 91, 255],
+ [142, 173, 255],
+ [162, 187, 255],
+ [ 31, 92, 255],
+ [ 80, 128, 255],
+ [125, 160, 255],
+ [165, 189, 255],
+ [200, 215, 255],
+ [119, 155, 255],
+ [ 60, 113, 255],
+ [ 32, 92, 255]],
+
+ [[144, 173, 255],
+ [125, 160, 255],
+ [127, 162, 255],
+ [163, 187, 255],
+ [185, 204, 255],
+ [205, 218, 255],
+ [216, 226, 255],
+ [210, 222, 255],
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+ [186, 204, 255],
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+ [ 40, 99, 255],
+ [142, 173, 255],
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+ [ 9, 76, 255],
+ [ 91, 135, 255],
+ [173, 195, 255],
+ [209, 221, 255],
+ [145, 175, 255],
+ [ 53, 108, 255],
+ [ 9, 76, 255],
+ [ 5, 73, 255]],
+
+ [[ 62, 114, 255],
+ [ 47, 103, 255],
+ [ 47, 103, 255],
+ [ 70, 120, 255],
+ [ 84, 130, 255],
+ [ 91, 135, 255],
+ [ 93, 137, 255],
+ [ 93, 137, 255],
+ [ 93, 137, 255],
+ [ 82, 129, 255],
+ [ 59, 112, 255],
+ [ 18, 82, 255],
+ [ 60, 113, 255],
+ [ 65, 116, 255],
+ [ 2, 71, 255],
+ [ 38, 97, 255],
+ [ 74, 123, 255],
+ [ 89, 134, 255],
+ [ 55, 109, 255],
+ [ 17, 81, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 1, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 3, 71, 255],
+ [ 3, 71, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]]], dtype=uint8)], 'font_config': (1, 29, 8, 0.33, 1)}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255]))) @ ModelUtils.py:148 in model_process
+ 11:39:26.176 [ERROR] - 算法模型分析异常：Traceback (most recent call last):
+ File "/home/thsw/chenbw/DrGraph/appIOs/conf/ModelUtils.py", line 152, in model_process
+ return aiHelper.AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
+ File "/home/thsw/chenbw/DrGraph/DrUtils/aiHelper.py", line 135, in AI_process
+ half,device,conf_thres,iou_thres,allowedList = objectPar['half'],objectPar['device'],objectPar['conf_thres'],objectPar['iou_thres'],objectPar['allowedList']
+KeyError: 'allowedList'
+, requestId:1234 @ ModelUtils.py:161 in model_process
+ 11:39:26.177 [ERROR] - 异常编码：SP018, 异常描述：算法模型分析异常! @ ModelUtils.py:44 in __str__
+ 11:41:40.257 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 11:41:40.279 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:55 in __init__
+ 11:41:40.304 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 11:41:40.305 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:65 in __init__
+ 11:41:40.465 [INFO] - 模型初始化时间：0.18572115898132324, requestId:1234 @ ModelUtils.py:107 in __init__
+ 11:41:40.465 [INFO] - 模型编号: 019, 模型参数: ( at 0x7fe5b4cf2b00>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:52 in
+ 11:41:40.466 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:290 in get_label_arraylist
+ 11:41:40.470 [INFO] - model_process([None]) @ aiHelper.py:101 in AI_process
+ 11:41:40.470 [ERROR] - 算法模型分析异常：Traceback (most recent call last):
+ File "/home/thsw/chenbw/DrGraph/appIOs/conf/ModelUtils.py", line 151, in model_process
+ return aiHelper.AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
+ File "/home/thsw/chenbw/DrGraph/DrUtils/aiHelper.py", line 138, in AI_process
+ half,device,conf_thres,iou_thres,allowedList = objectPar['half'],objectPar['device'],objectPar['conf_thres'],objectPar['iou_thres'],objectPar['allowedList']
+KeyError: 'allowedList'
+, requestId:1234 @ ModelUtils.py:160 in model_process
+ 11:41:40.472 [ERROR] - 异常编码：SP018, 异常描述：算法模型分析异常! @ ModelUtils.py:44 in __str__
+ 11:41:58.329 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 11:41:58.350 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:55 in __init__
+ 11:41:58.375 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 11:41:58.375 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:65 in __init__
+ 11:41:58.543 [INFO] - 模型初始化时间：0.19279718399047852, requestId:1234 @ ModelUtils.py:107 in __init__
+ 11:41:58.543 [INFO] - 模型编号: 019, 模型参数: ( at 0x7f0b69966710>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:52 in
+ 11:41:58.544 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:290 in get_label_arraylist
+ 11:41:58.547 [INFO] - model_process() @ aiHelper.py:101 in AI_process
+ 11:41:58.548 [ERROR] - 算法模型分析异常：Traceback (most recent call last):
+ File "/home/thsw/chenbw/DrGraph/appIOs/conf/ModelUtils.py", line 151, in model_process
+ return aiHelper.AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
+ File "/home/thsw/chenbw/DrGraph/DrUtils/aiHelper.py", line 138, in AI_process
+ half,device,conf_thres,iou_thres,allowedList = objectPar['half'],objectPar['device'],objectPar['conf_thres'],objectPar['iou_thres'],objectPar['allowedList']
+KeyError: 'allowedList'
+, requestId:1234 @ ModelUtils.py:160 in model_process
+ 11:41:58.549 [ERROR] - 异常编码：SP018, 异常描述：算法模型分析异常! @ ModelUtils.py:44 in __str__
+ 11:42:30.066 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 11:42:30.087 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:55 in __init__
+ 11:42:30.113 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 11:42:30.113 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:65 in __init__
+ 11:42:30.271 [INFO] - 模型初始化时间：0.18350505828857422, requestId:1234 @ ModelUtils.py:107 in __init__
+ 11:42:30.271 [INFO] - 模型编号: 019, 模型参数: ( at 0x7f8c24abe680>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:52 in
+ 11:42:30.272 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:290 in get_label_arraylist
+ 11:42:30.279 [ERROR] - 算法模型分析异常：Traceback (most recent call last):
+ File "/home/thsw/chenbw/DrGraph/appIOs/conf/ModelUtils.py", line 151, in model_process
+ return aiHelper.AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
+ File "/home/thsw/chenbw/DrGraph/DrUtils/aiHelper.py", line 101, in AI_process
+ logger.info("model_process({}, {},{},{},{},{},{},{},{},{},{},{})", im0s, model, segmodel, names, label_arraylist, rainbows,
+ File "/home/thsw/anaconda3/envs/alg_py310/lib/python3.10/site-packages/loguru/_logger.py", line 2014, in info
+ __self._log("INFO", False, __self._options, __message, args, kwargs)
+ File "/home/thsw/anaconda3/envs/alg_py310/lib/python3.10/site-packages/loguru/_logger.py", line 1991, in _log
+ log_record["message"] = message.format(*args, **kwargs)
+IndexError: Replacement index 11 out of range for positional args tuple
+, requestId:1234 @ ModelUtils.py:160 in model_process
+ 11:42:30.281 [ERROR] - 异常编码：SP018, 异常描述：算法模型分析异常! @ ModelUtils.py:44 in __str__
+ 11:42:58.565 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 11:42:58.586 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:55 in __init__
+ 11:42:58.612 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 11:42:58.612 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:65 in __init__
+ 11:42:58.771 [INFO] - 模型初始化时间：0.18506455421447754, requestId:1234 @ ModelUtils.py:107 in __init__
+ 11:42:58.772 [INFO] - 模型编号: 019, 模型参数: ( at 0x7fda98846680>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:52 in
+ 11:42:58.772 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:290 in get_label_arraylist
+ 11:42:58.775 [INFO] - model_process([None], ,None,['车', 'T角点', 'L角点', '违停'],[array([[[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 15, 15],
+ [255, 48, 48],
+ [255, 61, 61],
+ [255, 33, 33],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 34, 34],
+ [255, 50, 50],
+ [255, 83, 83],
+ [255, 129, 129],
+ [255, 143, 143],
+ [255, 104, 104],
+ [255, 61, 61],
+ [255, 61, 61],
+ [255, 61, 61],
+ [255, 48, 48],
+ [255, 29, 29]],
+
+ [[255, 104, 104],
+ [255, 154, 154],
+ [255, 203, 203],
+ [255, 232, 232],
+ [255, 222, 222],
+ [255, 205, 205],
+ [255, 189, 189],
+ [255, 186, 186],
+ [255, 186, 186],
+ [255, 145, 145],
+ [255, 87, 87]],
+
+ [[255, 48, 48],
+ [255, 104, 104],
+ [255, 168, 168],
+ [255, 217, 217],
+ [255, 141, 141],
+ [255, 134, 134],
+ [255, 137, 137],
+ [255, 90, 90],
+ [255, 87, 87],
+ [255, 68, 68],
+ [255, 40, 40]],
+
+ [[255, 16, 16],
+ [255, 106, 106],
+ [255, 170, 170],
+ [255, 168, 168],
+ [255, 112, 112],
+ [255, 136, 136],
+ [255, 149, 149],
+ [255, 56, 56],
+ [255, 50, 50],
+ [255, 35, 35],
+ [255, 14, 14]],
+
+ [[255, 3, 3],
+ [255, 124, 124],
+ [255, 188, 188],
+ [255, 143, 143],
+ [255, 132, 132],
+ [255, 176, 176],
+ [255, 190, 190],
+ [255, 87, 87],
+ [255, 80, 80],
+ [255, 49, 49],
+ [255, 4, 4]],
+
+ [[255, 9, 9],
+ [255, 124, 124],
+ [255, 203, 203],
+ [255, 201, 201],
+ [255, 210, 210],
+ [255, 228, 228],
+ [255, 234, 234],
+ [255, 199, 199],
+ [255, 196, 196],
+ [255, 120, 120],
+ [255, 9, 9]],
+
+ [[255, 54, 54],
+ [255, 99, 99],
+ [255, 130, 130],
+ [255, 132, 132],
+ [255, 157, 157],
+ [255, 199, 199],
+ [255, 209, 209],
+ [255, 135, 135],
+ [255, 130, 130],
+ [255, 99, 99],
+ [255, 54, 54]],
+
+ [[255, 93, 93],
+ [255, 108, 108],
+ [255, 119, 119],
+ [255, 120, 120],
+ [255, 149, 149],
+ [255, 194, 194],
+ [255, 205, 205],
+ [255, 124, 124],
+ [255, 119, 119],
+ [255, 108, 108],
+ [255, 93, 93]],
+
+ [[255, 102, 102],
+ [255, 119, 119],
+ [255, 131, 131],
+ [255, 133, 133],
+ [255, 158, 158],
+ [255, 199, 199],
+ [255, 209, 209],
+ [255, 136, 136],
+ [255, 131, 131],
+ [255, 119, 119],
+ [255, 102, 102]],
+
+ [[255, 16, 16],
+ [255, 19, 19],
+ [255, 21, 21],
+ [255, 24, 24],
+ [255, 72, 72],
+ [255, 149, 149],
+ [255, 168, 168],
+ [255, 29, 29],
+ [255, 21, 21],
+ [255, 19, 19],
+ [255, 16, 16]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 1, 1],
+ [255, 25, 25],
+ [255, 63, 63],
+ [255, 73, 73],
+ [255, 4, 4],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 3, 3],
+ [255, 7, 7],
+ [255, 8, 8],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]]], dtype=uint8), array([[[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[212, 6, 150],
+ [212, 7, 151],
+ [212, 7, 151],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[224, 77, 180],
+ [225, 84, 183],
+ [226, 89, 185],
+ ...,
+ [225, 80, 182],
+ [223, 70, 177],
+ [221, 59, 173]],
+
+ ...,
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [218, 40, 165],
+ [219, 49, 168],
+ [221, 61, 173]],
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]]], dtype=uint8), array([[[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 2, 128, 2],
+ [ 5, 129, 5],
+ [ 4, 129, 4],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 26, 140, 26],
+ [ 63, 159, 63],
+ [ 60, 157, 60],
+ ...,
+ [ 79, 167, 79],
+ [ 64, 159, 64],
+ [ 59, 157, 59]],
+
+ ...,
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 38, 146, 38],
+ [ 55, 155, 55],
+ [ 61, 157, 61]],
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]]], dtype=uint8), array([[[ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 25, 87, 255],
+ [ 30, 90, 255],
+ [ 22, 85, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 36, 95, 255],
+ [ 63, 115, 255],
+ [ 33, 93, 255],
+ [ 10, 76, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 34, 94, 255],
+ [ 47, 103, 255],
+ [ 24, 86, 255],
+ [ 6, 73, 255],
+ [ 25, 87, 255],
+ [ 50, 105, 255],
+ [ 50, 105, 255],
+ [ 16, 80, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 53, 107, 255],
+ [ 87, 132, 255],
+ [ 90, 135, 255],
+ [ 49, 104, 255],
+ [ 67, 118, 255],
+ [118, 155, 255],
+ [151, 179, 255],
+ [113, 151, 255],
+ [ 85, 131, 255],
+ [ 62, 114, 255],
+ [ 41, 99, 255],
+ [ 7, 74, 255],
+ [ 83, 129, 255],
+ [121, 157, 255],
+ [ 80, 127, 255],
+ [ 67, 118, 255],
+ [ 93, 137, 255],
+ [122, 158, 255],
+ [122, 157, 255],
+ [ 82, 129, 255],
+ [ 59, 112, 255],
+ [ 53, 107, 255]],
+
+ [[ 73, 121, 255],
+ [160, 185, 255],
+ [196, 212, 255],
+ [147, 177, 255],
+ [205, 218, 255],
+ [235, 240, 255],
+ [243, 246, 255],
+ [233, 239, 255],
+ [226, 233, 255],
+ [190, 208, 255],
+ [124, 160, 255],
+ [ 24, 86, 255],
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+ [190, 208, 255],
+ [201, 215, 255],
+ [201, 215, 255],
+ [201, 215, 255],
+ [201, 215, 255],
+ [184, 203, 255],
+ [160, 185, 255]],
+
+ [[ 17, 81, 255],
+ [ 66, 117, 255],
+ [ 88, 133, 255],
+ [ 66, 117, 255],
+ [123, 159, 255],
+ [183, 203, 255],
+ [214, 225, 255],
+ [178, 199, 255],
+ [152, 180, 255],
+ [104, 146, 255],
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+ [156, 183, 255],
+ [176, 197, 255],
+ [ 73, 122, 255],
+ [179, 200, 255],
+ [197, 213, 255],
+ [182, 201, 255],
+ [182, 201, 255],
+ [202, 216, 255],
+ [155, 182, 255],
+ [ 75, 124, 255]],
+
+ [[ 93, 137, 255],
+ [121, 157, 255],
+ [108, 147, 255],
+ [ 40, 98, 255],
+ [ 98, 141, 255],
+ [167, 191, 255],
+ [205, 218, 255],
+ [162, 187, 255],
+ [130, 164, 255],
+ [ 77, 126, 255],
+ [ 36, 95, 255],
+ [113, 152, 255],
+ [185, 204, 255],
+ [166, 190, 255],
+ [ 47, 103, 255],
+ [178, 199, 255],
+ [198, 213, 255],
+ [176, 197, 255],
+ [176, 197, 255],
+ [204, 217, 255],
+ [148, 177, 255],
+ [ 50, 105, 255]],
+
+ [[150, 178, 255],
+ [196, 212, 255],
+ [176, 197, 255],
+ [ 68, 118, 255],
+ [125, 160, 255],
+ [184, 203, 255],
+ [214, 225, 255],
+ [179, 200, 255],
+ [154, 182, 255],
+ [108, 148, 255],
+ [ 73, 122, 255],
+ [165, 189, 255],
+ [207, 220, 255],
+ [175, 196, 255],
+ [ 77, 125, 255],
+ [184, 203, 255],
+ [199, 215, 255],
+ [181, 201, 255],
+ [181, 201, 255],
+ [204, 218, 255],
+ [159, 185, 255],
+ [ 78, 126, 255]],
+
+ [[ 37, 96, 255],
+ [165, 189, 255],
+ [223, 231, 255],
+ [160, 186, 255],
+ [211, 223, 255],
+ [237, 242, 255],
+ [245, 248, 255],
+ [236, 241, 255],
+ [234, 239, 255],
+ [207, 219, 255],
+ [169, 192, 255],
+ [170, 193, 255],
+ [202, 216, 255],
+ [207, 220, 255],
+ [173, 195, 255],
+ [194, 211, 255],
+ [200, 215, 255],
+ [199, 214, 255],
+ [199, 214, 255],
+ [200, 215, 255],
+ [188, 206, 255],
+ [169, 193, 255]],
+
+ [[ 12, 78, 255],
+ [156, 183, 255],
+ [190, 208, 255],
+ [ 53, 108, 255],
+ [ 69, 119, 255],
+ [141, 171, 255],
+ [196, 212, 255],
+ [157, 184, 255],
+ [189, 207, 255],
+ [187, 206, 255],
+ [150, 179, 255],
+ [ 87, 133, 255],
+ [163, 188, 255],
+ [201, 215, 255],
+ [152, 180, 255],
+ [150, 179, 255],
+ [145, 175, 255],
+ [140, 171, 255],
+ [140, 171, 255],
+ [147, 176, 255],
+ [151, 179, 255],
+ [153, 180, 255]],
+
+ [[ 44, 101, 255],
+ [169, 192, 255],
+ [188, 206, 255],
+ [ 45, 102, 255],
+ [ 23, 85, 255],
+ [ 97, 140, 255],
+ [162, 187, 255],
+ [111, 150, 255],
+ [143, 174, 255],
+ [140, 171, 255],
+ [100, 142, 255],
+ [ 39, 98, 255],
+ [144, 174, 255],
+ [183, 202, 255],
+ [ 97, 140, 255],
+ [108, 148, 255],
+ [118, 155, 255],
+ [130, 164, 255],
+ [152, 180, 255],
+ [126, 161, 255],
+ [107, 147, 255],
+ [ 99, 141, 255]],
+
+ [[109, 148, 255],
+ [182, 202, 255],
+ [197, 212, 255],
+ [118, 155, 255],
+ [ 76, 124, 255],
+ [117, 155, 255],
+ [159, 185, 255],
+ [117, 154, 255],
+ [127, 162, 255],
+ [104, 145, 255],
+ [ 57, 111, 255],
+ [ 29, 91, 255],
+ [142, 173, 255],
+ [162, 187, 255],
+ [ 31, 92, 255],
+ [ 80, 128, 255],
+ [125, 160, 255],
+ [165, 189, 255],
+ [200, 215, 255],
+ [119, 155, 255],
+ [ 60, 113, 255],
+ [ 32, 92, 255]],
+
+ [[144, 173, 255],
+ [125, 160, 255],
+ [127, 162, 255],
+ [163, 187, 255],
+ [185, 204, 255],
+ [205, 218, 255],
+ [216, 226, 255],
+ [210, 222, 255],
+ [212, 223, 255],
+ [186, 204, 255],
+ [129, 163, 255],
+ [ 40, 99, 255],
+ [142, 173, 255],
+ [154, 181, 255],
+ [ 9, 76, 255],
+ [ 91, 135, 255],
+ [173, 195, 255],
+ [209, 221, 255],
+ [145, 175, 255],
+ [ 53, 108, 255],
+ [ 9, 76, 255],
+ [ 5, 73, 255]],
+
+ [[ 62, 114, 255],
+ [ 47, 103, 255],
+ [ 47, 103, 255],
+ [ 70, 120, 255],
+ [ 84, 130, 255],
+ [ 91, 135, 255],
+ [ 93, 137, 255],
+ [ 93, 137, 255],
+ [ 93, 137, 255],
+ [ 82, 129, 255],
+ [ 59, 112, 255],
+ [ 18, 82, 255],
+ [ 60, 113, 255],
+ [ 65, 116, 255],
+ [ 2, 71, 255],
+ [ 38, 97, 255],
+ [ 74, 123, 255],
+ [ 89, 134, 255],
+ [ 55, 109, 255],
+ [ 17, 81, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 1, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 3, 71, 255],
+ [ 3, 71, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]]], dtype=uint8)],([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255]),{'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []},{'line_thickness': 1, 'boxLine_thickness': 1, 'fontSize': 0.4, 'waterLineColor': (0, 255, 255), 'segLineShow': False, 'waterLineWidth': 1, 'wordSize': 8, 'label_location': 'leftTop'},{'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4},others,None) @ aiHelper.py:101 in AI_process
+ 11:42:58.780 [ERROR] - 算法模型分析异常：Traceback (most recent call last):
+ File "/home/thsw/chenbw/DrGraph/appIOs/conf/ModelUtils.py", line 151, in model_process
+ return aiHelper.AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
+ File "/home/thsw/chenbw/DrGraph/DrUtils/aiHelper.py", line 138, in AI_process
+ half,device,conf_thres,iou_thres,allowedList = objectPar['half'],objectPar['device'],objectPar['conf_thres'],objectPar['iou_thres'],objectPar['allowedList']
+KeyError: 'allowedList'
+, requestId:1234 @ ModelUtils.py:160 in model_process
+ 11:42:58.781 [ERROR] - 异常编码：SP018, 异常描述：算法模型分析异常! @ ModelUtils.py:44 in __str__
+ 13:31:46.854 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 13:31:46.874 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:55 in __init__
+ 13:31:46.899 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 13:31:46.900 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:65 in __init__
+ 13:31:47.072 [INFO] - 模型初始化时间：0.1983504295349121, requestId:1234 @ ModelUtils.py:107 in __init__
+ 13:31:47.073 [INFO] - 模型编号: 019
+模型参数: ( at 0x7f749cc4e7a0>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:52 in
+ 13:31:47.073 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:290 in get_label_arraylist
+ 13:31:47.076 [INFO] - model_process([None], ,None,['车', 'T角点', 'L角点', '违停'],[array([[[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 15, 15],
+ [255, 48, 48],
+ [255, 61, 61],
+ [255, 33, 33],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 34, 34],
+ [255, 50, 50],
+ [255, 83, 83],
+ [255, 129, 129],
+ [255, 143, 143],
+ [255, 104, 104],
+ [255, 61, 61],
+ [255, 61, 61],
+ [255, 61, 61],
+ [255, 48, 48],
+ [255, 29, 29]],
+
+ [[255, 104, 104],
+ [255, 154, 154],
+ [255, 203, 203],
+ [255, 232, 232],
+ [255, 222, 222],
+ [255, 205, 205],
+ [255, 189, 189],
+ [255, 186, 186],
+ [255, 186, 186],
+ [255, 145, 145],
+ [255, 87, 87]],
+
+ [[255, 48, 48],
+ [255, 104, 104],
+ [255, 168, 168],
+ [255, 217, 217],
+ [255, 141, 141],
+ [255, 134, 134],
+ [255, 137, 137],
+ [255, 90, 90],
+ [255, 87, 87],
+ [255, 68, 68],
+ [255, 40, 40]],
+
+ [[255, 16, 16],
+ [255, 106, 106],
+ [255, 170, 170],
+ [255, 168, 168],
+ [255, 112, 112],
+ [255, 136, 136],
+ [255, 149, 149],
+ [255, 56, 56],
+ [255, 50, 50],
+ [255, 35, 35],
+ [255, 14, 14]],
+
+ [[255, 3, 3],
+ [255, 124, 124],
+ [255, 188, 188],
+ [255, 143, 143],
+ [255, 132, 132],
+ [255, 176, 176],
+ [255, 190, 190],
+ [255, 87, 87],
+ [255, 80, 80],
+ [255, 49, 49],
+ [255, 4, 4]],
+
+ [[255, 9, 9],
+ [255, 124, 124],
+ [255, 203, 203],
+ [255, 201, 201],
+ [255, 210, 210],
+ [255, 228, 228],
+ [255, 234, 234],
+ [255, 199, 199],
+ [255, 196, 196],
+ [255, 120, 120],
+ [255, 9, 9]],
+
+ [[255, 54, 54],
+ [255, 99, 99],
+ [255, 130, 130],
+ [255, 132, 132],
+ [255, 157, 157],
+ [255, 199, 199],
+ [255, 209, 209],
+ [255, 135, 135],
+ [255, 130, 130],
+ [255, 99, 99],
+ [255, 54, 54]],
+
+ [[255, 93, 93],
+ [255, 108, 108],
+ [255, 119, 119],
+ [255, 120, 120],
+ [255, 149, 149],
+ [255, 194, 194],
+ [255, 205, 205],
+ [255, 124, 124],
+ [255, 119, 119],
+ [255, 108, 108],
+ [255, 93, 93]],
+
+ [[255, 102, 102],
+ [255, 119, 119],
+ [255, 131, 131],
+ [255, 133, 133],
+ [255, 158, 158],
+ [255, 199, 199],
+ [255, 209, 209],
+ [255, 136, 136],
+ [255, 131, 131],
+ [255, 119, 119],
+ [255, 102, 102]],
+
+ [[255, 16, 16],
+ [255, 19, 19],
+ [255, 21, 21],
+ [255, 24, 24],
+ [255, 72, 72],
+ [255, 149, 149],
+ [255, 168, 168],
+ [255, 29, 29],
+ [255, 21, 21],
+ [255, 19, 19],
+ [255, 16, 16]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 1, 1],
+ [255, 25, 25],
+ [255, 63, 63],
+ [255, 73, 73],
+ [255, 4, 4],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 3, 3],
+ [255, 7, 7],
+ [255, 8, 8],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]]], dtype=uint8), array([[[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[212, 6, 150],
+ [212, 7, 151],
+ [212, 7, 151],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[224, 77, 180],
+ [225, 84, 183],
+ [226, 89, 185],
+ ...,
+ [225, 80, 182],
+ [223, 70, 177],
+ [221, 59, 173]],
+
+ ...,
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [218, 40, 165],
+ [219, 49, 168],
+ [221, 61, 173]],
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]]], dtype=uint8), array([[[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 2, 128, 2],
+ [ 5, 129, 5],
+ [ 4, 129, 4],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 26, 140, 26],
+ [ 63, 159, 63],
+ [ 60, 157, 60],
+ ...,
+ [ 79, 167, 79],
+ [ 64, 159, 64],
+ [ 59, 157, 59]],
+
+ ...,
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 38, 146, 38],
+ [ 55, 155, 55],
+ [ 61, 157, 61]],
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]]], dtype=uint8), array([[[ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 25, 87, 255],
+ [ 30, 90, 255],
+ [ 22, 85, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 36, 95, 255],
+ [ 63, 115, 255],
+ [ 33, 93, 255],
+ [ 10, 76, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 34, 94, 255],
+ [ 47, 103, 255],
+ [ 24, 86, 255],
+ [ 6, 73, 255],
+ [ 25, 87, 255],
+ [ 50, 105, 255],
+ [ 50, 105, 255],
+ [ 16, 80, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 53, 107, 255],
+ [ 87, 132, 255],
+ [ 90, 135, 255],
+ [ 49, 104, 255],
+ [ 67, 118, 255],
+ [118, 155, 255],
+ [151, 179, 255],
+ [113, 151, 255],
+ [ 85, 131, 255],
+ [ 62, 114, 255],
+ [ 41, 99, 255],
+ [ 7, 74, 255],
+ [ 83, 129, 255],
+ [121, 157, 255],
+ [ 80, 127, 255],
+ [ 67, 118, 255],
+ [ 93, 137, 255],
+ [122, 158, 255],
+ [122, 157, 255],
+ [ 82, 129, 255],
+ [ 59, 112, 255],
+ [ 53, 107, 255]],
+
+ [[ 73, 121, 255],
+ [160, 185, 255],
+ [196, 212, 255],
+ [147, 177, 255],
+ [205, 218, 255],
+ [235, 240, 255],
+ [243, 246, 255],
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+ [ 24, 86, 255],
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+ [190, 208, 255],
+ [201, 215, 255],
+ [201, 215, 255],
+ [201, 215, 255],
+ [201, 215, 255],
+ [184, 203, 255],
+ [160, 185, 255]],
+
+ [[ 17, 81, 255],
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+ [ 88, 133, 255],
+ [ 66, 117, 255],
+ [123, 159, 255],
+ [183, 203, 255],
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+ [ 73, 122, 255],
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+ [197, 213, 255],
+ [182, 201, 255],
+ [182, 201, 255],
+ [202, 216, 255],
+ [155, 182, 255],
+ [ 75, 124, 255]],
+
+ [[ 93, 137, 255],
+ [121, 157, 255],
+ [108, 147, 255],
+ [ 40, 98, 255],
+ [ 98, 141, 255],
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+ [205, 218, 255],
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+ [130, 164, 255],
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+ [166, 190, 255],
+ [ 47, 103, 255],
+ [178, 199, 255],
+ [198, 213, 255],
+ [176, 197, 255],
+ [176, 197, 255],
+ [204, 217, 255],
+ [148, 177, 255],
+ [ 50, 105, 255]],
+
+ [[150, 178, 255],
+ [196, 212, 255],
+ [176, 197, 255],
+ [ 68, 118, 255],
+ [125, 160, 255],
+ [184, 203, 255],
+ [214, 225, 255],
+ [179, 200, 255],
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+ [207, 220, 255],
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+ [ 77, 125, 255],
+ [184, 203, 255],
+ [199, 215, 255],
+ [181, 201, 255],
+ [181, 201, 255],
+ [204, 218, 255],
+ [159, 185, 255],
+ [ 78, 126, 255]],
+
+ [[ 37, 96, 255],
+ [165, 189, 255],
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+ [160, 186, 255],
+ [211, 223, 255],
+ [237, 242, 255],
+ [245, 248, 255],
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+ [234, 239, 255],
+ [207, 219, 255],
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+ [207, 220, 255],
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+ [194, 211, 255],
+ [200, 215, 255],
+ [199, 214, 255],
+ [199, 214, 255],
+ [200, 215, 255],
+ [188, 206, 255],
+ [169, 193, 255]],
+
+ [[ 12, 78, 255],
+ [156, 183, 255],
+ [190, 208, 255],
+ [ 53, 108, 255],
+ [ 69, 119, 255],
+ [141, 171, 255],
+ [196, 212, 255],
+ [157, 184, 255],
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+ [187, 206, 255],
+ [150, 179, 255],
+ [ 87, 133, 255],
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+ [145, 175, 255],
+ [140, 171, 255],
+ [140, 171, 255],
+ [147, 176, 255],
+ [151, 179, 255],
+ [153, 180, 255]],
+
+ [[ 44, 101, 255],
+ [169, 192, 255],
+ [188, 206, 255],
+ [ 45, 102, 255],
+ [ 23, 85, 255],
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+ [162, 187, 255],
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+ [118, 155, 255],
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+ [152, 180, 255],
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+ [107, 147, 255],
+ [ 99, 141, 255]],
+
+ [[109, 148, 255],
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+ [197, 212, 255],
+ [118, 155, 255],
+ [ 76, 124, 255],
+ [117, 155, 255],
+ [159, 185, 255],
+ [117, 154, 255],
+ [127, 162, 255],
+ [104, 145, 255],
+ [ 57, 111, 255],
+ [ 29, 91, 255],
+ [142, 173, 255],
+ [162, 187, 255],
+ [ 31, 92, 255],
+ [ 80, 128, 255],
+ [125, 160, 255],
+ [165, 189, 255],
+ [200, 215, 255],
+ [119, 155, 255],
+ [ 60, 113, 255],
+ [ 32, 92, 255]],
+
+ [[144, 173, 255],
+ [125, 160, 255],
+ [127, 162, 255],
+ [163, 187, 255],
+ [185, 204, 255],
+ [205, 218, 255],
+ [216, 226, 255],
+ [210, 222, 255],
+ [212, 223, 255],
+ [186, 204, 255],
+ [129, 163, 255],
+ [ 40, 99, 255],
+ [142, 173, 255],
+ [154, 181, 255],
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+ [ 91, 135, 255],
+ [173, 195, 255],
+ [209, 221, 255],
+ [145, 175, 255],
+ [ 53, 108, 255],
+ [ 9, 76, 255],
+ [ 5, 73, 255]],
+
+ [[ 62, 114, 255],
+ [ 47, 103, 255],
+ [ 47, 103, 255],
+ [ 70, 120, 255],
+ [ 84, 130, 255],
+ [ 91, 135, 255],
+ [ 93, 137, 255],
+ [ 93, 137, 255],
+ [ 93, 137, 255],
+ [ 82, 129, 255],
+ [ 59, 112, 255],
+ [ 18, 82, 255],
+ [ 60, 113, 255],
+ [ 65, 116, 255],
+ [ 2, 71, 255],
+ [ 38, 97, 255],
+ [ 74, 123, 255],
+ [ 89, 134, 255],
+ [ 55, 109, 255],
+ [ 17, 81, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 1, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 3, 71, 255],
+ [ 3, 71, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]]], dtype=uint8)],([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255]),{'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []},{'line_thickness': 1, 'boxLine_thickness': 1, 'fontSize': 0.4, 'waterLineColor': (0, 255, 255), 'segLineShow': False, 'waterLineWidth': 1, 'wordSize': 8, 'label_location': 'leftTop'},{'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4},others,None) @ aiHelper.py:101 in AI_process
+ 13:31:47.081 [ERROR] - 算法模型分析异常：Traceback (most recent call last):
+ File "/home/thsw/chenbw/DrGraph/appIOs/conf/ModelUtils.py", line 151, in model_process
+ return aiHelper.AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
+ File "/home/thsw/chenbw/DrGraph/DrUtils/aiHelper.py", line 138, in AI_process
+ half,device,conf_thres,iou_thres,allowedList = objectPar['half'],objectPar['device'],objectPar['conf_thres'],objectPar['iou_thres'],objectPar['allowedList']
+KeyError: 'allowedList'
+, requestId:1234 @ ModelUtils.py:160 in model_process
+ 13:31:47.082 [ERROR] - 异常编码：SP018, 异常描述：算法模型分析异常! @ ModelUtils.py:44 in __str__
+ 13:36:09.591 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 13:36:09.613 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:55 in __init__
+ 13:36:09.638 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 13:36:09.638 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:65 in __init__
+ 13:36:09.792 [INFO] - 模型初始化时间：0.17864322662353516, requestId:1234 @ ModelUtils.py:107 in __init__
+ 13:36:09.793 [INFO] - 模型编号: 019
+模型参数: ( at 0x7fadae6665f0>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:52 in
+ 13:36:09.794 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:290 in get_label_arraylist
+ 13:36:09.797 [INFO] - model_process([None], model=,None,['车', 'T角点', 'L角点', '违停'],[array([[[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 15, 15],
+ [255, 48, 48],
+ [255, 61, 61],
+ [255, 33, 33],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 34, 34],
+ [255, 50, 50],
+ [255, 83, 83],
+ [255, 129, 129],
+ [255, 143, 143],
+ [255, 104, 104],
+ [255, 61, 61],
+ [255, 61, 61],
+ [255, 61, 61],
+ [255, 48, 48],
+ [255, 29, 29]],
+
+ [[255, 104, 104],
+ [255, 154, 154],
+ [255, 203, 203],
+ [255, 232, 232],
+ [255, 222, 222],
+ [255, 205, 205],
+ [255, 189, 189],
+ [255, 186, 186],
+ [255, 186, 186],
+ [255, 145, 145],
+ [255, 87, 87]],
+
+ [[255, 48, 48],
+ [255, 104, 104],
+ [255, 168, 168],
+ [255, 217, 217],
+ [255, 141, 141],
+ [255, 134, 134],
+ [255, 137, 137],
+ [255, 90, 90],
+ [255, 87, 87],
+ [255, 68, 68],
+ [255, 40, 40]],
+
+ [[255, 16, 16],
+ [255, 106, 106],
+ [255, 170, 170],
+ [255, 168, 168],
+ [255, 112, 112],
+ [255, 136, 136],
+ [255, 149, 149],
+ [255, 56, 56],
+ [255, 50, 50],
+ [255, 35, 35],
+ [255, 14, 14]],
+
+ [[255, 3, 3],
+ [255, 124, 124],
+ [255, 188, 188],
+ [255, 143, 143],
+ [255, 132, 132],
+ [255, 176, 176],
+ [255, 190, 190],
+ [255, 87, 87],
+ [255, 80, 80],
+ [255, 49, 49],
+ [255, 4, 4]],
+
+ [[255, 9, 9],
+ [255, 124, 124],
+ [255, 203, 203],
+ [255, 201, 201],
+ [255, 210, 210],
+ [255, 228, 228],
+ [255, 234, 234],
+ [255, 199, 199],
+ [255, 196, 196],
+ [255, 120, 120],
+ [255, 9, 9]],
+
+ [[255, 54, 54],
+ [255, 99, 99],
+ [255, 130, 130],
+ [255, 132, 132],
+ [255, 157, 157],
+ [255, 199, 199],
+ [255, 209, 209],
+ [255, 135, 135],
+ [255, 130, 130],
+ [255, 99, 99],
+ [255, 54, 54]],
+
+ [[255, 93, 93],
+ [255, 108, 108],
+ [255, 119, 119],
+ [255, 120, 120],
+ [255, 149, 149],
+ [255, 194, 194],
+ [255, 205, 205],
+ [255, 124, 124],
+ [255, 119, 119],
+ [255, 108, 108],
+ [255, 93, 93]],
+
+ [[255, 102, 102],
+ [255, 119, 119],
+ [255, 131, 131],
+ [255, 133, 133],
+ [255, 158, 158],
+ [255, 199, 199],
+ [255, 209, 209],
+ [255, 136, 136],
+ [255, 131, 131],
+ [255, 119, 119],
+ [255, 102, 102]],
+
+ [[255, 16, 16],
+ [255, 19, 19],
+ [255, 21, 21],
+ [255, 24, 24],
+ [255, 72, 72],
+ [255, 149, 149],
+ [255, 168, 168],
+ [255, 29, 29],
+ [255, 21, 21],
+ [255, 19, 19],
+ [255, 16, 16]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 1, 1],
+ [255, 25, 25],
+ [255, 63, 63],
+ [255, 73, 73],
+ [255, 4, 4],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 3, 3],
+ [255, 7, 7],
+ [255, 8, 8],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]]], dtype=uint8), array([[[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[212, 6, 150],
+ [212, 7, 151],
+ [212, 7, 151],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[224, 77, 180],
+ [225, 84, 183],
+ [226, 89, 185],
+ ...,
+ [225, 80, 182],
+ [223, 70, 177],
+ [221, 59, 173]],
+
+ ...,
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [218, 40, 165],
+ [219, 49, 168],
+ [221, 61, 173]],
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]]], dtype=uint8), array([[[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 2, 128, 2],
+ [ 5, 129, 5],
+ [ 4, 129, 4],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 26, 140, 26],
+ [ 63, 159, 63],
+ [ 60, 157, 60],
+ ...,
+ [ 79, 167, 79],
+ [ 64, 159, 64],
+ [ 59, 157, 59]],
+
+ ...,
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 38, 146, 38],
+ [ 55, 155, 55],
+ [ 61, 157, 61]],
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]]], dtype=uint8), array([[[ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
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+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 25, 87, 255],
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+ [ 22, 85, 255],
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+ [ 0, 69, 255],
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+ [ 63, 115, 255],
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+ [ 25, 87, 255],
+ [ 50, 105, 255],
+ [ 50, 105, 255],
+ [ 16, 80, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 53, 107, 255],
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+ [ 93, 137, 255],
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+ [122, 157, 255],
+ [ 82, 129, 255],
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+ [ 53, 107, 255]],
+
+ [[ 73, 121, 255],
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+ [201, 215, 255],
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+ [201, 215, 255],
+ [201, 215, 255],
+ [184, 203, 255],
+ [160, 185, 255]],
+
+ [[ 17, 81, 255],
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+ [202, 216, 255],
+ [155, 182, 255],
+ [ 75, 124, 255]],
+
+ [[ 93, 137, 255],
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+ [ 77, 126, 255],
+ [ 36, 95, 255],
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+ [ 47, 103, 255],
+ [178, 199, 255],
+ [198, 213, 255],
+ [176, 197, 255],
+ [176, 197, 255],
+ [204, 217, 255],
+ [148, 177, 255],
+ [ 50, 105, 255]],
+
+ [[150, 178, 255],
+ [196, 212, 255],
+ [176, 197, 255],
+ [ 68, 118, 255],
+ [125, 160, 255],
+ [184, 203, 255],
+ [214, 225, 255],
+ [179, 200, 255],
+ [154, 182, 255],
+ [108, 148, 255],
+ [ 73, 122, 255],
+ [165, 189, 255],
+ [207, 220, 255],
+ [175, 196, 255],
+ [ 77, 125, 255],
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+ [199, 215, 255],
+ [181, 201, 255],
+ [181, 201, 255],
+ [204, 218, 255],
+ [159, 185, 255],
+ [ 78, 126, 255]],
+
+ [[ 37, 96, 255],
+ [165, 189, 255],
+ [223, 231, 255],
+ [160, 186, 255],
+ [211, 223, 255],
+ [237, 242, 255],
+ [245, 248, 255],
+ [236, 241, 255],
+ [234, 239, 255],
+ [207, 219, 255],
+ [169, 192, 255],
+ [170, 193, 255],
+ [202, 216, 255],
+ [207, 220, 255],
+ [173, 195, 255],
+ [194, 211, 255],
+ [200, 215, 255],
+ [199, 214, 255],
+ [199, 214, 255],
+ [200, 215, 255],
+ [188, 206, 255],
+ [169, 193, 255]],
+
+ [[ 12, 78, 255],
+ [156, 183, 255],
+ [190, 208, 255],
+ [ 53, 108, 255],
+ [ 69, 119, 255],
+ [141, 171, 255],
+ [196, 212, 255],
+ [157, 184, 255],
+ [189, 207, 255],
+ [187, 206, 255],
+ [150, 179, 255],
+ [ 87, 133, 255],
+ [163, 188, 255],
+ [201, 215, 255],
+ [152, 180, 255],
+ [150, 179, 255],
+ [145, 175, 255],
+ [140, 171, 255],
+ [140, 171, 255],
+ [147, 176, 255],
+ [151, 179, 255],
+ [153, 180, 255]],
+
+ [[ 44, 101, 255],
+ [169, 192, 255],
+ [188, 206, 255],
+ [ 45, 102, 255],
+ [ 23, 85, 255],
+ [ 97, 140, 255],
+ [162, 187, 255],
+ [111, 150, 255],
+ [143, 174, 255],
+ [140, 171, 255],
+ [100, 142, 255],
+ [ 39, 98, 255],
+ [144, 174, 255],
+ [183, 202, 255],
+ [ 97, 140, 255],
+ [108, 148, 255],
+ [118, 155, 255],
+ [130, 164, 255],
+ [152, 180, 255],
+ [126, 161, 255],
+ [107, 147, 255],
+ [ 99, 141, 255]],
+
+ [[109, 148, 255],
+ [182, 202, 255],
+ [197, 212, 255],
+ [118, 155, 255],
+ [ 76, 124, 255],
+ [117, 155, 255],
+ [159, 185, 255],
+ [117, 154, 255],
+ [127, 162, 255],
+ [104, 145, 255],
+ [ 57, 111, 255],
+ [ 29, 91, 255],
+ [142, 173, 255],
+ [162, 187, 255],
+ [ 31, 92, 255],
+ [ 80, 128, 255],
+ [125, 160, 255],
+ [165, 189, 255],
+ [200, 215, 255],
+ [119, 155, 255],
+ [ 60, 113, 255],
+ [ 32, 92, 255]],
+
+ [[144, 173, 255],
+ [125, 160, 255],
+ [127, 162, 255],
+ [163, 187, 255],
+ [185, 204, 255],
+ [205, 218, 255],
+ [216, 226, 255],
+ [210, 222, 255],
+ [212, 223, 255],
+ [186, 204, 255],
+ [129, 163, 255],
+ [ 40, 99, 255],
+ [142, 173, 255],
+ [154, 181, 255],
+ [ 9, 76, 255],
+ [ 91, 135, 255],
+ [173, 195, 255],
+ [209, 221, 255],
+ [145, 175, 255],
+ [ 53, 108, 255],
+ [ 9, 76, 255],
+ [ 5, 73, 255]],
+
+ [[ 62, 114, 255],
+ [ 47, 103, 255],
+ [ 47, 103, 255],
+ [ 70, 120, 255],
+ [ 84, 130, 255],
+ [ 91, 135, 255],
+ [ 93, 137, 255],
+ [ 93, 137, 255],
+ [ 93, 137, 255],
+ [ 82, 129, 255],
+ [ 59, 112, 255],
+ [ 18, 82, 255],
+ [ 60, 113, 255],
+ [ 65, 116, 255],
+ [ 2, 71, 255],
+ [ 38, 97, 255],
+ [ 74, 123, 255],
+ [ 89, 134, 255],
+ [ 55, 109, 255],
+ [ 17, 81, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 1, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 3, 71, 255],
+ [ 3, 71, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]]], dtype=uint8)],([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255]),{'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []},{'line_thickness': 1, 'boxLine_thickness': 1, 'fontSize': 0.4, 'waterLineColor': (0, 255, 255), 'segLineShow': False, 'waterLineWidth': 1, 'wordSize': 8, 'label_location': 'leftTop'},{'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4},others,None) @ aiHelper.py:101 in AI_process
+ 13:36:09.801 [ERROR] - 算法模型分析异常：Traceback (most recent call last):
+ File "/home/thsw/chenbw/DrGraph/appIOs/conf/ModelUtils.py", line 151, in model_process
+ return aiHelper.AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
+ File "/home/thsw/chenbw/DrGraph/DrUtils/aiHelper.py", line 138, in AI_process
+ half,device,conf_thres,iou_thres,allowedList = objectPar['half'],objectPar['device'],objectPar['conf_thres'],objectPar['iou_thres'],objectPar['allowedList']
+KeyError: 'allowedList'
+, requestId:1234 @ ModelUtils.py:160 in model_process
+ 13:36:09.802 [ERROR] - 异常编码：SP018, 异常描述：算法模型分析异常! @ ModelUtils.py:44 in __str__
+ 13:36:46.828 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 13:36:46.848 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:55 in __init__
+ 13:36:46.877 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 13:36:46.878 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:65 in __init__
+ 13:36:47.039 [INFO] - 模型初始化时间：0.1906752586364746, requestId:1234 @ ModelUtils.py:107 in __init__
+ 13:36:47.040 [INFO] - 模型编号: 019
+模型参数: ( at 0x7fa043e9e5f0>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:52 in
+ 13:36:47.040 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:290 in get_label_arraylist
+ 13:36:47.044 [INFO] - model_process([None], model=,segmodel=None,names=['车', 'T角点', 'L角点', '违停'],[array([[[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 15, 15],
+ [255, 48, 48],
+ [255, 61, 61],
+ [255, 33, 33],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 34, 34],
+ [255, 50, 50],
+ [255, 83, 83],
+ [255, 129, 129],
+ [255, 143, 143],
+ [255, 104, 104],
+ [255, 61, 61],
+ [255, 61, 61],
+ [255, 61, 61],
+ [255, 48, 48],
+ [255, 29, 29]],
+
+ [[255, 104, 104],
+ [255, 154, 154],
+ [255, 203, 203],
+ [255, 232, 232],
+ [255, 222, 222],
+ [255, 205, 205],
+ [255, 189, 189],
+ [255, 186, 186],
+ [255, 186, 186],
+ [255, 145, 145],
+ [255, 87, 87]],
+
+ [[255, 48, 48],
+ [255, 104, 104],
+ [255, 168, 168],
+ [255, 217, 217],
+ [255, 141, 141],
+ [255, 134, 134],
+ [255, 137, 137],
+ [255, 90, 90],
+ [255, 87, 87],
+ [255, 68, 68],
+ [255, 40, 40]],
+
+ [[255, 16, 16],
+ [255, 106, 106],
+ [255, 170, 170],
+ [255, 168, 168],
+ [255, 112, 112],
+ [255, 136, 136],
+ [255, 149, 149],
+ [255, 56, 56],
+ [255, 50, 50],
+ [255, 35, 35],
+ [255, 14, 14]],
+
+ [[255, 3, 3],
+ [255, 124, 124],
+ [255, 188, 188],
+ [255, 143, 143],
+ [255, 132, 132],
+ [255, 176, 176],
+ [255, 190, 190],
+ [255, 87, 87],
+ [255, 80, 80],
+ [255, 49, 49],
+ [255, 4, 4]],
+
+ [[255, 9, 9],
+ [255, 124, 124],
+ [255, 203, 203],
+ [255, 201, 201],
+ [255, 210, 210],
+ [255, 228, 228],
+ [255, 234, 234],
+ [255, 199, 199],
+ [255, 196, 196],
+ [255, 120, 120],
+ [255, 9, 9]],
+
+ [[255, 54, 54],
+ [255, 99, 99],
+ [255, 130, 130],
+ [255, 132, 132],
+ [255, 157, 157],
+ [255, 199, 199],
+ [255, 209, 209],
+ [255, 135, 135],
+ [255, 130, 130],
+ [255, 99, 99],
+ [255, 54, 54]],
+
+ [[255, 93, 93],
+ [255, 108, 108],
+ [255, 119, 119],
+ [255, 120, 120],
+ [255, 149, 149],
+ [255, 194, 194],
+ [255, 205, 205],
+ [255, 124, 124],
+ [255, 119, 119],
+ [255, 108, 108],
+ [255, 93, 93]],
+
+ [[255, 102, 102],
+ [255, 119, 119],
+ [255, 131, 131],
+ [255, 133, 133],
+ [255, 158, 158],
+ [255, 199, 199],
+ [255, 209, 209],
+ [255, 136, 136],
+ [255, 131, 131],
+ [255, 119, 119],
+ [255, 102, 102]],
+
+ [[255, 16, 16],
+ [255, 19, 19],
+ [255, 21, 21],
+ [255, 24, 24],
+ [255, 72, 72],
+ [255, 149, 149],
+ [255, 168, 168],
+ [255, 29, 29],
+ [255, 21, 21],
+ [255, 19, 19],
+ [255, 16, 16]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 1, 1],
+ [255, 25, 25],
+ [255, 63, 63],
+ [255, 73, 73],
+ [255, 4, 4],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 3, 3],
+ [255, 7, 7],
+ [255, 8, 8],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]]], dtype=uint8), array([[[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[212, 6, 150],
+ [212, 7, 151],
+ [212, 7, 151],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[224, 77, 180],
+ [225, 84, 183],
+ [226, 89, 185],
+ ...,
+ [225, 80, 182],
+ [223, 70, 177],
+ [221, 59, 173]],
+
+ ...,
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [218, 40, 165],
+ [219, 49, 168],
+ [221, 61, 173]],
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]]], dtype=uint8), array([[[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 2, 128, 2],
+ [ 5, 129, 5],
+ [ 4, 129, 4],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 26, 140, 26],
+ [ 63, 159, 63],
+ [ 60, 157, 60],
+ ...,
+ [ 79, 167, 79],
+ [ 64, 159, 64],
+ [ 59, 157, 59]],
+
+ ...,
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 38, 146, 38],
+ [ 55, 155, 55],
+ [ 61, 157, 61]],
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]]], dtype=uint8), array([[[ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 25, 87, 255],
+ [ 30, 90, 255],
+ [ 22, 85, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 36, 95, 255],
+ [ 63, 115, 255],
+ [ 33, 93, 255],
+ [ 10, 76, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
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+ [ 6, 73, 255],
+ [ 25, 87, 255],
+ [ 50, 105, 255],
+ [ 50, 105, 255],
+ [ 16, 80, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 53, 107, 255],
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+ [ 90, 135, 255],
+ [ 49, 104, 255],
+ [ 67, 118, 255],
+ [118, 155, 255],
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+ [ 80, 127, 255],
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+ [ 93, 137, 255],
+ [122, 158, 255],
+ [122, 157, 255],
+ [ 82, 129, 255],
+ [ 59, 112, 255],
+ [ 53, 107, 255]],
+
+ [[ 73, 121, 255],
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+ [190, 208, 255],
+ [201, 215, 255],
+ [201, 215, 255],
+ [201, 215, 255],
+ [201, 215, 255],
+ [184, 203, 255],
+ [160, 185, 255]],
+
+ [[ 17, 81, 255],
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+ [ 66, 117, 255],
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+ [152, 180, 255],
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+ [ 73, 122, 255],
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+ [197, 213, 255],
+ [182, 201, 255],
+ [182, 201, 255],
+ [202, 216, 255],
+ [155, 182, 255],
+ [ 75, 124, 255]],
+
+ [[ 93, 137, 255],
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+ [ 40, 98, 255],
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+ [ 77, 126, 255],
+ [ 36, 95, 255],
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+ [166, 190, 255],
+ [ 47, 103, 255],
+ [178, 199, 255],
+ [198, 213, 255],
+ [176, 197, 255],
+ [176, 197, 255],
+ [204, 217, 255],
+ [148, 177, 255],
+ [ 50, 105, 255]],
+
+ [[150, 178, 255],
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+ [176, 197, 255],
+ [ 68, 118, 255],
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+ [214, 225, 255],
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+ [199, 215, 255],
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+ [181, 201, 255],
+ [204, 218, 255],
+ [159, 185, 255],
+ [ 78, 126, 255]],
+
+ [[ 37, 96, 255],
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+ [207, 220, 255],
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+ [194, 211, 255],
+ [200, 215, 255],
+ [199, 214, 255],
+ [199, 214, 255],
+ [200, 215, 255],
+ [188, 206, 255],
+ [169, 193, 255]],
+
+ [[ 12, 78, 255],
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+ [190, 208, 255],
+ [ 53, 108, 255],
+ [ 69, 119, 255],
+ [141, 171, 255],
+ [196, 212, 255],
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+ [189, 207, 255],
+ [187, 206, 255],
+ [150, 179, 255],
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+ [163, 188, 255],
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+ [152, 180, 255],
+ [150, 179, 255],
+ [145, 175, 255],
+ [140, 171, 255],
+ [140, 171, 255],
+ [147, 176, 255],
+ [151, 179, 255],
+ [153, 180, 255]],
+
+ [[ 44, 101, 255],
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+ [188, 206, 255],
+ [ 45, 102, 255],
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+ [ 97, 140, 255],
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+ [ 97, 140, 255],
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+ [118, 155, 255],
+ [130, 164, 255],
+ [152, 180, 255],
+ [126, 161, 255],
+ [107, 147, 255],
+ [ 99, 141, 255]],
+
+ [[109, 148, 255],
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+ [197, 212, 255],
+ [118, 155, 255],
+ [ 76, 124, 255],
+ [117, 155, 255],
+ [159, 185, 255],
+ [117, 154, 255],
+ [127, 162, 255],
+ [104, 145, 255],
+ [ 57, 111, 255],
+ [ 29, 91, 255],
+ [142, 173, 255],
+ [162, 187, 255],
+ [ 31, 92, 255],
+ [ 80, 128, 255],
+ [125, 160, 255],
+ [165, 189, 255],
+ [200, 215, 255],
+ [119, 155, 255],
+ [ 60, 113, 255],
+ [ 32, 92, 255]],
+
+ [[144, 173, 255],
+ [125, 160, 255],
+ [127, 162, 255],
+ [163, 187, 255],
+ [185, 204, 255],
+ [205, 218, 255],
+ [216, 226, 255],
+ [210, 222, 255],
+ [212, 223, 255],
+ [186, 204, 255],
+ [129, 163, 255],
+ [ 40, 99, 255],
+ [142, 173, 255],
+ [154, 181, 255],
+ [ 9, 76, 255],
+ [ 91, 135, 255],
+ [173, 195, 255],
+ [209, 221, 255],
+ [145, 175, 255],
+ [ 53, 108, 255],
+ [ 9, 76, 255],
+ [ 5, 73, 255]],
+
+ [[ 62, 114, 255],
+ [ 47, 103, 255],
+ [ 47, 103, 255],
+ [ 70, 120, 255],
+ [ 84, 130, 255],
+ [ 91, 135, 255],
+ [ 93, 137, 255],
+ [ 93, 137, 255],
+ [ 93, 137, 255],
+ [ 82, 129, 255],
+ [ 59, 112, 255],
+ [ 18, 82, 255],
+ [ 60, 113, 255],
+ [ 65, 116, 255],
+ [ 2, 71, 255],
+ [ 38, 97, 255],
+ [ 74, 123, 255],
+ [ 89, 134, 255],
+ [ 55, 109, 255],
+ [ 17, 81, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 1, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 3, 71, 255],
+ [ 3, 71, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]]], dtype=uint8)],([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255]),{'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []},{'line_thickness': 1, 'boxLine_thickness': 1, 'fontSize': 0.4, 'waterLineColor': (0, 255, 255), 'segLineShow': False, 'waterLineWidth': 1, 'wordSize': 8, 'label_location': 'leftTop'},{'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4},others,None) @ aiHelper.py:101 in AI_process
+ 13:36:47.050 [ERROR] - 算法模型分析异常：Traceback (most recent call last):
+ File "/home/thsw/chenbw/DrGraph/appIOs/conf/ModelUtils.py", line 151, in model_process
+ return aiHelper.AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
+ File "/home/thsw/chenbw/DrGraph/DrUtils/aiHelper.py", line 138, in AI_process
+ half,device,conf_thres,iou_thres,allowedList = objectPar['half'],objectPar['device'],objectPar['conf_thres'],objectPar['iou_thres'],objectPar['allowedList']
+KeyError: 'allowedList'
+, requestId:1234 @ ModelUtils.py:160 in model_process
+ 13:36:47.051 [ERROR] - 异常编码：SP018, 异常描述：算法模型分析异常! @ ModelUtils.py:44 in __str__
+ 13:37:14.981 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 13:37:15.026 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:55 in __init__
+ 13:37:15.050 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 13:37:15.051 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:65 in __init__
+ 13:37:15.202 [INFO] - 模型初始化时间：0.17597317695617676, requestId:1234 @ ModelUtils.py:107 in __init__
+ 13:37:15.202 [INFO] - 模型编号: 019
+模型参数: ( at 0x7f604b9f2680>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:52 in
+ 13:37:15.203 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:290 in get_label_arraylist
+ 13:37:15.206 [INFO] - model_process(
+
im0s=[None],
+
model=,
+
segmodel=None,
+
names=['车', 'T角点', 'L角点', '违停'],[array([[[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 15, 15],
+ [255, 48, 48],
+ [255, 61, 61],
+ [255, 33, 33],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 34, 34],
+ [255, 50, 50],
+ [255, 83, 83],
+ [255, 129, 129],
+ [255, 143, 143],
+ [255, 104, 104],
+ [255, 61, 61],
+ [255, 61, 61],
+ [255, 61, 61],
+ [255, 48, 48],
+ [255, 29, 29]],
+
+ [[255, 104, 104],
+ [255, 154, 154],
+ [255, 203, 203],
+ [255, 232, 232],
+ [255, 222, 222],
+ [255, 205, 205],
+ [255, 189, 189],
+ [255, 186, 186],
+ [255, 186, 186],
+ [255, 145, 145],
+ [255, 87, 87]],
+
+ [[255, 48, 48],
+ [255, 104, 104],
+ [255, 168, 168],
+ [255, 217, 217],
+ [255, 141, 141],
+ [255, 134, 134],
+ [255, 137, 137],
+ [255, 90, 90],
+ [255, 87, 87],
+ [255, 68, 68],
+ [255, 40, 40]],
+
+ [[255, 16, 16],
+ [255, 106, 106],
+ [255, 170, 170],
+ [255, 168, 168],
+ [255, 112, 112],
+ [255, 136, 136],
+ [255, 149, 149],
+ [255, 56, 56],
+ [255, 50, 50],
+ [255, 35, 35],
+ [255, 14, 14]],
+
+ [[255, 3, 3],
+ [255, 124, 124],
+ [255, 188, 188],
+ [255, 143, 143],
+ [255, 132, 132],
+ [255, 176, 176],
+ [255, 190, 190],
+ [255, 87, 87],
+ [255, 80, 80],
+ [255, 49, 49],
+ [255, 4, 4]],
+
+ [[255, 9, 9],
+ [255, 124, 124],
+ [255, 203, 203],
+ [255, 201, 201],
+ [255, 210, 210],
+ [255, 228, 228],
+ [255, 234, 234],
+ [255, 199, 199],
+ [255, 196, 196],
+ [255, 120, 120],
+ [255, 9, 9]],
+
+ [[255, 54, 54],
+ [255, 99, 99],
+ [255, 130, 130],
+ [255, 132, 132],
+ [255, 157, 157],
+ [255, 199, 199],
+ [255, 209, 209],
+ [255, 135, 135],
+ [255, 130, 130],
+ [255, 99, 99],
+ [255, 54, 54]],
+
+ [[255, 93, 93],
+ [255, 108, 108],
+ [255, 119, 119],
+ [255, 120, 120],
+ [255, 149, 149],
+ [255, 194, 194],
+ [255, 205, 205],
+ [255, 124, 124],
+ [255, 119, 119],
+ [255, 108, 108],
+ [255, 93, 93]],
+
+ [[255, 102, 102],
+ [255, 119, 119],
+ [255, 131, 131],
+ [255, 133, 133],
+ [255, 158, 158],
+ [255, 199, 199],
+ [255, 209, 209],
+ [255, 136, 136],
+ [255, 131, 131],
+ [255, 119, 119],
+ [255, 102, 102]],
+
+ [[255, 16, 16],
+ [255, 19, 19],
+ [255, 21, 21],
+ [255, 24, 24],
+ [255, 72, 72],
+ [255, 149, 149],
+ [255, 168, 168],
+ [255, 29, 29],
+ [255, 21, 21],
+ [255, 19, 19],
+ [255, 16, 16]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 1, 1],
+ [255, 25, 25],
+ [255, 63, 63],
+ [255, 73, 73],
+ [255, 4, 4],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 3, 3],
+ [255, 7, 7],
+ [255, 8, 8],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]]], dtype=uint8), array([[[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[212, 6, 150],
+ [212, 7, 151],
+ [212, 7, 151],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[224, 77, 180],
+ [225, 84, 183],
+ [226, 89, 185],
+ ...,
+ [225, 80, 182],
+ [223, 70, 177],
+ [221, 59, 173]],
+
+ ...,
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [218, 40, 165],
+ [219, 49, 168],
+ [221, 61, 173]],
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]]], dtype=uint8), array([[[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 2, 128, 2],
+ [ 5, 129, 5],
+ [ 4, 129, 4],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 26, 140, 26],
+ [ 63, 159, 63],
+ [ 60, 157, 60],
+ ...,
+ [ 79, 167, 79],
+ [ 64, 159, 64],
+ [ 59, 157, 59]],
+
+ ...,
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 38, 146, 38],
+ [ 55, 155, 55],
+ [ 61, 157, 61]],
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]]], dtype=uint8), array([[[ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 25, 87, 255],
+ [ 30, 90, 255],
+ [ 22, 85, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 36, 95, 255],
+ [ 63, 115, 255],
+ [ 33, 93, 255],
+ [ 10, 76, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 34, 94, 255],
+ [ 47, 103, 255],
+ [ 24, 86, 255],
+ [ 6, 73, 255],
+ [ 25, 87, 255],
+ [ 50, 105, 255],
+ [ 50, 105, 255],
+ [ 16, 80, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 53, 107, 255],
+ [ 87, 132, 255],
+ [ 90, 135, 255],
+ [ 49, 104, 255],
+ [ 67, 118, 255],
+ [118, 155, 255],
+ [151, 179, 255],
+ [113, 151, 255],
+ [ 85, 131, 255],
+ [ 62, 114, 255],
+ [ 41, 99, 255],
+ [ 7, 74, 255],
+ [ 83, 129, 255],
+ [121, 157, 255],
+ [ 80, 127, 255],
+ [ 67, 118, 255],
+ [ 93, 137, 255],
+ [122, 158, 255],
+ [122, 157, 255],
+ [ 82, 129, 255],
+ [ 59, 112, 255],
+ [ 53, 107, 255]],
+
+ [[ 73, 121, 255],
+ [160, 185, 255],
+ [196, 212, 255],
+ [147, 177, 255],
+ [205, 218, 255],
+ [235, 240, 255],
+ [243, 246, 255],
+ [233, 239, 255],
+ [226, 233, 255],
+ [190, 208, 255],
+ [124, 160, 255],
+ [ 24, 86, 255],
+ [138, 169, 255],
+ [206, 220, 255],
+ [158, 184, 255],
+ [190, 208, 255],
+ [201, 215, 255],
+ [201, 215, 255],
+ [201, 215, 255],
+ [201, 215, 255],
+ [184, 203, 255],
+ [160, 185, 255]],
+
+ [[ 17, 81, 255],
+ [ 66, 117, 255],
+ [ 88, 133, 255],
+ [ 66, 117, 255],
+ [123, 159, 255],
+ [183, 203, 255],
+ [214, 225, 255],
+ [178, 199, 255],
+ [152, 180, 255],
+ [104, 146, 255],
+ [ 52, 107, 255],
+ [ 53, 108, 255],
+ [156, 183, 255],
+ [176, 197, 255],
+ [ 73, 122, 255],
+ [179, 200, 255],
+ [197, 213, 255],
+ [182, 201, 255],
+ [182, 201, 255],
+ [202, 216, 255],
+ [155, 182, 255],
+ [ 75, 124, 255]],
+
+ [[ 93, 137, 255],
+ [121, 157, 255],
+ [108, 147, 255],
+ [ 40, 98, 255],
+ [ 98, 141, 255],
+ [167, 191, 255],
+ [205, 218, 255],
+ [162, 187, 255],
+ [130, 164, 255],
+ [ 77, 126, 255],
+ [ 36, 95, 255],
+ [113, 152, 255],
+ [185, 204, 255],
+ [166, 190, 255],
+ [ 47, 103, 255],
+ [178, 199, 255],
+ [198, 213, 255],
+ [176, 197, 255],
+ [176, 197, 255],
+ [204, 217, 255],
+ [148, 177, 255],
+ [ 50, 105, 255]],
+
+ [[150, 178, 255],
+ [196, 212, 255],
+ [176, 197, 255],
+ [ 68, 118, 255],
+ [125, 160, 255],
+ [184, 203, 255],
+ [214, 225, 255],
+ [179, 200, 255],
+ [154, 182, 255],
+ [108, 148, 255],
+ [ 73, 122, 255],
+ [165, 189, 255],
+ [207, 220, 255],
+ [175, 196, 255],
+ [ 77, 125, 255],
+ [184, 203, 255],
+ [199, 215, 255],
+ [181, 201, 255],
+ [181, 201, 255],
+ [204, 218, 255],
+ [159, 185, 255],
+ [ 78, 126, 255]],
+
+ [[ 37, 96, 255],
+ [165, 189, 255],
+ [223, 231, 255],
+ [160, 186, 255],
+ [211, 223, 255],
+ [237, 242, 255],
+ [245, 248, 255],
+ [236, 241, 255],
+ [234, 239, 255],
+ [207, 219, 255],
+ [169, 192, 255],
+ [170, 193, 255],
+ [202, 216, 255],
+ [207, 220, 255],
+ [173, 195, 255],
+ [194, 211, 255],
+ [200, 215, 255],
+ [199, 214, 255],
+ [199, 214, 255],
+ [200, 215, 255],
+ [188, 206, 255],
+ [169, 193, 255]],
+
+ [[ 12, 78, 255],
+ [156, 183, 255],
+ [190, 208, 255],
+ [ 53, 108, 255],
+ [ 69, 119, 255],
+ [141, 171, 255],
+ [196, 212, 255],
+ [157, 184, 255],
+ [189, 207, 255],
+ [187, 206, 255],
+ [150, 179, 255],
+ [ 87, 133, 255],
+ [163, 188, 255],
+ [201, 215, 255],
+ [152, 180, 255],
+ [150, 179, 255],
+ [145, 175, 255],
+ [140, 171, 255],
+ [140, 171, 255],
+ [147, 176, 255],
+ [151, 179, 255],
+ [153, 180, 255]],
+
+ [[ 44, 101, 255],
+ [169, 192, 255],
+ [188, 206, 255],
+ [ 45, 102, 255],
+ [ 23, 85, 255],
+ [ 97, 140, 255],
+ [162, 187, 255],
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+ [140, 171, 255],
+ [100, 142, 255],
+ [ 39, 98, 255],
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+ [183, 202, 255],
+ [ 97, 140, 255],
+ [108, 148, 255],
+ [118, 155, 255],
+ [130, 164, 255],
+ [152, 180, 255],
+ [126, 161, 255],
+ [107, 147, 255],
+ [ 99, 141, 255]],
+
+ [[109, 148, 255],
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+ [197, 212, 255],
+ [118, 155, 255],
+ [ 76, 124, 255],
+ [117, 155, 255],
+ [159, 185, 255],
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+ [127, 162, 255],
+ [104, 145, 255],
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+ [ 31, 92, 255],
+ [ 80, 128, 255],
+ [125, 160, 255],
+ [165, 189, 255],
+ [200, 215, 255],
+ [119, 155, 255],
+ [ 60, 113, 255],
+ [ 32, 92, 255]],
+
+ [[144, 173, 255],
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+ [163, 187, 255],
+ [185, 204, 255],
+ [205, 218, 255],
+ [216, 226, 255],
+ [210, 222, 255],
+ [212, 223, 255],
+ [186, 204, 255],
+ [129, 163, 255],
+ [ 40, 99, 255],
+ [142, 173, 255],
+ [154, 181, 255],
+ [ 9, 76, 255],
+ [ 91, 135, 255],
+ [173, 195, 255],
+ [209, 221, 255],
+ [145, 175, 255],
+ [ 53, 108, 255],
+ [ 9, 76, 255],
+ [ 5, 73, 255]],
+
+ [[ 62, 114, 255],
+ [ 47, 103, 255],
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+ [ 70, 120, 255],
+ [ 84, 130, 255],
+ [ 91, 135, 255],
+ [ 93, 137, 255],
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+ [ 93, 137, 255],
+ [ 82, 129, 255],
+ [ 59, 112, 255],
+ [ 18, 82, 255],
+ [ 60, 113, 255],
+ [ 65, 116, 255],
+ [ 2, 71, 255],
+ [ 38, 97, 255],
+ [ 74, 123, 255],
+ [ 89, 134, 255],
+ [ 55, 109, 255],
+ [ 17, 81, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 1, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 3, 71, 255],
+ [ 3, 71, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]]], dtype=uint8)],([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255]),{'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []},{'line_thickness': 1, 'boxLine_thickness': 1, 'fontSize': 0.4, 'waterLineColor': (0, 255, 255), 'segLineShow': False, 'waterLineWidth': 1, 'wordSize': 8, 'label_location': 'leftTop'},{'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4},others,None) @ aiHelper.py:101 in AI_process
+ 13:37:15.210 [ERROR] - 算法模型分析异常：Traceback (most recent call last):
+ File "/home/thsw/chenbw/DrGraph/appIOs/conf/ModelUtils.py", line 151, in model_process
+ return aiHelper.AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
+ File "/home/thsw/chenbw/DrGraph/DrUtils/aiHelper.py", line 138, in AI_process
+ half,device,conf_thres,iou_thres,allowedList = objectPar['half'],objectPar['device'],objectPar['conf_thres'],objectPar['iou_thres'],objectPar['allowedList']
+KeyError: 'allowedList'
+, requestId:1234 @ ModelUtils.py:160 in model_process
+ 13:37:15.211 [ERROR] - 异常编码：SP018, 异常描述：算法模型分析异常! @ ModelUtils.py:44 in __str__
+ 13:38:03.395 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 13:38:03.416 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:55 in __init__
+ 13:38:03.443 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 13:38:03.444 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:65 in __init__
+ 13:38:03.596 [INFO] - 模型初始化时间：0.1793203353881836, requestId:1234 @ ModelUtils.py:107 in __init__
+ 13:38:03.596 [INFO] - 模型编号: 019
+模型参数: ( at 0x7f9367c56680>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:52 in
+ 13:38:03.597 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:290 in get_label_arraylist
+ 13:38:03.600 [INFO] - model_process(
+
im0s=[None],
+
model=,
+
segmodel=None,
+
names=['车', 'T角点', 'L角点', '违停'],[array([[[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 15, 15],
+ [255, 48, 48],
+ [255, 61, 61],
+ [255, 33, 33],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 34, 34],
+ [255, 50, 50],
+ [255, 83, 83],
+ [255, 129, 129],
+ [255, 143, 143],
+ [255, 104, 104],
+ [255, 61, 61],
+ [255, 61, 61],
+ [255, 61, 61],
+ [255, 48, 48],
+ [255, 29, 29]],
+
+ [[255, 104, 104],
+ [255, 154, 154],
+ [255, 203, 203],
+ [255, 232, 232],
+ [255, 222, 222],
+ [255, 205, 205],
+ [255, 189, 189],
+ [255, 186, 186],
+ [255, 186, 186],
+ [255, 145, 145],
+ [255, 87, 87]],
+
+ [[255, 48, 48],
+ [255, 104, 104],
+ [255, 168, 168],
+ [255, 217, 217],
+ [255, 141, 141],
+ [255, 134, 134],
+ [255, 137, 137],
+ [255, 90, 90],
+ [255, 87, 87],
+ [255, 68, 68],
+ [255, 40, 40]],
+
+ [[255, 16, 16],
+ [255, 106, 106],
+ [255, 170, 170],
+ [255, 168, 168],
+ [255, 112, 112],
+ [255, 136, 136],
+ [255, 149, 149],
+ [255, 56, 56],
+ [255, 50, 50],
+ [255, 35, 35],
+ [255, 14, 14]],
+
+ [[255, 3, 3],
+ [255, 124, 124],
+ [255, 188, 188],
+ [255, 143, 143],
+ [255, 132, 132],
+ [255, 176, 176],
+ [255, 190, 190],
+ [255, 87, 87],
+ [255, 80, 80],
+ [255, 49, 49],
+ [255, 4, 4]],
+
+ [[255, 9, 9],
+ [255, 124, 124],
+ [255, 203, 203],
+ [255, 201, 201],
+ [255, 210, 210],
+ [255, 228, 228],
+ [255, 234, 234],
+ [255, 199, 199],
+ [255, 196, 196],
+ [255, 120, 120],
+ [255, 9, 9]],
+
+ [[255, 54, 54],
+ [255, 99, 99],
+ [255, 130, 130],
+ [255, 132, 132],
+ [255, 157, 157],
+ [255, 199, 199],
+ [255, 209, 209],
+ [255, 135, 135],
+ [255, 130, 130],
+ [255, 99, 99],
+ [255, 54, 54]],
+
+ [[255, 93, 93],
+ [255, 108, 108],
+ [255, 119, 119],
+ [255, 120, 120],
+ [255, 149, 149],
+ [255, 194, 194],
+ [255, 205, 205],
+ [255, 124, 124],
+ [255, 119, 119],
+ [255, 108, 108],
+ [255, 93, 93]],
+
+ [[255, 102, 102],
+ [255, 119, 119],
+ [255, 131, 131],
+ [255, 133, 133],
+ [255, 158, 158],
+ [255, 199, 199],
+ [255, 209, 209],
+ [255, 136, 136],
+ [255, 131, 131],
+ [255, 119, 119],
+ [255, 102, 102]],
+
+ [[255, 16, 16],
+ [255, 19, 19],
+ [255, 21, 21],
+ [255, 24, 24],
+ [255, 72, 72],
+ [255, 149, 149],
+ [255, 168, 168],
+ [255, 29, 29],
+ [255, 21, 21],
+ [255, 19, 19],
+ [255, 16, 16]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 1, 1],
+ [255, 25, 25],
+ [255, 63, 63],
+ [255, 73, 73],
+ [255, 4, 4],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 3, 3],
+ [255, 7, 7],
+ [255, 8, 8],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]]], dtype=uint8), array([[[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[212, 6, 150],
+ [212, 7, 151],
+ [212, 7, 151],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[224, 77, 180],
+ [225, 84, 183],
+ [226, 89, 185],
+ ...,
+ [225, 80, 182],
+ [223, 70, 177],
+ [221, 59, 173]],
+
+ ...,
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [218, 40, 165],
+ [219, 49, 168],
+ [221, 61, 173]],
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]]], dtype=uint8), array([[[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 2, 128, 2],
+ [ 5, 129, 5],
+ [ 4, 129, 4],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 26, 140, 26],
+ [ 63, 159, 63],
+ [ 60, 157, 60],
+ ...,
+ [ 79, 167, 79],
+ [ 64, 159, 64],
+ [ 59, 157, 59]],
+
+ ...,
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 38, 146, 38],
+ [ 55, 155, 55],
+ [ 61, 157, 61]],
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]]], dtype=uint8), array([[[ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 25, 87, 255],
+ [ 30, 90, 255],
+ [ 22, 85, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 36, 95, 255],
+ [ 63, 115, 255],
+ [ 33, 93, 255],
+ [ 10, 76, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 34, 94, 255],
+ [ 47, 103, 255],
+ [ 24, 86, 255],
+ [ 6, 73, 255],
+ [ 25, 87, 255],
+ [ 50, 105, 255],
+ [ 50, 105, 255],
+ [ 16, 80, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 53, 107, 255],
+ [ 87, 132, 255],
+ [ 90, 135, 255],
+ [ 49, 104, 255],
+ [ 67, 118, 255],
+ [118, 155, 255],
+ [151, 179, 255],
+ [113, 151, 255],
+ [ 85, 131, 255],
+ [ 62, 114, 255],
+ [ 41, 99, 255],
+ [ 7, 74, 255],
+ [ 83, 129, 255],
+ [121, 157, 255],
+ [ 80, 127, 255],
+ [ 67, 118, 255],
+ [ 93, 137, 255],
+ [122, 158, 255],
+ [122, 157, 255],
+ [ 82, 129, 255],
+ [ 59, 112, 255],
+ [ 53, 107, 255]],
+
+ [[ 73, 121, 255],
+ [160, 185, 255],
+ [196, 212, 255],
+ [147, 177, 255],
+ [205, 218, 255],
+ [235, 240, 255],
+ [243, 246, 255],
+ [233, 239, 255],
+ [226, 233, 255],
+ [190, 208, 255],
+ [124, 160, 255],
+ [ 24, 86, 255],
+ [138, 169, 255],
+ [206, 220, 255],
+ [158, 184, 255],
+ [190, 208, 255],
+ [201, 215, 255],
+ [201, 215, 255],
+ [201, 215, 255],
+ [201, 215, 255],
+ [184, 203, 255],
+ [160, 185, 255]],
+
+ [[ 17, 81, 255],
+ [ 66, 117, 255],
+ [ 88, 133, 255],
+ [ 66, 117, 255],
+ [123, 159, 255],
+ [183, 203, 255],
+ [214, 225, 255],
+ [178, 199, 255],
+ [152, 180, 255],
+ [104, 146, 255],
+ [ 52, 107, 255],
+ [ 53, 108, 255],
+ [156, 183, 255],
+ [176, 197, 255],
+ [ 73, 122, 255],
+ [179, 200, 255],
+ [197, 213, 255],
+ [182, 201, 255],
+ [182, 201, 255],
+ [202, 216, 255],
+ [155, 182, 255],
+ [ 75, 124, 255]],
+
+ [[ 93, 137, 255],
+ [121, 157, 255],
+ [108, 147, 255],
+ [ 40, 98, 255],
+ [ 98, 141, 255],
+ [167, 191, 255],
+ [205, 218, 255],
+ [162, 187, 255],
+ [130, 164, 255],
+ [ 77, 126, 255],
+ [ 36, 95, 255],
+ [113, 152, 255],
+ [185, 204, 255],
+ [166, 190, 255],
+ [ 47, 103, 255],
+ [178, 199, 255],
+ [198, 213, 255],
+ [176, 197, 255],
+ [176, 197, 255],
+ [204, 217, 255],
+ [148, 177, 255],
+ [ 50, 105, 255]],
+
+ [[150, 178, 255],
+ [196, 212, 255],
+ [176, 197, 255],
+ [ 68, 118, 255],
+ [125, 160, 255],
+ [184, 203, 255],
+ [214, 225, 255],
+ [179, 200, 255],
+ [154, 182, 255],
+ [108, 148, 255],
+ [ 73, 122, 255],
+ [165, 189, 255],
+ [207, 220, 255],
+ [175, 196, 255],
+ [ 77, 125, 255],
+ [184, 203, 255],
+ [199, 215, 255],
+ [181, 201, 255],
+ [181, 201, 255],
+ [204, 218, 255],
+ [159, 185, 255],
+ [ 78, 126, 255]],
+
+ [[ 37, 96, 255],
+ [165, 189, 255],
+ [223, 231, 255],
+ [160, 186, 255],
+ [211, 223, 255],
+ [237, 242, 255],
+ [245, 248, 255],
+ [236, 241, 255],
+ [234, 239, 255],
+ [207, 219, 255],
+ [169, 192, 255],
+ [170, 193, 255],
+ [202, 216, 255],
+ [207, 220, 255],
+ [173, 195, 255],
+ [194, 211, 255],
+ [200, 215, 255],
+ [199, 214, 255],
+ [199, 214, 255],
+ [200, 215, 255],
+ [188, 206, 255],
+ [169, 193, 255]],
+
+ [[ 12, 78, 255],
+ [156, 183, 255],
+ [190, 208, 255],
+ [ 53, 108, 255],
+ [ 69, 119, 255],
+ [141, 171, 255],
+ [196, 212, 255],
+ [157, 184, 255],
+ [189, 207, 255],
+ [187, 206, 255],
+ [150, 179, 255],
+ [ 87, 133, 255],
+ [163, 188, 255],
+ [201, 215, 255],
+ [152, 180, 255],
+ [150, 179, 255],
+ [145, 175, 255],
+ [140, 171, 255],
+ [140, 171, 255],
+ [147, 176, 255],
+ [151, 179, 255],
+ [153, 180, 255]],
+
+ [[ 44, 101, 255],
+ [169, 192, 255],
+ [188, 206, 255],
+ [ 45, 102, 255],
+ [ 23, 85, 255],
+ [ 97, 140, 255],
+ [162, 187, 255],
+ [111, 150, 255],
+ [143, 174, 255],
+ [140, 171, 255],
+ [100, 142, 255],
+ [ 39, 98, 255],
+ [144, 174, 255],
+ [183, 202, 255],
+ [ 97, 140, 255],
+ [108, 148, 255],
+ [118, 155, 255],
+ [130, 164, 255],
+ [152, 180, 255],
+ [126, 161, 255],
+ [107, 147, 255],
+ [ 99, 141, 255]],
+
+ [[109, 148, 255],
+ [182, 202, 255],
+ [197, 212, 255],
+ [118, 155, 255],
+ [ 76, 124, 255],
+ [117, 155, 255],
+ [159, 185, 255],
+ [117, 154, 255],
+ [127, 162, 255],
+ [104, 145, 255],
+ [ 57, 111, 255],
+ [ 29, 91, 255],
+ [142, 173, 255],
+ [162, 187, 255],
+ [ 31, 92, 255],
+ [ 80, 128, 255],
+ [125, 160, 255],
+ [165, 189, 255],
+ [200, 215, 255],
+ [119, 155, 255],
+ [ 60, 113, 255],
+ [ 32, 92, 255]],
+
+ [[144, 173, 255],
+ [125, 160, 255],
+ [127, 162, 255],
+ [163, 187, 255],
+ [185, 204, 255],
+ [205, 218, 255],
+ [216, 226, 255],
+ [210, 222, 255],
+ [212, 223, 255],
+ [186, 204, 255],
+ [129, 163, 255],
+ [ 40, 99, 255],
+ [142, 173, 255],
+ [154, 181, 255],
+ [ 9, 76, 255],
+ [ 91, 135, 255],
+ [173, 195, 255],
+ [209, 221, 255],
+ [145, 175, 255],
+ [ 53, 108, 255],
+ [ 9, 76, 255],
+ [ 5, 73, 255]],
+
+ [[ 62, 114, 255],
+ [ 47, 103, 255],
+ [ 47, 103, 255],
+ [ 70, 120, 255],
+ [ 84, 130, 255],
+ [ 91, 135, 255],
+ [ 93, 137, 255],
+ [ 93, 137, 255],
+ [ 93, 137, 255],
+ [ 82, 129, 255],
+ [ 59, 112, 255],
+ [ 18, 82, 255],
+ [ 60, 113, 255],
+ [ 65, 116, 255],
+ [ 2, 71, 255],
+ [ 38, 97, 255],
+ [ 74, 123, 255],
+ [ 89, 134, 255],
+ [ 55, 109, 255],
+ [ 17, 81, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 1, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 3, 71, 255],
+ [ 3, 71, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]]], dtype=uint8)],([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255]),{'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []},{'line_thickness': 1, 'boxLine_thickness': 1, 'fontSize': 0.4, 'waterLineColor': (0, 255, 255), 'segLineShow': False, 'waterLineWidth': 1, 'wordSize': 8, 'label_location': 'leftTop'},{'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4},others,None) @ aiHelper.py:101 in AI_process
+ 13:38:03.604 [ERROR] - 算法模型分析异常：Traceback (most recent call last):
+ File "/home/thsw/chenbw/DrGraph/appIOs/conf/ModelUtils.py", line 151, in model_process
+ return aiHelper.AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
+ File "/home/thsw/chenbw/DrGraph/DrUtils/aiHelper.py", line 139, in AI_process
+ half,device,conf_thres,iou_thres,allowedList = objectPar['half'],objectPar['device'],objectPar['conf_thres'],objectPar['iou_thres'],objectPar['allowedList']
+KeyError: 'allowedList'
+, requestId:1234 @ ModelUtils.py:160 in model_process
+ 13:38:03.605 [ERROR] - 异常编码：SP018, 异常描述：算法模型分析异常! @ ModelUtils.py:44 in __str__
+ 13:38:45.764 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 13:38:45.785 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:55 in __init__
+ 13:38:45.812 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 13:38:45.812 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:65 in __init__
+ 13:38:45.979 [INFO] - 模型初始化时间：0.19443655014038086, requestId:1234 @ ModelUtils.py:107 in __init__
+ 13:38:45.980 [INFO] - 模型编号: 019
+模型参数: ( at 0x7fbee58625f0>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:52 in
+ 13:38:45.980 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:290 in get_label_arraylist
+ 13:38:45.984 [INFO] - model_process(
+
im0s=[None],
+
model=,
+
segmodel=None,
+
names=['车', 'T角点', 'L角点', '违停'],
+ label_arraylist=[array([[[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 15, 15],
+ [255, 48, 48],
+ [255, 61, 61],
+ [255, 33, 33],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 34, 34],
+ [255, 50, 50],
+ [255, 83, 83],
+ [255, 129, 129],
+ [255, 143, 143],
+ [255, 104, 104],
+ [255, 61, 61],
+ [255, 61, 61],
+ [255, 61, 61],
+ [255, 48, 48],
+ [255, 29, 29]],
+
+ [[255, 104, 104],
+ [255, 154, 154],
+ [255, 203, 203],
+ [255, 232, 232],
+ [255, 222, 222],
+ [255, 205, 205],
+ [255, 189, 189],
+ [255, 186, 186],
+ [255, 186, 186],
+ [255, 145, 145],
+ [255, 87, 87]],
+
+ [[255, 48, 48],
+ [255, 104, 104],
+ [255, 168, 168],
+ [255, 217, 217],
+ [255, 141, 141],
+ [255, 134, 134],
+ [255, 137, 137],
+ [255, 90, 90],
+ [255, 87, 87],
+ [255, 68, 68],
+ [255, 40, 40]],
+
+ [[255, 16, 16],
+ [255, 106, 106],
+ [255, 170, 170],
+ [255, 168, 168],
+ [255, 112, 112],
+ [255, 136, 136],
+ [255, 149, 149],
+ [255, 56, 56],
+ [255, 50, 50],
+ [255, 35, 35],
+ [255, 14, 14]],
+
+ [[255, 3, 3],
+ [255, 124, 124],
+ [255, 188, 188],
+ [255, 143, 143],
+ [255, 132, 132],
+ [255, 176, 176],
+ [255, 190, 190],
+ [255, 87, 87],
+ [255, 80, 80],
+ [255, 49, 49],
+ [255, 4, 4]],
+
+ [[255, 9, 9],
+ [255, 124, 124],
+ [255, 203, 203],
+ [255, 201, 201],
+ [255, 210, 210],
+ [255, 228, 228],
+ [255, 234, 234],
+ [255, 199, 199],
+ [255, 196, 196],
+ [255, 120, 120],
+ [255, 9, 9]],
+
+ [[255, 54, 54],
+ [255, 99, 99],
+ [255, 130, 130],
+ [255, 132, 132],
+ [255, 157, 157],
+ [255, 199, 199],
+ [255, 209, 209],
+ [255, 135, 135],
+ [255, 130, 130],
+ [255, 99, 99],
+ [255, 54, 54]],
+
+ [[255, 93, 93],
+ [255, 108, 108],
+ [255, 119, 119],
+ [255, 120, 120],
+ [255, 149, 149],
+ [255, 194, 194],
+ [255, 205, 205],
+ [255, 124, 124],
+ [255, 119, 119],
+ [255, 108, 108],
+ [255, 93, 93]],
+
+ [[255, 102, 102],
+ [255, 119, 119],
+ [255, 131, 131],
+ [255, 133, 133],
+ [255, 158, 158],
+ [255, 199, 199],
+ [255, 209, 209],
+ [255, 136, 136],
+ [255, 131, 131],
+ [255, 119, 119],
+ [255, 102, 102]],
+
+ [[255, 16, 16],
+ [255, 19, 19],
+ [255, 21, 21],
+ [255, 24, 24],
+ [255, 72, 72],
+ [255, 149, 149],
+ [255, 168, 168],
+ [255, 29, 29],
+ [255, 21, 21],
+ [255, 19, 19],
+ [255, 16, 16]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 1, 1],
+ [255, 25, 25],
+ [255, 63, 63],
+ [255, 73, 73],
+ [255, 4, 4],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 3, 3],
+ [255, 7, 7],
+ [255, 8, 8],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]],
+
+ [[255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0],
+ [255, 0, 0]]], dtype=uint8), array([[[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[212, 6, 150],
+ [212, 7, 151],
+ [212, 7, 151],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[224, 77, 180],
+ [225, 84, 183],
+ [226, 89, 185],
+ ...,
+ [225, 80, 182],
+ [223, 70, 177],
+ [221, 59, 173]],
+
+ ...,
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [218, 40, 165],
+ [219, 49, 168],
+ [221, 61, 173]],
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]],
+
+ [[211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148],
+ ...,
+ [211, 0, 148],
+ [211, 0, 148],
+ [211, 0, 148]]], dtype=uint8), array([[[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 2, 128, 2],
+ [ 5, 129, 5],
+ [ 4, 129, 4],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 26, 140, 26],
+ [ 63, 159, 63],
+ [ 60, 157, 60],
+ ...,
+ [ 79, 167, 79],
+ [ 64, 159, 64],
+ [ 59, 157, 59]],
+
+ ...,
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 38, 146, 38],
+ [ 55, 155, 55],
+ [ 61, 157, 61]],
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]],
+
+ [[ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ ...,
+ [ 0, 127, 0],
+ [ 0, 127, 0],
+ [ 0, 127, 0]]], dtype=uint8), array([[[ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 25, 87, 255],
+ [ 30, 90, 255],
+ [ 22, 85, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 36, 95, 255],
+ [ 63, 115, 255],
+ [ 33, 93, 255],
+ [ 10, 76, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 34, 94, 255],
+ [ 47, 103, 255],
+ [ 24, 86, 255],
+ [ 6, 73, 255],
+ [ 25, 87, 255],
+ [ 50, 105, 255],
+ [ 50, 105, 255],
+ [ 16, 80, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 53, 107, 255],
+ [ 87, 132, 255],
+ [ 90, 135, 255],
+ [ 49, 104, 255],
+ [ 67, 118, 255],
+ [118, 155, 255],
+ [151, 179, 255],
+ [113, 151, 255],
+ [ 85, 131, 255],
+ [ 62, 114, 255],
+ [ 41, 99, 255],
+ [ 7, 74, 255],
+ [ 83, 129, 255],
+ [121, 157, 255],
+ [ 80, 127, 255],
+ [ 67, 118, 255],
+ [ 93, 137, 255],
+ [122, 158, 255],
+ [122, 157, 255],
+ [ 82, 129, 255],
+ [ 59, 112, 255],
+ [ 53, 107, 255]],
+
+ [[ 73, 121, 255],
+ [160, 185, 255],
+ [196, 212, 255],
+ [147, 177, 255],
+ [205, 218, 255],
+ [235, 240, 255],
+ [243, 246, 255],
+ [233, 239, 255],
+ [226, 233, 255],
+ [190, 208, 255],
+ [124, 160, 255],
+ [ 24, 86, 255],
+ [138, 169, 255],
+ [206, 220, 255],
+ [158, 184, 255],
+ [190, 208, 255],
+ [201, 215, 255],
+ [201, 215, 255],
+ [201, 215, 255],
+ [201, 215, 255],
+ [184, 203, 255],
+ [160, 185, 255]],
+
+ [[ 17, 81, 255],
+ [ 66, 117, 255],
+ [ 88, 133, 255],
+ [ 66, 117, 255],
+ [123, 159, 255],
+ [183, 203, 255],
+ [214, 225, 255],
+ [178, 199, 255],
+ [152, 180, 255],
+ [104, 146, 255],
+ [ 52, 107, 255],
+ [ 53, 108, 255],
+ [156, 183, 255],
+ [176, 197, 255],
+ [ 73, 122, 255],
+ [179, 200, 255],
+ [197, 213, 255],
+ [182, 201, 255],
+ [182, 201, 255],
+ [202, 216, 255],
+ [155, 182, 255],
+ [ 75, 124, 255]],
+
+ [[ 93, 137, 255],
+ [121, 157, 255],
+ [108, 147, 255],
+ [ 40, 98, 255],
+ [ 98, 141, 255],
+ [167, 191, 255],
+ [205, 218, 255],
+ [162, 187, 255],
+ [130, 164, 255],
+ [ 77, 126, 255],
+ [ 36, 95, 255],
+ [113, 152, 255],
+ [185, 204, 255],
+ [166, 190, 255],
+ [ 47, 103, 255],
+ [178, 199, 255],
+ [198, 213, 255],
+ [176, 197, 255],
+ [176, 197, 255],
+ [204, 217, 255],
+ [148, 177, 255],
+ [ 50, 105, 255]],
+
+ [[150, 178, 255],
+ [196, 212, 255],
+ [176, 197, 255],
+ [ 68, 118, 255],
+ [125, 160, 255],
+ [184, 203, 255],
+ [214, 225, 255],
+ [179, 200, 255],
+ [154, 182, 255],
+ [108, 148, 255],
+ [ 73, 122, 255],
+ [165, 189, 255],
+ [207, 220, 255],
+ [175, 196, 255],
+ [ 77, 125, 255],
+ [184, 203, 255],
+ [199, 215, 255],
+ [181, 201, 255],
+ [181, 201, 255],
+ [204, 218, 255],
+ [159, 185, 255],
+ [ 78, 126, 255]],
+
+ [[ 37, 96, 255],
+ [165, 189, 255],
+ [223, 231, 255],
+ [160, 186, 255],
+ [211, 223, 255],
+ [237, 242, 255],
+ [245, 248, 255],
+ [236, 241, 255],
+ [234, 239, 255],
+ [207, 219, 255],
+ [169, 192, 255],
+ [170, 193, 255],
+ [202, 216, 255],
+ [207, 220, 255],
+ [173, 195, 255],
+ [194, 211, 255],
+ [200, 215, 255],
+ [199, 214, 255],
+ [199, 214, 255],
+ [200, 215, 255],
+ [188, 206, 255],
+ [169, 193, 255]],
+
+ [[ 12, 78, 255],
+ [156, 183, 255],
+ [190, 208, 255],
+ [ 53, 108, 255],
+ [ 69, 119, 255],
+ [141, 171, 255],
+ [196, 212, 255],
+ [157, 184, 255],
+ [189, 207, 255],
+ [187, 206, 255],
+ [150, 179, 255],
+ [ 87, 133, 255],
+ [163, 188, 255],
+ [201, 215, 255],
+ [152, 180, 255],
+ [150, 179, 255],
+ [145, 175, 255],
+ [140, 171, 255],
+ [140, 171, 255],
+ [147, 176, 255],
+ [151, 179, 255],
+ [153, 180, 255]],
+
+ [[ 44, 101, 255],
+ [169, 192, 255],
+ [188, 206, 255],
+ [ 45, 102, 255],
+ [ 23, 85, 255],
+ [ 97, 140, 255],
+ [162, 187, 255],
+ [111, 150, 255],
+ [143, 174, 255],
+ [140, 171, 255],
+ [100, 142, 255],
+ [ 39, 98, 255],
+ [144, 174, 255],
+ [183, 202, 255],
+ [ 97, 140, 255],
+ [108, 148, 255],
+ [118, 155, 255],
+ [130, 164, 255],
+ [152, 180, 255],
+ [126, 161, 255],
+ [107, 147, 255],
+ [ 99, 141, 255]],
+
+ [[109, 148, 255],
+ [182, 202, 255],
+ [197, 212, 255],
+ [118, 155, 255],
+ [ 76, 124, 255],
+ [117, 155, 255],
+ [159, 185, 255],
+ [117, 154, 255],
+ [127, 162, 255],
+ [104, 145, 255],
+ [ 57, 111, 255],
+ [ 29, 91, 255],
+ [142, 173, 255],
+ [162, 187, 255],
+ [ 31, 92, 255],
+ [ 80, 128, 255],
+ [125, 160, 255],
+ [165, 189, 255],
+ [200, 215, 255],
+ [119, 155, 255],
+ [ 60, 113, 255],
+ [ 32, 92, 255]],
+
+ [[144, 173, 255],
+ [125, 160, 255],
+ [127, 162, 255],
+ [163, 187, 255],
+ [185, 204, 255],
+ [205, 218, 255],
+ [216, 226, 255],
+ [210, 222, 255],
+ [212, 223, 255],
+ [186, 204, 255],
+ [129, 163, 255],
+ [ 40, 99, 255],
+ [142, 173, 255],
+ [154, 181, 255],
+ [ 9, 76, 255],
+ [ 91, 135, 255],
+ [173, 195, 255],
+ [209, 221, 255],
+ [145, 175, 255],
+ [ 53, 108, 255],
+ [ 9, 76, 255],
+ [ 5, 73, 255]],
+
+ [[ 62, 114, 255],
+ [ 47, 103, 255],
+ [ 47, 103, 255],
+ [ 70, 120, 255],
+ [ 84, 130, 255],
+ [ 91, 135, 255],
+ [ 93, 137, 255],
+ [ 93, 137, 255],
+ [ 93, 137, 255],
+ [ 82, 129, 255],
+ [ 59, 112, 255],
+ [ 18, 82, 255],
+ [ 60, 113, 255],
+ [ 65, 116, 255],
+ [ 2, 71, 255],
+ [ 38, 97, 255],
+ [ 74, 123, 255],
+ [ 89, 134, 255],
+ [ 55, 109, 255],
+ [ 17, 81, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 1, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 3, 71, 255],
+ [ 3, 71, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]],
+
+ [[ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255],
+ [ 0, 69, 255]]], dtype=uint8)],([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255]),{'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []},{'line_thickness': 1, 'boxLine_thickness': 1, 'fontSize': 0.4, 'waterLineColor': (0, 255, 255), 'segLineShow': False, 'waterLineWidth': 1, 'wordSize': 8, 'label_location': 'leftTop'},{'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4},others,None) @ aiHelper.py:101 in AI_process
+ 13:38:45.988 [ERROR] - 算法模型分析异常：Traceback (most recent call last):
+ File "/home/thsw/chenbw/DrGraph/appIOs/conf/ModelUtils.py", line 151, in model_process
+ return aiHelper.AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
+ File "/home/thsw/chenbw/DrGraph/DrUtils/aiHelper.py", line 139, in AI_process
+ half,device,conf_thres,iou_thres,allowedList = objectPar['half'],objectPar['device'],objectPar['conf_thres'],objectPar['iou_thres'],objectPar['allowedList']
+KeyError: 'allowedList'
+, requestId:1234 @ ModelUtils.py:160 in model_process
+ 13:38:45.990 [ERROR] - 异常编码：SP018, 异常描述：算法模型分析异常! @ ModelUtils.py:44 in __str__
+ 13:39:31.577 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 13:39:31.598 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:55 in __init__
+ 13:39:31.623 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 13:39:31.623 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:65 in __init__
+ 13:39:31.778 [INFO] - 模型初始化时间：0.18051886558532715, requestId:1234 @ ModelUtils.py:107 in __init__
+ 13:39:31.779 [INFO] - 模型编号: 019
+模型参数: ( at 0x7feee6aa25f0>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:52 in
+ 13:39:31.779 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:290 in get_label_arraylist
+ 13:39:31.782 [INFO] - model_process(
+
im0s=[None],
+
model=,
+
segmodel=None,
+
names=['车', 'T角点', 'L角点', '违停'],
+
rainbows=([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255]),{'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []},{'line_thickness': 1, 'boxLine_thickness': 1, 'fontSize': 0.4, 'waterLineColor': (0, 255, 255), 'segLineShow': False, 'waterLineWidth': 1, 'wordSize': 8, 'label_location': 'leftTop'},{'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4},others,None) @ aiHelper.py:101 in AI_process
+ 13:39:31.783 [ERROR] - 算法模型分析异常：Traceback (most recent call last):
+ File "/home/thsw/chenbw/DrGraph/appIOs/conf/ModelUtils.py", line 151, in model_process
+ return aiHelper.AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
+ File "/home/thsw/chenbw/DrGraph/DrUtils/aiHelper.py", line 139, in AI_process
+ half,device,conf_thres,iou_thres,allowedList = objectPar['half'],objectPar['device'],objectPar['conf_thres'],objectPar['iou_thres'],objectPar['allowedList']
+KeyError: 'allowedList'
+, requestId:1234 @ ModelUtils.py:160 in model_process
+ 13:39:31.784 [ERROR] - 异常编码：SP018, 异常描述：算法模型分析异常! @ ModelUtils.py:44 in __str__
+ 13:40:31.679 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 13:40:31.702 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:55 in __init__
+ 13:40:31.728 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 13:40:31.728 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:65 in __init__
+ 13:40:31.887 [INFO] - 模型初始化时间：0.18517684936523438, requestId:1234 @ ModelUtils.py:107 in __init__
+ 13:40:31.888 [INFO] - 模型编号: 019
+模型参数: ( at 0x7f8f4d962830>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:52 in
+ 13:40:31.888 [INFO] - at 0x7f8f4d97c3a0> @ main.py:67 in
+ 13:40:31.889 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:290 in get_label_arraylist
+ 13:40:31.892 [INFO] - model_process(
+
im0s=[None],
+
model=,
+
segmodel=None,
+
names=['车', 'T角点', 'L角点', '违停'],
+
rainbows=([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255]),{'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []},{'line_thickness': 1, 'boxLine_thickness': 1, 'fontSize': 0.4, 'waterLineColor': (0, 255, 255), 'segLineShow': False, 'waterLineWidth': 1, 'wordSize': 8, 'label_location': 'leftTop'},{'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4},others,None) @ aiHelper.py:101 in AI_process
+ 13:40:31.893 [ERROR] - 算法模型分析异常：Traceback (most recent call last):
+ File "/home/thsw/chenbw/DrGraph/appIOs/conf/ModelUtils.py", line 151, in model_process
+ return aiHelper.AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
+ File "/home/thsw/chenbw/DrGraph/DrUtils/aiHelper.py", line 139, in AI_process
+ half,device,conf_thres,iou_thres,allowedList = objectPar['half'],objectPar['device'],objectPar['conf_thres'],objectPar['iou_thres'],objectPar['allowedList']
+KeyError: 'allowedList'
+, requestId:1234 @ ModelUtils.py:160 in model_process
+ 13:40:31.894 [ERROR] - 异常编码：SP018, 异常描述：算法模型分析异常! @ ModelUtils.py:44 in __str__
+ 13:41:19.104 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 13:41:19.126 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:55 in __init__
+ 13:41:19.152 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 13:41:19.152 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:65 in __init__
+ 13:41:19.320 [INFO] - 模型初始化时间：0.19472265243530273, requestId:1234 @ ModelUtils.py:107 in __init__
+ 13:41:19.321 [INFO] - 模型编号: 019
+模型参数: ( at 0x7f93bf8fa830>)>, {'model': , 'segmodel': None, 'objectPar': {'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []}, 'segPar': {'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4}, 'mode': 'others', 'postPar': None}, [0, 1, 2, 3], ['车', 'T角点', 'L角点', '违停'], ([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255])) @ main.py:52 in
+ 13:41:19.321 [INFO] - model= at 0x7f93bf9103a0> @ main.py:67 in
+ 13:41:19.321 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:290 in get_label_arraylist
+ 13:41:19.325 [INFO] - model_process(
+
im0s=[None],
+
model=,
+
segmodel=None,
+
names=['车', 'T角点', 'L角点', '违停'],
+
rainbows=([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255]),{'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []},{'line_thickness': 1, 'boxLine_thickness': 1, 'fontSize': 0.4, 'waterLineColor': (0, 255, 255), 'segLineShow': False, 'waterLineWidth': 1, 'wordSize': 8, 'label_location': 'leftTop'},{'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4},others,None) @ aiHelper.py:101 in AI_process
+ 13:41:19.326 [ERROR] - 算法模型分析异常：Traceback (most recent call last):
+ File "/home/thsw/chenbw/DrGraph/appIOs/conf/ModelUtils.py", line 151, in model_process
+ return aiHelper.AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
+ File "/home/thsw/chenbw/DrGraph/DrUtils/aiHelper.py", line 139, in AI_process
+ half,device,conf_thres,iou_thres,allowedList = objectPar['half'],objectPar['device'],objectPar['conf_thres'],objectPar['iou_thres'],objectPar['allowedList']
+KeyError: 'allowedList'
+, requestId:1234 @ ModelUtils.py:160 in model_process
+ 13:41:19.327 [ERROR] - 异常编码：SP018, 异常描述：算法模型分析异常! @ ModelUtils.py:44 in __str__
+ 13:41:33.399 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 13:41:33.420 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:55 in __init__
+ 13:41:33.448 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 13:41:33.448 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:65 in __init__
+ 13:41:33.625 [INFO] - 模型初始化时间：0.2046341896057129, requestId:1234 @ ModelUtils.py:107 in __init__
+ 13:41:33.625 [INFO] - model= at 0x7fe14cec03a0> @ main.py:67 in
+ 13:41:33.626 [INFO] - fontPath:./appIOs/conf/platech.ttf @ ModelUtils.py:290 in get_label_arraylist
+ 13:41:33.629 [INFO] - model_process(
+
im0s=[None],
+
model=,
+
segmodel=None,
+
names=['车', 'T角点', 'L角点', '违停'],
+
rainbows=([255, 0, 0], [211, 0, 148], [0, 127, 0], [0, 69, 255], [0, 255, 0], [255, 0, 255], [0, 0, 127], [127, 0, 255], [255, 129, 0], [139, 139, 0], [255, 255, 0], [127, 255, 0], [0, 127, 255], [0, 255, 127], [255, 127, 255], [8, 101, 139], [171, 130, 255], [139, 112, 74], [205, 205, 180], [0, 0, 255]),{'half': True, 'device': device(type='cuda', index=0), 'conf_thres': 0.25, 'ovlap_thres_crossCategory': None, 'iou_thres': 0.25, 'segRegionCnt': 2, 'trtFlag_det': True, 'trtFlag_seg': False, 'score_byClass': None, 'fiterList': []},{'line_thickness': 1, 'boxLine_thickness': 1, 'fontSize': 0.4, 'waterLineColor': (0, 255, 255), 'segLineShow': False, 'waterLineWidth': 1, 'wordSize': 8, 'label_location': 'leftTop'},{'mixFunction': {'function': , 'pars': {}}, 'seg_nclass': 4},others,None) @ aiHelper.py:101 in AI_process
+ 13:41:33.630 [ERROR] - 算法模型分析异常：Traceback (most recent call last):
+ File "/home/thsw/chenbw/DrGraph/appIOs/conf/ModelUtils.py", line 151, in model_process
+ return aiHelper.AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
+ File "/home/thsw/chenbw/DrGraph/DrUtils/aiHelper.py", line 139, in AI_process
+ half,device,conf_thres,iou_thres,allowedList = objectPar['half'],objectPar['device'],objectPar['conf_thres'],objectPar['iou_thres'],objectPar['allowedList']
+KeyError: 'allowedList'
+, requestId:1234 @ ModelUtils.py:160 in model_process
+ 13:41:33.631 [ERROR] - 异常编码：SP018, 异常描述：算法模型分析异常! @ ModelUtils.py:44 in __str__
+ 13:46:52.738 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 13:47:04.293 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:55 in __init__
+ 13:47:28.563 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 13:47:31.817 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:55 in __init__
+ 13:47:35.475 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 13:47:36.362 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:65 in __init__
+ 13:48:03.364 [INFO] - 模型初始化时间：34.780102014541626, requestId:1234 @ ModelUtils.py:107 in __init__
+ 14:04:47.515 [INFO] - 模型编号: 019, 检查目标: [0, 1, 2, 3], requestId: 1234 @ main.py:21 in get_model
+ 14:04:47.535 [INFO] - ########################加载车辆违停模型########################, requestId:1234 @ ModelUtils.py:56 in __init__
+ 14:04:47.535 [INFO] - __init__(device=0, allowedList=[0, 1, 2, 3], requestId=1234, modeType=ModelType.ILLPARKING_MODEL, gpu_name=3090, base_dir=/home/thsw/chenbw/DrGraph, env=test) @ ModelUtils.py:58 in __init__
+ 14:04:47.559 [INFO] - select_device YOLOv5 🚀 2025-9-19 torch 2.0.0+cu117 CUDA:0 (NVIDIA GeForce RTX 3090, 24258.625MB)
+ @ torchHelper.py:73 in select_device
+ 14:04:47.560 [INFO] - 加载模型：./weights/illParking/yolov5_3090_fp16.engine @ ModelUtils.py:68 in __init__
+ 14:04:47.705 [INFO] - 模型初始化时间：0.16959834098815918, requestId:1234 @ ModelUtils.py:110 in __init__
+ 14:04:47.705 [INFO] - model= at 0x7fcede974550> @ main.py:67 in