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z#o~wOXAv8=U<@C0@FF63F@cDG8BttBV1yBKgy2cbvjh@9e6Um)w@M;jK3SX+`Y29K z8ofNsb_jDOO8Y_#V;ee7`nTwY!3d92PzTs&F;NPN_#s{+9Etrj$P6DuJYU3%3uFwH zkZ7|d2QM0sZWn8KhM>8X9b4JJbHt9M!6h9A3}VOY)dVbP%2CXin3N(d_)+u))^!?D z8U9n!h$r{R1F*(*nY~R^=y|-~C|2_M>}`G|pEs)$`Me*g+C%&tkVf{y_Vm1i2vG?B z$m}8hWW?`8d}y{*bxOr?yicQ?9}d1M$PX@82nQg<_NewDo4-X7)?$C-t>+LS$9TZu z;H4-d<;lp*_Xk#i@b_30>1X802^*B+MZ$z=-uf6ljh7t(YNuK)*^IW2Z?D+s9aMaX z60$Q|*nq4*uG*UmL_S_V-@?@^=7YYgTs7xQ)^6fJ9s<+icsxRu0+v|((s($6fH3w diff --git a/contrast/feat_infer.py b/contrast/feat_infer.py index 6fef3c6..988e34b 100644 --- a/contrast/feat_infer.py +++ b/contrast/feat_infer.py @@ -16,11 +16,6 @@ from feat_extract.inference import FeatsInterface #, inference_image Encoder = FeatsInterface(conf) - - - - - def main(): imgpaths = r"D:\全实时\202502\result\Yolos_Tracking\20250228-160049-188_6921168558018_6921168558018\a" featDict = {} diff --git a/contrast/genfeats.py b/contrast/genfeats.py index e11bd6c..2ef0802 100644 --- a/contrast/genfeats.py +++ b/contrast/genfeats.py @@ -191,10 +191,19 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None): return -def gen_bcd_features(imgpath, bcdpath, featpath, bcdSet=None): +def gen_bcd_features(imgpath, bcdpath, featpath, eventSourcePath): ''' 生成标准特征集 ''' '''1. 提取 imgpath 中样本地址，生成字典{barcode: [imgpath1, imgpath1, ...]} 并存储于: bcdpath, 格式为 barcode.pickle''' + + bcdList = [] + for evtname in os.listdir(eventSourcePath): + bname, ext = os.path.splitext(evtname) + evt = bname.split('_') + if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10: + bcdList.append(evt[-1]) + + bcdSet = set(bcdList) get_std_barcodeDict(imgpath, bcdpath, bcdSet) '''2. 特征提取，并保存至文件夹 featpath 中，也根据 bcdSet 交集执行''' diff --git a/contrast/one2n_contrast.py b/contrast/one2n_contrast.py index 003c0af..fa1739c 100644 --- a/contrast/one2n_contrast.py +++ b/contrast/one2n_contrast.py @@ -14,49 +14,47 @@ from scipy.spatial.distance import cdist from utils.event import ShoppingEvent -def init_eventdict(sourcePath, stype="data"): +def init_eventDict(sourcePath, eventDataPath, stype="data"): '''stype: str, 'source': 由 videos 或 images 生成的 pickle 文件 'data': 从 data 文件中读取的现场运行数据 "realtime": 全实时数据，从 data 文件中读取的现场运行数据 - ''' + + sourcePath:事件文件夹，事件类型包含2种： + (1) pipeline生成的 pickle 文件 + (2) 直接采集的事件文件夹 + ''' k, errEvents = 0, [] - for bname in os.listdir(sourcePath): - # bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479" + for evtname in os.listdir(sourcePath): + bname, ext = os.path.splitext(evtname) + source_path = os.path.join(sourcePath, evtname) - source_path = os.path.join(sourcePath, bname) - if stype=="source" and not os.path.isfile(source_path): continue + if stype=="source" and ext not in ['.pkl', '.pickle']: continue if stype=="data" and os.path.isfile(source_path): continue if stype=="realtime" and os.path.isfile(source_path): continue - - if os.path.isdir(source_path): - pickpath = os.path.join(eventDataPath, f"{bname}.pickle") - else: - pickpath = os.path.join(eventDataPath, bname) - if os.path.isfile(pickpath): - continue + evt = bname.split('_') + condt = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10 + if not condt: continue - evt = os.path.splitext(os.path.split(pickpath)[-1])[0].split('_') - cont = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10 - if not cont: - continue + pickpath = os.path.join(eventDataPath, f"{bname}.pickle") + if os.path.isfile(pickpath): continue # event = ShoppingEvent(source_path, stype) try: event = ShoppingEvent(source_path, stype) with open(pickpath, 'wb') as f: pickle.dump(event, f) - print(bname) + print(evtname) except Exception as e: errEvents.append(source_path) - print(f"Error: {bname}, {e}") + print(f"Error: {evtname}, {e}") # k += 1 # if k==1: # break - - errfile = os.path.join(resultPath, 'error_events.txt') - with open(errfile, 'a', encoding='utf-8') as f: + + errfile = Path(eventDataPath).parent / 'error_events.txt' + with open(str(errfile), 'a', encoding='utf-8') as f: for line in errEvents: f.write(line + '\n') @@ -185,7 +183,7 @@ def one2n_pr(evtDicts, pattern=1): elif bcd!=event.barcode and simi!=maxsim: tnsimi.append(simi) tnevents.append(evtname) - elif bcd!=event.barcode and simi==maxsim: + elif bcd!=event.barcode and simi==maxsim and event.barcode in evt_barcodes: fpsimi.append(simi) fpevents.append(evtname) else: @@ -216,7 +214,11 @@ def one2n_pr(evtDicts, pattern=1): 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_xticks(np.arange(0, 1, 0.1)) + ax.set_yticks(np.arange(0, 1, 0.1)) + ax.grid(True, linestyle='--') + ax.set_title('1:n Precise & Recall') ax.set_xlabel(f"Event Num: {len(one2nFile)}") ax.legend() @@ -241,7 +243,7 @@ def one2n_pr(evtDicts, pattern=1): def main(): '''1. 生成事件字典并保存至 eventDataPath, 只需运行一次 ''' - init_eventdict(eventSourcePath, stype="realtime") # 'source', 'data', 'realtime' + init_eventDict(eventSourcePath, eventDataPath, stype="realtime") # 'source', 'data', 'realtime' # for pfile in os.listdir(eventDataPath): # evt = os.path.splitext(pfile)[0].split('_') @@ -264,7 +266,7 @@ def main(): if __name__ == '__main__': - eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-2-27" + 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" resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\testing" eventDataPath = os.path.join(resultPath, "evtobjs_wang") diff --git a/contrast/one2one_contrast.py b/contrast/one2one_contrast.py index b90b8b8..fdce5f1 100644 --- a/contrast/one2one_contrast.py +++ b/contrast/one2one_contrast.py @@ -58,6 +58,7 @@ from feat_extract.inference import FeatsInterface from utils.event import ShoppingEvent, save_data from genfeats import gen_bcd_features from event_test import calc_simil +from one2n_contrast import init_eventDict @@ -271,8 +272,12 @@ def build_std_evt_dict(): evtDict = {} for evtname, barcode in evtList: evtpath = os.path.join(eventDataPath, evtname+'.pickle') - with open(evtpath, 'rb') as f: - evtdata = pickle.load(f) + try: + with open(evtpath, 'rb') as f: + evtdata = pickle.load(f) + except Exception as e: + print(evtname) + evtDict[evtname] = evtdata return evtList, evtDict, stdDict @@ -300,7 +305,8 @@ def one2SN_pr(evtList, evtDict, stdDict): event = evtDict[evtname] ## 无轨迹判断 if len(event.front_feats)+len(event.back_feats)==0: - print(evtname) + errorFile_one2SN.append(evtname) + print(f"No trajectory: {evtname}") continue barcodes, similars = [], [] @@ -351,10 +357,10 @@ def one2SN_pr(evtList, evtDict, stdDict): 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)) + PRecallX.append(TPX/(TPX+FNX+1e-6)) NPreciseX.append(TNX/(TNX+FNX+1e-6)) - NRecallX.append(TNX/(len(tn_simi)+len(fp_simi)+1e-6)) + NRecallX.append(TNX/(TNX+FPX+1e-6)) fig, ax = plt.subplots() ax.plot(Thresh, PPreciseX, 'r', label='Precise_Pos: TP/TPFP') @@ -363,9 +369,11 @@ def one2SN_pr(evtList, evtDict, stdDict): 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_xticks(np.arange(0, 1, 0.1)) + ax.set_yticks(np.arange(0, 1, 0.1)) + ax.grid(True, linestyle='--') ax.set_title('1:SN Precise & Recall') - ax.set_xlabel(f"Event Num: {len(evtList)}") + ax.set_xlabel(f"Event Num: {len(tp_events) + len(fn_events)}") ax.legend() plt.show() ## ============================= 1:N 展厅 直方图''' @@ -403,10 +411,14 @@ def one2one_simi(evtList, evtDict, stdDict): '''======2 计算事件、标准特征集相似度 ==================''' rltdata = [] + errorFile_one2one = [] for i in range(len(mergePairs)): evtname, stdbcd, label = mergePairs[i] event = evtDict[evtname] - if len(event.feats_compose)==0: continue + if len(event.feats_compose)==0: + errorFile_one2one.append(evtname) + + continue stdfeat = stdDict[stdbcd] # float32 @@ -418,11 +430,16 @@ def one2one_simi(evtList, evtDict, stdDict): '''================ float32、16、int8 精度比较与存储 =============''' # data_precision_compare(stdfeat, evtfeat, mergePairs[i], save=True) - - return rltdata + + errorFile_one2one = list(set(errorFile_one2one)) + + return rltdata, errorFile_one2one -def one2one_pr(rltdata): +def one2one_pr(evtList, evtDict, stdDict): + + rltdata, errorFile_one2one = one2one_simi(evtList, evtDict, stdDict) + Same, Cross = [], [] for label, stdbcd, evtname, simi_mean, simi_max, simi_mft in rltdata: if label == "same": @@ -451,27 +468,41 @@ def one2one_pr(rltdata): Correct = [] Thresh = np.linspace(-0.2, 1, 100) for th in Thresh: - TP = np.sum(Same > th) - FN = TPFN - TP + TP = np.sum(Same >= th) + FN = np.sum(Same < th) + # FN = TPFN - TP + TN = np.sum(Cross < th) - FP = TNFP - TN + FP = np.sum(Cross >= th) + # FP = TNFP - TN - Recall_Pos.append(TP/TPFN) - Recall_Neg.append(TN/TNFP) + Precision_Pos.append(TP/(TP+FP+1e-6)) Precision_Neg.append(TN/(TN+FN+1e-6)) + Recall_Pos.append(TP/(TP+FN+1e-6)) + Recall_Neg.append(TN/(TN+FP+1e-6)) + + # Recall_Pos.append(TP/TPFN) + # Recall_Neg.append(TN/TNFP) + + Correct.append((TN+TP)/(TPFN+TNFP)) fig, ax = plt.subplots() - ax.plot(Thresh, Correct, 'r', label='Correct: (TN+TP)/(TPFN+TNFP)') + + ax.plot(Thresh, Precision_Pos, 'r', label='Precision_Pos: TP/(TP+FP)') ax.plot(Thresh, Recall_Pos, 'b', label='Recall_Pos: TP/TPFN') ax.plot(Thresh, Recall_Neg, 'g', label='Recall_Neg: TN/TNFP') - ax.plot(Thresh, Precision_Pos, 'c', label='Precision_Pos: TP/(TP+FP)') + ax.plot(Thresh, Correct, 'c', label='Correct: (TN+TP)/(TPFN+TNFP)') ax.plot(Thresh, Precision_Neg, 'm', label='Precision_Neg: TN/(TN+FN)') ax.set_xlim([0, 1]) ax.set_ylim([0, 1]) - ax.grid(True) + + ax.set_xticks(np.arange(0, 1, 0.1)) + ax.set_yticks(np.arange(0, 1, 0.1)) + ax.grid(True, linestyle='--') + ax.set_title('PrecisePos & PreciseNeg') ax.set_xlabel(f"Same Num: {TPFN}, Cross Num: {TNFP}") ax.legend() @@ -506,23 +537,17 @@ def gen_eventdict(sourcePath, saveimg=True): ## 兼容事件的两种情况：文件夹 和 Yolo-Resnet-Tracker 的输出 if os.path.isfile(source_path): bname, ext = os.path.splitext(bname) - evt = bname.split("_") + # evt = bname.split("_") evt = bname.split('_') condt = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10 if not condt: continue - # bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479" - # source_path = os.path.join(evtpath, bname) - + # 如果已完成事件生成，则不执行 pickpath = os.path.join(eventDataPath, f"{bname}.pickle") if os.path.isfile(pickpath): continue - # event = ShoppingEvent(source_path, stype=source_type) - # with open(pickpath, 'wb') as f: - # pickle.dump(event, f) - try: event = ShoppingEvent(source_path, stype=source_type) # save_data(event, resultPath) @@ -538,16 +563,44 @@ def gen_eventdict(sourcePath, saveimg=True): # if k==1: # break - errfile = os.path.join(resultPath, 'error_events.txt') # with open(errfile, 'w', encoding='utf-8') as f: # for line in errEvents: # f.write(line + '\n') -def init_std_evt_dict(): - '''==== 0. 生成事件列表和对应的 Barcodes列表 ===========''' - bcdList, event_spath = [], [] +# def init_std_evt_dict(): +# '''==== 0. 生成事件列表和对应的 Barcodes列表 ===========''' +# bcdList, event_spath = [], [] +# for evtname in os.listdir(eventSourcePath): +# bname, ext = os.path.splitext(evtname) + +# ## 处理事件的两种情况：文件夹 和 Yolo-Resnet-Tracker 的输出 +# fpath = os.path.join(eventSourcePath, evtname) +# if os.path.isfile(fpath) and (ext==".pkl" or ext==".pickle"): +# evt = bname.split('_') +# elif os.path.isdir(fpath): +# evt = evtname.split('_') +# else: +# continue + +# if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10: +# bcdList.append(evt[-1]) +# event_spath.append(fpath) + +# '''==== 1. 生成标准特征集, 只需运行一次, 在 genfeats.py 中实现 ===========''' +# bcdSet = set(bcdList) +# gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet) +# print("stdFeats have generated and saved!") + +# '''==== 2. 生成事件字典, 只需运行一次 ===============''' +# gen_eventdict(event_spath) +# print("eventList have generated and saved!") + +def get_evtList(): + + '''==== 0. 生成事件列表和对应的 Barcodes 集合 ===========''' + bcdList, evtpaths = [], [] for evtname in os.listdir(eventSourcePath): bname, ext = os.path.splitext(evtname) @@ -562,46 +615,73 @@ def init_std_evt_dict(): if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10: bcdList.append(evt[-1]) - event_spath.append(fpath) - - '''==== 1. 生成标准特征集, 只需运行一次, 在 genfeats.py 中实现 ===========''' - bcdSet = set(bcdList) - gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet) - print("stdFeats have generated and saved!") - - '''==== 2. 生成事件字典, 只需运行一次 ===============''' - gen_eventdict(event_spath) - print("eventList have generated and saved!") + evtpaths.append(fpath) + bcdSet = set(bcdList) + + return evtpaths, bcdSet -def test_one2one(): +# def init_stdDict(): +# evtpaths, bcdSet = get_evtList() +# gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet) +# print("stdFeats have generated and saved!") + + +# def init_evtDict(): +# '''==== 0. 生成事件列表和对应的 Barcodes列表 ===========''' +# bcdList, event_spath = [], [] +# for evtname in os.listdir(eventSourcePath): +# bname, ext = os.path.splitext(evtname) + +# ## 处理事件的两种情况：文件夹 和 Yolo-Resnet-Tracker 的输出 +# fpath = os.path.join(eventSourcePath, evtname) +# if os.path.isfile(fpath) and (ext==".pkl" or ext==".pickle"): +# evt = bname.split('_') +# elif os.path.isdir(fpath): +# evt = evtname.split('_') +# else: +# continue + +# if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10: +# bcdList.append(evt[-1]) +# event_spath.append(fpath) + +# '''==== 2. 生成事件字典, 只需运行一次 ===============''' +# gen_eventdict(event_spath) +# print("eventList have generated and saved!") + + + + + +def test_one2one_one2SN(): '''1:1性能评估''' - # 1. 只需运行一次，生成事件字典和相应的标准特征库字典 - # init_std_evt_dict() + # evtpaths, bcdSet = get_evtList() - # 2. 基于事件barcode集和标准库barcode交集构造事件集合 - evtList, evtDict, stdDict = build_std_evt_dict() + '''=== 1. 只需运行一次，生成事件对应的标准特征库字典,如已生成，无需运行 ====''' + # gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, eventSourcePath) - rltdata = one2one_simi(evtList, evtDict, stdDict) + '''==== 2. 生成事件字典, 只需运行一次 ====================''' - one2one_pr(rltdata) + # date_ = ['2025-3-4_1', '2025-3-5_1', '2025-3-5_2'] + # for dt in date_: + # evtpaths = os.path.join(eventSourcePath, dt) + # init_eventDict(evtpaths, eventDataPath, source_type) + init_eventDict(eventSourcePath, eventDataPath, source_type) + + -def test_one2SN(): - '''1:SN性能评估''' - - # 1. 只需运行一次，生成事件字典和相应的标准特征库字典 - # init_std_evt_dict() - - # 2. 事件barcode集和标准库barcode求交集 + '''==== 2. 基于事件barcode集和标准库barcode交集构造事件集合 =========''' evtList, evtDict, stdDict = build_std_evt_dict() - - one2SN_pr(evtList, evtDict, stdDict) + one2one_pr(evtList, evtDict, stdDict) + one2SN_pr(evtList, evtDict, stdDict) + if __name__ == '__main__': ''' 共7个地址： @@ -627,32 +707,30 @@ if __name__ == '__main__': stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\比对数据\barcode\all_totalBarocde\totalBarcode" stdBarcodePath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\testing\bcdpath" - stdFeaturePath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\testing\stdfeats" + stdFeaturePath = r"\\192.168.1.28\share\数据\已完成数据\比对数据\barcode\all_totalBarocde\features_json\v11_barcode_0304" if not os.path.exists(stdBarcodePath): os.makedirs(stdBarcodePath) if not os.path.exists(stdFeaturePath): os.makedirs(stdFeaturePath) + '''source_type: + "source": eventSourcePath 为 Yolo-Resnet-Tracker 输出的 pickle 文件 + "data": 基于事件切分的原 data 文件版本 + "realtime": 全实时生成的 data 文件 ''' - source_type: - "source": eventSourcePath 为 Yolo-Resnet-Tracker 输出的 pickle 文件 - "data": eventSourcePath 为 包含 data 文件的文件夹 - ''' - source_type = 'realtime' # 'source', 'data', 'realtime' - eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-2-21\比对\video" - + source_type = 'realtime' # 'source', 'data', 'realtime' + eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\基准数据集\2025-3-4_1" resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\testing" - eventDataPath = os.path.join(resultPath, "evtobjs_data") - similPath = os.path.join(resultPath, "simidata_data") + + eventDataPath = os.path.join(resultPath, "evtobjs_0304_1") + similPath = os.path.join(resultPath, "simidata_0304_1") if not os.path.exists(eventDataPath): os.makedirs(eventDataPath) if not os.path.exists(similPath): os.makedirs(similPath) - test_one2one() - - test_one2SN() + test_one2one_one2SN() diff --git a/contrast/onsite_contrast_pr.py b/contrast/onsite_contrast_pr.py index 691a5ee..e228bf6 100644 --- a/contrast/onsite_contrast_pr.py +++ b/contrast/onsite_contrast_pr.py @@ -109,24 +109,31 @@ def test_compare(): plot_pr_curve(simiList) -def contrast_pr(paths): +def contrast_pr(evtPaths): ''' 1:1 - ''' - paths = Path(paths) - + ''' evtpaths = [] - for p in paths.iterdir(): + # date_ = ['2025-3-4_1', '2025-3-5_1', '2025-3-5_2'] + # for dt in date_: + # paths = Path(evtPaths) / dt + abc = [] + for p in Path(evtPaths).iterdir(): condt1 = p.is_dir() condt2 = len(p.name.split('_'))>=2 - condt3 = len(p.name.split('_')[-1])>8 + condt3 = len(p.name.split('_')[-1])>=8 condt4 = p.name.split('_')[-1].isdigit() if condt1 and condt2 and condt3 and condt4: evtpaths.append(p) + elif p.is_dir(): + abc.append(p.stem) + # 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()] + alg_times = [] + events, similars = [], [] ##===================================== 扫A放A, 扫A放B场景() one2oneAA, one2oneAB = [], [] @@ -157,7 +164,7 @@ def contrast_pr(paths): barcode = path.stem.split('_')[-1] datapath = path.joinpath('process.data') - if not barcode.isdigit() or len(barcode)<10: continue + if not barcode.isdigit() or len(barcode)<8: continue if not datapath.is_file(): continue bcdList.append(barcode) @@ -175,10 +182,15 @@ def contrast_pr(paths): if len(one2one)+len(one2SN)+len(one2n) == 0: errorFile.append(path.stem) + + dtime = SimiDict["algroStartToEnd"] + if dtime >= 0: + alg_times.append((dtime, path.stem)) '''================== 0. 1:1 ===================''' barcodes, similars = [], [] + barcodes_ = [] for dt in one2one: one2onePath.append((path.stem)) if dt['similar']==0: @@ -186,6 +198,12 @@ def contrast_pr(paths): continue barcodes.append(dt['barcode']) similars.append(dt['similar']) + + + barcodes_.append(path.stem) + + + if len(barcodes)==len(similars) and len(barcodes)!=0: @@ -216,6 +234,8 @@ def contrast_pr(paths): _fp_events.append(path.stem) else: errorFile_one2one.append(path.stem) + elif len(one2SN)+len(one2n) == 0: + errorFile_one2one.append(path.stem) '''================== 2. 取出场景下的 1 : Small N ===================''' @@ -223,6 +243,7 @@ def contrast_pr(paths): 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 @@ -231,11 +252,11 @@ def contrast_pr(paths): one2SNAA.extend(simAA) one2SNAB.extend(simAB) + one2SNPath.append(path.stem) if len(simAA)==0: - one2SNPath1.append(path.stem) - - + errorFile_one2SN.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] @@ -256,6 +277,7 @@ def contrast_pr(paths): fp_events.append(path.stem) else: errorFile_one2SN.append(path.stem) + @@ -266,10 +288,17 @@ def contrast_pr(paths): 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: + + + if len(events)==len(evt_barcodes)==len(evt_similars)==len(evt_types) and len(events)>0: + if not barcode in evt_barcodes: + errorFile_one2n.append(path.stem) + continue + + if len(barcodes_): + print("do") + one2nPath.append(path.stem) maxsim = evt_similars[evt_similars.index(max(evt_similars))] for i in range(len(one2n)): @@ -324,9 +353,9 @@ def contrast_pr(paths): _TN = sum(np.array(one2oneAB) < th) _PPrecise.append(_TP/(_TP+_FP+1e-6)) - _PRecall.append(_TP/(len(one2oneAA)+1e-6)) + _PRecall.append(_TP/(_TP+_FN+1e-6)) _NPrecise.append(_TN/(_TN+_FN+1e-6)) - _NRecall.append(_TN/(len(one2oneAB)+1e-6)) + _NRecall.append(_TN/(_TN+_FP+1e-6)) '''===================================== 1:SN 均值''' TP_ = sum(np.array(one2SNAA) >= th) @@ -346,10 +375,10 @@ def contrast_pr(paths): 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)) + PRecallX.append(TPX/(TPX+FNX+1e-6)) NPreciseX.append(TNX/(TNX+FNX+1e-6)) - NRecallX.append(TNX/(len(tn_simi)+len(fp_simi)+1e-6)) + NRecallX.append(TNX/(TNX+FPX+1e-6)) '''===================================== 1:n''' @@ -359,13 +388,19 @@ def contrast_pr(paths): TN = sum(np.array(tnsimi) < th) PPrecise.append(TP/(TP+FP+1e-6)) - PRecall.append(TP/(len(tpsimi)+len(fnsimi)+1e-6)) + PRecall.append(TP/(TP+FN+1e-6)) NPrecise.append(TN/(TN+FN+1e-6)) - NRecall.append(TN/(len(tnsimi)+len(fpsimi)+1e-6)) + NRecall.append(TN/(TN+FP+1e-6)) + + algtime = [] + for tm, _ in alg_times: + algtime.append(tm) + fig, ax = plt.subplots() + ax.hist(np.array(algtime), bins=100, edgecolor='black') + ax.set_title('Algorthm Spend Time') + ax.set_xlabel(f"Event Num: {len(alg_times)}") + plt.show() - - - '''1. ============================= 1:1 最大值方案 曲线''' fig, ax = plt.subplots() ax.plot(Thresh, _PPrecise, 'r', label='Precise_Pos: TP/TPFP') @@ -374,7 +409,9 @@ def contrast_pr(paths): 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_xticks(np.arange(0, 1, 0.1)) + ax.set_yticks(np.arange(0, 1, 0.1)) + ax.grid(True, linestyle='--') ax.set_title('1:1 Precise & Recall') ax.set_xlabel(f"Event Num: {len(one2oneAA)+len(one2oneAB)}") ax.legend() @@ -393,30 +430,30 @@ def contrast_pr(paths): '''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)}") + # 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() + # 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() @@ -426,7 +463,9 @@ def contrast_pr(paths): 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_xticks(np.arange(0, 1, 0.1)) + ax.set_yticks(np.arange(0, 1, 0.1)) + ax.grid(True, linestyle='--') ax.set_title('1:SN Precise & Recall') ax.set_xlabel(f"Event Num: {len(one2SNAA)}") ax.legend() @@ -456,7 +495,9 @@ def contrast_pr(paths): 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_xticks(np.arange(0, 1, 0.1)) + ax.set_yticks(np.arange(0, 1, 0.1)) + ax.grid(True, linestyle='--') ax.set_title('1:n Precise & Recall') ax.set_xlabel(f"Event Num: {len(tpsimi)+len(fnsimi)}") ax.legend() @@ -473,11 +514,11 @@ def contrast_pr(paths): 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: @@ -487,27 +528,24 @@ def contrast_pr(paths): # 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 = os.path.join(evtPaths, "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") + one2NErrFile = os.path.join(evtPaths, "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\全实时测试\V12\2025-2-26_2" + 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" contrast_pr(evtpaths) diff --git 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'source', 'data', 'realtime', 共三种 ''' + '''stype: str, 'source', 'data', 'realtime', 共三种 + source: 前后摄视频经 pipeline 生成的文件 + data: 基于事件切分的原 data 文件版本 + realtime: 全实时生成的 data 文件 + ''' self.eventpath = eventpath self.evtname = str(Path(eventpath).stem) diff --git a/dataset/multi-trajs.py b/dataset/multi-trajs.py new file mode 100644 index 0000000..107f408 --- /dev/null +++ b/dataset/multi-trajs.py @@ -0,0 +1,72 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Mar 10 09:33:35 2025 +基准数据集筛选，选取tracking输出多个轨迹的事件 + +@author: ym +""" +import os +import numpy as np + +import sys +sys.path.append(r"D:\DetectTracking") +from tracking.utils.read_data import extract_data, read_tracking_output_realtime + + +def get_multitraj_file(spath, pattern): + multi_traj_events = [] + n = 0 + for evtname in os.listdir(spath): + name, ext = os.path.splitext(evtname) + eventpath = os.path.join(spath, evtname) + + evt = name.split('_') + condt = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=8 + if not condt: continue + if not os.path.isdir(eventpath): continue + + trackingboxes = [] + for dataname in os.listdir(eventpath): + if os.path.splitext(dataname)[-1] in [".jpg", ".png"]: + continue + + datapath = os.path.join(eventpath, dataname) + if not os.path.isfile(datapath): continue + CamerType = dataname.split('_')[0] + + if pattern=="realtime" and dataname.find("_tracking_output.data")>0: + trackingboxes, trackingfeats, tracking_outboxes, tracking_outfeats = read_tracking_output_realtime(datapath) + if pattern=="evtsplit" and dataname.find("_track.data")>0: + bboxes, ffeats, trackerboxes, trackerfeats, trackingboxes, trackingfeats = extract_data(datapath) + + if len(trackingboxes)>=2: + multi_traj_events.append(evtname) + n += 1 + print(f"{n}: {evtname}") + break + + + multi_traj_file = os.path.join(spath, "multi_traj_file.txt") + with open(multi_traj_file, "w") as file: + for item in multi_traj_events: + file.write(item + "\n") + +def main(): + spaths = [r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\比对测试\1212", + r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\比对测试\1216", + r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\比对测试\1218", + r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\比对测试\202412" + ] + + pattern = "evtsplit" # realtime # 全实时版、事件切分版数据读取方式 + for spath in spaths: + get_multitraj_file(spath, pattern) + + + + + + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/pipeline.py b/pipeline.py index 99acf22..df0fb3e 100644 --- a/pipeline.py +++ b/pipeline.py @@ -166,12 +166,15 @@ def pipeline( '''================= 4. tracking =================''' '''(1) 生成用于 tracking 模块的 boxes、feats''' + bboxes = np.empty((0, 6), dtype=np.float64) trackerboxes = np.empty((0, 9), dtype=np.float64) trackefeats = {} for frameDict in yrtOut: tboxes = frameDict["tboxes"] ffeats = frameDict["feats"] + boxes = frameDict["bboxes"] + bboxes = np.concatenate((bboxes, np.array(boxes)), axis=0) trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)), axis=0) for i in range(len(tboxes)): fid, bid = int(tboxes[i, 7]), int(tboxes[i, 8]) @@ -266,7 +269,7 @@ def main(): 函数：pipeline()，遍历事件文件夹，选择类型 image 或 video, ''' parmDict = {} - evtdir = r"D:\全实时\202502" + evtdir = 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" parmDict["SourceType"] = "video" # video, image parmDict["savepath"] = r"D:\全实时\202502\result" parmDict["weights"] = r'D:\DetectTracking\ckpts\best_cls10_0906.pt' @@ -275,10 +278,9 @@ def main(): k, errEvents = 0, [] for item in evtdir.iterdir(): if item.is_dir(): - item = evtdir/Path("20250228-160049-188_6921168558018_6921168558018") + item = evtdir/Path("20250303-103058-074_6914973604223_6914973604223") parmDict["eventpath"] = item # pipeline(**parmDict) - try: pipeline(**parmDict) except Exception as e: diff --git a/practice/6924743915817.txt b/practice/6924743915817.txt new file mode 100644 index 0000000..e937507 --- /dev/null +++ b/practice/6924743915817.txt @@ -0,0 +1,78 @@ +-0.012108 -0.005081 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zPqw}<%Jei*W3*wup=4BKpz#DctQtCVO(N_IGNBggq=xt7*eJB@1cJ4^=d<}1m*WM|()aaGbu-{%nMD+*fM z&ERdVp=%WLz&D}vun=PEv9V~XdnVn?xNaS%-*Aq|l(MF`j&?RC-ZP$c^7FuU z&uDKot`P2q1Gq9080+x;F`ruz`9irDtftokpSlu?r(20enqJ}lVDmhs2N939Y8%8q z=*`6`AloiaN#<-rX-beKh$6E%tt@tA=qtA<% zxdviaF$$AS>SLaT7y6m7NNE>Qce5YP-p@t-;2Ps2>8;q$GlJceGSp&;$MBY+=VPq56uhrxEXmu_;CI6@=H+C_<(ybVqzaoFD{vRRpQ1Ac% literal 0 HcmV?d00001 diff --git a/practice/json_to_data.py b/practice/json_to_data.py new file mode 100644 index 0000000..fd5d2af --- /dev/null +++ b/practice/json_to_data.py @@ -0,0 +1,90 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Dec 18 15:44:33 2024 + +@author: ieemoo-zl003 +""" + +import json +import struct +import numpy as np + +json_path = r"D:\DetectTracking\practice\resv11_test.json" + +def write_binary_file(filename, datas): + with open(filename, 'wb') as f: + # 先写入数据中的key数量（为C++读取提供便利） + key_count = len(datas) + f.write(struct.pack('I', key_count)) # 'I'代表无符号整型（4字节） + + feats_32, feats_16 = [], [] + for data in datas: + key = data['key'] + feats = data['value'] + key_bytes = key.encode('utf-8') + key_len = len(key) + length_byte = struct.pack('=0 and evtpath.name.find(".txt")>0: - weight_data = read_weight_sensor(evtpath) - - if not evtpath.is_dir(): - continue - - ## 2. 读取事件data数据 - for fpath in evtpath.iterdir(): - - fname = fpath.name - if fname.find("tracker.data"): - pass - - if fname.find("tracking_output.data"): - pass - - - if fname.find("process.data") >=0: - pass - - - fpath = str(fpath) - - - - - - - - - - - - - - - pass - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/realtime/tracker_test.py b/realtime/tracker_test.py new file mode 100644 index 0000000..7a1a4dc --- /dev/null +++ b/realtime/tracker_test.py @@ -0,0 +1,281 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun Mar 2 14:15:57 2025 + +@author: ym +""" +import numpy as np +import cv2 +import os +from pathlib import Path + +import sys +sys.path.append(r"D:\DetectTracking") + +# from tracking.utils.read_data import extract_data_realtime, read_tracking_output_realtime +from tracking.utils.plotting import Annotator, colors +from tracking.utils import Boxes, IterableSimpleNamespace, yaml_load, boxes_add_fid +from tracking.trackers import BOTSORT, BYTETracker +from tracking.utils.showtrack import drawtracks +from hands.hand_inference import hand_pose +from tracking.utils.read_data import read_weight_sensor, extract_data_realtime, read_tracking_output_realtime +from contrast.feat_extract.config import config as conf +from contrast.feat_extract.inference import FeatsInterface +from tracking.utils.drawtracks import drawTrack + + +ReIDEncoder = FeatsInterface(conf) + + +W, H = 1024, 1280 +Mode = 'front' #'back' +ImgFormat = ['.jpg', '.jpeg', '.png', '.bmp'] + + +'''调用tracking()函数，利用本地跟踪算法获取各目标轨迹，可以比较本地跟踪算法与现场跟踪算法的区别。''' +def init_tracker(tracker_yaml = None, bs=1): + """ + Initialize tracker for object tracking during prediction. + """ + TRACKER_MAP = {'bytetrack': BYTETracker, 'botsort': BOTSORT} + cfg = IterableSimpleNamespace(**yaml_load(tracker_yaml)) + + tracker = TRACKER_MAP[cfg.tracker_type](args=cfg, frame_rate=30) + + return tracker + + +def init_trackers(tracker_yaml = None, bs=1): + """ + Initialize trackers for object tracking during prediction. + """ + # tracker_yaml = r"./tracking/trackers/cfg/botsort.yaml" + + TRACKER_MAP = {'bytetrack': BYTETracker, 'botsort': BOTSORT} + + cfg = IterableSimpleNamespace(**yaml_load(tracker_yaml)) + trackers = [] + for _ in range(bs): + tracker = TRACKER_MAP[cfg.tracker_type](args=cfg, frame_rate=30) + trackers.append(tracker) + + return trackers + + +def draw_box(img, tracks): + annotator = Annotator(img.copy(), line_width=2) + # for *xyxy, conf, cls in reversed(tracks): + # name = f'{int(cls)} {conf:.2f}' + # color = colors(int(cls), True) + # annotator.box_label(xyxy, name, color=color) + + for *xyxy, id, conf, cls, fid, bid in reversed(tracks): + name = f'ID:{int(id)} {int(cls)} {conf:.2f}' + color = colors(int(cls), True) + annotator.box_label(xyxy, name, color=color) + + + + im0 = annotator.result() + + return im0 + + + + +def tracking(bboxes, ffeats): + tracker_yaml = "./tracking/trackers/cfg/botsort.yaml" + + tracker = init_tracker(tracker_yaml) + + TrackBoxes = np.empty((0, 9), dtype = np.float32) + TracksDict = {} + + frmIds = [] + '''========================== 执行跟踪处理 =============================''' + # dets 与 feats 应保持严格对应 + k=0 + for dets, feats in zip(bboxes, ffeats): + + frmIds.append(np.unique(dets[:, 6]).astype(np.int64)[0]) + + boxes = dets[:, :6] + det_tracking = Boxes(boxes).cpu().numpy() + tracks, outfeats = tracker.update(det_tracking, features=feats) + '''tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index] + 0 1 2 3 4 5 6 7 8 + 这里，frame_index 也可以用视频的 帧ID 代替, box_index 保持不变 + ''' + k += 1 + imgpath = r"D:\全实时\202502\tracker\Yolos_Tracking\tracker\1_1740891284792\1_1740891284792_{}.png".format(int(k)) + img = cv2.imread(imgpath) + + im0 = draw_box(img, tracks) + savepath = r"D:\全实时\202502\tracker\Yolos_Tracking\tracker\1_1740891284792\b\1_1740891284792_{}_b.png".format(k) + cv2.imwrite(savepath, im0) + + + if len(tracks): + TrackBoxes = np.concatenate([TrackBoxes, tracks], axis=0) + +# ============================================================================= +# FeatDict = {} +# for track in tracks: +# tid = int(track[8]) +# FeatDict.update({tid: feats[tid, :]}) +# +# frameID = tracks[0, 7] +# +# # print(f"frameID: {int(frameID)}") +# assert len(tracks) == len(FeatDict), f"Please check the func: tracker.update() at frameID({int(frameID)})" +# +# TracksDict[f"frame_{int(frameID)}"] = {"feats":FeatDict} +# ============================================================================= + + + return TrackBoxes, TracksDict + +def dotrack(): + + datapath = r"D:\全实时\202502\tracker\1_tracker_in.data" + + bboxes, ffeats = extract_data_realtime(datapath) + trackerboxes, tracker_feat_dict = tracking(bboxes, ffeats) + + print("done!") + + +# def plotbox(): + +# fpath = 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\20250303-103833-338_6928804010091_6928804010091\1_tracking_output.data" +# imgpath = r"D:\全实时\202502\result\Yolos_Tracking\20250303-103833-338_6928804010091_6928804010091\1_1740969517953" +# trackingboxes, trackingfeats, tracking_outboxes, tracking_outfeats = read_tracking_output_realtime(fpath) + +# for *xyxy, id, conf, cls, fid, bid in tracking_outboxes[0]: +# imgname = f"1_1740969517953_{int(fid)}.png" + +# img_path = os.path.join(imgpath, imgname) +# img = cv2.imread(img_path) +# annotator = Annotator(img.copy(), line_width=2) + +# name = f'ID:{int(id)} {int(cls)} {conf:.2f}' +# color = colors(int(cls), True) +# annotator.box_label(xyxy, name, color=color) +# im0 = annotator.result() +# cv2.imwrite(os.path.join(imgpath, f"1_1740969517953_{int(fid)}_.png"), im0) + +# print(f"1_1740969676295_{int(fid)}_.png") + +# print("done") + +def video2imgs(videopath): + cap = cv2.VideoCapture(str(videopath)) + k = 0 + while True: + ret, frame = cap.read() + if frame is None: + break + k += 1 + imgpath = videopath.parent / f"{videopath.stem}_{k}.png" + cv2.imwrite(str(imgpath), frame) + + + +def extract_evtimgs(evtpath): + vidpaths = [v for v in evtpath.iterdir() if v.suffix == '.mp4'] + for vidpath in vidpaths: + video2imgs(vidpath) + + stamps = [name.stem.split('_')[1] for name in vidpaths] + + if len(set(stamps)==1): + return stamps[0] + return None + +def draw_tracking_boxes(evtpath, stamp): + for datapath in evtpath.iterdir(): + if datapath.name.find('_tracking_output.data')<=0: + continue + + camera = datapath.stem.split('_')[0] + trackingboxes, trackingfeats, tracking_outboxes, tracking_outfeats = read_tracking_output_realtime(str(datapath)) + + ## 该模块先读取轨迹数据，再根据帧ID读取相应图像 + for *xyxy, id, conf, cls, fid, bid in tracking_outboxes[0]: + imgpath = evtpath / f"{camera}_{stamp}_{int(fid)}.png" + + img = cv2.imread(str(imgpath)) + annotator = Annotator(img.copy(), line_width=2) + + name = f'ID:{int(id)} {int(cls)} {conf:.2f}' + color = colors(int(cls), True) + annotator.box_label(xyxy, name, color=color) + im0 = annotator.result() + cv2.imwrite(imgpath, im0) + + print(datapath.name) + +def draw_traj(evtpath): + for datapath in evtpath.iterdir(): + if datapath.name.find('_tracking_output.data')<=0: + continue + + fname = datapath.name + trackingboxes, trackingfeats, tracking_outboxes, tracking_outfeats = read_tracking_output_realtime(datapath) + + CamerType = fname.split('_')[0] + if CamerType == '1': + edgeline = cv2.imread("./CartTemp/board_ftmp_line.png") + if CamerType == '0': + edgeline = cv2.imread("./CartTemp/edgeline.png") + edgeline = drawTrack(tracking_outboxes, edgeline) + + + imgpath = datapath.parent / f"{datapath.stem}.png" + cv2.imwrite(str(imgpath), edgeline) + + +def main(): + + path = 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\20250303-104225-381_6920459958674" + evtpaths = [p for p in Path(path).iterdir() if p.is_dir()] + for evtpath in evtpaths: + #1. 从事件的前后摄视频提取图像 + stamp = extract_evtimgs(evtpath) + + #2. 根据 0/1_tracking_output.data 中提取的轨迹在img中绘制box + draw_tracking_boxes(evtpath, stamp) + + #3. 根据 0/1_tracking_output.data 中提取的轨迹在edgeline中绘制box + draw_traj(evtpath) + + + + +if __name__ == '__main__': + # dotrack() + # plotbox() + + vpath = r"D:\datasets\ym\VID_20250307_105606" + extract_evtimgs(Path(vpath)) + + + + + + + + + + + + + + + + + + + + + diff --git a/track_reid.py b/track_reid.py index 536c53c..df91966 100644 --- a/track_reid.py +++ b/track_reid.py @@ -279,7 +279,7 @@ def yolo_resnet_tracker( color = colors(int(id), True) else: color = colors(19, True) # 19为调色板的最后一个元素 - # annotator.box_label(xyxy, label, color=color) + annotator.box_label(xyxy, label, color=color) '''====== Save results (image and video) ======''' # save_path = str(save_dir / Path(path).name) # 带有后缀名 @@ -727,8 +727,8 @@ def main(): # p = r"D:\exhibition\images\153112511_0_seek_105.mp4" # p = r"D:\exhibition\images\image" - p = r"\\192.168.1.28\share\数据\原始数据\小物品数据\视频\82654976401_20241213-143457_front_addGood_5478c9a53bbe_40_17700000001.mp4" - optdict["project"] = r"D:\小物品入侵检测\result" + p = r"D:\全实时\202502\tracker\1_1740891284792.mp4" + optdict["project"] = r"D:\全实时\202502\tracker" # optdict["project"] = r"D:\exhibition\result" if os.path.isdir(p): diff --git a/tracking/module_analysis.py b/tracking/module_analysis.py index 8318163..1752100 100644 --- a/tracking/module_analysis.py +++ b/tracking/module_analysis.py @@ -17,6 +17,7 @@ import warnings import sys sys.path.append(r"D:\DetectTracking") +from tracking.utils.read_data import extract_data_realtime, read_tracking_output_realtime from tracking.utils.plotting import Annotator, colors, draw_tracking_boxes from tracking.utils import Boxes, IterableSimpleNamespace, yaml_load @@ -85,6 +86,12 @@ def tracking(bboxes, ffeats): return TrackBoxes, TracksDict + + + + + + def read_imgs(imgspath, CamerType): ''' inputs: @@ -440,6 +447,7 @@ def main(): if __name__ == "__main__": + # main_loop() main() # try: diff --git a/tracking/utils/__pycache__/read_data.cpython-39.pyc b/tracking/utils/__pycache__/read_data.cpython-39.pyc index 4c463e0a0ec3a93b11c86e1551e516402d608962..0d7146fa8fe43526d862c6a335a370d433c27082 100644 GIT binary patch delta 3149 zcmaJ@Yj7J^72d1IYW1=nc4FC%s@Sm{%ZXo!W6QRjq^;XD%`{E0(2x|};13K741^!0Q~ImZKV^o2bM9KI z?UqQh-=1^tx#zX#o_k*`epGD8`28LMewF?==f6GsgNE0g(X>mane5qvlW8|6W>i^A zi@CYNlZuvR|8O2A+pB}(4Hp@x{@(uzA!$|!oEPJugm$s_0wbie+7S#Ba-zB?^f%(& zffG9sdReUTdV4pL1fd0C2w@LEB0!T!kFvisM#XU?_p$#rMrQXTc`raBL?@B%K-h-x zD8j=ChX73RaXqJMI(-mF4j>#sIEv7TU^$7@W`xfmOtA}1DmhiX(^OvXY+>^} z-wAV<2S7OCuKD<8b4M6`On5Dyrcu=~_MhgSHttW|N7M0OE;G;lqiJ?uL?g4+A4fj7 z-c+phW|AA}L6v^fR?tkxVn#n>imLXwnp0NIqLDF#2833GarRN`9&(8Fx6P6>)k>Rp zz2;zZ6Wix^v+MD0_OC7e&7-i!bWvq7m&q!x*{BhC`BFVWHI!sbPQ_a7ShWQs5v#hoQ^IjU_B#ZJP_`2=F(_2*THk;``2QrJL8gR6jLo$l z9y48qys`@(Q$%9wu<68io;qMdnSo4ho))I`4ArL#59DRKw)1}62NGTtr0D`e0XIH5 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read_similar(filePath): Dict['barcode'] = '' if label.find("_") > 0: bcd = label.split('_')[-1] - if len(bcd)>=10 and bcd.isdigit(): + if len(bcd)>=8 and bcd.isdigit(): Dict['barcode'] = bcd