179 lines
7.1 KiB
Python
179 lines
7.1 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Tue Nov 26 17:35:05 2024
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@author: ym
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"""
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import os
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import numpy as np
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from pathlib import Path
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import sys
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sys.path.append(r"D:\DetectTracking")
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from tracking.utils.read_data import extract_data, read_tracking_output, read_similar
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IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
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VID_FORMAT = ['.mp4', '.avi']
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class Event:
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def __init__(self, eventpath, stype="data"):
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'''stype: str, 'video', 'image', 'data', '''
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self.eventpath = eventpath
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self.evtname = str(Path(eventpath).stem)
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self.barcode = ''
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self.evtType = ''
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'''=========== path of image and video =========== '''
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self.back_videopath = ''
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self.front_videopath = ''
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self.back_imgpaths = []
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self.front_imgpaths = []
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'''=========== process.data ==============================='''
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self.one2one = None
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self.one2n = None
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'''=========== 0/1_track.data ============================='''
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self.back_yolobboxes = np.empty((0, 6), dtype=np.float64)
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self.back_yolofeats = np.empty((0, 256), dtype=np.float64)
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self.back_trackerboxes = np.empty((0, 9), dtype=np.float64)
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self.back_trackerfeats = np.empty((0, 256), dtype=np.float64)
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self.back_trackingboxes = np.empty((0, 9), dtype=np.float64)
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self.back_trackingfeats = np.empty((0, 256), dtype=np.float64)
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self.front_yolobboxes = np.empty((0, 6), dtype=np.float64)
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self.front_yolofeats = np.empty((0, 256), dtype=np.float64)
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self.front_trackerboxes = np.empty((0, 9), dtype=np.float64)
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self.front_trackerfeats = np.empty((0, 256), dtype=np.float64)
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self.front_trackingboxes = np.empty((0, 9), dtype=np.float64)
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self.front_trackingfeats = np.empty((0, 256), dtype=np.float64)
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'''=========== 0/1_tracking_output.data ==================='''
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self.back_boxes = np.empty((0, 9), dtype=np.float64)
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self.front_boxes = np.empty((0, 9), dtype=np.float64)
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self.back_feats = np.empty((0, 256), dtype=np.float64)
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self.front_feats = np.empty((0, 256), dtype=np.float64)
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self.feats_compose = np.empty((0, 256), dtype=np.float64)
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self.feats_select = np.empty((0, 256), dtype=np.float64)
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if stype=="data":
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self.from_datafile(eventpath)
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if stype=="video":
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self.from_video(eventpath)
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if stype=="image":
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self.from_image(eventpath)
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def from_datafile(self, eventpath):
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evtList = self.evtname.split('_')
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if len(evtList)>=2 and len(evtList[-1])>=10 and evtList[-1].isdigit():
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self.barcode = evtList[-1]
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if len(evtList)==3 and evtList[-1]== evtList[-2]:
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self.evtType = 'input'
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else:
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self.evtType = 'other'
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'''================ path of image ============='''
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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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if len(name.split('_')) != 3 and not name.split('_')[3].isdigit(): 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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## 生成依据帧 ID 排序的前后摄图像地址列表
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frontIdx = np.argsort(np.array(frontFid))
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backIdx = np.argsort(np.array(backFid))
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self.front_imgpaths = [frontImgs[i] for i in frontIdx]
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self.back_imgpaths = [backImgs[i] for i in backIdx]
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'''================ path of video ============='''
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for vidname in os.listdir(eventpath):
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name, ext = os.path.splitext(vidname)
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if ext not in VID_FORMAT: continue
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vidpath = os.path.join(eventpath, vidname)
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CamerType = name.split('_')[0]
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if CamerType == '0':
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self.back_videopath = vidpath
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if CamerType == '1':
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self.front_videopath = vidpath
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'''================ process.data ============='''
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procpath = Path(eventpath).joinpath('process.data')
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if procpath.is_file():
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SimiDict = read_similar(procpath)
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self.one2one = SimiDict['one2one']
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self.one2n = SimiDict['one2n']
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'''=========== 0/1_track.data & 0/1_tracking_output.data ======='''
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for dataname in os.listdir(eventpath):
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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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'''========== 0/1_track.data =========='''
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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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if CamerType == '0':
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self.back_yolobboxes = bboxes
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self.back_yolofeats = ffeats
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self.back_trackerboxes = trackerboxes
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self.back_trackerfeats = tracker_feat_dict
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self.back_trackingboxes = trackingboxes
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self.back_trackingfeats = tracking_feat_dict
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if CamerType == '1':
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self.front_yolobboxes = bboxes
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self.front_yolofeats = ffeats
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self.front_trackerboxes = trackerboxes
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self.front_trackerfeats = tracker_feat_dict
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self.front_trackingboxes = trackingboxes
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self.front_trackingfeats = tracking_feat_dict
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'''========== 0/1_tracking_output.data =========='''
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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 CamerType == '0':
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self.back_boxes = tracking_output_boxes
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self.back_feats = tracking_output_feats
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elif CamerType == '1':
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self.front_boxes = tracking_output_boxes
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self.front_feats = tracking_output_feats
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self.select_feat()
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self.compose_feats()
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def compose_feats(self):
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'''事件的特征集成'''
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feats_compose = np.empty((0, 256), dtype=np.float64)
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if len(self.front_feats):
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feats_compose = np.concatenate((feats_compose, self.front_feats), axis=0)
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if len(self.back_feats):
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feats_compose = np.concatenate((feats_compose, self.back_feats), axis=0)
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self.feats_compose = feats_compose
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def select_feats(self):
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'''事件的特征选择'''
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if len(self.front_feats):
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self.feats_select = self.front_feats
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else:
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self.feats_select = self.back_feats
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