# -*- coding: utf-8 -*- """ Created on Wed Sep 11 11:57:30 2024 永辉现场试验输出数据的 1:1 性能评估 适用于202410前数据保存版本的，需调用 OneToOneCompare.txt @author: ym """ import os import numpy as np from pathlib import Path import matplotlib.pyplot as plt import sys sys.path.append(r"D:\DetectTracking") from tracking.utils.read_data import read_similar def read_one2one_data(filepath): simiList = [] with open(filepath, 'r', encoding='utf-8') as f: lines = f.readlines() split_flag = False simi_dict = {} for i, line in enumerate(lines): line = line.strip() if not line: if len(simi_dict): simiList.append(simi_dict) simi_dict = {} continue label = line.split(':')[0].strip() value = line.split(':')[1].strip() if label.find("SeqDir") >= 0: simi_dict["SeqDir"] = value if label.isdigit() and len(label) >= 8: simi_max, simi_min = value.strip(',').split('.') simi_dict["barcode"] = label simi_dict["simi_max"] = float(simi_max) / 1000 simi_dict["simi_min"] = float(simi_min) / 1000 if len(simi_dict): simiList.append(simi_dict) return simiList def plot_pr_curve(matrix): simimax, simimean = [], [] need_analysis = [] for simidict in matrix: simimax.append(simidict["simi_max"]) simimean.append(simidict["simi_min"]) if simidict["simi_max"]>0.6: need_analysis.append(simidict) simimax = np.array(simimax) simimean = np.array(simimean) TPFN_max = len(simimax) TPFN_mean = len(simimean) fig, axs = plt.subplots(2, 1) axs[0].hist(simimax, bins=60, edgecolor='black') axs[0].set_xlim([-0.2, 1]) axs[0].set_title(f'Same Barcode, Num: {TPFN_max}') axs[1].hist(simimean, bins=60, edgecolor='black') axs[1].set_xlim([-0.2, 1]) axs[1].set_title(f'Cross Barcode, Num: {TPFN_mean}') # plt.savefig(f'./result/{file}_hist.png') # svg, png, pdf Recall_Neg = [] Thresh = np.linspace(-0.2, 1, 100) for th in Thresh: TN = np.sum(simimax < th) Recall_Neg.append(TN/TPFN_max) fig, ax = plt.subplots() ax.plot(Thresh, Recall_Neg, 'b', label='Recall_Pos: TP/TPFN') ax.set_xlim([0, 1]) ax.set_ylim([0, 1]) ax.grid(True) ax.set_title('Positive recall') ax.set_xlabel(f"Num: {TPFN_max}") ax.legend() plt.show() # plt.savefig(f'./result/{file}_pr.png') # svg, png, pdf print("Have done!") pass def test_compare(): filepaths = [r"\\192.168.1.28\share\测试_202406\0913_扫A放B\0913_1\OneToOneCompare.txt", r"\\192.168.1.28\share\测试_202406\0913_扫A放B\0913_2\OneToOneCompare.txt", r"\\192.168.1.28\share\测试_202406\0914_扫A放B\0914_1\OneToOneCompare.txt", r"\\192.168.1.28\share\测试_202406\0914_扫A放B\0914_2\OneToOneCompare.txt" ] simiList = [] for fp in filepaths: slist = read_one2one_data(fp) simiList.extend(slist) plot_pr_curve(simiList) def contrast_pr(paths): ''' 1:1 ''' paths = Path(paths) evtpaths = [] for p in paths.iterdir(): condt1 = p.is_dir() condt2 = len(p.name.split('_'))>=2 condt3 = len(p.name.split('_')[-1])>8 condt4 = p.name.split('_')[-1].isdigit() if condt1 and condt2 and condt3 and condt4: evtpaths.append(p) # evtpaths = [p for p in paths.iterdir() if p.is_dir() and len(p.name.split('_'))>=2 and len(p.name.split('_')[-1])>8] # evtpaths = [p for p in paths.iterdir() if p.is_dir()] events, similars = [], [] ##===================================== 扫A放A, 扫A放B场景() one2oneAA, one2oneAB = [], [] one2SNAA, one2SNAB = [], [] ##===================================== 应用于 1：1 _tp_events, _fn_events, _fp_events, _tn_events = [], [], [], [] _tp_simi, _fn_simi, _tn_simi, _fp_simi = [], [], [], [] ##===================================== 应用于 1：SN tp_events, fn_events, fp_events, tn_events = [], [], [], [] tp_simi, fn_simi, tn_simi, fp_simi = [], [], [], [] ##===================================== 应用于1:n tpevents, fnevents, fpevents, tnevents = [], [], [], [] tpsimi, fnsimi, tnsimi, fpsimi = [], [], [], [] ##===================================== barcodes总数、比对错误事件 bcdList = [] one2onePath, one2onePath1 = [], [] one2SNPath, one2SNPath1 = [], [] one2nPath = [] errorFile_one2one, errorFile_one2SN, errorFile_one2n = [], [], [] for path in evtpaths: barcode = path.stem.split('_')[-1] datapath = path.joinpath('process.data') if not barcode.isdigit() or len(barcode)<10: continue if not datapath.is_file(): continue bcdList.append(barcode) try: SimiDict = read_similar(datapath) except Exception as e: print(f"{path.stem}, Error: {e}") '''放入为 1:1，相似度取最大值；取出时为 1:SN, 相似度取均值''' one2one = SimiDict['one2one'] one2SN = SimiDict['one2SN'] one2n = SimiDict['one2n'] '''================== 0. 1:1 ===================''' barcodes, similars = [], [] for dt in one2one: one2onePath.append((path.stem)) if dt['similar']==0: one2onePath1.append((path.stem)) continue barcodes.append(dt['barcode']) similars.append(dt['similar']) if len(barcodes)==len(similars) and len(barcodes)!=0: ## 扫A放A, 扫A放B场景 simAA = [similars[i] for i in range(len(barcodes)) if barcodes[i]==barcode] simAB = [similars[i] for i in range(len(barcodes)) if barcodes[i]!=barcode] one2oneAA.extend(simAA) one2oneAB.extend(simAB) ## 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 max_idx = similars.index(max(similars)) max_sim = similars[max_idx] # max_bcd = barcodes[max_idx] for i in range(len(one2one)): bcd, simi = barcodes[i], similars[i] if bcd==barcode and simi==max_sim: _tp_simi.append(simi) _tp_events.append(path.stem) elif bcd==barcode and simi!=max_sim: _fn_simi.append(simi) _fn_events.append(path.stem) elif bcd!=barcode and simi!=max_sim: _tn_simi.append(simi) _tn_events.append(path.stem) elif bcd!=barcode and simi==max_sim and barcode in barcodes: _fp_simi.append(simi) _fp_events.append(path.stem) else: errorFile_one2one.append(path.stem) '''================== 2. 取出场景下的 1 : Small N ===================''' barcodes, similars = [], [] for dt in one2SN: barcodes.append(dt['barcode']) similars.append(dt['similar']) if len(barcodes)==len(similars) and len(barcodes)!=0: ## 扫A放A, 扫A放B场景 simAA = [similars[i] for i in range(len(barcodes)) if barcodes[i]==barcode] simAB = [similars[i] for i in range(len(barcodes)) if barcodes[i]!=barcode] one2SNAA.extend(simAA) one2SNAB.extend(simAB) one2SNPath.append(path.stem) if len(simAA)==0: one2SNPath1.append(path.stem) ## 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 max_idx = similars.index(max(similars)) max_sim = similars[max_idx] # max_bcd = barcodes[max_idx] for i in range(len(one2SN)): bcd, simi = barcodes[i], similars[i] if bcd==barcode and simi==max_sim: tp_simi.append(simi) tp_events.append(path.stem) elif bcd==barcode and simi!=max_sim: fn_simi.append(simi) fn_events.append(path.stem) elif bcd!=barcode and simi!=max_sim: tn_simi.append(simi) tn_events.append(path.stem) elif bcd!=barcode and simi==max_sim and barcode in barcodes: fp_simi.append(simi) fp_events.append(path.stem) else: errorFile_one2SN.append(path.stem) '''===================== 3. 取出场景下的 1:n ========================''' events, evt_barcodes, evt_similars, evt_types = [], [], [], [] for dt in one2n: events.append(dt["event"]) evt_barcodes.append(dt["barcode"]) evt_similars.append(dt["similar"]) evt_types.append(dt["type"]) if len(events)==len(evt_barcodes) and len(evt_barcodes)==len(evt_similars) \ and len(evt_similars)==len(evt_types) and len(events)>0: one2nPath.append(path.stem) maxsim = evt_similars[evt_similars.index(max(evt_similars))] for i in range(len(one2n)): bcd, simi = evt_barcodes[i], evt_similars[i] if bcd==barcode and simi==maxsim: tpsimi.append(simi) tpevents.append(path.stem) elif bcd==barcode and simi!=maxsim: fnsimi.append(simi) fnevents.append(path.stem) elif bcd!=barcode and simi!=maxsim: tnsimi.append(simi) tnevents.append(path.stem) elif bcd!=barcode and simi==maxsim and barcode in evt_barcodes: fpsimi.append(simi) fpevents.append(path.stem) else: errorFile_one2n.append(path.stem) '''命名规则: 1:1 (max) 1:1 (max) 1:n 1:N _TP TP_ TP TPX _PPrecise PPrecise_ PPrecise PPreciseX tpsimi tp_simi ''' ''' 1:1 数据存储, 相似度计算方式：最大值、均值''' _PPrecise, _PRecall = [], [] _NPrecise, _NRecall = [], [] PPrecise_, PRecall_ = [], [] NPrecise_, NRecall_ = [], [] ''' 1:SN 数据存储，需根据相似度排序''' PPreciseX, PRecallX = [], [] NPreciseX, NRecallX = [], [] ''' 1:n 数据存储，需根据相似度排序''' PPrecise, PRecall = [], [] NPrecise, NRecall = [], [] Thresh = np.linspace(-0.2, 1, 100) for th in Thresh: '''(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''' '''===================================== 1:1 最大值''' _TP = sum(np.array(one2oneAA) >= th) _FP = sum(np.array(one2oneAB) >= th) _FN = sum(np.array(one2oneAA) < th) _TN = sum(np.array(one2oneAB) < th) _PPrecise.append(_TP/(_TP+_FP+1e-6)) _PRecall.append(_TP/(len(one2oneAA)+1e-6)) _NPrecise.append(_TN/(_TN+_FN+1e-6)) _NRecall.append(_TN/(len(one2oneAB)+1e-6)) '''===================================== 1:SN 均值''' TP_ = sum(np.array(one2SNAA) >= th) FP_ = sum(np.array(one2SNAB) >= th) FN_ = sum(np.array(one2SNAA) < th) TN_ = sum(np.array(one2SNAB) < th) PPrecise_.append(TP_/(TP_+FP_+1e-6)) PRecall_.append(TP_/(len(one2SNAA)+1e-6)) NPrecise_.append(TN_/(TN_+FN_+1e-6)) NRecall_.append(TN_/(len(one2SNAB)+1e-6)) '''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 ''' '''===================================== 1:SN ''' TPX = sum(np.array(tp_simi) >= th) FPX = sum(np.array(fp_simi) >= th) FNX = sum(np.array(fn_simi) < th) TNX = sum(np.array(tn_simi) < th) PPreciseX.append(TPX/(TPX+FPX+1e-6)) PRecallX.append(TPX/(len(tp_simi)+len(fn_simi)+1e-6)) NPreciseX.append(TNX/(TNX+FNX+1e-6)) NRecallX.append(TNX/(len(tn_simi)+len(fp_simi)+1e-6)) '''===================================== 1:n''' TP = sum(np.array(tpsimi) >= th) FP = sum(np.array(fpsimi) >= th) FN = sum(np.array(fnsimi) < th) TN = sum(np.array(tnsimi) < th) PPrecise.append(TP/(TP+FP+1e-6)) PRecall.append(TP/(len(tpsimi)+len(fnsimi)+1e-6)) NPrecise.append(TN/(TN+FN+1e-6)) NRecall.append(TN/(len(tnsimi)+len(fpsimi)+1e-6)) '''1. ============================= 1:1 最大值方案 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') 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('1:1 Precise & Recall') ax.set_xlabel(f"Event Num: {len(one2oneAA)+len(one2oneAB)}") ax.legend() plt.show() ## ============================= 1:1 最大值方案 直方图''' fig, axes = plt.subplots(2, 1) axes[0].hist(np.array(one2oneAA), bins=60, edgecolor='black') axes[0].set_xlim([-0.2, 1]) axes[0].set_title('AA') axes[1].hist(np.array(one2oneAB), bins=60, edgecolor='black') axes[1].set_xlim([-0.2, 1]) axes[1].set_title('BB') plt.show() '''2. ============================= 1:1 均值方案 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') 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('1:1 Precise & Recall') ax.set_xlabel(f"Event Num: {len(one2SNAA)}") ax.legend() plt.show() ## ============================= 1:1 均值方案 直方图''' fig, axes = plt.subplots(2, 1) axes[0].hist(np.array(one2SNAA), bins=60, edgecolor='black') axes[0].set_xlim([-0.2, 1]) axes[0].set_title('AA') axes[0].set_xlabel(f"Event Num: {len(one2SNAA)}") axes[1].hist(np.array(one2SNAB), bins=60, edgecolor='black') axes[1].set_xlim([-0.2, 1]) axes[1].set_title('BB') axes[1].set_xlabel(f"Event Num: {len(one2SNAB)}") plt.show() ''''3. ============================= 1:SN 曲线''' fig, ax = plt.subplots() ax.plot(Thresh, PPreciseX, 'r', label='Precise_Pos: TP/TPFP') ax.plot(Thresh, PRecallX, 'b', label='Recall_Pos: TP/TPFN') ax.plot(Thresh, NPreciseX, 'g', label='Precise_Neg: TN/TNFP') ax.plot(Thresh, NRecallX, 'c', label='Recall_Neg: TN/TNFN') ax.set_xlim([0, 1]) ax.set_ylim([0, 1]) ax.grid(True) ax.set_title('1:SN Precise & Recall') ax.set_xlabel(f"Event Num: {len(one2SNAA)}") ax.legend() plt.show() ## ============================= 1:N 展厅 直方图''' 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(f'TP({len(tp_simi)})') axes[0, 1].hist(fp_simi, bins=60, edgecolor='black') axes[0, 1].set_xlim([-0.2, 1]) axes[0, 1].set_title(f'FP({len(fp_simi)})') axes[1, 0].hist(tn_simi, bins=60, edgecolor='black') axes[1, 0].set_xlim([-0.2, 1]) axes[1, 0].set_title(f'TN({len(tn_simi)})') axes[1, 1].hist(fn_simi, bins=60, edgecolor='black') axes[1, 1].set_xlim([-0.2, 1]) axes[1, 1].set_title(f'FN({len(fn_simi)})') 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') 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(f'TP({len(tpsimi)})') axes[0, 1].hist(fpsimi, bins=60, edgecolor='black') axes[0, 1].set_xlim([-0.2, 1]) axes[0, 1].set_title(f'FP({len(fpsimi)})') axes[1, 0].hist(tnsimi, bins=60, edgecolor='black') axes[1, 0].set_xlim([-0.2, 1]) axes[1, 0].set_title(f'TN({len(tnsimi)})') axes[1, 1].hist(fnsimi, bins=60, edgecolor='black') axes[1, 1].set_xlim([-0.2, 1]) axes[1, 1].set_title(f'FN({len(fnsimi)})') 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"D:\全实时\source_data\2024122416" contrast_pr(evtpaths)