modify pipeline.py
This commit is contained in:
王庆刚
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@ -174,16 +174,6 @@ def calc_simil(event, stdfeat):
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def simi_matrix():
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resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result\single_event"
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@ -347,20 +337,6 @@ def main():
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if __name__ == "__main__":
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main()
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# cluster()
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@ -136,8 +136,8 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
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continue
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featpath = os.path.join(featPath, f"{bcd}.pickle")
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# if os.path.isfile(featpath):
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# continue
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if os.path.isfile(featpath):
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continue
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stdbDict = {}
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t1 = time.time()
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@ -24,10 +24,15 @@ def init_eventdict(sourcePath, stype="data"):
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# bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479"
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source_path = os.path.join(sourcePath, bname)
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if not os.path.isdir(source_path): continue
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if stype=="data":
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pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
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if not os.path.isdir(source_path) or os.path.isfile(pickpath):
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continue
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if stype=="source":
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pickpath = os.path.join(eventDataPath, bname)
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if not os.path.isfile(source_path) or os.path.isfile(pickpath):
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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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try:
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event = ShoppingEvent(source_path, stype)
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@ -86,20 +91,28 @@ def simi_calc(event, o2nevt, typee=None):
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feat1 = event.back_feats
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feat2 = o2nevt.front_feats
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'''自定义事件特征选择'''
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if typee==3:
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feat1 = event.feats_compose
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feat2 = o2nevt.feats_compose
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if len(feat1) and len(feat2):
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matrix = 1 - cdist(feat1[0], feat2[0], 'cosine')
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simi = np.mean(matrix)
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else:
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simi = None
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return simi
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def one2n_pr(evtDicts, pattern=1):
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'''
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pattern:
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1: process.data 中记录的相似度
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2: 根据 process.data 中标记的 type 选择特征计算
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3: 以其它方式选择特征计算
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'''
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def one2n_pr(evtDicts):
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tpevents, fnevents, fpevents, tnevents = [], [], [], []
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tpsimi, fnsimi, tnsimi, fpsimi = [], [], [], []
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errorFile_one2n = []
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@ -112,21 +125,29 @@ def one2n_pr(evtDicts):
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similar = ndict["similar"]
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typee = ndict["type"].strip()
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o2n_evt = [evt for name, evt in evtDicts.items() if name.find(nname[:15])==0]
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if len(o2n_evt)==1:
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o2nevt = o2n_evt[0]
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else:
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continue
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simival = simi_calc(event, o2nevt, typee)
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if simival==None:
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continue
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evt_names.append(nname)
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evt_barcodes.append(barcode)
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evt_similars.append(simival)
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evt_types.append(typee)
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if pattern==1:
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evt_similars.append(similar)
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if pattern==2 or pattern==3:
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o2n_evt = [evt for name, evt in evtDicts.items() if name.find(nname[:15])==0]
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if len(o2n_evt)==1:
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o2nevt = o2n_evt[0]
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else:
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continue
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if pattern==2:
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simival = simi_calc(event, o2nevt, typee)
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if pattern==3:
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simival = simi_calc(event, o2nevt, typee=pattern)
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if simival==None:
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continue
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evt_similars.append(simival)
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if len(evt_names)==len(evt_barcodes) and len(evt_barcodes)==len(evt_similars) \
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and len(evt_similars)==len(evt_types) and len(evt_names)>0:
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@ -205,14 +226,14 @@ def one2n_pr(evtDicts):
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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)
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init_eventdict(eventSourcePath, stype="source")
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'''2. 读取事件字典 '''
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evtDicts = read_eventdict(eventDataPath)
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'''3. 1:n 比对事件评估 '''
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fpevents = one2n_pr(evtDicts)
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fpevents = one2n_pr(evtDicts, pattern=3)
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fpErrFile = str(Path(resultPath).joinpath("one2n_fp_Error.txt"))
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with open(fpErrFile, "w") as file:
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@ -221,14 +242,9 @@ 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\算法全流程测试\202412\images"
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resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result"
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eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result\ShoppingDict_pkfile"
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resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result\contrast"
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eventDataPath = os.path.join(resultPath, "evtobjs")
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similPath = os.path.join(resultPath, "simidata")
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@ -194,6 +194,7 @@ def simi_calc(event, stdfeat):
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if len(evtfeat)==0 or len(stdfeat)==0:
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return None, None, None
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evtfeat /= np.linalg.norm(evtfeat, axis=1)[:, None]
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stdfeat /= np.linalg.norm(stdfeat, axis=1)[:, None]
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@ -305,8 +306,8 @@ def one2SN_pr(evtList, evtDict, stdDict):
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barcodes, similars = [], []
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for stdbcd in bcd_selected:
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stdfeat = stdDict[stdbcd]
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# simi_mean, simi_max, simi_mfeat = simi_calc(event, stdfeat)
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simi_mean = calc_simil(event, stdfeat)
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simi_mean, simi_max, simi_mfeat = simi_calc(event, stdfeat)
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# simi_mean = calc_simil(event, stdfeat)
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## 在event.front_feats和event.back_feats同时为空时，此处不需要保护
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# if simi_mean==None:
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@ -629,10 +630,8 @@ if __name__ == '__main__':
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stdBarcodePath = r"D:\全实时\source_data\bcdpath"
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stdFeaturePath = r"D:\全实时\source_data\stdfeats"
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eventSourcePath = [r"D:\全实时\result\pipeline\pipeline"]
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resultPath = r"D:\全实时\result\pipeline"
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eventSourcePath = [r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result\ShoppingDict_pkfile"]
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resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result\contrast"
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eventDataPath = os.path.join(resultPath, "evtobjs")
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similPath = os.path.join(resultPath, "simidata")
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if not os.path.exists(eventDataPath):
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@ -640,7 +639,7 @@ if __name__ == '__main__':
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if not os.path.exists(similPath):
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os.makedirs(similPath)
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test_one2one()
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# test_one2one()
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test_one2SN()
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@ -76,7 +76,7 @@ def array2list(bboxes):
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class ShoppingEvent:
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def __init__(self, eventpath, stype="data"):
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'''stype: str, 'source', 'data', '''
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'''stype: str, 'source', 'data', 'realtime', 共三种 '''
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self.eventpath = eventpath
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self.evtname = str(Path(eventpath).stem)
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@ -167,21 +167,23 @@ class ShoppingEvent:
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def from_source_pkl(self, eventpath):
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with open(eventpath, 'rb') as f:
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ShoppingDict = pickle.load(f)
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self.eventpath = ShoppingDict["eventPath"]
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self.evtname = ShoppingDict["eventName"]
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self.barcode = ShoppingDict["barcode"]
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if len(ShoppingDict["one2n"]):
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self.one2n = ShoppingDict["one2n"]
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'''=========== path of image and video =========== '''
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self.back_videopath = ShoppingDict["backCamera"]["videoPath"]
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self.front_videopath = ShoppingDict["frontCamera"]["videoPath"]
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self.back_imgpaths = ShoppingDict["backCamera"]["imagePaths"]
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self.front_imgpaths = ShoppingDict["frontCamera"]["imagePaths"]
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'''===========对应于 0/1_track.data ============================='''
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backdata, back_outdata = self.kerndata(ShoppingDict, "backCamera")
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frontdata, front_outdata = self.kerndata(ShoppingDict, "frontCamera")
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111
events/evt_rename.py
Normal file
111
events/evt_rename.py
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@ -0,0 +1,111 @@
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# -*- coding: utf-8 -*-
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"""
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Created on Thu Jan 9 14:34:41 2025
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@author: ym
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"""
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import os
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def read_deletedBarcode_file(filePath):
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with open(filePath, 'r', encoding='utf-8') as f:
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lines = f.readlines()
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split_flag, all_list = False, []
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dict, barcode_list, similarity_list = {}, [], []
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clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]
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for i, line in enumerate(clean_lines):
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if line.endswith(','):
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line = line[:-1]
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stripped_line = line.strip()
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if not stripped_line:
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if len(barcode_list): dict['barcode'] = barcode_list
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if len(similarity_list): dict['similarity'] = similarity_list
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if len(dict): all_list.append(dict)
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split_flag = False
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dict, barcode_list, similarity_list = {}, [], []
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continue
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if line.find(':')<0: continue
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label = line.split(':')[0]
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value = line.split(':')[1]
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if label == 'SeqDir':
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dict['SeqDir'] = value
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dict['filetype'] = "deletedBarcode"
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if label == 'Deleted':
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dict['Deleted'] = value
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if label == 'List':
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split_flag = True
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continue
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if split_flag:
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barcode_list.append(label)
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similarity_list.append(value)
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if len(barcode_list): dict['barcode'] = barcode_list
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if len(similarity_list): dict['similarity'] = similarity_list
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if len(dict): all_list.append(dict)
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return all_list
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def event_rename(path):
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for filename in os.listdir(path):
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fpath = os.path.join(path, filename)
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if os.path.isfile(fpath): continue
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flist = filename.split("_")
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if len(flist)==3 and flist[-1].isdigit() and len(flist[-1])>=8:
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print(f"{path}: renamed!")
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return
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dpath = os.path.join(path, "deletedBarcode.txt")
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alist = read_deletedBarcode_file(dpath)
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input_events, getout_events = [], []
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for filename in os.listdir(path):
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fpath = os.path.join(path, filename)
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if os.path.isfile(fpath): continue
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flist = filename.split("_")
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bcd = flist[-1]
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if len(flist)==2 and flist[0].find("2024")==0 and len(bcd)==0:
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bcds = [dt['Deleted'] for dt in alist if dt['SeqDir'].strip()==filename]
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if len(bcds)==1:
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getout_events.append((filename, filename + bcds[0].strip()))
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if len(flist)==2 and flist[0].find("2024")==0 and len(bcd)>=10:
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input_events.append((filename, filename + '_' + bcd))
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events = (input_events, getout_events)
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for evts in events:
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for name_old, name_new in evts:
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path_old = os.path.join(path, name_old)
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path_new = os.path.join(path, name_new)
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try:
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os.rename(path_old, path_new)
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except Exception as e:
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print(f"发生错误：{e}")
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def main():
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path = r"\\192.168.1.28\share\测试视频数据以及日志\测试_202406\0910\images"
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event_rename(path)
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if __name__ == "__main__":
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main()
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203
pipeline.py
203
pipeline.py
@ -5,67 +5,48 @@ Created on Sun Sep 29 08:59:21 2024
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@author: ym
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"""
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import os
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import sys
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# import sys
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import cv2
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import pickle
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import argparse
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import numpy as np
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from pathlib import Path
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from track_reid import parse_opt
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from track_reid import yolo_resnet_tracker
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# FILE = Path(__file__).resolve()
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# ROOT = FILE.parents[0] # YOLOv5 root directory
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# if str(ROOT) not in sys.path:
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# sys.path.append(str(ROOT)) # add ROOT to PATH
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# ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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from tracking.dotrack.dotracks_back import doBackTracks
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from tracking.dotrack.dotracks_front import doFrontTracks
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from tracking.utils.drawtracks import plot_frameID_y2, draw_all_trajectories
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from utils.getsource import get_image_pairs, get_video_pairs
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def get_interbcd_inputenents():
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bcdpath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes_2192"
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eventpath = r"\\192.168.1.28\share\测试_202406\0918"
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barcodes = []
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eventpaths = []
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for featname in os.listdir(bcdpath):
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barcode, ext = os.path.splitext(featname)
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barcodes.append(barcode)
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input_enents = []
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for root, dirs, files in os.walk(eventpath):
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input_enent = [os.path.join(root, d) for d in dirs if d.split('_')[-1] in barcodes]
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input_enents.extend(input_enent)
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from tracking.utils.read_data import read_similar
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return input_enents
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def save_subimgs(imgdict, boxes, spath, ctype):
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for i in range(len(boxes)):
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fid, bid = int(boxes[i, 7]), int(boxes[i, 8])
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if f"{fid}_{bid}" in imgdict.keys():
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img = imgdict[f"{fid}_{bid}"]
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imgpath = spath / f"{ctype}_{fid}_{bid}.png"
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cv2.imwrite(imgpath, img)
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def pipeline(
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eventpath,
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savepath = '',
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SourceType = "image", # video
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stdfeat_path = None
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savepath,
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SourceType,
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weights
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):
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if SourceType == "video":
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vpaths = get_video_pairs(eventpath)
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elif SourceType == "image":
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vpaths = get_image_pairs(eventpath)
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'''
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eventpath: 单个事件的存储路径
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'''
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'''======== 函数 yolo_resnet_tracker() 的参数字典 ========'''
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opt = parse_opt()
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optdict = vars(opt)
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if SourceType == "video":
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vpaths = get_video_pairs(eventpath)
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elif SourceType == "image":
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vpaths = get_image_pairs(eventpath)
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optdict = {}
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optdict["weights"] = weights
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optdict["weights"] = r'D:\DetectTracking\ckpts\best_cls10_0906.pt'
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optdict["is_save_img"] = True
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optdict["is_save_video"] = True
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event_tracks = []
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@ -76,21 +57,35 @@ def pipeline(
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and evtname.split('_')[-1].isdigit() else ''
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'''事件结果存储文件夹'''
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if not savepath:
|
||||
savepath = Path(__file__).resolve().parents[0] / "evtresult"
|
||||
savepath = Path(__file__).resolve().parents[0] / "events_result"
|
||||
|
||||
save_dir_event = Path(savepath) / evtname
|
||||
savepath_pipeline = Path(savepath) / Path("Yolos_Tracking") / evtname
|
||||
|
||||
pickpath = Path(savepath)/"pickfile"
|
||||
if not pickpath.exists():
|
||||
pickpath.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
"""ShoppingDict pickle 文件保存地址 """
|
||||
savepath_spdict = Path(savepath) / "ShoppingDict_pkfile"
|
||||
if not savepath_spdict.exists():
|
||||
savepath_spdict.mkdir(parents=True, exist_ok=True)
|
||||
pf_path = Path(savepath_spdict) / Path(str(evtname)+".pickle")
|
||||
|
||||
# if pf_path.exists():
|
||||
# return
|
||||
|
||||
ShoppingDict = {"eventPath": eventpath,
|
||||
"eventName": evtname,
|
||||
"barcode": barcode,
|
||||
"eventType": '', # "input", "output", "other"
|
||||
"frontCamera": {},
|
||||
"backCamera": {}}
|
||||
"backCamera": {},
|
||||
"one2n": []
|
||||
}
|
||||
|
||||
|
||||
procpath = Path(eventpath).joinpath('process.data')
|
||||
if procpath.is_file():
|
||||
SimiDict = read_similar(procpath)
|
||||
ShoppingDict["one2n"] = SimiDict['one2n']
|
||||
|
||||
|
||||
for vpath in vpaths:
|
||||
'''相机事件字典构造'''
|
||||
@ -115,16 +110,20 @@ def pipeline(
|
||||
'''事件结果存储文件夹'''
|
||||
|
||||
if isinstance(vpath, list):
|
||||
save_dir_video = save_dir_event / Path("images")
|
||||
savepath_pipeline_imgs = savepath_pipeline / Path("images")
|
||||
else:
|
||||
save_dir_video = save_dir_event / Path(str(Path(vpath).stem))
|
||||
if not save_dir_video.exists():
|
||||
save_dir_video.mkdir(parents=True, exist_ok=True)
|
||||
savepath_pipeline_imgs = savepath_pipeline / Path(str(Path(vpath).stem))
|
||||
if not savepath_pipeline_imgs.exists():
|
||||
savepath_pipeline_imgs.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
savepath_pipeline_subimgs = savepath_pipeline / Path("subimgs")
|
||||
if not savepath_pipeline_subimgs.exists():
|
||||
savepath_pipeline_subimgs.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
|
||||
'''Yolo + Resnet + Tracker'''
|
||||
optdict["source"] = vpath
|
||||
optdict["save_dir"] = save_dir_video
|
||||
optdict["save_dir"] = savepath_pipeline_imgs
|
||||
|
||||
yrtOut = yolo_resnet_tracker(**optdict)
|
||||
|
||||
@ -164,14 +163,31 @@ def pipeline(
|
||||
CameraEvent["tracking"] = vts
|
||||
ShoppingDict["frontCamera"] = CameraEvent
|
||||
|
||||
# pklpath = save_dir_event / "ShoppingDict.pkl"
|
||||
# with open(str(pklpath), 'wb') as f:
|
||||
# pickle.dump(ShoppingDict, f)
|
||||
pf_path = Path(pickpath) / Path(str(evtname)+".pkl")
|
||||
|
||||
with open(str(pf_path), 'wb') as f:
|
||||
pickle.dump(ShoppingDict, f)
|
||||
|
||||
|
||||
for CamerType, vts in event_tracks:
|
||||
if len(vts.tracks)==0: continue
|
||||
if CamerType == 'front':
|
||||
yolos = ShoppingDict["frontCamera"]["yoloResnetTracker"]
|
||||
ctype = 1
|
||||
if CamerType == 'back':
|
||||
yolos = ShoppingDict["backCamera"]["yoloResnetTracker"]
|
||||
ctype = 0
|
||||
|
||||
imgdict = {}
|
||||
for y in yolos:
|
||||
imgdict.update(y["imgs"])
|
||||
|
||||
for track in vts.Residual:
|
||||
if isinstance(track, np.ndarray):
|
||||
save_subimgs(imgdict, track, savepath_pipeline_subimgs, ctype)
|
||||
else:
|
||||
save_subimgs(imgdict, track.boxes, savepath_pipeline_subimgs, ctype)
|
||||
|
||||
|
||||
'''轨迹显示模块'''
|
||||
illus = [None, None]
|
||||
for CamerType, vts in event_tracks:
|
||||
@ -181,24 +197,23 @@ def pipeline(
|
||||
edgeline = cv2.imread("./tracking/shopcart/cart_tempt/board_ftmp_line.png")
|
||||
|
||||
h, w = edgeline.shape[:2]
|
||||
nh, nw = h//2, w//2
|
||||
edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA)
|
||||
# nh, nw = h//2, w//2
|
||||
# edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA)
|
||||
|
||||
img_tracking = draw_all_trajectories(vts, edgeline, save_dir_event, CamerType, draw5p=True)
|
||||
img_tracking = draw_all_trajectories(vts, edgeline, savepath_pipeline, CamerType, draw5p=True)
|
||||
illus[0] = img_tracking
|
||||
|
||||
|
||||
plt = plot_frameID_y2(vts)
|
||||
plt.savefig(os.path.join(save_dir_event, "front_y2.png"))
|
||||
plt.savefig(os.path.join(savepath_pipeline, "front_y2.png"))
|
||||
|
||||
if CamerType == 'back':
|
||||
edgeline = cv2.imread("./tracking/shopcart/cart_tempt/edgeline.png")
|
||||
|
||||
h, w = edgeline.shape[:2]
|
||||
nh, nw = h//2, w//2
|
||||
edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA)
|
||||
# nh, nw = h//2, w//2
|
||||
# edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA)
|
||||
|
||||
img_tracking = draw_all_trajectories(vts, edgeline, save_dir_event, CamerType, draw5p=True)
|
||||
img_tracking = draw_all_trajectories(vts, edgeline, savepath_pipeline, CamerType, draw5p=True)
|
||||
illus[1] = img_tracking
|
||||
|
||||
illus = [im for im in illus if im is not None]
|
||||
@ -208,64 +223,25 @@ def pipeline(
|
||||
H, W = img_cat.shape[:2]
|
||||
cv2.line(img_cat, (int(W/2), 0), (int(W/2), int(H)), (128, 128, 255), 3)
|
||||
|
||||
trajpath = os.path.join(save_dir_event, "traj.png")
|
||||
trajpath = os.path.join(savepath_pipeline, "trajectory.png")
|
||||
cv2.imwrite(trajpath, img_cat)
|
||||
|
||||
|
||||
def main_loop():
|
||||
bcdpath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes_2192"
|
||||
eventpath = r"\\192.168.1.28\share\测试_202406\0918\images1"
|
||||
SourceType = "image" # video, image
|
||||
|
||||
barcodes = []
|
||||
input_enents = []
|
||||
output_events = []
|
||||
|
||||
|
||||
'''1. 获得barcode标准特征集列表'''
|
||||
for featname in os.listdir(bcdpath):
|
||||
barcode, ext = os.path.splitext(featname)
|
||||
if not barcode.isdigit() or len(barcode)<=8 or ext != ".pickle" :
|
||||
continue
|
||||
barcodes.append(barcode)
|
||||
|
||||
'''2. 构造（放入事件，标准特征）对'''
|
||||
for filename in os.listdir(eventpath):
|
||||
'''barcode为时间文件夹的最后一个字段'''
|
||||
bcd = filename.split('_')[-1]
|
||||
|
||||
event_path = os.path.join(eventpath, filename)
|
||||
stdfeat_path = None
|
||||
if bcd in barcodes:
|
||||
stdfeat_path = os.path.join(bcdpath, f"{bcd}.pickle")
|
||||
input_enents.append((event_path, stdfeat_path))
|
||||
|
||||
parmDict = {}
|
||||
parmDict["SourceType"] = "image"
|
||||
parmDict["savepath"] = r"D:\contrast\detect"
|
||||
for eventpath, stdfeat_path in input_enents:
|
||||
parmDict["eventpath"] = eventpath
|
||||
parmDict["stdfeat_path"] = stdfeat_path
|
||||
|
||||
pipeline(**parmDict)
|
||||
|
||||
|
||||
def main():
|
||||
'''
|
||||
函数：pipeline()，遍历事件文件夹，选择类型 image 或 video,
|
||||
'''
|
||||
evtdir = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images"
|
||||
evtdir = Path(evtdir)
|
||||
parmDict = {}
|
||||
parmDict["savepath"] = r"D:\contrast\202412测试"
|
||||
parmDict["SourceType"] = "video" # video, image
|
||||
parmDict["stdfeat_path"] = None
|
||||
|
||||
k = 0
|
||||
errEvents = []
|
||||
parmDict = {}
|
||||
evtdir = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images"
|
||||
parmDict["SourceType"] = "video" # video, image
|
||||
parmDict["savepath"] = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result"
|
||||
parmDict["weights"] = r'D:\DetectTracking\ckpts\best_cls10_0906.pt'
|
||||
|
||||
evtdir = Path(evtdir)
|
||||
k, errEvents = 0, []
|
||||
for item in evtdir.iterdir():
|
||||
if item.is_dir():
|
||||
# item = r"D:\exhibition\images\images2\images2"
|
||||
# item = evtdir/Path("20241209-160201-b97f7a0e-7322-4375-9f17-c475500097e9_6926265317292")
|
||||
parmDict["eventpath"] = item
|
||||
# pipeline(**parmDict)
|
||||
|
||||
@ -273,10 +249,9 @@ def main():
|
||||
pipeline(**parmDict)
|
||||
except Exception as e:
|
||||
errEvents.append(str(item))
|
||||
|
||||
# k+=1
|
||||
# if k==1:
|
||||
# break
|
||||
k+=1
|
||||
if k==1:
|
||||
break
|
||||
|
||||
errfile = os.path.join(parmDict["savepath"], f'error_events.txt')
|
||||
with open(errfile, 'w', encoding='utf-8') as f:
|
||||
|
||||
@ -62,6 +62,7 @@ from hands.hand_inference import hand_pose
|
||||
|
||||
from contrast.feat_extract.config import config as conf
|
||||
from contrast.feat_extract.inference import FeatsInterface
|
||||
|
||||
ReIDEncoder = FeatsInterface(conf)
|
||||
|
||||
IMG_FORMATS = '.bmp', '.dng', '.jpeg', '.jpg', '.mpo', '.png', '.tif', '.tiff', '.webp', '.pfm' # include image suffixes
|
||||
@ -139,12 +140,8 @@ def init_trackers(tracker_yaml = None, bs=1):
|
||||
def yolo_resnet_tracker(
|
||||
weights=ROOT / 'yolov5s.pt', # model path or triton URL
|
||||
source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam)
|
||||
|
||||
project=ROOT / 'runs/detect', # save results to project/name
|
||||
name='exp', # save results to project/name
|
||||
save_dir = '',
|
||||
|
||||
is_save_img = False,
|
||||
is_save_img = True,
|
||||
is_save_video = True,
|
||||
|
||||
tracker_yaml = "./tracking/trackers/cfg/botsort.yaml",
|
||||
@ -154,19 +151,9 @@ def yolo_resnet_tracker(
|
||||
max_det=1000, # maximum detections per image
|
||||
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||||
|
||||
view_img=False, # show results
|
||||
save_txt=False, # save results to *.txt
|
||||
save_csv=False, # save results in CSV format
|
||||
save_conf=False, # save confidences in --save-txt labels
|
||||
save_crop=False, # save cropped prediction boxes
|
||||
nosave=False, # do not save images/videos
|
||||
update=False, # update all models
|
||||
exist_ok=False, # existing project/name ok, do not increment
|
||||
|
||||
classes=None, # filter by class: --class 0, or --class 0 2 3
|
||||
agnostic_nms=False, # class-agnostic NMS
|
||||
augment=False, # augmented inference
|
||||
visualize=False, # visualize features
|
||||
|
||||
line_thickness=3, # bounding box thickness (pixels)
|
||||
hide_labels=False, # hide labels
|
||||
@ -208,7 +195,7 @@ def yolo_resnet_tracker(
|
||||
|
||||
# Inference
|
||||
with dt[1]:
|
||||
visualize = increment_path(project / Path(path).stem, mkdir=True) if visualize else False
|
||||
# visualize = increment_path(project / Path(path).stem, mkdir=True) if visualize else False
|
||||
pred = model(im, augment=augment, visualize=False)
|
||||
|
||||
# NMS
|
||||
@ -242,15 +229,16 @@ def yolo_resnet_tracker(
|
||||
# trackerBoxes = np.concatenate([trackerBoxes, tracks], axis=0)
|
||||
'''================== 1. 存储 dets/subimgs/features Dict ============='''
|
||||
imgs, features = ReIDEncoder.inference(im0, tracks)
|
||||
featdict = {}
|
||||
imgdict, featdict = {}, {}
|
||||
for ii, bid in enumerate(tracks[:, 8]):
|
||||
featdict.update({f"{int(frameId)}_{int(bid)}": features[ii, :]}) # [f"feat_{int(bid)}"] = features[i, :]
|
||||
imgdict.update({f"{int(frameId)}_{int(bid)}": imgs[ii]})
|
||||
|
||||
frameDict = {"path": path,
|
||||
"fid": int(frameId),
|
||||
"bboxes": det,
|
||||
"tboxes": tracks,
|
||||
"imgs": imgs,
|
||||
"imgs": imgdict,
|
||||
"feats": featdict}
|
||||
yoloResnetTracker.append(frameDict)
|
||||
|
||||
|
||||
48
说明文档.txt
48
说明文档.txt
@ -26,9 +26,53 @@
|
||||
|
||||
4. 整体流程仿真
|
||||
pipeline.py
|
||||
SourceType: "image", "video", yolo+resent+tracker模块输入数据类型
|
||||
需要指定是个变量：
|
||||
(1) evtdir: 保存各个事件的文件夹
|
||||
(2) SourceType: "image", "video", yolo+resent+tracker模块输入数据类型
|
||||
(3) savepath: 结果数据保存位置
|
||||
(4) weights: YoloV5 模型权重
|
||||
|
||||
保存为 pickle 文件，该文件可用于 ShoppingEvent 对象构建。
|
||||
savepath
|
||||
(1) Yolos_Tracking
|
||||
eventname文件夹，保存yolo+resent+tracker模块输出，包括：
|
||||
(1) images 或 {videoname}
|
||||
(2) subimgs
|
||||
(3) trajectory
|
||||
(4) front_y2.png
|
||||
(5) trajectory.png
|
||||
(2) ShoppingDict_pkfile
|
||||
保存 ShoppingDict 字典的 pickle 文件，可用于构造 ShoppingEvent 对象。
|
||||
|
||||
(3) contrast
|
||||
保存1:1、1:SN、1:n数据
|
||||
|
||||
(4) error_events.txt
|
||||
|
||||
事件字典结构：
|
||||
ShoppingDict = {"eventPath": eventpath,
|
||||
"eventName": evtname,
|
||||
"barcode": barcode,
|
||||
"eventType": '', # "input", "output", "other"
|
||||
"frontCamera": {},
|
||||
"backCamera": {},
|
||||
"one2n": []
|
||||
}
|
||||
|
||||
相机字典：
|
||||
CameraEvent = {"cameraType": '', # "front", "back"
|
||||
"videoPath": '',
|
||||
"imagePaths": [],
|
||||
"yoloResnetTracker": [],
|
||||
"tracking": [],
|
||||
}
|
||||
|
||||
yoloResnetTracker，列表，元素 "frameDict" 为有关帧数据的字典结构
|
||||
frameDict = {"path": path,
|
||||
"fid": int(frameId),
|
||||
"bboxes": det,
|
||||
"tboxes": tracks,
|
||||
"imgs": imgdict,
|
||||
"feats": featdict}
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user