525 lines
18 KiB
Python
525 lines
18 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Wed Sep 11 11:57:30 2024
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永辉现场试验输出数据的 1:1 性能评估
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适用于202410前数据保存版本的，需调用 OneToOneCompare.txt
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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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from pathlib import Path
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import matplotlib.pyplot as plt
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import sys
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sys.path.append(r"D:\DetectTracking")
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from tracking.utils.read_data import read_similar
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def read_one2one_data(filepath):
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simiList = []
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with open(filepath, 'r', encoding='utf-8') as f:
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lines = f.readlines()
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split_flag = False
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simi_dict = {}
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for i, line in enumerate(lines):
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line = line.strip()
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if not line:
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if len(simi_dict): simiList.append(simi_dict)
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simi_dict = {}
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continue
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label = line.split(':')[0].strip()
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value = line.split(':')[1].strip()
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if label.find("SeqDir") >= 0:
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simi_dict["SeqDir"] = value
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if label.isdigit() and len(label) >= 8:
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simi_max, simi_min = value.strip(',').split('.')
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simi_dict["barcode"] = label
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simi_dict["simi_max"] = float(simi_max) / 1000
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simi_dict["simi_min"] = float(simi_min) / 1000
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if len(simi_dict): simiList.append(simi_dict)
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return simiList
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def plot_pr_curve(matrix):
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simimax, simimean = [], []
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need_analysis = []
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for simidict in matrix:
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simimax.append(simidict["simi_max"])
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simimean.append(simidict["simi_min"])
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if simidict["simi_max"]>0.6:
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need_analysis.append(simidict)
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simimax = np.array(simimax)
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simimean = np.array(simimean)
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TPFN_max = len(simimax)
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TPFN_mean = len(simimean)
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fig, axs = plt.subplots(2, 1)
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axs[0].hist(simimax, bins=60, edgecolor='black')
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axs[0].set_xlim([-0.2, 1])
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axs[0].set_title(f'Same Barcode, Num: {TPFN_max}')
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axs[1].hist(simimean, bins=60, edgecolor='black')
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axs[1].set_xlim([-0.2, 1])
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axs[1].set_title(f'Cross Barcode, Num: {TPFN_mean}')
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# plt.savefig(f'./result/{file}_hist.png') # svg, png, pdf
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Recall_Neg = []
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Thresh = np.linspace(-0.2, 1, 100)
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for th in Thresh:
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TN = np.sum(simimax < th)
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Recall_Neg.append(TN/TPFN_max)
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fig, ax = plt.subplots()
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ax.plot(Thresh, Recall_Neg, 'b', label='Recall_Pos: TP/TPFN')
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ax.set_xlim([0, 1])
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ax.set_ylim([0, 1])
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ax.grid(True)
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ax.set_title('Positive recall')
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ax.set_xlabel(f"Num: {TPFN_max}")
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ax.legend()
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plt.show()
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# plt.savefig(f'./result/{file}_pr.png') # svg, png, pdf
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print("Have done!")
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pass
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def test_compare():
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filepaths = [r"\\192.168.1.28\share\测试_202406\0913_扫A放B\0913_1\OneToOneCompare.txt",
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r"\\192.168.1.28\share\测试_202406\0913_扫A放B\0913_2\OneToOneCompare.txt",
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r"\\192.168.1.28\share\测试_202406\0914_扫A放B\0914_1\OneToOneCompare.txt",
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r"\\192.168.1.28\share\测试_202406\0914_扫A放B\0914_2\OneToOneCompare.txt"
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]
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simiList = []
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for fp in filepaths:
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slist = read_one2one_data(fp)
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simiList.extend(slist)
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plot_pr_curve(simiList)
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def contrast_pr(paths):
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'''
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1:1
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'''
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paths = Path(paths)
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evtpaths = []
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for p in paths.iterdir():
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condt1 = p.is_dir()
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condt2 = len(p.name.split('_'))>=2
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condt3 = len(p.name.split('_')[-1])>8
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condt4 = p.name.split('_')[-1].isdigit()
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if condt1 and condt2 and condt3 and condt4:
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evtpaths.append(p)
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# evtpaths = [p for p in paths.iterdir() if p.is_dir() and len(p.name.split('_'))>=2 and len(p.name.split('_')[-1])>8]
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# evtpaths = [p for p in paths.iterdir() if p.is_dir()]
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events, similars = [], []
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##===================================== 扫A放A, 扫A放B场景()
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one2oneAA, one2oneAB = [], []
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one2SNAA, one2SNAB = [], []
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##===================================== 应用于 1：1
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_tp_events, _fn_events, _fp_events, _tn_events = [], [], [], []
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_tp_simi, _fn_simi, _tn_simi, _fp_simi = [], [], [], []
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##===================================== 应用于 1：SN
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tp_events, fn_events, fp_events, tn_events = [], [], [], []
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tp_simi, fn_simi, tn_simi, fp_simi = [], [], [], []
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##===================================== 应用于1:n
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tpevents, fnevents, fpevents, tnevents = [], [], [], []
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tpsimi, fnsimi, tnsimi, fpsimi = [], [], [], []
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##===================================== barcodes总数、比对错误事件
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bcdList = []
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one2onePath, one2onePath1 = [], []
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one2SNPath, one2SNPath1 = [], []
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one2nPath = []
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errorFile_one2one, errorFile_one2SN, errorFile_one2n = [], [], []
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for path in evtpaths:
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barcode = path.stem.split('_')[-1]
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datapath = path.joinpath('process.data')
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if not barcode.isdigit() or len(barcode)<10: continue
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if not datapath.is_file(): continue
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bcdList.append(barcode)
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try:
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SimiDict = read_similar(datapath)
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except Exception as e:
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print(f"{path.stem}, Error: {e}")
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'''放入为 1:1，相似度取最大值；取出时为 1:SN, 相似度取均值'''
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one2one = SimiDict['one2one']
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one2SN = SimiDict['one2SN']
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one2n = SimiDict['one2n']
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'''================== 0. 1:1 ==================='''
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barcodes, similars = [], []
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for dt in one2one:
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one2onePath.append((path.stem))
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if dt['similar']==0:
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one2onePath1.append((path.stem))
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continue
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barcodes.append(dt['barcode'])
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similars.append(dt['similar'])
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if len(barcodes)==len(similars) and len(barcodes)!=0:
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## 扫A放A, 扫A放B场景
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simAA = [similars[i] for i in range(len(barcodes)) if barcodes[i]==barcode]
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simAB = [similars[i] for i in range(len(barcodes)) if barcodes[i]!=barcode]
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one2oneAA.extend(simAA)
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one2oneAB.extend(simAB)
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## 相似度排序，barcode相等且排名第一为TP，适用于多的barcode相似度比较
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max_idx = similars.index(max(similars))
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max_sim = similars[max_idx]
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# max_bcd = barcodes[max_idx]
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for i in range(len(one2one)):
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bcd, simi = barcodes[i], similars[i]
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if bcd==barcode and simi==max_sim:
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_tp_simi.append(simi)
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_tp_events.append(path.stem)
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elif bcd==barcode and simi!=max_sim:
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_fn_simi.append(simi)
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_fn_events.append(path.stem)
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elif bcd!=barcode and simi!=max_sim:
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_tn_simi.append(simi)
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_tn_events.append(path.stem)
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elif bcd!=barcode and simi==max_sim and barcode in barcodes:
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_fp_simi.append(simi)
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_fp_events.append(path.stem)
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else:
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errorFile_one2one.append(path.stem)
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'''================== 2. 取出场景下的 1 : Small N ==================='''
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barcodes, similars = [], []
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for dt in one2SN:
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barcodes.append(dt['barcode'])
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similars.append(dt['similar'])
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if len(barcodes)==len(similars) and len(barcodes)!=0:
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## 扫A放A, 扫A放B场景
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simAA = [similars[i] for i in range(len(barcodes)) if barcodes[i]==barcode]
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simAB = [similars[i] for i in range(len(barcodes)) if barcodes[i]!=barcode]
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one2SNAA.extend(simAA)
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one2SNAB.extend(simAB)
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one2SNPath.append(path.stem)
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if len(simAA)==0:
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one2SNPath1.append(path.stem)
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## 相似度排序，barcode相等且排名第一为TP，适用于多的barcode相似度比较
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max_idx = similars.index(max(similars))
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max_sim = similars[max_idx]
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# max_bcd = barcodes[max_idx]
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for i in range(len(one2SN)):
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bcd, simi = barcodes[i], similars[i]
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if bcd==barcode and simi==max_sim:
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tp_simi.append(simi)
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tp_events.append(path.stem)
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elif bcd==barcode and simi!=max_sim:
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fn_simi.append(simi)
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fn_events.append(path.stem)
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elif bcd!=barcode and simi!=max_sim:
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tn_simi.append(simi)
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tn_events.append(path.stem)
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elif bcd!=barcode and simi==max_sim and barcode in barcodes:
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fp_simi.append(simi)
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fp_events.append(path.stem)
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else:
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errorFile_one2SN.append(path.stem)
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'''===================== 3. 取出场景下的 1:n ========================'''
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events, evt_barcodes, evt_similars, evt_types = [], [], [], []
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for dt in one2n:
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events.append(dt["event"])
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evt_barcodes.append(dt["barcode"])
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evt_similars.append(dt["similar"])
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evt_types.append(dt["type"])
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if len(events)==len(evt_barcodes) and len(evt_barcodes)==len(evt_similars) \
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and len(evt_similars)==len(evt_types) and len(events)>0:
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one2nPath.append(path.stem)
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maxsim = evt_similars[evt_similars.index(max(evt_similars))]
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for i in range(len(one2n)):
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bcd, simi = evt_barcodes[i], evt_similars[i]
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if bcd==barcode and simi==maxsim:
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tpsimi.append(simi)
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tpevents.append(path.stem)
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elif bcd==barcode and simi!=maxsim:
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fnsimi.append(simi)
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fnevents.append(path.stem)
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elif bcd!=barcode and simi!=maxsim:
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tnsimi.append(simi)
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tnevents.append(path.stem)
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elif bcd!=barcode and simi==maxsim and barcode in evt_barcodes:
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fpsimi.append(simi)
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fpevents.append(path.stem)
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else:
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errorFile_one2n.append(path.stem)
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'''命名规则:
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1:1 (max) 1:1 (max) 1:n 1:N
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_TP TP_ TP TPX
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_PPrecise PPrecise_ PPrecise PPreciseX
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tpsimi tp_simi
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'''
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''' 1:1 数据存储, 相似度计算方式：最大值、均值'''
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_PPrecise, _PRecall = [], []
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_NPrecise, _NRecall = [], []
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PPrecise_, PRecall_ = [], []
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NPrecise_, NRecall_ = [], []
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''' 1:SN 数据存储，需根据相似度排序'''
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PPreciseX, PRecallX = [], []
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NPreciseX, NRecallX = [], []
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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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'''(Precise, Recall) 计算方式, 若 1:1 与 1:SN 相似度选择方式相同，则可以合并'''
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'''===================================== 1:1 最大值'''
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_TP = sum(np.array(one2oneAA) >= th)
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_FP = sum(np.array(one2oneAB) >= th)
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_FN = sum(np.array(one2oneAA) < th)
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_TN = sum(np.array(one2oneAB) < th)
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_PPrecise.append(_TP/(_TP+_FP+1e-6))
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_PRecall.append(_TP/(len(one2oneAA)+1e-6))
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_NPrecise.append(_TN/(_TN+_FN+1e-6))
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_NRecall.append(_TN/(len(one2oneAB)+1e-6))
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'''===================================== 1:SN 均值'''
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TP_ = sum(np.array(one2SNAA) >= th)
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FP_ = sum(np.array(one2SNAB) >= th)
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FN_ = sum(np.array(one2SNAA) < th)
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TN_ = sum(np.array(one2SNAB) < th)
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PPrecise_.append(TP_/(TP_+FP_+1e-6))
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PRecall_.append(TP_/(len(one2SNAA)+1e-6))
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NPrecise_.append(TN_/(TN_+FN_+1e-6))
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NRecall_.append(TN_/(len(one2SNAB)+1e-6))
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'''适用于 (Precise, Recall) 计算方式：多个相似度计算并排序，barcode相等且排名第一为 TP '''
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'''===================================== 1:SN '''
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TPX = sum(np.array(tp_simi) >= th)
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FPX = sum(np.array(fp_simi) >= th)
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FNX = sum(np.array(fn_simi) < th)
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TNX = sum(np.array(tn_simi) < th)
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PPreciseX.append(TPX/(TPX+FPX+1e-6))
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PRecallX.append(TPX/(len(tp_simi)+len(fn_simi)+1e-6))
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NPreciseX.append(TNX/(TNX+FNX+1e-6))
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NRecallX.append(TNX/(len(tn_simi)+len(fp_simi)+1e-6))
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'''===================================== 1:n'''
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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/(len(tpsimi)+len(fnsimi)+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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'''1. ============================= 1:1 最大值方案 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.grid(True)
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ax.set_title('1:1 Precise & Recall')
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ax.set_xlabel(f"Event Num: {len(one2oneAA)+len(one2oneAB)}")
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ax.legend()
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plt.show()
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## ============================= 1:1 最大值方案 直方图'''
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fig, axes = plt.subplots(2, 1)
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axes[0].hist(np.array(one2oneAA), bins=60, edgecolor='black')
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axes[0].set_xlim([-0.2, 1])
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axes[0].set_title('AA')
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axes[1].hist(np.array(one2oneAB), bins=60, edgecolor='black')
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axes[1].set_xlim([-0.2, 1])
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axes[1].set_title('BB')
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plt.show()
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'''2. ============================= 1:1 均值方案 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.grid(True)
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ax.set_title('1:1 Precise & Recall')
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ax.set_xlabel(f"Event Num: {len(one2SNAA)}")
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ax.legend()
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plt.show()
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## ============================= 1:1 均值方案 直方图'''
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fig, axes = plt.subplots(2, 1)
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axes[0].hist(np.array(one2SNAA), bins=60, edgecolor='black')
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axes[0].set_xlim([-0.2, 1])
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axes[0].set_title('AA')
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axes[1].hist(np.array(one2SNAB), bins=60, edgecolor='black')
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axes[1].set_xlim([-0.2, 1])
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axes[1].set_title('BB')
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plt.show()
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''''3. ============================= 1:SN 曲线'''
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fig, ax = plt.subplots()
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ax.plot(Thresh, PPreciseX, 'r', label='Precise_Pos: TP/TPFP')
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ax.plot(Thresh, PRecallX, 'b', label='Recall_Pos: TP/TPFN')
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ax.plot(Thresh, NPreciseX, 'g', label='Precise_Neg: TN/TNFP')
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ax.plot(Thresh, NRecallX, '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.grid(True)
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ax.set_title('1:SN Precise & Recall')
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ax.set_xlabel(f"Event Num: {len(one2SNAA)}")
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ax.legend()
|
||
plt.show()
|
||
## ============================= 1:N 展厅 直方图'''
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||
fig, axes = plt.subplots(2, 2)
|
||
axes[0, 0].hist(tp_simi, bins=60, edgecolor='black')
|
||
axes[0, 0].set_xlim([-0.2, 1])
|
||
axes[0, 0].set_title('TP')
|
||
axes[0, 1].hist(fp_simi, bins=60, edgecolor='black')
|
||
axes[0, 1].set_xlim([-0.2, 1])
|
||
axes[0, 1].set_title('FP')
|
||
axes[1, 0].hist(tn_simi, bins=60, edgecolor='black')
|
||
axes[1, 0].set_xlim([-0.2, 1])
|
||
axes[1, 0].set_title('TN')
|
||
axes[1, 1].hist(fn_simi, bins=60, edgecolor='black')
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||
axes[1, 1].set_xlim([-0.2, 1])
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axes[1, 1].set_title('FN')
|
||
plt.show()
|
||
|
||
|
||
'''4. ============================= 1:n 曲线,'''
|
||
fig, ax = plt.subplots()
|
||
ax.plot(Thresh, PPrecise, 'r', label='Precise_Pos: TP/TPFP')
|
||
ax.plot(Thresh, PRecall, 'b', label='Recall_Pos: TP/TPFN')
|
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ax.plot(Thresh, NPrecise, 'g', label='Precise_Neg: TN/TNFP')
|
||
ax.plot(Thresh, NRecall, 'c', label='Recall_Neg: TN/TNFN')
|
||
ax.set_xlim([0, 1])
|
||
ax.set_ylim([0, 1])
|
||
ax.grid(True)
|
||
ax.set_title('1:n Precise & Recall')
|
||
ax.set_xlabel(f"Event Num: {len(tpsimi)+len(fnsimi)}")
|
||
ax.legend()
|
||
plt.show()
|
||
## ============================= 1:n 直方图'''
|
||
fig, axes = plt.subplots(2, 2)
|
||
axes[0, 0].hist(tpsimi, bins=60, edgecolor='black')
|
||
axes[0, 0].set_xlim([-0.2, 1])
|
||
axes[0, 0].set_title('TP')
|
||
axes[0, 1].hist(fpsimi, bins=60, edgecolor='black')
|
||
axes[0, 1].set_xlim([-0.2, 1])
|
||
axes[0, 1].set_title('FP')
|
||
axes[1, 0].hist(tnsimi, bins=60, edgecolor='black')
|
||
axes[1, 0].set_xlim([-0.2, 1])
|
||
axes[1, 0].set_title('TN')
|
||
axes[1, 1].hist(fnsimi, bins=60, edgecolor='black')
|
||
axes[1, 1].set_xlim([-0.2, 1])
|
||
axes[1, 1].set_title('FN')
|
||
plt.show()
|
||
|
||
|
||
fpsnErrFile = str(paths.joinpath("one2SN_Error.txt"))
|
||
with open(fpsnErrFile, "w") as file:
|
||
for item in fp_events:
|
||
file.write(item + "\n")
|
||
|
||
fpErrFile = str(paths.joinpath("one2n_Error.txt"))
|
||
with open(fpErrFile, "w") as file:
|
||
for item in fpevents:
|
||
file.write(item + "\n")
|
||
|
||
|
||
|
||
|
||
# bcdSet = set(bcdList)
|
||
# one2nErrFile = str(paths.joinpath("one_2_Small_n_Error.txt"))
|
||
# with open(one2nErrFile, "w") as file:
|
||
# for item in fnevents:
|
||
# file.write(item + "\n")
|
||
|
||
# one2NErrFile = str(paths.joinpath("one_2_Big_N_Error.txt"))
|
||
# with open(one2NErrFile, "w") as file:
|
||
# for item in fn_events:
|
||
# file.write(item + "\n")
|
||
|
||
print('Done!')
|
||
|
||
|
||
|
||
if __name__ == "__main__":
|
||
evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images"
|
||
contrast_pr(evtpaths)
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
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|
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|
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|
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|
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