129 lines
4.0 KiB
Python
129 lines
4.0 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Thu Oct 31 15:17:01 2024
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@author: ym
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"""
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import os
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import numpy as np
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import pickle
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from pathlib import Path
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import matplotlib.pyplot as plt
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from .event import ShoppingEvent
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def init_eventDict(sourcePath, eventDataPath, stype="data"):
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'''
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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 get_evtList(evtpath):
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'''==== 0. 生成事件列表和对应的 Barcodes 集合 ==========='''
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bcdList, evtpaths = [], []
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for evtname in os.listdir(evtpath):
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bname, ext = os.path.splitext(evtname)
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## 处理事件的两种情况：文件夹 和 Yolo-Resnet-Tracker 的输出
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fpath = os.path.join(evtpath, evtname)
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if os.path.isfile(fpath) and (ext==".pkl" or ext==".pickle"):
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evt = bname.split('_')
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elif os.path.isdir(fpath):
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evt = evtname.split('_')
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else:
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continue
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if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10:
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bcdList.append(evt[-1])
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evtpaths.append(fpath)
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bcdSet = set(bcdList)
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return evtpaths, bcdSet
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def showHist(err, correct):
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err = np.array(err)
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correct = np.array(correct)
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fig, axs = plt.subplots(2, 1)
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axs[0].hist(err, bins=50, edgecolor='black')
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axs[0].set_xlim([0, 1])
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axs[0].set_title('err')
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axs[1].hist(correct, bins=50, edgecolor='black')
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axs[1].set_xlim([0, 1])
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axs[1].set_title('correct')
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# plt.show()
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return plt
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def show_recall_prec(recall, prec, ths):
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# x = np.linspace(start=-0, stop=1, num=11, endpoint=True).tolist()
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fig = plt.figure(figsize=(10, 6))
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plt.plot(ths, recall, color='red', label='recall')
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plt.plot(ths, prec, color='blue', label='PrecisePos')
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plt.legend()
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plt.xlabel(f'threshold')
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# plt.ylabel('Similarity')
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plt.grid(True, linestyle='--', alpha=0.5)
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# plt.savefig('accuracy_recall_grid.png')
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# plt.show()
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# plt.close()
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return plt
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def compute_recall_precision(err_similarity, correct_similarity):
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ths = np.linspace(0, 1, 51)
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recall, prec = [], []
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for th in ths:
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TP = len([num for num in correct_similarity if num >= th])
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FP = len([num for num in err_similarity if num >= th])
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if (TP+FP) == 0:
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prec.append(1)
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recall.append(0)
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else:
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prec.append(TP / (TP + FP))
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recall.append(TP / (len(err_similarity) + len(correct_similarity)))
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return recall, prec, ths |