1:n modified
This commit is contained in:
王庆刚
@ -6,6 +6,7 @@ Created on Wed Dec 18 11:49:01 2024
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"""
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import os
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import pickle
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import copy
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import numpy as np
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from pathlib import Path
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import matplotlib.pyplot as plt
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@ -17,44 +18,47 @@ def init_eventdict(sourcePath, stype="data"):
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'''stype: str,
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'source': 由 videos 或 images 生成的 pickle 文件
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'data': 从 data 文件中读取的现场运行数据
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'''
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"realtime": 全实时数据，从 data 文件中读取的现场运行数据
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'''
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k, errEvents = 0, []
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for bname in os.listdir(sourcePath):
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# bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479"
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source_path = os.path.join(sourcePath, bname)
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if stype=="data" or stype=="realtime":
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if stype=="source" and not os.path.isfile(source_path): continue
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if stype=="data" and os.path.isfile(source_path): continue
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if stype=="realtime" and os.path.isfile(source_path): continue
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if os.path.isdir(source_path):
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pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
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if not os.path.isdir(source_path) or os.path.isfile(pickpath):
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continue
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if stype=="source":
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else:
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pickpath = os.path.join(eventDataPath, bname)
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if not os.path.isfile(source_path) or os.path.isfile(pickpath):
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continue
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if os.path.isfile(pickpath):
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continue
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evt = os.path.splitext(os.path.split(pickpath)[-1])[0].split('_')
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cont = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10
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if not cont:
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continue
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# event = ShoppingEvent(source_path, stype)
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try:
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event = ShoppingEvent(source_path, stype)
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with open(pickpath, 'wb') as f:
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pickle.dump(event, f)
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print(bname)
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except Exception as e:
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errEvents.append(source_path)
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print(e)
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print(f"Error: {bname}, {e}")
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# k += 1
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# if k==1:
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# break
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# errfile = os.path.join(resultPath, 'error_events.txt')
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# with open(errfile, 'a', encoding='utf-8') as f:
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# for line in errEvents:
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# f.write(line + '\n')
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errfile = os.path.join(resultPath, 'error_events.txt')
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with open(errfile, 'a', encoding='utf-8') as f:
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for line in errEvents:
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f.write(line + '\n')
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def read_eventdict(eventDataPath):
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evtDict = {}
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@ -70,38 +74,38 @@ def read_eventdict(eventDataPath):
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return evtDict
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def simi_calc(event, o2nevt, typee=None):
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if typee == "11":
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boxes1 = event.front_boxes
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boxes2 = o2nevt.front_boxes
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feat1 = event.front_feats
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feat2 = o2nevt.front_feats
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if typee == "10":
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boxes1 = event.front_boxes
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boxes2 = o2nevt.back_boxes
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feat1 = event.front_feats
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feat2 = o2nevt.back_feats
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if typee == "00":
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boxes1 = event.back_boxes
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boxes2 = o2nevt.back_boxes
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feat1 = event.back_feats
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feat2 = o2nevt.back_feats
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if typee == "01":
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boxes1 = event.back_boxes
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boxes2 = o2nevt.front_boxes
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feat1 = event.back_feats
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feat2 = o2nevt.front_feats
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def simi_calc(event, o2nevt, pattern, typee=None):
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if pattern==1 or pattern==2:
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if typee == "11":
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boxes1 = event.front_boxes
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boxes2 = o2nevt.front_boxes
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feat1 = event.front_feats
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feat2 = o2nevt.front_feats
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if typee == "10":
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boxes1 = event.front_boxes
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boxes2 = o2nevt.back_boxes
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feat1 = event.front_feats
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feat2 = o2nevt.back_feats
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if typee == "00":
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boxes1 = event.back_boxes
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boxes2 = o2nevt.back_boxes
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feat1 = event.back_feats
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feat2 = o2nevt.back_feats
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if typee == "01":
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boxes1 = event.back_boxes
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boxes2 = o2nevt.front_boxes
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feat1 = event.back_feats
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feat2 = o2nevt.front_feats
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'''自定义事件特征选择'''
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if typee==3 and len(event.feats_compose) and len(o2nevt.feats_compose):
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if pattern==3 and len(event.feats_compose) and len(o2nevt.feats_compose):
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feat1 = [event.feats_compose]
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feat2 = [o2nevt.feats_compose]
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if len(feat1) and len(feat2):
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matrix = 1 - cdist(feat1[0], feat2[0], 'cosine')
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simi = np.mean(matrix)
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@ -114,64 +118,64 @@ def one2n_pr(evtDicts, pattern=1):
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'''
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pattern:
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1: process.data 中记录的相似度
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2: 根据 process.data 中标记的 type 选择特征计算相似度
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3: 以其它方式选择特征计算相似度
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2: 根据 process.data 中标记的 type 选择特征组合方式计算相似度
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3: 利用 process.data 中的轨迹特征，以其它方式计算相似度
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'''
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tpevents, fnevents, fpevents, tnevents = [], [], [], []
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tpsimi, fnsimi, tnsimi, fpsimi = [], [], [], []
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one2nFile, errorFile_one2n = [], []
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errorFile_one2n_ = []
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evts_output = []
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for evtname, event in evtDicts.items():
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evt_names, evt_barcodes, evt_similars, evt_types = [], [], [], []
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if len(event.barcode)==0:
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evt_names, evt_barcodes, evt_similars, evt_types = [], [], [], []
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if len(event.one2n)==0 or len(event.barcode)==0:
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continue
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evts_output.append(evtname)
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for ndict in event.one2n:
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nname = ndict["event"]
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barcode = ndict["barcode"]
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similar = ndict["similar"]
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typee = ndict["type"].strip()
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if len(barcode)==0:
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continue
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if typee.find(",") >=0:
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typee = typee.split(",")[-1]
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if pattern==1:
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evt_similars.append(similar)
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if pattern==2 or pattern==3:
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o2n_evt = [evt for name, evt in evtDicts.items() if name.find(nname[:15])==0]
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if len(o2n_evt)!=1:
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continue
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simival = simi_calc(event, o2n_evt[0], pattern, typee)
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if simival==None:
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continue
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evt_similars.append(simival)
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evt_names.append(nname)
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evt_barcodes.append(barcode)
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evt_types.append(typee)
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if pattern==1:
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evt_similars.append(similar)
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if pattern==2 or pattern==3:
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o2n_evt = [evt for name, evt in evtDicts.items() if name.find(nname[:15])==0]
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if len(o2n_evt)==1:
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o2nevt = o2n_evt[0]
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else:
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continue
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if pattern==2:
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simival = simi_calc(event, o2nevt, typee)
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if pattern==3:
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simival = simi_calc(event, o2nevt, typee=pattern)
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# if evtname == "20250226-170321-327_6903244678377":
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# print("evtname")
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if simival==None:
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continue
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evt_similars.append(simival)
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## process.data的oneTon的各项中，均不包括当前事件的barcode
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if event.barcode not in evt_barcodes:
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errorFile_one2n.append(evtname)
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continue
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else:
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one2nFile.append(evtname)
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if len(evt_names)==len(evt_barcodes) and len(evt_barcodes)==len(evt_similars) \
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and len(evt_similars)==len(evt_types) and len(evt_names)>0:
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if len(evt_names)==len(evt_barcodes)==len(evt_similars)==len(evt_types) and len(evt_names)>0:
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# maxsim = evt_similars[evt_similars.index(max(evt_similars))]
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maxsim = max(evt_similars)
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for i in range(len(evt_names)):
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bcd, simi = evt_barcodes[i], evt_similars[i]
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if bcd==event.barcode and simi==maxsim:
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tpsimi.append(simi)
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tpevents.append(evtname)
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@ -185,14 +189,11 @@ def one2n_pr(evtDicts, pattern=1):
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fpsimi.append(simi)
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fpevents.append(evtname)
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else:
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errorFile_one2n.append(evtname)
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errorFile_one2n_.append(evtname)
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''' 1:n 数据存储，需根据相似度排序'''
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PPrecise, PRecall = [], []
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NPrecise, NRecall = [], []
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Thresh = np.linspace(-0.2, 1, 100)
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for th in Thresh:
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'''============================= 1:n 计算'''
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@ -202,9 +203,9 @@ def one2n_pr(evtDicts, pattern=1):
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TN = sum(np.array(tnsimi) < th)
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PPrecise.append(TP/(TP+FP+1e-6))
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PRecall.append(TP/(len(one2nFile)+1e-6))
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PRecall.append(TP/(TP+FN+1e-6))
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NPrecise.append(TN/(TN+FN+1e-6))
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NRecall.append(TN/(len(tnsimi)+len(fpsimi)+1e-6))
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NRecall.append(TN/(TN+FP+1e-6))
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'''4. ============================= 1:n 曲线,'''
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@ -238,25 +239,22 @@ def one2n_pr(evtDicts, pattern=1):
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return fpevents
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def main():
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def main():
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'''1. 生成事件字典并保存至 eventDataPath, 只需运行一次 '''
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init_eventdict(eventSourcePath, stype="source") # 'source', 'data', 'realtime'
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init_eventdict(eventSourcePath, stype="realtime") # 'source', 'data', 'realtime'
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# for pfile in os.listdir(eventDataPath):
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# evt = os.path.splitext(pfile)[0].split('_')
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# cont = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10
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# if not cont:
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# continue
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'''2. 读取事件字典 '''
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evtDicts = read_eventdict(eventDataPath)
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'''3. 1:n 比对事件评估 '''
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fpevents = one2n_pr(evtDicts, pattern=2)
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fpevents = one2n_pr(evtDicts, pattern=1)
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fpErrFile = str(Path(resultPath).joinpath("one2n_fp_Error.txt"))
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with open(fpErrFile, "w") as file:
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@ -266,10 +264,10 @@ def main():
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if __name__ == '__main__':
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eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\result_V12\ShoppingDict_pkfile"
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eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-2-27"
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resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\testing"
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eventDataPath = os.path.join(resultPath, "evtobjs_data")
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eventDataPath = os.path.join(resultPath, "evtobjs_wang")
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if not os.path.exists(eventDataPath):
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os.makedirs(eventDataPath)
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