301 lines
9.9 KiB
Python
301 lines
9.9 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Wed Dec 18 11:49:01 2024
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@author: ym
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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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from scipy.spatial.distance import cdist
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from utils.event import ShoppingEvent
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def init_eventDict(sourcePath, eventDataPath, 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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"realtime": 全实时数据，从 data 文件中读取的现场运行数据
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sourcePath:事件文件夹，事件类型包含2种：
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(1) pipeline生成的 pickle 文件
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(2) 直接采集的事件文件夹
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'''
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k, errEvents = 0, []
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for evtname in os.listdir(sourcePath):
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bname, ext = os.path.splitext(evtname)
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source_path = os.path.join(sourcePath, evtname)
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if stype=="source" and ext not in ['.pkl', '.pickle']: 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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evt = bname.split('_')
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condt = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10
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if not condt: continue
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pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
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if os.path.isfile(pickpath): 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(evtname)
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except Exception as e:
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errEvents.append(source_path)
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print(f"Error: {evtname}, {e}")
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# k += 1
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# if k==1:
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# break
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errfile = Path(eventDataPath).parent / 'error_events.txt'
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with open(str(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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for filename in os.listdir(eventDataPath):
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evtname, ext = os.path.splitext(filename)
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if ext != ".pickle": continue
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evtpath = os.path.join(eventDataPath, filename)
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with open(evtpath, 'rb') as f:
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evtdata = pickle.load(f)
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evtDict[evtname] = evtdata
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return evtDict
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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 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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else:
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simi = None
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return simi
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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.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 evtname == "20250226-170321-327_6903244678377":
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# print("evtname")
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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)==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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elif bcd==event.barcode and simi!=maxsim:
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fnsimi.append(simi)
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fnevents.append(evtname)
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elif bcd!=event.barcode and simi!=maxsim:
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tnsimi.append(simi)
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tnevents.append(evtname)
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elif bcd!=event.barcode and simi==maxsim and event.barcode in evt_barcodes:
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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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''' 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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TP = sum(np.array(tpsimi) >= th)
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FP = sum(np.array(fpsimi) >= th)
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FN = sum(np.array(fnsimi) < th)
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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/(TP+FN+1e-6))
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NPrecise.append(TN/(TN+FN+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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fig, ax = plt.subplots()
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ax.plot(Thresh, PPrecise, 'r', label='Precise_Pos: TP/TPFP')
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ax.plot(Thresh, PRecall, 'b', label='Recall_Pos: TP/TPFN')
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ax.plot(Thresh, NPrecise, 'g', label='Precise_Neg: TN/TNFP')
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ax.plot(Thresh, NRecall, 'c', label='Recall_Neg: TN/TNFN')
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ax.set_xlim([0, 1])
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ax.set_ylim([0, 1])
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ax.set_xticks(np.arange(0, 1, 0.1))
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ax.set_yticks(np.arange(0, 1, 0.1))
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ax.grid(True, linestyle='--')
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ax.set_title('1:n Precise & Recall')
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ax.set_xlabel(f"Event Num: {len(one2nFile)}")
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ax.legend()
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plt.show()
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## ============================= 1:n 直方图'''
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fig, axes = plt.subplots(2, 2)
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axes[0, 0].hist(tpsimi, bins=60, range=(-0.2, 1), edgecolor='black')
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axes[0, 0].set_xlim([-0.2, 1])
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axes[0, 0].set_title(f'TP: {len(tpsimi)}')
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axes[0, 1].hist(fpsimi, bins=60, range=(-0.2, 1), edgecolor='black')
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axes[0, 1].set_xlim([-0.2, 1])
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axes[0, 1].set_title(f'FP: {len(fpsimi)}')
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axes[1, 0].hist(tnsimi, bins=60, range=(-0.2, 1), edgecolor='black')
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axes[1, 0].set_xlim([-0.2, 1])
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axes[1, 0].set_title(f'TN: {len(tnsimi)}')
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axes[1, 1].hist(fnsimi, bins=60, range=(-0.2, 1), edgecolor='black')
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axes[1, 1].set_xlim([-0.2, 1])
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axes[1, 1].set_title(f'FN: {len(fnsimi)}')
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plt.show()
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return fpevents
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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, eventDataPath, 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=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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for item in fpevents:
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file.write(item + "\n")
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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\V12\2025-3-4_2"
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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_wang")
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if not os.path.exists(eventDataPath):
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os.makedirs(eventDataPath)
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# similPath = os.path.join(resultPath, "simidata")
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# if not os.path.exists(similPath):
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# os.makedirs(similPath)
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main()
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