first push

sample_server.py

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