20250313
This commit is contained in:
王庆刚
@ -109,24 +109,31 @@ def test_compare():
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plot_pr_curve(simiList)
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def contrast_pr(paths):
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def contrast_pr(evtPaths):
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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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'''
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evtpaths = []
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for p in paths.iterdir():
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# date_ = ['2025-3-4_1', '2025-3-5_1', '2025-3-5_2']
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# for dt in date_:
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# paths = Path(evtPaths) / dt
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abc = []
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for p in Path(evtPaths).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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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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elif p.is_dir():
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abc.append(p.stem)
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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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alg_times = []
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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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@ -157,7 +164,7 @@ def contrast_pr(paths):
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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 barcode.isdigit() or len(barcode)<8: continue
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if not datapath.is_file(): continue
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bcdList.append(barcode)
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@ -175,10 +182,15 @@ def contrast_pr(paths):
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if len(one2one)+len(one2SN)+len(one2n) == 0:
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errorFile.append(path.stem)
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dtime = SimiDict["algroStartToEnd"]
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if dtime >= 0:
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alg_times.append((dtime, path.stem))
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'''================== 0. 1:1 ==================='''
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barcodes, similars = [], []
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barcodes_ = []
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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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@ -186,6 +198,12 @@ def contrast_pr(paths):
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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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barcodes_.append(path.stem)
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if len(barcodes)==len(similars) and len(barcodes)!=0:
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@ -216,6 +234,8 @@ def contrast_pr(paths):
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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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elif len(one2SN)+len(one2n) == 0:
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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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@ -223,6 +243,7 @@ def contrast_pr(paths):
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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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@ -231,11 +252,11 @@ def contrast_pr(paths):
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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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errorFile_one2SN.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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@ -256,6 +277,7 @@ def contrast_pr(paths):
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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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@ -266,10 +288,17 @@ def contrast_pr(paths):
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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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if len(events)==len(evt_barcodes)==len(evt_similars)==len(evt_types) and len(events)>0:
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if not barcode in evt_barcodes:
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errorFile_one2n.append(path.stem)
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continue
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if len(barcodes_):
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print("do")
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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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@ -324,9 +353,9 @@ def contrast_pr(paths):
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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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_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(one2oneAB)+1e-6))
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_NRecall.append(_TN/(_TN+_FP+1e-6))
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'''===================================== 1:SN 均值'''
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TP_ = sum(np.array(one2SNAA) >= th)
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@ -346,10 +375,10 @@ def contrast_pr(paths):
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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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PRecallX.append(TPX/(TPX+FNX+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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NRecallX.append(TNX/(TNX+FPX+1e-6))
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'''===================================== 1:n'''
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@ -359,13 +388,19 @@ def contrast_pr(paths):
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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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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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algtime = []
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for tm, _ in alg_times:
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algtime.append(tm)
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fig, ax = plt.subplots()
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ax.hist(np.array(algtime), bins=100, edgecolor='black')
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ax.set_title('Algorthm Spend Time')
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ax.set_xlabel(f"Event Num: {len(alg_times)}")
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plt.show()
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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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@ -374,7 +409,9 @@ def contrast_pr(paths):
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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_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('1:1 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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@ -393,30 +430,30 @@ def contrast_pr(paths):
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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[0].set_xlabel(f"Event Num: {len(one2SNAA)}")
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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[0].set_xlabel(f"Event Num: {len(one2SNAA)}")
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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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axes[1].set_xlabel(f"Event Num: {len(one2SNAB)}")
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plt.show()
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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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# axes[1].set_xlabel(f"Event Num: {len(one2SNAB)}")
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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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@ -426,7 +463,9 @@ def contrast_pr(paths):
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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_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:SN Precise & Recall')
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ax.set_xlabel(f"Event Num: {len(one2SNAA)}")
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ax.legend()
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@ -456,7 +495,9 @@ def contrast_pr(paths):
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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_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(tpsimi)+len(fnsimi)}")
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ax.legend()
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@ -473,11 +514,11 @@ def contrast_pr(paths):
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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, 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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# fpsnErrFile = str(paths.joinpath("one2SN_Error.txt"))
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# with open(fpsnErrFile, "w") as file:
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# for item in fp_events:
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@ -487,27 +528,24 @@ def contrast_pr(paths):
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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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# bcdSet = set(bcdList)
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# one2nErrFile = str(paths.joinpath("one_2_Small_n_Error.txt"))
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# with open(one2nErrFile, "w") as file:
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# for item in fnevents:
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# file.write(item + "\n")
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one2nErrFile = os.path.join(evtPaths, "one_2_Small_n_Error.txt")
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with open(one2nErrFile, "w") as file:
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for item in fnevents:
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file.write(item + "\n")
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# one2NErrFile = str(paths.joinpath("one_2_Big_N_Error.txt"))
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# with open(one2NErrFile, "w") as file:
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# for item in fn_events:
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# file.write(item + "\n")
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one2NErrFile = os.path.join(evtPaths, "one_2_Big_N_Error.txt")
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with open(one2NErrFile, "w") as file:
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for item in fn_events:
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file.write(item + "\n")
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print('Done!')
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if __name__ == "__main__":
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evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-2-26_2"
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evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-3-3"
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contrast_pr(evtpaths)
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