Files
ieemoo-ai-imageassessment/tools/config.py
2024-11-27 15:37:10 +08:00

94 lines
2.7 KiB
Python

# from yacs.config import CfgNode as CfgNode
import torchvision.transforms as T
import torch
import os
class globalVal:
tempLibList = []
tempLibLists = {}
track_y_lists = []
mac_id = None
back_return_similarity = []
back_add_similarity = []
front_return_similarity = []
front_add_similarity = []
comprehensive_similarity = []
class config:
save_videos_dir = 'videos'
#url
# push_url = 'http://api.test2.ieemoo.cn/emoo-api/intelligence/addVideoPathBySequenceId.do'
push_url = 'https://api.test2.ieemoo.cn/emoo-api/intelligence/addVideoPathBySequenceId.do' # 闲时上传
get_config_url = 'https://api.test2.ieemoo.cn/emoo-api/intelligence/addVideoPathByStoreId.do' # 闲时上传相应配置
storidPth = 'tools/storeId.txt'
#obs update
obs_access_key_id = 'LHXJC7GIC2NNUUHHTNVI'
obs_secret_access_key = 'sVWvEItrFKWPp5DxeMvX8jLFU69iXPpzkjuMX3iM'
obs_server = 'https://obs.cn-east-3.myhuaweicloud.com'
obs_bucketName = 'ieemoo-ai'
keys = ['x', 'y', 'w', 'h', 'track_id', 'score', 'cls', 'frame_index']
obs_root_dir = 'ieemoo_ai_data'
#contrast config
host = "192.168.1.28"
port = "19530"
embedding_size = 256
img_size = 224
test_transform = T.Compose([
T.ToTensor(),
T.Resize((224, 224)),
T.ConvertImageDtype(torch.float32),
T.Normalize(mean=[0.5], std=[0.5]),
])
# test_model = "./tools/ckpts/MobilenetV3Large_noParallel_2624.pth"
test_model = "./tools/ckpts/resnet18_0721_best.pth"
tracking_model = "./tools/ckpts/best_158734_cls11_noaug10.pt"
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
httpHost = '0.0.0.0'
httpPort = 8088
#tracking config
botsort = './ytracking/tracking/trackers/cfg/botsort.yaml'
incart = './tools/Template_images/incart.png'
outcart = './tools/Template_images/outcart.png'
cartboarder = './tools/Template_images/cartboarder.png'
edgeline = './tools/Template_images/edgeline.png'
cartedge = './tools/Template_images/cartedge.png'
incart_ftmp = './tools/Template_images/incart_ftmp.png'
action_type = {
"1": 'purchase',
'2': 'jettison',
'3': 'unswept_purchase',
'4': 'unswept_jettison'
}
camera_id = {
'0': 'back',
'1': 'front',
}
recognize_result = {
'01': 'uncatalogued',
'02': 'fail',
'03': 'exception',
'04': 'pass',
}
# reid config
backbone = 'resnet18' # [resnet18, mobilevit_s, mobilenet_v2, mobilenetv3]
batch_size = 8
model_path = './tools/ckpts/best_resnet18_0515.pth'
temp_video_name = None
cfg = config()
gvalue = globalVal()