20250313
This commit is contained in:
王庆刚
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@ -16,11 +16,6 @@ from feat_extract.inference import FeatsInterface #, inference_image
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Encoder = FeatsInterface(conf)
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def main():
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imgpaths = r"D:\全实时\202502\result\Yolos_Tracking\20250228-160049-188_6921168558018_6921168558018\a"
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featDict = {}
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@ -191,10 +191,19 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
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return
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def gen_bcd_features(imgpath, bcdpath, featpath, bcdSet=None):
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def gen_bcd_features(imgpath, bcdpath, featpath, eventSourcePath):
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''' 生成标准特征集 '''
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'''1. 提取 imgpath 中样本地址，生成字典{barcode: [imgpath1, imgpath1, ...]}
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并存储于: bcdpath, 格式为 barcode.pickle'''
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bcdList = []
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for evtname in os.listdir(eventSourcePath):
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bname, ext = os.path.splitext(evtname)
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evt = bname.split('_')
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if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10:
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bcdList.append(evt[-1])
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bcdSet = set(bcdList)
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get_std_barcodeDict(imgpath, bcdpath, bcdSet)
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'''2. 特征提取，并保存至文件夹 featpath 中，也根据 bcdSet 交集执行'''
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@ -14,49 +14,47 @@ from scipy.spatial.distance import cdist
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from utils.event import ShoppingEvent
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def init_eventdict(sourcePath, stype="data"):
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def init_eventDict(sourcePath, eventDataPath, stype="data"):
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'''stype: str,
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'source': 由 videos 或 images 生成的 pickle 文件
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'data': 从 data 文件中读取的现场运行数据
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"realtime": 全实时数据，从 data 文件中读取的现场运行数据
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sourcePath:事件文件夹，事件类型包含2种：
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(1) pipeline生成的 pickle 文件
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(2) 直接采集的事件文件夹
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'''
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k, errEvents = 0, []
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for bname in os.listdir(sourcePath):
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# bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479"
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for evtname in os.listdir(sourcePath):
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bname, ext = os.path.splitext(evtname)
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source_path = os.path.join(sourcePath, evtname)
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source_path = os.path.join(sourcePath, bname)
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if stype=="source" and not os.path.isfile(source_path): continue
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if stype=="source" and ext not in ['.pkl', '.pickle']: continue
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if stype=="data" and os.path.isfile(source_path): continue
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if stype=="realtime" and os.path.isfile(source_path): continue
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if os.path.isdir(source_path):
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pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
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else:
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pickpath = os.path.join(eventDataPath, bname)
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evt = bname.split('_')
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condt = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10
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if not condt: continue
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if os.path.isfile(pickpath):
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continue
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evt = os.path.splitext(os.path.split(pickpath)[-1])[0].split('_')
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cont = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10
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if not cont:
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continue
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pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
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if os.path.isfile(pickpath): continue
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# event = ShoppingEvent(source_path, stype)
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try:
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event = ShoppingEvent(source_path, stype)
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with open(pickpath, 'wb') as f:
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pickle.dump(event, f)
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print(bname)
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print(evtname)
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except Exception as e:
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errEvents.append(source_path)
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print(f"Error: {bname}, {e}")
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print(f"Error: {evtname}, {e}")
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# k += 1
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# if k==1:
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# break
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errfile = os.path.join(resultPath, 'error_events.txt')
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with open(errfile, 'a', encoding='utf-8') as f:
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errfile = Path(eventDataPath).parent / 'error_events.txt'
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with open(str(errfile), 'a', encoding='utf-8') as f:
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for line in errEvents:
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f.write(line + '\n')
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@ -185,7 +183,7 @@ def one2n_pr(evtDicts, pattern=1):
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elif bcd!=event.barcode and simi!=maxsim:
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tnsimi.append(simi)
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tnevents.append(evtname)
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elif bcd!=event.barcode and simi==maxsim:
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elif bcd!=event.barcode and simi==maxsim and event.barcode in evt_barcodes:
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fpsimi.append(simi)
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fpevents.append(evtname)
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else:
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@ -216,7 +214,11 @@ def one2n_pr(evtDicts, pattern=1):
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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(one2nFile)}")
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ax.legend()
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@ -241,7 +243,7 @@ def one2n_pr(evtDicts, pattern=1):
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def main():
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'''1. 生成事件字典并保存至 eventDataPath, 只需运行一次 '''
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init_eventdict(eventSourcePath, stype="realtime") # 'source', 'data', 'realtime'
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init_eventDict(eventSourcePath, eventDataPath, stype="realtime") # 'source', 'data', 'realtime'
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# for pfile in os.listdir(eventDataPath):
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# evt = os.path.splitext(pfile)[0].split('_')
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@ -264,7 +266,7 @@ def main():
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if __name__ == '__main__':
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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"
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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"
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resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\testing"
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eventDataPath = os.path.join(resultPath, "evtobjs_wang")
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@ -58,6 +58,7 @@ from feat_extract.inference import FeatsInterface
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from utils.event import ShoppingEvent, save_data
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from genfeats import gen_bcd_features
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from event_test import calc_simil
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from one2n_contrast import init_eventDict
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@ -271,8 +272,12 @@ def build_std_evt_dict():
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evtDict = {}
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for evtname, barcode in evtList:
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evtpath = os.path.join(eventDataPath, evtname+'.pickle')
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with open(evtpath, 'rb') as f:
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evtdata = pickle.load(f)
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try:
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with open(evtpath, 'rb') as f:
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evtdata = pickle.load(f)
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except Exception as e:
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print(evtname)
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evtDict[evtname] = evtdata
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return evtList, evtDict, stdDict
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@ -300,7 +305,8 @@ def one2SN_pr(evtList, evtDict, stdDict):
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event = evtDict[evtname]
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## 无轨迹判断
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if len(event.front_feats)+len(event.back_feats)==0:
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print(evtname)
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errorFile_one2SN.append(evtname)
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print(f"No trajectory: {evtname}")
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continue
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barcodes, similars = [], []
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@ -351,10 +357,10 @@ def one2SN_pr(evtList, evtDict, stdDict):
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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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fig, ax = plt.subplots()
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ax.plot(Thresh, PPreciseX, 'r', label='Precise_Pos: TP/TPFP')
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@ -363,9 +369,11 @@ def one2SN_pr(evtList, evtDict, stdDict):
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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(evtList)}")
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ax.set_xlabel(f"Event Num: {len(tp_events) + len(fn_events)}")
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ax.legend()
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plt.show()
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## ============================= 1:N 展厅 直方图'''
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@ -403,10 +411,14 @@ def one2one_simi(evtList, evtDict, stdDict):
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'''======2 计算事件、标准特征集相似度 =================='''
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rltdata = []
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errorFile_one2one = []
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for i in range(len(mergePairs)):
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evtname, stdbcd, label = mergePairs[i]
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event = evtDict[evtname]
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if len(event.feats_compose)==0: continue
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if len(event.feats_compose)==0:
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errorFile_one2one.append(evtname)
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continue
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stdfeat = stdDict[stdbcd] # float32
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@ -419,10 +431,15 @@ def one2one_simi(evtList, evtDict, stdDict):
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'''================ float32、16、int8 精度比较与存储 ============='''
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# data_precision_compare(stdfeat, evtfeat, mergePairs[i], save=True)
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return rltdata
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errorFile_one2one = list(set(errorFile_one2one))
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return rltdata, errorFile_one2one
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def one2one_pr(rltdata):
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def one2one_pr(evtList, evtDict, stdDict):
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rltdata, errorFile_one2one = one2one_simi(evtList, evtDict, stdDict)
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Same, Cross = [], []
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for label, stdbcd, evtname, simi_mean, simi_max, simi_mft in rltdata:
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if label == "same":
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@ -451,27 +468,41 @@ def one2one_pr(rltdata):
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Correct = []
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Thresh = np.linspace(-0.2, 1, 100)
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for th in Thresh:
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TP = np.sum(Same > th)
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FN = TPFN - TP
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TN = np.sum(Cross < th)
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FP = TNFP - TN
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TP = np.sum(Same >= th)
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FN = np.sum(Same < th)
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# FN = TPFN - TP
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TN = np.sum(Cross < th)
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FP = np.sum(Cross >= th)
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# FP = TNFP - TN
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Recall_Pos.append(TP/TPFN)
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Recall_Neg.append(TN/TNFP)
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Precision_Pos.append(TP/(TP+FP+1e-6))
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Precision_Neg.append(TN/(TN+FN+1e-6))
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Recall_Pos.append(TP/(TP+FN+1e-6))
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Recall_Neg.append(TN/(TN+FP+1e-6))
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# Recall_Pos.append(TP/TPFN)
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# Recall_Neg.append(TN/TNFP)
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Correct.append((TN+TP)/(TPFN+TNFP))
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fig, ax = plt.subplots()
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ax.plot(Thresh, Correct, 'r', label='Correct: (TN+TP)/(TPFN+TNFP)')
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ax.plot(Thresh, Precision_Pos, 'r', label='Precision_Pos: TP/(TP+FP)')
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ax.plot(Thresh, Recall_Pos, 'b', label='Recall_Pos: TP/TPFN')
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ax.plot(Thresh, Recall_Neg, 'g', label='Recall_Neg: TN/TNFP')
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ax.plot(Thresh, Precision_Pos, 'c', label='Precision_Pos: TP/(TP+FP)')
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ax.plot(Thresh, Correct, 'c', label='Correct: (TN+TP)/(TPFN+TNFP)')
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ax.plot(Thresh, Precision_Neg, 'm', label='Precision_Neg: TN/(TN+FN)')
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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('PrecisePos & PreciseNeg')
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ax.set_xlabel(f"Same Num: {TPFN}, Cross Num: {TNFP}")
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ax.legend()
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@ -506,23 +537,17 @@ def gen_eventdict(sourcePath, saveimg=True):
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## 兼容事件的两种情况：文件夹 和 Yolo-Resnet-Tracker 的输出
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if os.path.isfile(source_path):
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bname, ext = os.path.splitext(bname)
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evt = bname.split("_")
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# evt = bname.split("_")
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evt = bname.split('_')
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condt = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10
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if not condt: continue
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# bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479"
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# source_path = os.path.join(evtpath, bname)
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# 如果已完成事件生成，则不执行
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pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
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if os.path.isfile(pickpath): continue
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# event = ShoppingEvent(source_path, stype=source_type)
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# with open(pickpath, 'wb') as f:
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# pickle.dump(event, f)
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try:
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event = ShoppingEvent(source_path, stype=source_type)
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# save_data(event, resultPath)
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@ -538,16 +563,44 @@ def gen_eventdict(sourcePath, saveimg=True):
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# if k==1:
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# break
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errfile = os.path.join(resultPath, 'error_events.txt')
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# with open(errfile, 'w', encoding='utf-8') as f:
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# for line in errEvents:
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# f.write(line + '\n')
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def init_std_evt_dict():
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'''==== 0. 生成事件列表和对应的 Barcodes列表 ==========='''
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bcdList, event_spath = [], []
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# def init_std_evt_dict():
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# '''==== 0. 生成事件列表和对应的 Barcodes列表 ==========='''
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# bcdList, event_spath = [], []
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# for evtname in os.listdir(eventSourcePath):
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# bname, ext = os.path.splitext(evtname)
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# ## 处理事件的两种情况：文件夹 和 Yolo-Resnet-Tracker 的输出
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# fpath = os.path.join(eventSourcePath, evtname)
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# if os.path.isfile(fpath) and (ext==".pkl" or ext==".pickle"):
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# evt = bname.split('_')
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# elif os.path.isdir(fpath):
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# evt = evtname.split('_')
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# else:
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# continue
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# if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10:
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# bcdList.append(evt[-1])
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# event_spath.append(fpath)
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# '''==== 1. 生成标准特征集, 只需运行一次, 在 genfeats.py 中实现 ==========='''
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# bcdSet = set(bcdList)
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# gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet)
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# print("stdFeats have generated and saved!")
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# '''==== 2. 生成事件字典, 只需运行一次 ==============='''
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# gen_eventdict(event_spath)
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# print("eventList have generated and saved!")
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def get_evtList():
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'''==== 0. 生成事件列表和对应的 Barcodes 集合 ==========='''
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bcdList, evtpaths = [], []
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for evtname in os.listdir(eventSourcePath):
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bname, ext = os.path.splitext(evtname)
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@ -562,46 +615,73 @@ def init_std_evt_dict():
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if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10:
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bcdList.append(evt[-1])
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event_spath.append(fpath)
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evtpaths.append(fpath)
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'''==== 1. 生成标准特征集, 只需运行一次, 在 genfeats.py 中实现 ==========='''
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bcdSet = set(bcdList)
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gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet)
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print("stdFeats have generated and saved!")
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'''==== 2. 生成事件字典, 只需运行一次 ==============='''
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gen_eventdict(event_spath)
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print("eventList have generated and saved!")
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return evtpaths, bcdSet
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# def init_stdDict():
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# evtpaths, bcdSet = get_evtList()
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# gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet)
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# print("stdFeats have generated and saved!")
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# def init_evtDict():
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# '''==== 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():
|
||||
|
||||
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
|
||||
'''=== 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)
|
||||
|
||||
'''==== 2. 生成事件字典, 只需运行一次 ===================='''
|
||||
|
||||
# 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)
|
||||
|
||||
|
||||
|
||||
'''==== 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()
|
||||
|
||||
rltdata = one2one_simi(evtList, evtDict, stdDict)
|
||||
|
||||
one2one_pr(rltdata)
|
||||
|
||||
|
||||
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
|
||||
evtList, evtDict, stdDict = build_std_evt_dict()
|
||||
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": eventSourcePath 为 包含 data 文件的文件夹
|
||||
'''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 = '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"
|
||||
|
||||
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()
|
||||
|
||||
|
||||
|
||||
|
||||
@ -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)
|
||||
@ -176,9 +183,14 @@ 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:
|
||||
@ -188,6 +200,12 @@ def contrast_pr(paths):
|
||||
similars.append(dt['similar'])
|
||||
|
||||
|
||||
barcodes_.append(path.stem)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
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]
|
||||
@ -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 ==================='''
|
||||
@ -224,6 +244,7 @@ def contrast_pr(paths):
|
||||
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]
|
||||
@ -231,10 +252,10 @@ 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))
|
||||
@ -259,6 +280,7 @@ def contrast_pr(paths):
|
||||
|
||||
|
||||
|
||||
|
||||
'''===================== 3. 取出场景下的 1:n ========================'''
|
||||
events, evt_barcodes, evt_similars, evt_types = [], [], [], []
|
||||
for dt in one2n:
|
||||
@ -267,8 +289,15 @@ def contrast_pr(paths):
|
||||
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))]
|
||||
@ -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'''
|
||||
@ -359,12 +388,18 @@ 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()
|
||||
@ -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:
|
||||
@ -489,25 +530,22 @@ def contrast_pr(paths):
|
||||
# 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)
|
||||
|
||||
|
||||
|
||||
Binary file not shown.
@ -82,7 +82,11 @@ def array2list(bboxes):
|
||||
|
||||
class ShoppingEvent:
|
||||
def __init__(self, eventpath, stype="data"):
|
||||
'''stype: str, '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)
|
||||
|
||||
72
dataset/multi-trajs.py
Normal file
72
dataset/multi-trajs.py
Normal file
@ -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()
|
||||
@ -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:
|
||||
|
||||
78
practice/6924743915817.txt
Normal file
78
practice/6924743915817.txt
Normal file
@ -0,0 +1,78 @@
|
||||
-0.012108 -0.005081 0.040924 0.003206 0.048553 0.078918 -0.036774 -0.049957 -0.034515 -0.037689 -0.058533 -0.006462 -0.106995 0.023758 0.038818 -0.146240 0.001214 -0.077209 0.063110 0.023376 -0.085205 -0.067322 0.021179 -0.015556 0.073242 0.065247 0.014977 0.067261 0.139893 0.000464 0.109802 -0.064697 -0.071045 -0.029648 -0.111511 -0.142334 -0.067627 -0.025436 0.029633 0.066040 0.053192 0.057281 0.001234 -0.017181 -0.051910 0.037964 0.043549 -0.078247 -0.048981 -0.016296 0.035248 -0.083862 -0.072876 0.003597 0.028336 -0.002398 0.107483 0.006378 0.085327 -0.035217 -0.029861 0.000131 -0.062988 0.006710 -0.008194 0.023758 -0.032501 -0.020172 0.024170 0.025360 0.094116 -0.040558 0.105408 -0.062042 0.068726 -0.050781 -0.114441 0.040497 0.053406 0.029068 -0.078308 -0.042084 -0.049103 -0.064392 -0.070618 -0.080261 0.031403 0.029037 -0.032562 -0.031403 0.043365 0.098938 0.028519 -0.054016 -0.072083 -0.052643 0.085449 -0.047882 -0.015900 0.060547 -0.052704 -0.138916 -0.129517 -0.039856 -0.011246 -0.023804 -0.018372 -0.032410 -0.050903 -0.034393 -0.084961 0.021057 0.021820 0.017853 0.013176 -0.117126 -0.067688 0.056610 0.046783 -0.013290 0.103577 -0.041260 0.044434 -0.007912 0.085876 -0.070862 0.111511 -0.028915 0.057495 0.027283 0.020248 0.067871 -0.046204 0.054626 0.028427 -0.036469 -0.026749 -0.139526 0.009445 -0.054443 -0.028671 -0.056305 0.041260 -0.093201 -0.018829 0.037628 -0.005714 -0.018143 0.051910 0.042603 0.027008 -0.056427 0.070496 -0.029129 -0.063782 -0.089355 -0.046783 0.084656 -0.038849 0.090698 0.008919 -0.104309 -0.061005 0.014610 0.047913 -0.006104 -0.095459 -0.017731 0.019455 -0.004402 0.053284 -0.088501 0.057251 -0.054047 -0.017914 0.089661 0.008621 -0.014305 0.015915 0.037170 0.038727 -0.130249 0.015274 0.054504 -0.030716 0.023438 -0.041504 -0.003704 0.158325 0.067566 0.030365 -0.024719 -0.059082 -0.025925 0.050476 -0.016632 0.097839 -0.123474 0.064148 -0.004620 -0.088013 -0.036896 0.013901 0.084900 0.010056 0.102600 -0.083862 -0.117065 -0.007828 0.041779 -0.047638 0.051605 0.071167 -0.017288 -0.054962 -0.060852 0.016983 0.039307 -0.098389 -0.041931 -0.033112 0.116821 0.057831 -0.002653 0.012817 0.169678 0.053131 0.060333 -0.107605 -0.009209 0.014893 0.039185 0.021896 -0.046570 0.000801 -0.055817 0.138062 -0.038574 -0.163208 0.069702 0.098083 -0.007362 0.064392 -0.075867 0.073425 -0.058075 -0.101746 -0.039917 -0.010178 -0.008743 -0.017105 0.078308 0.149292 0.011856 0.003960 -0.000674
|
||||
-0.063477 -0.007069 -0.005245 0.043976 0.038483 0.101135 -0.004757 -0.054932 -0.051422 -0.036133 -0.048309 0.027176 -0.103699 -0.004608 0.084229 -0.182983 0.023010 -0.036285 0.033508 0.021790 -0.074341 -0.011391 -0.040161 -0.069519 0.094604 0.004799 -0.030457 0.060059 0.128906 0.026169 0.113586 -0.018616 -0.033478 -0.038574 -0.102966 -0.098999 -0.054718 0.003244 0.031738 0.131714 0.053589 0.042694 0.039795 -0.032562 -0.111450 0.017044 0.081055 -0.019135 -0.044891 -0.014748 0.060181 -0.151001 -0.130371 -0.011879 0.079102 0.053833 0.088257 0.000039 0.068909 -0.086487 -0.038635 0.045715 -0.073547 0.018677 -0.019897 0.001243 -0.059570 0.007343 0.019928 0.029938 0.123413 -0.050232 0.097961 -0.030304 0.034943 -0.046692 -0.151978 0.010780 0.073181 -0.010277 -0.048309 -0.025406 -0.028961 0.009995 -0.071777 -0.092896 -0.025650 0.069519 -0.050781 -0.034546 0.014282 0.098694 0.007473 -0.018600 -0.077515 0.020584 0.070129 -0.044800 -0.061768 0.088379 0.033112 -0.132690 -0.062408 0.024338 -0.002018 0.040039 -0.039001 -0.032806 -0.101868 -0.029999 -0.045227 0.028305 -0.005219 -0.003819 0.008522 -0.106812 -0.018646 0.089233 0.054169 -0.016068 0.053772 -0.081543 -0.048126 -0.014633 0.053986 -0.079407 0.055664 -0.039307 0.091797 0.008270 0.032928 0.002785 -0.072266 0.042084 0.021957 -0.082275 -0.010719 -0.054108 0.020813 -0.013069 -0.023575 -0.006931 0.003746 -0.063049 0.038239 0.017975 0.008720 0.039062 0.032166 0.006096 0.000866 -0.040741 0.063049 -0.025879 -0.061676 -0.102600 -0.004028 0.120789 -0.018600 0.109070 0.042145 -0.088501 -0.085327 0.067139 0.030060 0.057587 -0.105774 -0.033752 -0.003479 -0.071960 0.054749 -0.076416 0.059082 -0.061615 -0.074402 0.011024 0.036255 0.008629 0.022537 0.035675 0.038055 -0.098755 0.017593 0.083496 -0.071899 0.078064 -0.056641 -0.000371 0.142578 0.022171 0.017471 -0.028198 -0.018631 -0.034729 -0.004997 -0.010086 0.029083 -0.144043 0.048889 0.027649 -0.043732 -0.013733 0.032166 0.059937 0.065247 0.086365 -0.082214 -0.118225 0.016159 0.026978 -0.108765 0.075317 0.073730 -0.002377 -0.029755 -0.092407 0.007515 0.040344 -0.076843 -0.001449 -0.085571 0.139893 0.066040 0.032013 0.055267 0.190674 -0.031403 0.079285 -0.110779 -0.014015 0.053802 -0.021652 0.025146 -0.032776 -0.013557 -0.074951 0.108276 -0.014259 -0.162109 0.045227 0.068787 -0.029633 0.033112 -0.031174 0.089355 -0.045166 -0.028091 -0.011375 0.008003 0.033997 -0.005348 -0.004387 0.096558 -0.032715 -0.002901 -0.027359
|
||||
-0.077576 -0.053558 0.011093 -0.053497 0.116943 -0.086792 0.075867 -0.045746 -0.000218 -0.057861 -0.091492 0.010445 -0.062073 -0.062866 -0.031174 -0.150024 0.004971 -0.061249 0.024353 0.016251 -0.026306 0.090149 -0.075073 -0.025162 0.028854 -0.065552 0.041901 0.062683 -0.053772 0.017639 0.095154 -0.059906 0.041077 0.026886 -0.126099 -0.036194 -0.080444 -0.029373 -0.018250 0.068970 0.050110 0.062134 0.103149 -0.002596 -0.033783 0.024185 0.058624 0.058197 -0.106445 -0.039825 0.000286 -0.018631 -0.162476 0.019836 0.084473 -0.012520 0.028641 -0.012932 0.034882 -0.086609 0.089172 0.019257 -0.126099 -0.003334 -0.017380 0.016800 -0.025055 -0.046173 -0.045258 -0.032166 0.094666 0.063904 0.086304 -0.051453 0.015327 -0.037659 -0.069519 0.038483 0.021698 0.001744 0.003468 0.001429 0.038605 -0.032288 -0.028336 -0.085144 0.035767 0.028961 -0.001388 -0.052643 0.046967 0.049225 0.057220 -0.093018 -0.072083 0.013733 0.040741 -0.012871 -0.076965 0.140503 0.066833 -0.099792 0.046936 -0.073730 -0.021301 0.040710 -0.017029 -0.049927 -0.005035 0.020096 0.000294 -0.059784 -0.037323 0.005112 0.043823 -0.012642 0.035919 0.067261 0.013954 -0.066895 0.121582 -0.094360 0.035980 -0.079346 0.015457 0.031250 0.072083 0.038910 -0.011421 -0.055542 -0.067932 -0.012093 -0.023499 0.056702 -0.010284 0.040283 0.030228 -0.025574 0.068359 0.013748 -0.087952 0.062195 0.037903 -0.098389 -0.057770 0.079529 -0.028214 -0.088684 -0.014153 0.002840 0.044678 -0.008965 -0.038696 0.114807 -0.026810 0.000520 -0.017410 0.119385 -0.047760 0.092590 0.002943 -0.060791 -0.084351 0.116455 0.001213 -0.007164 -0.016769 -0.080750 -0.012779 -0.105835 -0.004128 0.012833 -0.084106 -0.078552 0.003675 -0.036682 0.045868 0.074219 -0.055664 0.066711 0.042511 -0.059845 -0.023758 0.004200 -0.011436 0.053497 -0.083374 -0.007359 0.106873 -0.058624 -0.051270 -0.001504 0.010979 0.106140 -0.083496 -0.005470 0.084595 -0.136353 0.100037 -0.094055 -0.118530 0.006615 0.097168 0.061401 0.171265 0.074707 -0.022614 -0.033234 0.000768 0.073120 -0.024551 0.022461 0.069702 0.018723 0.005615 0.111572 -0.059998 0.078064 -0.063538 -0.042786 -0.008965 0.087280 0.095154 -0.046509 0.111267 -0.000310 -0.076599 -0.038605 -0.029648 -0.001067 0.084473 0.078613 -0.070251 -0.036621 0.132446 -0.080200 0.064331 -0.073547 -0.057404 -0.019928 -0.001274 -0.003666 0.029327 -0.064880 0.073303 0.033936 -0.085388 -0.101685 -0.072693 -0.088257 0.097290 -0.092285 0.058624 0.083618 -0.001839 0.002214
|
||||
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-0.039215 -0.067261 0.094055 -0.064087 0.059204 -0.040588 -0.004719 -0.043976 -0.040375 0.006104 -0.084229 -0.029160 -0.164185 0.001306 0.024918 -0.141968 -0.058075 -0.036530 0.089294 0.020020 -0.039429 0.009346 0.022797 0.000039 0.045959 -0.077148 0.057648 0.022858 0.077393 -0.075012 0.054077 -0.072083 -0.042786 0.010666 -0.177368 -0.047852 -0.090759 -0.008438 0.018585 0.064026 0.038452 0.020096 0.077148 -0.079285 -0.039581 0.061005 0.077576 -0.025085 -0.065613 -0.094971 0.011909 0.048462 -0.112488 -0.000763 -0.017792 -0.027252 0.058228 -0.030045 0.029221 -0.021332 0.008820 -0.056274 -0.120544 -0.008987 -0.070496 0.001659 0.014915 -0.063232 -0.118469 0.045441 0.052368 -0.028946 0.052734 -0.067017 0.122498 -0.068542 -0.069641 0.016251 0.082214 -0.012611 -0.082092 -0.028564 0.052734 0.062256 0.025452 -0.066101 0.062866 0.030960 -0.004002 -0.079895 0.003546 0.091309 0.051483 -0.108765 -0.112549 -0.041412 -0.020218 -0.046265 -0.027588 0.148315 -0.014610 -0.169067 -0.013329 -0.013206 -0.105103 -0.082336 -0.026978 -0.146606 -0.031464 -0.043732 -0.024704 -0.042877 0.009636 0.002218 0.044769 -0.041443 0.087524 0.066895 -0.008499 -0.061340 0.165283 0.025574 -0.020477 -0.053375 0.052490 0.004951 0.055481 0.045013 0.047241 0.031494 -0.040131 -0.001564 -0.050476 0.064880 0.008156 0.017136 -0.073730 -0.138916 0.030197 -0.003706 -0.076477 0.003685 0.006142 -0.031616 -0.073914 0.134155 -0.013878 -0.037811 -0.032990 -0.037109 0.002953 -0.051514 0.000969 0.029892 0.021820 -0.051971 -0.006363 0.112793 0.029617 0.013397 0.036743 -0.077454 -0.070007 0.090759 0.009613 -0.065186 -0.046967 -0.044037 0.014175 -0.028412 0.096252 -0.030090 -0.072571 -0.059479 0.109558 0.024170 0.024841 -0.001913 0.044861 0.033081 0.063354 -0.066040 -0.055328 0.026306 -0.077393 0.016174 -0.059357 0.000043 0.123230 -0.007015 -0.043549 -0.053009 -0.068298 0.030396 -0.021835 -0.059265 0.101685 -0.119019 0.038910 -0.106201 -0.064331 -0.011925 0.016785 0.110779 0.070557 0.111938 -0.042145 -0.030884 -0.067566 0.029221 -0.007294 0.128784 0.121887 -0.074097 -0.081421 0.020447 0.013786 0.037659 -0.068787 -0.013481 -0.035614 0.033630 0.006725 -0.046417 0.061432 0.023666 0.006065 -0.009491 -0.026978 -0.022049 0.107727 -0.006538 0.009056 -0.015793 0.110046 -0.008354 0.036865 -0.082458 -0.034393 0.011353 -0.011482 -0.016418 0.030472 -0.069458 0.033051 0.090698 -0.068787 0.010857 -0.011711 -0.089050 0.087891 -0.033722 0.123901 -0.053345 -0.069336 -0.062683
|
||||
-0.112854 -0.025482 0.005482 0.020279 0.059692 -0.042267 0.031586 -0.048950 -0.040253 -0.008797 -0.152100 -0.019165 -0.092712 0.010941 0.067322 -0.196411 -0.033844 -0.128418 0.088440 -0.000941 -0.011795 0.060852 -0.057220 -0.080078 0.030167 -0.071960 0.015747 0.114380 0.041992 -0.030792 0.071228 -0.067261 -0.022034 -0.100281 -0.171021 -0.104553 -0.062744 -0.014526 0.041565 0.075012 0.055084 0.038727 0.058868 -0.008957 -0.048981 0.021347 0.071350 -0.002934 -0.083008 -0.048096 0.058441 0.016479 -0.157471 0.041962 0.062988 0.012779 0.034943 0.006897 0.017471 -0.050293 -0.016953 0.004257 -0.076538 -0.017776 -0.031067 -0.000585 -0.045929 -0.025711 -0.016251 -0.004734 0.053497 0.028519 0.076660 -0.033356 0.027313 -0.052063 -0.125488 -0.010010 0.077148 -0.005856 -0.081116 0.015457 0.038208 -0.011597 -0.062988 -0.092346 0.008179 0.033844 -0.002117 -0.068359 0.028885 0.069336 0.012299 -0.041962 -0.082153 0.001546 0.020584 -0.035828 -0.025558 0.138916 0.010719 -0.124268 -0.024048 -0.078857 -0.009933 0.028809 0.010895 -0.094849 -0.002447 -0.027924 0.041931 0.009644 0.069641 0.064941 0.050018 -0.048584 0.053802 0.081238 0.006062 -0.069580 0.112488 -0.035706 -0.007183 -0.000055 -0.043152 -0.015388 0.080627 -0.061707 0.012703 -0.020157 -0.009735 -0.017563 -0.061432 0.052826 -0.030060 -0.027008 0.017593 -0.034180 0.079041 -0.005634 -0.054993 0.007370 -0.028214 -0.054626 0.047760 0.097351 0.009506 0.006466 -0.007553 0.018219 0.018982 0.004433 0.056000 0.005699 -0.029144 -0.038330 -0.012337 0.068604 -0.049377 0.138794 0.038452 -0.048859 -0.057190 0.119629 -0.011131 -0.016388 -0.085999 -0.041748 -0.026672 -0.086365 -0.005589 -0.055542 -0.055328 -0.126465 -0.010002 -0.000075 0.053864 -0.018692 -0.035675 0.012138 0.006001 -0.150146 -0.037598 0.029358 -0.077698 0.111450 -0.063232 -0.025040 0.106018 0.013931 -0.026550 -0.011070 -0.001914 0.011086 -0.045319 -0.014580 0.110291 -0.079956 0.046265 -0.014252 -0.047577 -0.076416 0.076904 0.119873 0.131226 0.067993 -0.004433 -0.053619 -0.027161 0.069031 0.002426 0.054901 0.091003 -0.032990 -0.026718 0.008980 -0.078064 0.097534 -0.083130 -0.061493 -0.047638 0.164429 0.107849 -0.063660 0.074524 0.083435 -0.049347 -0.061462 -0.059631 0.017822 0.106140 0.058350 0.019424 -0.014931 0.043335 -0.115051 0.099854 -0.094849 -0.084900 0.024323 0.053223 -0.014671 0.043610 -0.033905 0.098694 0.012497 -0.041504 -0.034546 -0.106934 -0.053223 0.045807 -0.100769 0.110535 0.067444 -0.041168 -0.007671
|
||||
0.017929 -0.018723 -0.028030 -0.025391 -0.024414 0.104248 0.000623 -0.090576 0.012886 -0.088074 -0.033875 0.031174 -0.089233 0.002180 0.017212 -0.142944 0.062256 0.000796 -0.041046 -0.027954 -0.096313 0.043091 -0.066956 -0.069275 0.053711 0.016785 0.011047 0.129639 0.107422 0.004948 0.141479 -0.057739 -0.037903 -0.012688 -0.108948 -0.051483 -0.035034 0.022781 0.030731 0.154053 -0.013000 0.054474 0.030518 0.028152 -0.082092 0.015396 0.034088 -0.048859 -0.096619 0.042938 0.058502 -0.138428 -0.170166 -0.067566 0.056458 -0.012047 0.028152 -0.009102 0.133301 -0.045746 0.004971 -0.000458 -0.037506 0.070374 -0.028275 -0.009819 -0.040436 -0.026962 0.068970 -0.034058 0.070068 -0.070984 0.132202 -0.013420 0.021973 -0.052765 -0.103882 0.072327 0.060364 0.022430 -0.001070 -0.031494 -0.021042 -0.002335 -0.120117 -0.080688 0.014008 0.025497 -0.028107 -0.007694 -0.004406 0.115601 0.030579 -0.062622 -0.068481 0.046753 0.018555 -0.059967 -0.034088 0.076965 0.049377 -0.135620 -0.026871 -0.005478 -0.021561 0.064209 0.002422 -0.012718 -0.115234 -0.056885 -0.036591 0.015289 -0.011833 -0.022507 0.001439 -0.073853 -0.038391 0.088257 0.024506 -0.002724 0.050873 -0.104797 -0.018936 -0.078918 0.032776 -0.081909 0.076416 -0.085388 0.011993 -0.019592 0.017456 0.025284 -0.053833 0.039703 0.056122 -0.031097 0.040771 -0.040741 0.060303 0.008430 -0.068237 0.038300 0.034637 -0.087036 0.021988 0.003504 -0.006550 -0.035339 0.009674 -0.004513 0.029251 0.043396 0.049835 -0.022720 -0.046173 -0.121399 -0.010719 0.112610 -0.057343 0.103699 0.038666 -0.120850 -0.086792 -0.016953 0.029556 0.070862 -0.096436 -0.106140 -0.059906 -0.092346 0.007980 -0.026062 0.008110 -0.095581 -0.107666 0.038788 0.058289 0.006073 0.033997 0.066467 0.055206 -0.080505 0.024368 0.040283 -0.058960 0.023438 -0.107239 0.017838 0.144287 0.062073 0.005550 -0.036774 -0.038910 -0.003952 -0.001529 0.014221 -0.024261 -0.099182 0.055969 -0.010254 -0.140259 -0.009117 0.089600 0.045227 0.080200 0.082581 -0.085815 -0.054718 -0.025909 0.009064 -0.076782 0.021667 0.086670 -0.027649 -0.095337 -0.069458 0.016449 0.102173 -0.077820 -0.062378 -0.053497 0.096436 0.087646 0.025070 0.020370 0.190552 -0.091797 0.025452 -0.078735 -0.017929 0.026031 0.049042 -0.031311 -0.075256 -0.015388 -0.050415 0.075134 -0.027069 -0.144165 0.044617 0.023788 0.020142 0.003881 0.007057 0.047974 -0.027649 -0.059631 -0.002188 -0.046631 0.020798 0.024246 0.027603 0.028336 0.039246 -0.015083 -0.034363
|
||||
BIN
practice/data_.bin
Normal file
BIN
practice/data_.bin
Normal file
Binary file not shown.
90
practice/json_to_data.py
Normal file
90
practice/json_to_data.py
Normal file
@ -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('<B', key_len)
|
||||
f.write(length_byte)
|
||||
# f.write(struct.pack('Q', len(key_bytes)))
|
||||
f.write(key_bytes)
|
||||
value_count = len(feats)
|
||||
f.write(struct.pack('I', (value_count * 256)))
|
||||
# 遍历字典，写入每个key及其对应的浮点数值列表
|
||||
for values in feats:
|
||||
# 写入每个浮点数值（保留小数点后六位）
|
||||
feat16 = []
|
||||
for value in values:
|
||||
# 使用'f'格式（单精度浮点，4字节），并四舍五入保留六位小数
|
||||
value_half = np.float16(value)
|
||||
# print(value_half.tobytes())
|
||||
f.write(value_half.tobytes())
|
||||
|
||||
feat16.append(value_half)
|
||||
|
||||
feats_16.append(feat16)
|
||||
feats_32.append(values)
|
||||
|
||||
feats16 = np.array(feats_16)
|
||||
feats32 = np.array(feats_32)
|
||||
|
||||
|
||||
print("Done")
|
||||
|
||||
|
||||
def main_xh():
|
||||
|
||||
# 1. 打开JSON文件
|
||||
with open(json_path, 'r', encoding='utf-8') as file:
|
||||
# 2. 读取并解析JSON文件内容
|
||||
data = json.load(file)
|
||||
|
||||
for flag, values in data.items():
|
||||
# 逐个写入values中的每个值，保留小数点后六位，每个值占一行
|
||||
write_binary_file(r"D:\DetectTracking\practice\data_.bin", values)
|
||||
|
||||
|
||||
|
||||
'''
|
||||
with open('D://07Test/全实时/output.txt', 'w', encoding='utf-8') as txt_file:
|
||||
for flag, values in data.items():
|
||||
# 逐个写入values中的每个值，保留小数点后六位，每个值占一行
|
||||
for value in values:
|
||||
key = value['key']
|
||||
feats = value['value']
|
||||
# 写入key
|
||||
txt_file.write(key)
|
||||
txt_file.write("\n")
|
||||
for feat in feats:
|
||||
for number in feat:
|
||||
# 使用格式化字符串保留六位小数，并写入文件
|
||||
txt_file.write(f"{number:.6f} ")
|
||||
txt_file.write("\n")
|
||||
|
||||
'''
|
||||
def main_wang():
|
||||
pass
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main_xh()
|
||||
|
||||
@ -1,86 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Fri Feb 21 14:28:59 2025
|
||||
|
||||
@author: ym
|
||||
"""
|
||||
|
||||
import os
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
|
||||
import sys
|
||||
sys.path.append(r"D:\DetectTracking")
|
||||
from contrast.utils.event import ShoppingEvent
|
||||
from tracking.utils.read_data import read_weight_sensor, extract_data_realtime, read_tracking_output_realtime
|
||||
from tracking.utils.read_data import read_process
|
||||
|
||||
|
||||
|
||||
def read_tracker_data(filepath):
|
||||
pass
|
||||
|
||||
|
||||
def read_tracking_output_data(filepath):
|
||||
pass
|
||||
|
||||
|
||||
def read_process_data(filepath):
|
||||
path
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def main():
|
||||
evtPaths = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-2-21\persist"
|
||||
evtPaths = Path(evtPaths)
|
||||
|
||||
for evtpath in evtPaths.iterdir():
|
||||
|
||||
## 1. 读取重力数据
|
||||
if evtpath.name.find("Weight")>=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()
|
||||
281
realtime/tracker_test.py
Normal file
281
realtime/tracker_test.py
Normal file
@ -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))
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -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):
|
||||
|
||||
@ -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:
|
||||
|
||||
Binary file not shown.
@ -330,6 +330,7 @@ def read_similar(filePath):
|
||||
SimiDict['one2one'] = []
|
||||
SimiDict['one2SN'] = []
|
||||
SimiDict['one2n'] = []
|
||||
SimiDict['algroStartToEnd'] = -1
|
||||
|
||||
with open(filePath, 'r', encoding='utf-8') as f:
|
||||
lines = f.readlines()
|
||||
@ -353,6 +354,13 @@ def read_similar(filePath):
|
||||
Flag_1to1, Flag_1toSN, Flag_1ton = False, False, False
|
||||
continue
|
||||
|
||||
if line.find("algroStartToEnd")>=0:
|
||||
alg_during = line.split(':')[1].strip()
|
||||
SimiDict['algroStartToEnd'] = int(alg_during)
|
||||
|
||||
|
||||
|
||||
|
||||
if line.find('oneToOne')>=0:
|
||||
Flag_1to1, Flag_1toSN, Flag_1ton = True, False,False
|
||||
continue
|
||||
@ -387,7 +395,7 @@ def 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
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user