113 lines
4.0 KiB
Python
Executable File
113 lines
4.0 KiB
Python
Executable File
import sys
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import argparse
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#from utils.retrieval_index import EvaluteMap
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from utils.tools import EvaluteMap
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from utils.retrieval_feature import AntiFraudFeatureDataset
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from utils.monitor import Moniting
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from utils.updateObs import *
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#from utils.decide import Decide
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from utils.config import cfg
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from utils.tools import createNet
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from flask import request,Flask, jsonify
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from utils.forsegmentation import analysis
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from gevent.pywsgi import WSGIServer
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import os, base64, stat, shutil
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import pdb
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sys.path.append('RAFT')
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sys.path.append('RAFT/core')
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sys.path.append('RAFT/core/utils')
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from RAFT.analysis_video import *
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import time
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os.environ["CUDA_VISIBLE_DEVICES"] = '0,1'
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app = Flask(__name__)
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parser = argparse.ArgumentParser()
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parser.add_argument('--model', default='RAFT/models/raft-things.pth',help="restore checkpoint")
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parser.add_argument('--small', action='store_true', help='use small model')
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parser.add_argument('--mixed_precision', action='store_true', help='use mixed precision')
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parser.add_argument('--alternate_corr', action='store_true', help='use efficent correlation implementation')
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opt, unknown = parser.parse_known_args()
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'''
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status 状态码
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00: 视频未解析成功(视频截取错误)
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01: 未纳入监查列表
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02: 未检测出商品
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03: 异常输出
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04: 正确识别
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'''
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status = ['00', '01', '02', '03', '04']
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net, transform, ms = createNet()
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raft_model = raft_init_model(opt)
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def get_video():
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url = "https://api.ieemoo.com/emoo-train/collection/getVideoCollectByTime.do"
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data = {"startTime":"2022-01-25", "endTime":"2022-01-26"}
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r = requests.post(url=url, data=data)
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videonames = []
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filename = cfg.SAVIDEOPATH
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for dictdata in r.json()['data']:
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urlpath = dictdata["videoPath"]
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videonames.append(urlpath)
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for urlname in videonames:
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videoname = os.path.basename(urlname)
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savepath = os.sep.join([filename, videoname])
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filepath, _ = urllib.request.urlretrieve(urlname, savepath, _progress)
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def search(video_name):
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#get_video()
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T1 = time.time()
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pre_status = False
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try:
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video_path = os.sep.join([cfg.SAVIDEOPATH, video_name])
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uuid_barcode = video_name.split('.')[0]
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barcode_name = uuid_barcode.split('_')[-1]
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#pdb.set_trace()
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photo_nu = analysis_video(raft_model, video_path, cfg.SAMPLEIMGS, uuid_barcode)
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if not Moniting(barcode_name).search() == 'nomatch':
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if photo_nu == 0:
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deleteimg(uuid_barcode)
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return uuid_barcode+'_0.90_!'+status[0]+'_'+video_name
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#Addimg(uuid_barcode)
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feature_dict = AntiFraudFeatureDataset(uuid_barcode, cfg.SAMPLEIMGS, 'sample').extractFeature(net, transform, ms)
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res = EvaluteMap().match_images(feature_dict, barcode_name)
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if res<cfg.THRESHOLD: pre_status = status[2]
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else: pre_status = status[4]
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else:
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pre_status = status[1]
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res = '0.90'
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except:
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return uuid_barcode+'_0.90_!'+'_'+status[3]+'_'+video_name
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data = uuid_barcode+'_'+str(res)+'_!'
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print(data)
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if pre_status == '04':#去除异常与识别正确
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deleteimg(uuid_barcode)
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result = data+'_'+pre_status+'_'+video_name
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T2 = time.time()
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print('程序运行总时间:%s秒' % ((T2 - T1) ))
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print(result)
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return result
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def match():
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n = 0
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total = len(os.listdir(cfg.SAVIDEOPATH))
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f = open('tmp.txt', 'a')
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for video_name in os.listdir(cfg.SAVIDEOPATH):
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result = search(video_name)
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score = result.split('!')[0].split('_')[-2]
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if float(score) >cfg.THRESHOLD:
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if not float(score) == 0.90:
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#print('video_name',video_name)
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f.write(result+'\n')
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n += 1
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else:
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total -= 1
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if not n == 0:
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print(n/total)
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f.close()
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def deleteimg(uuid_barcode):
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for img_name in os.listdir(cfg.SAMPLEIMGS):
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if uuid_barcode in img_name:
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os.remove(os.sep.join([cfg.SAMPLEIMGS, img_name]))
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if __name__ == '__main__':
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match()
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