1088 lines
40 KiB
Python
1088 lines
40 KiB
Python
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import sys
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from enum import Enum, unique
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from common.Constant import COLOR
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sys.path.extend(['..', '../AIlib2'])
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from DMPR import DMPRModel
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from DMPRUtils.jointUtil import dmpr_yolo
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from segutils.segmodel import SegModel
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from utilsK.queRiver import riverDetSegMixProcess
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from utilsK.crowdGather import gather_post_process
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from segutils.trafficUtils import tracfficAccidentMixFunction,mixTraffic_postprocess
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from utilsK.drownUtils import mixDrowing_water_postprocess
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from utilsK.noParkingUtils import mixNoParking_road_postprocess
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from utilsK.illParkingUtils import illParking_postprocess
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from utilsK.pannelpostUtils import pannel_post_process
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from stdc import stdcModel
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from yolov5 import yolov5Model
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from p2pNet import p2NnetModel
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from DMPRUtils.jointUtil import dmpr_yolo_stdc
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from AI import default_mix
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from ocr import ocrModel
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from utilsK.channel2postUtils import channel2_post_process
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'''
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参数说明
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1. 编号
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2. 模型编号
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3. 模型名称
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4. 选用的模型名称
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5. 模型配置
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6. 模型引用配置[Detweights文件, Segweights文件, 引用计数]
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'''
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@unique
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class ModelType(Enum):
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WATER_SURFACE_MODEL = ("1", "001", "河道模型", 'river', lambda device, gpuName: {
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'device': device,
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'labelnames': ["排口", "水生植被", "其它", "漂浮物", "污染排口", "菜地", "违建", "岸坡垃圾"],
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'seg_nclass': 2,
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'trtFlag_seg': True,
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'trtFlag_det': True,
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'segRegionCnt': 1,
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'segPar': {
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'modelSize': (640, 360),
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'mean': (0.485, 0.456, 0.406),
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'std': (0.229, 0.224, 0.225),
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'numpy': False,
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'RGB_convert_first': True,
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'mixFunction': {
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'function': riverDetSegMixProcess,
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'pars': {
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'slopeIndex': [5, 6, 7],
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'riverIou': 0.1
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}
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}
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},
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'postFile': {
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"name": "post_process",
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"conf_thres": 0.25,
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"iou_thres": 0.45,
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"classes": 5,
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"rainbows": COLOR
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},
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'Detweights': "../weights/trt/AIlib2/river/yolov5_%s_fp16.engine" % gpuName,
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'Segweights': '../weights/trt/AIlib2/river/stdc_360X640_%s_fp16.engine' % gpuName
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})
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# FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
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# 'device': device,
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# 'gpu_name': gpuName,
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# 'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
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# 'trtFlag_det': True,
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# 'trtFlag_seg': False,
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# 'Detweights': "../weights/trt/AIlib2/forest2/yolov5_%s_fp16.engine" % gpuName,
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# 'seg_nclass': 2,
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# 'segRegionCnt': 0,
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# 'slopeIndex': [],
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# 'segPar': None,
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# 'postFile': {
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# "name": "post_process",
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# "conf_thres": 0.25,
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# "iou_thres": 0.45,
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# "classes": 6,
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# "rainbows": COLOR
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# },
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# 'Segweights': None
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# })
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FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
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'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
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'postProcess':{'function':default_mix,'pars':{}},
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'models':
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[
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{
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'weight':"../weights/trt/AIlib2/forest2/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
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'name':'yolov5',
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'model':yolov5Model,
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'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
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}
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],
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'postFile': {
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"name": "post_process",
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"conf_thres": 0.25,
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"iou_thres": 0.45,
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"classes": 5,
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"rainbows": COLOR
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},
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'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9] ],###控制哪些检测类别显示、输出
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'segRegionCnt':2,###分割模型结果需要保留的等值线数目
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"pixScale": 1.2,
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})
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TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'highWay2', lambda device, gpuName: {
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'device': str(device),
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'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",
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"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"],
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'trtFlag_seg': True,
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'trtFlag_det': True,
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'seg_nclass': 3,
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'segRegionCnt': 2,
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'segPar': {
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'modelSize': (640, 360),
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'mean': (0.485, 0.456, 0.406),
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'std': (0.229, 0.224, 0.225),
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'predResize': True,
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'numpy': False,
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'RGB_convert_first': True,
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'mixFunction': {
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'function': tracfficAccidentMixFunction,
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'pars': {
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'modelSize': (640, 360),
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'RoadArea': 16000,
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'roadVehicleAngle': 15,
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'speedRoadVehicleAngleMax': 75,
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'roundness': 1.0,
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'cls': 10,
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'CarId':1,
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'CthcId':12,
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'vehicleFactor': 0.1,
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'confThres': 0.25,
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'roadIou': 0.6,
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'radius': 50,
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'vehicleFlag': False,
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'distanceFlag': False
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}
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}
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},
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'postFile': {
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"name": "post_process",
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"conf_thres": 0.25,
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"iou_thres": 0.25,
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"classes": 10,
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"rainbows": COLOR
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},
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'allowedList':[0,1,2,3,4,5,6,7,8,9,10,11,12,16,17,18,19,20,21,22],
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'Detweights': "../weights/trt/AIlib2/highWay2/yolov5_%s_fp16.engine" % gpuName,
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'Segweights': '../weights/trt/AIlib2/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
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})
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EPIDEMIC_PREVENTION_MODEL = ("4", "004", "防疫模型", None, None)
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PLATE_MODEL = ("5", "005", "车牌模型", None, None)
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VEHICLE_MODEL = ("6", "006", "车辆模型", 'vehicle', lambda device, gpuName: {
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'device': device,
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'gpu_name': gpuName,
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'labelnames': ["车辆"],
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'seg_nclass': 2,
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'segRegionCnt': 0,
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'slopeIndex': [],
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'trtFlag_det': True,
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'trtFlag_seg': False,
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'Detweights': "../weights/trt/AIlib2/vehicle/yolov5_%s_fp16.engine" % gpuName,
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'segPar': None,
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'postFile': {
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"name": "post_process",
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"conf_thres": 0.25,
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"iou_thres": 0.45,
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"classes": 5,
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"rainbows": COLOR
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},
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'Segweights': None
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})
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PEDESTRIAN_MODEL = ("7", "007", "行人模型", 'pedestrian', lambda device, gpuName: {
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'device': device,
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'gpu_name': gpuName,
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'labelnames': ["行人"],
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'seg_nclass': 2,
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'segRegionCnt': 0,
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'trtFlag_det': True,
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'trtFlag_seg': False,
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'Detweights': "../weights/trt/AIlib2/pedestrian/yolov5_%s_fp16.engine" % gpuName,
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'slopeIndex': [],
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'segPar': None,
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'postFile': {
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"name": "post_process",
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"conf_thres": 0.25,
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"iou_thres": 0.45,
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"classes": 5,
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"rainbows": COLOR
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},
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'Segweights': None
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})
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SMOGFIRE_MODEL = ("8", "008", "烟火模型", 'smogfire', lambda device, gpuName: {
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'device': device,
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'gpu_name': gpuName,
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'labelnames': ["火焰", "烟雾"],
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'seg_nclass': 2, # 分割模型类别数目，默认2类
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'segRegionCnt': 0,
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'trtFlag_det': True,
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'trtFlag_seg': False,
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'Detweights': "../weights/trt/AIlib2/smogfire/yolov5_%s_fp16.engine" % gpuName,
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'slopeIndex': [],
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'segPar': None,
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'postFile': {
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"name": "post_process",
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"conf_thres": 0.25,
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"iou_thres": 0.45,
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"classes": 5,
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"rainbows": COLOR
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},
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'Segweights': None
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})
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ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer', lambda device, gpuName: {
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'device': device,
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'gpu_name': gpuName,
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'labelnames': ["钓鱼", "游泳"],
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'seg_nclass': 2, # 分割模型类别数目，默认2类
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'segRegionCnt': 0,
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'slopeIndex': [],
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'trtFlag_det': True,
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'trtFlag_seg': False,
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'Detweights': "../weights/trt/AIlib2/AnglerSwimmer/yolov5_%s_fp16.engine" % gpuName,
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'segPar': None,
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'postFile': {
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"name": "post_process",
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"conf_thres": 0.25,
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"iou_thres": 0.45,
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"classes": 5,
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"rainbows": COLOR
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},
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'Segweights': None
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})
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COUNTRYROAD_MODEL = ("10", "010", "乡村模型", 'countryRoad', lambda device, gpuName: {
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'device': device,
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'gpu_name': gpuName,
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'labelnames': ["违法种植"],
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'seg_nclass': 2, # 分割模型类别数目，默认2类
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'segRegionCnt': 0,
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'slopeIndex': [],
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'trtFlag_det': True,
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'trtFlag_seg': False,
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'Detweights': "../weights/trt/AIlib2/countryRoad/yolov5_%s_fp16.engine" % gpuName,
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'segPar': None,
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'postFile': {
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"name": "post_process",
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"conf_thres": 0.25,
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"iou_thres": 0.45,
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"classes": 5,
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"rainbows": COLOR
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},
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'Segweights': None
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})
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SHIP_MODEL = ("11", "011", "船只模型", 'ship2', lambda device, gpuName: {
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'model_size': (608, 608),
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'K': 100,
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'conf_thresh': 0.18,
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'device': 'cuda:%s' % device,
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'down_ratio': 4,
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'num_classes': 15,
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'weights': '../weights/trt/AIlib2/ship2/obb_608X608_%s_fp16.engine' % gpuName,
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'dataset': 'dota',
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'half': False,
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'mean': (0.5, 0.5, 0.5),
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'std': (1, 1, 1),
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'heads': {'hm': None, 'wh': 10, 'reg': 2, 'cls_theta': 1},
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'decoder': None,
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'test_flag': True,
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"rainbows": COLOR,
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'postFile': {
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"name": "post_process",
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"conf_thres": 0.25,
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"iou_thres": 0.45,
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"classes": 5,
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"rainbows": COLOR
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},
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|||
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'drawBox': False,
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'label_array': None,
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'labelnames': ("0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "船只"),
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})
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BAIDU_MODEL = ("12", "012", "百度AI图片识别模型", None, None)
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CHANNEL_EMERGENCY_MODEL = ("13", "013", "航道模型", 'channelEmergency', lambda device, gpuName: {
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'device': device,
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'gpu_name': gpuName,
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|||
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'labelnames': ["人"],
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|||
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'seg_nclass': 2, # 分割模型类别数目，默认2类
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|||
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'segRegionCnt': 0,
|
|||
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'slopeIndex': [],
|
|||
|
|
'trtFlag_det': True,
|
|||
|
|
'trtFlag_seg': False,
|
|||
|
|
'Detweights': "../weights/trt/AIlib2/channelEmergency/yolov5_%s_fp16.engine" % gpuName,
|
|||
|
|
'segPar': None,
|
|||
|
|
'postFile': {
|
|||
|
|
"name": "post_process",
|
|||
|
|
"conf_thres": 0.25,
|
|||
|
|
"iou_thres": 0.45,
|
|||
|
|
"classes": 5,
|
|||
|
|
"rainbows": COLOR
|
|||
|
|
},
|
|||
|
|
'Segweights': None
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
RIVER2_MODEL = ("15", "015", "河道检测模型", 'river2', lambda device, gpuName: {
|
|||
|
|
'device': device,
|
|||
|
|
'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
|
|||
|
|
"蓝藻"],
|
|||
|
|
'trtFlag_seg': True,
|
|||
|
|
'trtFlag_det': True,
|
|||
|
|
'seg_nclass': 2,
|
|||
|
|
'segRegionCnt': 1,
|
|||
|
|
'segPar': {
|
|||
|
|
'modelSize': (640, 360),
|
|||
|
|
'mean': (0.485, 0.456, 0.406),
|
|||
|
|
'std': (0.229, 0.224, 0.225),
|
|||
|
|
'numpy': False,
|
|||
|
|
'RGB_convert_first': True,
|
|||
|
|
'mixFunction': {
|
|||
|
|
'function': riverDetSegMixProcess,
|
|||
|
|
'pars': {
|
|||
|
|
'slopeIndex': [1, 3, 4, 7],
|
|||
|
|
'riverIou': 0.1
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
'postFile': {
|
|||
|
|
"name": "post_process",
|
|||
|
|
"conf_thres": 0.25,
|
|||
|
|
"iou_thres": 0.3,
|
|||
|
|
"ovlap_thres_crossCategory": 0.65,
|
|||
|
|
"classes": 5,
|
|||
|
|
"rainbows": COLOR
|
|||
|
|
},
|
|||
|
|
'Detweights': "../weights/trt/AIlib2/river2/yolov5_%s_fp16.engine" % gpuName,
|
|||
|
|
'Segweights': '../weights/trt/AIlib2/river2/stdc_360X640_%s_fp16.engine' % gpuName
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
CITY_MANGEMENT_MODEL = ("16", "016", "城管模型", 'cityMangement2', lambda device, gpuName: {
|
|||
|
|
'labelnames': [ "车辆", "垃圾", "商贩", "违停","占道经营","裸土","未覆盖裸土","违建" ],
|
|||
|
|
'postProcess':{
|
|||
|
|
'function':dmpr_yolo_stdc,
|
|||
|
|
'pars':{
|
|||
|
|
'carCls':0 ,'illCls':7,'scaleRatio':0.5,'border':80,
|
|||
|
|
#"车辆","垃圾","商贩","裸土","占道经营","未覆盖裸土","违建"
|
|||
|
|
# key:实际训练index value:展示index
|
|||
|
|
'classReindex':{ 0:0,1:1,2:2,7:3,4:4,3:5,5:6,6:7}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
'models':[
|
|||
|
|
{
|
|||
|
|
'weight':'../weights/trt/AIlib2/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
|
|||
|
|
'name':'yolov5',
|
|||
|
|
'model':yolov5Model,
|
|||
|
|
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3,4,5,6,7],'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':True, "score_byClass":{"0":0.8,"1":0.4,"2":0.5,"3":0.5 } }
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
'weight':'../weights/pth/AIlib2/cityMangement3/dmpr.pth',
|
|||
|
|
'par':{
|
|||
|
|
'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.1, 'dmprimg_size':640,
|
|||
|
|
'name':'dmpr'
|
|||
|
|
},
|
|||
|
|
'model':DMPRModel,
|
|||
|
|
'name':'dmpr'
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
'weight':'../weights/trt/AIlib2/cityMangement3/stdc_360X640_%s_fp16.engine'%(gpuName),
|
|||
|
|
'par':{
|
|||
|
|
'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'predResize':True,'numpy':False, 'RGB_convert_first':True,'seg_nclass':3},###分割模型预处理参数
|
|||
|
|
'model':stdcModel,
|
|||
|
|
'name':'stdc'
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
'postFile': {
|
|||
|
|
"name": "post_process",
|
|||
|
|
"conf_thres": 0.25,
|
|||
|
|
"iou_thres": 0.45,
|
|||
|
|
"classes": 8,
|
|||
|
|
"rainbows": COLOR
|
|||
|
|
},
|
|||
|
|
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9] ],###控制哪些检测类别显示、输出
|
|||
|
|
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
|
|||
|
|
"pixScale": 1.2,
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
DROWING_MODEL = ("17", "017", "人员落水模型", 'drowning', lambda device, gpuName: {
|
|||
|
|
'device': device,
|
|||
|
|
'labelnames': ["人头", "人", "船只"],
|
|||
|
|
'trtFlag_seg': True,
|
|||
|
|
'trtFlag_det': True,
|
|||
|
|
'seg_nclass': 2,
|
|||
|
|
'segRegionCnt': 2,
|
|||
|
|
'segPar': {
|
|||
|
|
'modelSize': (640, 360),
|
|||
|
|
'mean': (0.485, 0.456, 0.406),
|
|||
|
|
'std': (0.229, 0.224, 0.225),
|
|||
|
|
'predResize': True,
|
|||
|
|
'numpy': False,
|
|||
|
|
'RGB_convert_first': True,
|
|||
|
|
'mixFunction': {
|
|||
|
|
'function': mixDrowing_water_postprocess,
|
|||
|
|
'pars': {
|
|||
|
|
'modelSize': (640, 360)
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
'postFile': {
|
|||
|
|
"name": "post_process",
|
|||
|
|
"conf_thres": 0.25,
|
|||
|
|
"iou_thres": 0.25,
|
|||
|
|
"classes": 9,
|
|||
|
|
"rainbows": COLOR
|
|||
|
|
},
|
|||
|
|
'Detweights': "../weights/trt/AIlib2/drowning/yolov5_%s_fp16.engine" % gpuName,
|
|||
|
|
'Segweights': '../weights/trt/AIlib2/drowning/stdc_360X640_%s_fp16.engine' % gpuName
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
NOPARKING_MODEL = (
|
|||
|
|
"18", "018", "城市违章模型", 'noParking', lambda device, gpuName: {
|
|||
|
|
'device': device,
|
|||
|
|
'labelnames': ["车辆", "违停"],
|
|||
|
|
'trtFlag_seg': True,
|
|||
|
|
'trtFlag_det': True,
|
|||
|
|
'seg_nclass': 4,
|
|||
|
|
'segRegionCnt': 2,
|
|||
|
|
'segPar': {
|
|||
|
|
'modelSize': (640, 360),
|
|||
|
|
'mean': (0.485, 0.456, 0.406),
|
|||
|
|
'std': (0.229, 0.224, 0.225),
|
|||
|
|
'predResize': True,
|
|||
|
|
'numpy': False,
|
|||
|
|
'RGB_convert_first': True, ###分割模型预处理参数
|
|||
|
|
'mixFunction': {
|
|||
|
|
'function': mixNoParking_road_postprocess,
|
|||
|
|
'pars': {
|
|||
|
|
'modelSize': (640, 360),
|
|||
|
|
'roundness': 0.3,
|
|||
|
|
'cls': 9,
|
|||
|
|
'laneArea': 10,
|
|||
|
|
'laneAngleCha': 5,
|
|||
|
|
'RoadArea': 16000,
|
|||
|
|
'fitOrder':2
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
'postFile': {
|
|||
|
|
"name": "post_process",
|
|||
|
|
"conf_thres": 0.25,
|
|||
|
|
"iou_thres": 0.25,
|
|||
|
|
"classes": 9,
|
|||
|
|
"rainbows": COLOR
|
|||
|
|
},
|
|||
|
|
'Detweights': "../weights/trt/AIlib2/noParking/yolov5_%s_fp16.engine" % gpuName,
|
|||
|
|
'Segweights': '../weights/trt/AIlib2/noParking/stdc_360X640_%s_fp16.engine' % gpuName
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
ILLPARKING_MODEL = ("19", "019", "车辆违停模型", 'illParking', lambda device, gpuName: {
|
|||
|
|
'device': device,
|
|||
|
|
'labelnames': ["车", "T角点", "L角点", "违停"],
|
|||
|
|
'trtFlag_seg': False,
|
|||
|
|
'trtFlag_det': True,
|
|||
|
|
'seg_nclass': 4,
|
|||
|
|
'segRegionCnt': 2,
|
|||
|
|
'segPar': {
|
|||
|
|
'mixFunction': {
|
|||
|
|
'function': illParking_postprocess,
|
|||
|
|
'pars': {}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
'postFile': {
|
|||
|
|
"name": "post_process",
|
|||
|
|
"conf_thres": 0.25,
|
|||
|
|
"iou_thres": 0.25,
|
|||
|
|
"classes": 9,
|
|||
|
|
"rainbows": COLOR
|
|||
|
|
},
|
|||
|
|
'Detweights': "../weights/trt/AIlib2/illParking/yolov5_%s_fp16.engine" % gpuName,
|
|||
|
|
'Segweights': None
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
CITYROAD_MODEL = ("20", "020", "城市公路模型", 'cityRoad', lambda device, gpuName: {
|
|||
|
|
'device': device,
|
|||
|
|
'labelnames': ["护栏", "交通标志", "非交通标志", "施工锥桶", "施工水马"],
|
|||
|
|
'trtFlag_seg': False,
|
|||
|
|
'trtFlag_det': True,
|
|||
|
|
'slopeIndex': [],
|
|||
|
|
'seg_nclass': 2,
|
|||
|
|
'segRegionCnt': 0,
|
|||
|
|
'segPar': None,
|
|||
|
|
'postFile': {
|
|||
|
|
"name": "post_process",
|
|||
|
|
"conf_thres": 0.8,
|
|||
|
|
"iou_thres": 0.45,
|
|||
|
|
"classes": 5,
|
|||
|
|
"rainbows": COLOR
|
|||
|
|
},
|
|||
|
|
'Detweights': "../weights/trt/AIlib2/cityRoad/yolov5_%s_fp16.engine" % gpuName,
|
|||
|
|
'Segweights': None
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
POTHOLE_MODEL = ("23", "023", "坑槽检测模型", 'pothole', lambda device, gpuName: {
|
|||
|
|
'device': device,
|
|||
|
|
'gpu_name': gpuName,
|
|||
|
|
'labelnames': ["坑槽"],
|
|||
|
|
'seg_nclass': 2, # 分割模型类别数目，默认2类
|
|||
|
|
'segRegionCnt': 0,
|
|||
|
|
'slopeIndex': [],
|
|||
|
|
'trtFlag_det': True,
|
|||
|
|
'trtFlag_seg': False,
|
|||
|
|
'Detweights': "../weights/trt/AIlib2/pothole/yolov5_%s_fp16.engine" % gpuName,
|
|||
|
|
'segPar': None,
|
|||
|
|
'postFile': {
|
|||
|
|
"name": "post_process",
|
|||
|
|
"conf_thres": 0.25,
|
|||
|
|
"iou_thres": 0.45,
|
|||
|
|
"classes": 5,
|
|||
|
|
"rainbows": COLOR
|
|||
|
|
},
|
|||
|
|
'Segweights': None,
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
CHANNEL2_MODEL = ("24", "024", "船只综合检测模型", 'channel2', lambda device, gpuName: {
|
|||
|
|
'device': device,
|
|||
|
|
'gpu_name': gpuName,
|
|||
|
|
# 'labelnames': ["国旗", "浮标", "船名", "船只","未挂国旗船只"],
|
|||
|
|
'labelnames': ["国旗", "浮标", "船名", "船只", "未挂国旗船只","未封仓船只","未挂国旗且未封仓船只"],
|
|||
|
|
'segRegionCnt': 0,
|
|||
|
|
'postProcess':{'function':channel2_post_process,'name':'channel2','pars':{
|
|||
|
|
'objs':[2],
|
|||
|
|
'wRation':1/6.0,
|
|||
|
|
'hRation':1/6.0,
|
|||
|
|
'flagId':0,
|
|||
|
|
'boatId':3,
|
|||
|
|
'unflagId': 4, # 未挂国旗船只
|
|||
|
|
'uncoverId': 5, # 未封仓
|
|||
|
|
'unflagAndcoverId': 6, # 未挂国旗且未封仓
|
|||
|
|
'recScale':1.2,
|
|||
|
|
'target_cls': 3, # 船只目标种类
|
|||
|
|
'filter_cls': 4 # 被过滤的种类,模型文件中未封仓实际index
|
|||
|
|
}},
|
|||
|
|
'models':[
|
|||
|
|
{
|
|||
|
|
'weight':'../weights/trt/AIlib2/channel2/yolov5_%s_fp16.engine'%(gpuName),
|
|||
|
|
'name':'yolov5',
|
|||
|
|
'model':yolov5Model,
|
|||
|
|
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.1,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.7,"1":0.7,"2":0.8,"3":0.6} }
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
'weight' : '../weights/pth/AIlib2/ocr2/crnn_ch.pth',
|
|||
|
|
'name':'ocr',
|
|||
|
|
'model':ocrModel,
|
|||
|
|
'par':{
|
|||
|
|
'char_file':'../AIlib2/conf/ocr2/benchmark.txt',
|
|||
|
|
'mode':'ch',
|
|||
|
|
'nc':3,
|
|||
|
|
'imgH':32,
|
|||
|
|
'imgW':192,
|
|||
|
|
'hidden':256,
|
|||
|
|
'mean':[0.5,0.5,0.5],
|
|||
|
|
'std':[0.5,0.5,0.5],
|
|||
|
|
'dynamic':False,
|
|||
|
|
},
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]],
|
|||
|
|
'segPar': None,
|
|||
|
|
'postFile': {
|
|||
|
|
"name": "post_process",
|
|||
|
|
"conf_thres": 0.25,
|
|||
|
|
"iou_thres": 0.45,
|
|||
|
|
"classes": 5,
|
|||
|
|
"rainbows": COLOR
|
|||
|
|
},
|
|||
|
|
'Segweights': None,
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
RIVERT_MODEL = ("25", "025", "河道检测模型(T)", 'riverT', lambda device, gpuName: {
|
|||
|
|
'device': device,
|
|||
|
|
'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
|
|||
|
|
"蓝藻"],
|
|||
|
|
'trtFlag_seg': True,
|
|||
|
|
'trtFlag_det': True,
|
|||
|
|
'seg_nclass': 2,
|
|||
|
|
'segRegionCnt': 1,
|
|||
|
|
'segPar': {
|
|||
|
|
'modelSize': (640, 360),
|
|||
|
|
'mean': (0.485, 0.456, 0.406),
|
|||
|
|
'std': (0.229, 0.224, 0.225),
|
|||
|
|
'numpy': False,
|
|||
|
|
'RGB_convert_first': True,
|
|||
|
|
'mixFunction': {
|
|||
|
|
'function': riverDetSegMixProcess,
|
|||
|
|
'pars': {
|
|||
|
|
'slopeIndex': [1, 3, 4, 7],
|
|||
|
|
'riverIou': 0.1
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
'postFile': {
|
|||
|
|
"name": "post_process",
|
|||
|
|
"conf_thres": 0.25,
|
|||
|
|
"iou_thres": 0.3,
|
|||
|
|
"ovlap_thres_crossCategory": 0.65,
|
|||
|
|
"classes": 5,
|
|||
|
|
"rainbows": COLOR
|
|||
|
|
},
|
|||
|
|
'Detweights': "../weights/trt/AIlib2/riverT/yolov5_%s_fp16.engine" % gpuName,
|
|||
|
|
'Segweights': '../weights/trt/AIlib2/riverT/stdc_360X640_%s_fp16.engine' % gpuName
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
FORESTCROWD_FARM_MODEL = ("26", "026", "森林人群模型", 'forestCrowd', lambda device, gpuName: {
|
|||
|
|
'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","人群"],
|
|||
|
|
'postProcess':{'function':gather_post_process,'pars':{'pedestrianId':2,'crowdThreshold':4,'gatherId':5,'distancePersonScale':2.0}},
|
|||
|
|
'models':
|
|||
|
|
[
|
|||
|
|
{
|
|||
|
|
'weight':"../weights/trt/AIlib2/forestCrowd/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
|
|||
|
|
'name':'yolov5',
|
|||
|
|
'model':yolov5Model,
|
|||
|
|
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{ "0":0.25,"1":0.25,"2":0.6,"3":0.6,'4':0.6 ,'5':0.6 } },
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|
|||
|
|
],
|
|||
|
|
|
|||
|
|
'postFile': {
|
|||
|
|
"name": "post_process",
|
|||
|
|
"conf_thres": 0.25,
|
|||
|
|
"iou_thres": 0.45,
|
|||
|
|
"classes": 5,
|
|||
|
|
"rainbows": COLOR
|
|||
|
|
},
|
|||
|
|
'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9] ],###控制哪些检测类别显示、输出
|
|||
|
|
'segRegionCnt':2,###分割模型结果需要保留的等值线数目
|
|||
|
|
"pixScale": 1.2,
|
|||
|
|
|
|||
|
|
|
|||
|
|
})
|
|||
|
|
TRAFFICFORDSJ_FARM_MODEL = ("27", "027", "交通模型-大数据局", 'highWay2T', lambda device, gpuName: {
|
|||
|
|
'device': str(device),
|
|||
|
|
'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子",
|
|||
|
|
"事故", "桥梁外观","设施破损缺失","龙门架","防抛网","标识牌损坏","护栏损坏","钢筋裸露" ],
|
|||
|
|
'trtFlag_seg': True,
|
|||
|
|
'trtFlag_det': True,
|
|||
|
|
'seg_nclass': 3,
|
|||
|
|
'segRegionCnt': 2,
|
|||
|
|
'segPar': {
|
|||
|
|
'modelSize': (640, 360),
|
|||
|
|
'mean': (0.485, 0.456, 0.406),
|
|||
|
|
'std': (0.229, 0.224, 0.225),
|
|||
|
|
'predResize': True,
|
|||
|
|
'numpy': False,
|
|||
|
|
'RGB_convert_first': True,
|
|||
|
|
'mixFunction': {
|
|||
|
|
'function': tracfficAccidentMixFunction,
|
|||
|
|
'pars': {
|
|||
|
|
'modelSize': (640, 360),
|
|||
|
|
'RoadArea': 16000,
|
|||
|
|
'roadVehicleAngle': 15,
|
|||
|
|
'speedRoadVehicleAngleMax': 75,
|
|||
|
|
'roundness': 1.0,
|
|||
|
|
'cls': 10,
|
|||
|
|
'CarId':1,
|
|||
|
|
'CthcId':1,
|
|||
|
|
'vehicleFactor': 0.1,
|
|||
|
|
'confThres': 0.25,
|
|||
|
|
'roadIou': 0.6,
|
|||
|
|
'radius': 50,
|
|||
|
|
'vehicleFlag': False,
|
|||
|
|
'distanceFlag': False
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
'postFile': {
|
|||
|
|
"name": "post_process",
|
|||
|
|
"conf_thres": 0.25,
|
|||
|
|
"iou_thres": 0.25,
|
|||
|
|
"classes": 10,
|
|||
|
|
"rainbows": COLOR
|
|||
|
|
},
|
|||
|
|
'Detweights': "../weights/trt/AIlib2/highWay2T/yolov5_%s_fp16.engine" % gpuName,
|
|||
|
|
'Segweights': '../weights/trt/AIlib2/highWay2T/stdc_360X640_%s_fp16.engine' % gpuName
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
SMARTSITE_MODEL = ("28", "028", "智慧工地模型", 'smartSite', lambda device, gpuName: {
|
|||
|
|
'labelnames': [ "工人","塔式起重机","悬臂","起重机","压路机","推土机","挖掘机","卡车","装载机","泵车","混凝土搅拌车","打桩","其他车辆" ],
|
|||
|
|
'postProcess':{'function':default_mix,'pars':{}},
|
|||
|
|
'models':
|
|||
|
|
[
|
|||
|
|
{
|
|||
|
|
'weight':"../weights/trt/AIlib2/smartSite/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
|
|||
|
|
'name':'yolov5',
|
|||
|
|
'model':yolov5Model,
|
|||
|
|
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|
|||
|
|
],
|
|||
|
|
'postFile': {
|
|||
|
|
"rainbows": COLOR
|
|||
|
|
},
|
|||
|
|
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
RUBBISH_MODEL = ("29", "029", "垃圾模型", 'rubbish', lambda device, gpuName: {
|
|||
|
|
'labelnames': [ "建筑垃圾","白色垃圾","其他垃圾"],
|
|||
|
|
'postProcess':{'function':default_mix,'pars':{}},
|
|||
|
|
'models':
|
|||
|
|
[
|
|||
|
|
{
|
|||
|
|
'weight':"../weights/trt/AIlib2/rubbish/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
|
|||
|
|
'name':'yolov5',
|
|||
|
|
'model':yolov5Model,
|
|||
|
|
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|
|||
|
|
],
|
|||
|
|
'postFile': {
|
|||
|
|
"rainbows": COLOR
|
|||
|
|
},
|
|||
|
|
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
FIREWORK_MODEL = ("30", "030", "烟花模型", 'firework', lambda device, gpuName: {
|
|||
|
|
'labelnames': [ "烟花"],
|
|||
|
|
'postProcess':{'function':default_mix,'pars':{}},
|
|||
|
|
'models':
|
|||
|
|
[
|
|||
|
|
{
|
|||
|
|
'weight':"../weights/trt/AIlib2/firework/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
|
|||
|
|
'name':'yolov5',
|
|||
|
|
'model':yolov5Model,
|
|||
|
|
'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3 } },
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|
|||
|
|
],
|
|||
|
|
'postFile': {
|
|||
|
|
"rainbows": COLOR
|
|||
|
|
},
|
|||
|
|
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
TRAFFIC_SPILL_MODEL = ("50", "501", "高速公路抛洒物模型", 'highWaySpill', lambda device, gpuName: {
|
|||
|
|
'device': str(device),
|
|||
|
|
'labelnames': ["抛洒物","车辆"],
|
|||
|
|
'trtFlag_seg': True,
|
|||
|
|
'trtFlag_det': True,
|
|||
|
|
'seg_nclass': 3,
|
|||
|
|
'segRegionCnt': 2,
|
|||
|
|
'segPar': {
|
|||
|
|
'modelSize': (640, 360),
|
|||
|
|
'mean': (0.485, 0.456, 0.406),
|
|||
|
|
'std': (0.229, 0.224, 0.225),
|
|||
|
|
'predResize': True,
|
|||
|
|
'numpy': False,
|
|||
|
|
'RGB_convert_first': True,
|
|||
|
|
'mixFunction': {
|
|||
|
|
'function': mixTraffic_postprocess,
|
|||
|
|
'pars': {
|
|||
|
|
'modelSize': (640, 360),
|
|||
|
|
'RoadArea': 16000,
|
|||
|
|
'roadVehicleAngle': 15,
|
|||
|
|
'speedRoadVehicleAngleMax': 75,
|
|||
|
|
'roundness': 1.0,
|
|||
|
|
'cls': 0,
|
|||
|
|
'vehicleFactor': 0.1,
|
|||
|
|
'confThres': 0.25,
|
|||
|
|
'roadIou': 0.6,
|
|||
|
|
'radius': 50,
|
|||
|
|
'vehicleFlag': False,
|
|||
|
|
'distanceFlag': False
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
'postFile': {
|
|||
|
|
"name": "post_process",
|
|||
|
|
"conf_thres": 0.25,
|
|||
|
|
"iou_thres": 0.25,
|
|||
|
|
"classes": 2,
|
|||
|
|
"rainbows": COLOR
|
|||
|
|
},
|
|||
|
|
'detModelpara': [{"id": str(x), "config": {"k1": "v1", "k2": "v2"}} for x in [0]],
|
|||
|
|
###控制哪些检测类别显示、输出
|
|||
|
|
'Detweights': "../weights/trt/AIlib2/highWaySpill/yolov5_%s_fp16.engine" % gpuName,
|
|||
|
|
'Segweights': '../weights/trt/AIlib2/highWaySpill/stdc_360X640_%s_fp16.engine' % gpuName
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
TRAFFIC_CTHC_MODEL = ("50", "502", "高速公路危化品模型", 'highWayCthc', lambda device, gpuName: {
|
|||
|
|
'device': str(device),
|
|||
|
|
'labelnames': ["危化品","罐体","危险标识","普通车"],
|
|||
|
|
'trtFlag_seg': True,
|
|||
|
|
'trtFlag_det': True,
|
|||
|
|
'seg_nclass': 3,
|
|||
|
|
'segRegionCnt': 2,
|
|||
|
|
'segPar': {
|
|||
|
|
'modelSize': (640, 360),
|
|||
|
|
'mean': (0.485, 0.456, 0.406),
|
|||
|
|
'std': (0.229, 0.224, 0.225),
|
|||
|
|
'predResize': True,
|
|||
|
|
'numpy': False,
|
|||
|
|
'RGB_convert_first': True,
|
|||
|
|
'mixFunction': {
|
|||
|
|
'function': mixTraffic_postprocess,
|
|||
|
|
'pars': {
|
|||
|
|
'modelSize': (640, 360),
|
|||
|
|
'RoadArea': 16000,
|
|||
|
|
'roadVehicleAngle': 15,
|
|||
|
|
'speedRoadVehicleAngleMax': 75,
|
|||
|
|
'roundness': 1.0,
|
|||
|
|
'cls': 0,
|
|||
|
|
'vehicleFactor': 0.1,
|
|||
|
|
'confThres': 0.25,
|
|||
|
|
'roadIou': 0.6,
|
|||
|
|
'radius': 50,
|
|||
|
|
'vehicleFlag': False,
|
|||
|
|
'distanceFlag': False
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
'postFile': {
|
|||
|
|
"name": "post_process",
|
|||
|
|
"conf_thres": 0.25,
|
|||
|
|
"iou_thres": 0.25,
|
|||
|
|
"classes": 4,
|
|||
|
|
"rainbows": COLOR
|
|||
|
|
},
|
|||
|
|
'detModelpara': [{"id": str(x), "config": {"k1": "v1", "k2": "v2"}} for x in [0]],
|
|||
|
|
###控制哪些检测类别显示、输出
|
|||
|
|
'Detweights': "../weights/trt/AIlib2/highWayCthc/yolov5_%s_fp16.engine" % gpuName,
|
|||
|
|
'Segweights': '../weights/trt/AIlib2/highWayCthc/stdc_360X640_%s_fp16.engine' % gpuName
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
TRAFFIC_PANNEL_MODEL = ("50", "503", "光伏板模型", 'pannel', lambda device, gpuName: {
|
|||
|
|
'labelnames': ["光伏板","覆盖物","裂缝"],
|
|||
|
|
'postProcess': {'function': pannel_post_process, 'pars': {'objs': [0]}},
|
|||
|
|
'models':
|
|||
|
|
[
|
|||
|
|
{
|
|||
|
|
'weight': "../weights/trt/AIlib2/pannel/yolov5_%s_fp16.engine" % (gpuName), ###检测模型路径
|
|||
|
|
'name': 'yolov5',
|
|||
|
|
'model': yolov5Model,
|
|||
|
|
'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.25, 'iou_thres': 0.45,
|
|||
|
|
'allowedList': [0,1,2], 'segRegionCnt': 1, 'trtFlag_det': True,
|
|||
|
|
'trtFlag_seg': False, "score_byClass": {"0": 0.25, "1": 0.3, "2": 0.3, "3": 0.3}},
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
],
|
|||
|
|
'postFile': {
|
|||
|
|
"rainbows": COLOR
|
|||
|
|
},
|
|||
|
|
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
CITY_CARPLATE_MODEL = ("30", "301", "自研车牌检测", 'carplate', lambda device, gpuName: {
|
|||
|
|
'labelnames': ["车牌"],
|
|||
|
|
'device': str(device),
|
|||
|
|
'rainbows': COLOR,
|
|||
|
|
'models': [
|
|||
|
|
{
|
|||
|
|
'trtFlag_det': False,
|
|||
|
|
'weight': '../weights/pth/AIlib2/carplate/plate_yolov5s_v3.jit',
|
|||
|
|
'name': 'yolov5',
|
|||
|
|
'model': yolov5Model,
|
|||
|
|
'par': {
|
|||
|
|
'device': 'cuda:0',
|
|||
|
|
'half': False,
|
|||
|
|
'conf_thres': 0.4,
|
|||
|
|
'iou_thres': 0.45,
|
|||
|
|
'nc': 1,
|
|||
|
|
'plate_dilate': (0.5, 0.1)
|
|||
|
|
},
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
'trtFlag_ocr': False,
|
|||
|
|
'weight': '../weights/pth/AIlib2/ocr2/crnn_ch.pth',
|
|||
|
|
'name': 'ocr',
|
|||
|
|
'model': ocrModel,
|
|||
|
|
'par': {
|
|||
|
|
'char_file': '../AIlib2/conf/ocr2/benchmark.txt',
|
|||
|
|
'mode': 'ch',
|
|||
|
|
'nc': 3,
|
|||
|
|
'imgH': 32,
|
|||
|
|
'imgW': 192,
|
|||
|
|
'hidden': 256,
|
|||
|
|
'mean': [0.5, 0.5, 0.5],
|
|||
|
|
'std': [0.5, 0.5, 0.5],
|
|||
|
|
'dynamic': False,
|
|||
|
|
}
|
|||
|
|
}],
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
CITY_INFRAREDPERSON_MODEL = ("30", "302", "红外行人模型", 'infraredPerson', lambda device, gpuName: {
|
|||
|
|
'labelnames': ["行人"],
|
|||
|
|
'postProcess': {'function': default_mix, 'pars': {}},
|
|||
|
|
'models':
|
|||
|
|
[
|
|||
|
|
{
|
|||
|
|
'weight': "../weights/trt/AIlib2/infraredPerson/yolov5_%s_fp16.engine" % (gpuName), ###检测模型路径
|
|||
|
|
'name': 'yolov5',
|
|||
|
|
'model': yolov5Model,
|
|||
|
|
'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.50, 'iou_thres': 0.45,
|
|||
|
|
'allowedList': list(range(20)), 'segRegionCnt': 1, 'trtFlag_det': True,
|
|||
|
|
'trtFlag_seg': False, "score_byClass": {"0": 0.50, "1": 0.3, "2": 0.3, "3": 0.3}},
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
],
|
|||
|
|
'postFile': {
|
|||
|
|
"rainbows": COLOR
|
|||
|
|
},
|
|||
|
|
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
CITY_NIGHTFIRESMOKE_MODEL = ("30", "303", "夜间烟火模型", 'nightFireSmoke', lambda device, gpuName: {
|
|||
|
|
'labelnames': ["火","烟雾"],
|
|||
|
|
'postProcess': {'function': default_mix, 'pars': {}},
|
|||
|
|
'models':
|
|||
|
|
[
|
|||
|
|
{
|
|||
|
|
'weight': "../weights/trt/AIlib2/nightFireSmoke/yolov5_%s_fp16.engine" % (gpuName), ###检测模型路径
|
|||
|
|
'name': 'yolov5',
|
|||
|
|
'model': yolov5Model,
|
|||
|
|
'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.50, 'iou_thres': 0.45,
|
|||
|
|
'allowedList': list(range(20)), 'segRegionCnt': 1, 'trtFlag_det': True,
|
|||
|
|
'trtFlag_seg': False, "score_byClass": {"0": 0.50, "1": 0.3, "2": 0.3, "3": 0.3}},
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
],
|
|||
|
|
'postFile': {
|
|||
|
|
"rainbows": COLOR
|
|||
|
|
},
|
|||
|
|
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
CITY_DENSECROWDCOUNT_MODEL = ("30", "304", "密集人群计数", 'DenseCrowdCount', lambda device, gpuName: {
|
|||
|
|
'labelnames': ["人群计数"],
|
|||
|
|
'device': str(device),
|
|||
|
|
'rainbows': COLOR,
|
|||
|
|
'models': [
|
|||
|
|
{
|
|||
|
|
'trtFlag_det': False,
|
|||
|
|
'weight': "../weights/pth/AIlib2/DenseCrowd/SHTechA.pth", ###检测模型路径
|
|||
|
|
'vggweight': "../weights/pth/AIlib2/DenseCrowd/vgg16_bn-6c64b313.pth", ###检测模型路径
|
|||
|
|
'name': 'p2pnet',
|
|||
|
|
'model': p2NnetModel,
|
|||
|
|
'par': {
|
|||
|
|
'device': 'cuda:0',
|
|||
|
|
'row': 2,
|
|||
|
|
'line': 2,
|
|||
|
|
'point_loss_coef': 0.45,
|
|||
|
|
'conf': 0.65,
|
|||
|
|
'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,
|
|||
|
|
'allowedList': list(range(20)), 'segRegionCnt': 1, 'trtFlag_det': True,
|
|||
|
|
'trtFlag_seg': False, "score_byClass": {"0": 0.50, "1": 0.3, "2": 0.3, "3": 0.3}},
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
],
|
|||
|
|
'postFile': {
|
|||
|
|
"rainbows": COLOR
|
|||
|
|
},
|
|||
|
|
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
CITY_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,
|
|||
|
|
'allowedList': [0,1,2], 'segRegionCnt': 1, 'trtFlag_det': True,
|
|||
|
|
'trtFlag_seg': False, "score_byClass": {"0": 0.25, "1": 0.3, "2": 0.3, "3": 0.3}},
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
'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
|
|||
|
|
},
|
|||
|
|
}],
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
@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
|