modify at output data format
This commit is contained in:
王庆刚
@ -52,10 +52,10 @@ from tracking.utils.read_data import extract_data, read_tracking_output, read_si
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from tracking.utils.plotting import Annotator, colors
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from feat_extract.config import config as conf
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from feat_extract.inference import FeatsInterface
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from utils.event import Event
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from utils.event import ShoppingEvent
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from genfeats import gen_bcd_features
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IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
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@ -83,148 +83,6 @@ def ft16_to_uint8(arr_ft16):
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return arr_uint8, arr_ft16_
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def creat_shopping_event(eventPath):
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'''构造放入商品事件字典，这些事件需满足条件：
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1) 前后摄至少有一条轨迹输出
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2) 保存有帧图像，以便裁剪出 boxe 子图
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'''
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'''evtName 为一次购物事件'''
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evtName = os.path.basename(eventPath)
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evtList = evtName.split('_')
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'''================ 0. 检查 evtName 及 eventPath 正确性和有效性 ================'''
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if evtName.find('2024')<0 and len(evtList[0])!=15:
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return
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if not os.path.isdir(eventPath):
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return
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if len(evtList)==1 or (len(evtList)==2 and len(evtList[1])==0):
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barcode = ''
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else:
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barcode = evtList[-1]
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if len(evtList)==3 and evtList[-1]== evtList[-2]:
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evtType = 'input'
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else:
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evtType = 'other'
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'''================ 1. 构造事件描述字典，暂定 9 items ==============='''
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event = {}
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event['barcode'] = barcode
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event['type'] = evtType
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event['filepath'] = eventPath
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event['back_imgpaths'] = []
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event['front_imgpaths'] = []
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event['back_boxes'] = np.empty((0, 9), dtype=np.float64)
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event['front_boxes'] = np.empty((0, 9), dtype=np.float64)
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event['back_feats'] = np.empty((0, 256), dtype=np.float64)
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event['front_feats'] = np.empty((0, 256), dtype=np.float64)
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event['feats_compose'] = np.empty((0, 256), dtype=np.float64)
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event['one2one'] = None
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event['one2n'] = None
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event['feats_select'] = np.empty((0, 256), dtype=np.float64)
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'''================= 2. 读取 data 文件 ============================='''
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for dataname in os.listdir(eventPath):
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# filename = '1_track.data'
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datapath = os.path.join(eventPath, dataname)
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if not os.path.isfile(datapath): continue
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CamerType = dataname.split('_')[0]
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''' 2.1 读取 0/1_track.data 中数据，暂不考虑'''
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# if dataname.find("_track.data")>0:
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# bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict = extract_data(datapath)
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''' 2.2 读取 0/1_tracking_output.data 中数据'''
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if dataname.find("_tracking_output.data")>0:
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tracking_output_boxes, tracking_output_feats = read_tracking_output(datapath)
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if len(tracking_output_boxes) != len(tracking_output_feats): continue
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if CamerType == '0':
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event['back_boxes'] = tracking_output_boxes
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event['back_feats'] = tracking_output_feats
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elif CamerType == '1':
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event['front_boxes'] = tracking_output_boxes
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event['front_feats'] = tracking_output_feats
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if dataname.find("process.data")==0:
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simiDict = read_similar(datapath)
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event['one2one'] = simiDict['one2one']
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event['one2n'] = simiDict['one2n']
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if len(event['back_boxes'])==0 or len(event['front_boxes'])==0:
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return None
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'''2.3 事件的特征表征方式: 特征选择、特征集成'''
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bk_feats = event['back_feats']
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ft_feats = event['front_feats']
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'''2.3.1 特征集成'''
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feats_compose = np.empty((0, 256), dtype=np.float64)
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if len(ft_feats):
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feats_compose = np.concatenate((feats_compose, ft_feats), axis=0)
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if len(bk_feats):
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feats_compose = np.concatenate((feats_compose, bk_feats), axis=0)
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event['feats_compose'] = feats_compose
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'''2.3.1 特征选择'''
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if len(ft_feats):
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event['feats_select'] = ft_feats
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'''================ 3. 读取图像文件地址，并按照帧ID排序 ============='''
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frontImgs, frontFid = [], []
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backImgs, backFid = [], []
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for imgname in os.listdir(eventPath):
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name, ext = os.path.splitext(imgname)
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if ext not in IMG_FORMAT or name.find('frameId')<0: continue
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CamerType = name.split('_')[0]
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frameId = int(name.split('_')[3])
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imgpath = os.path.join(eventPath, imgname)
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if CamerType == '0':
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backImgs.append(imgpath)
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backFid.append(frameId)
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if CamerType == '1':
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frontImgs.append(imgpath)
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frontFid.append(frameId)
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frontIdx = np.argsort(np.array(frontFid))
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backIdx = np.argsort(np.array(backFid))
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'''3.1 生成依据帧 ID 排序的前后摄图像地址列表'''
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frontImgs = [frontImgs[i] for i in frontIdx]
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backImgs = [backImgs[i] for i in backIdx]
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'''3.2 将前、后摄图像路径添加至事件字典'''
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bfid = event['back_boxes'][:, 7].astype(np.int64)
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ffid = event['front_boxes'][:, 7].astype(np.int64)
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if len(bfid) and max(bfid) <= len(backImgs):
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event['back_imgpaths'] = [backImgs[i-1] for i in bfid]
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if len(ffid) and max(ffid) <= len(frontImgs):
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event['front_imgpaths'] = [frontImgs[i-1] for i in ffid]
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'''================ 4. 判断当前事件有效性，并添加至事件列表 =========='''
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condt1 = len(event['back_imgpaths'])==0 or len(event['front_imgpaths'])==0
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condt2 = len(event['front_feats'])==0 and len(event['back_feats'])==0
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if condt1 or condt2:
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print(f"Event: {evtName}, Error, condt1: {condt1}, condt2: {condt2}")
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return None
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return event
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def plot_save_image(event, savepath):
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cameras = ('front', 'back')
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@ -286,9 +144,6 @@ def save_event_subimg(event, savepath):
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else:
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boxes = event.back_boxes
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imgpaths = event.back_imgpaths
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for i, box in enumerate(boxes):
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x1, y1, x2, y2, tid, score, cls, fid, bid = box
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@ -491,8 +346,8 @@ def one2one_simi():
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##============================================ float32
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stdfeat = stdDict[stdbcd]["feats_ft32"]
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evtfeat = event.feats_compose
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evtfeat = event.feats_compose
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if len(evtfeat)==0: continue
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matrix = 1 - cdist(stdfeat, evtfeat, 'cosine')
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@ -585,28 +440,9 @@ def gen_eventdict(sourcePath, saveimg=True):
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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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# if bname != "20241129-100321-a9dae9e3-7db5-4e31-959c-d7dfc228923e_6972636670213":
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# continue
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# eventDict = creat_shopping_event(eventPath)
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# if eventDict:
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# eventList.append(eventDict)
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# with open(pickpath, 'wb') as f:
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# pickle.dump(eventDict, f)
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# print(f"Event: {eventName}, have saved!")
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# if saveimg and eventDict:
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# basename = os.path.basename(eventDict['filepath'])
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# savepath = os.path.join(subimgPath, basename)
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# if not os.path.exists(savepath):
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# os.makedirs(savepath)
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# save_event_subimg(eventDict, savepath)
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try:
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event = Event(source_path)
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event = ShoppingEvent(source_path, stype="data")
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eventList.append(event)
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with open(pickpath, 'wb') as f:
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pickle.dump(event, f)
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@ -624,9 +460,7 @@ def gen_eventdict(sourcePath, saveimg=True):
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for line in errEvents:
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f.write(line + '\n')
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def test_one2one():
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bcdList, event_spath = [], []
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