433 lines
18 KiB
Python
433 lines
18 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Sat Jul 27 14:07:25 2024
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现场测试数据，在不同特征组合情况下的精度、召回率分析程序
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@author: ym
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"""
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import os.path
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import numpy as np
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from scipy.spatial.distance import cdist
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import matplotlib.pyplot as plt
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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_deletedBarcode_file, read_tracking_output
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# from tracking.dotrack.dotracks import Track
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from one2n_contrast import compute_recall_precision, show_recall_prec
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from one2n_contrast import performance_evaluate, one2n_return, one2n_deleted
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def compute_similar(feat1, feat2):
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if len(feat1)==0 or len(feat2)==0:
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return 0
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similar = 1 - np.maximum(0.0, cdist(feat1, feat2, metric = 'cosine'))
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smean = np.mean(similar)
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return smean
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def update_event(datapath):
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'''一次购物事件，包含 8 个keys
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back_sole_boxes：后摄boxes
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front_sole_boxes：前摄boxes
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back_sole_feats：后摄特征
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front_sole_feats：前摄特征
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feats_compose：将前后摄特征进行合并
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feats_select：特征选择，优先选择前摄特征
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'''
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event = {}
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# event['front_tracking_boxes'] = []
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# event['front_tracking_feats'] = {}
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# event['back_tracking_boxes'] = []
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# event['back_tracking_feats'] = {}
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event['back_sole_boxes'] = np.empty((0, 9), dtype=np.float64)
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event['front_sole_boxes'] = np.empty((0, 9), dtype=np.float64)
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event['back_sole_feats'] = np.empty((0, 256), dtype=np.float64)
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event['front_sole_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['feats_select'] = np.empty((0, 256), dtype=np.float64)
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# '''读取 track.data 文件中的数据'''
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# fpath_0_track = os.path.join(datapath, '0_track.data')
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# fpath_1_track = os.path.join(datapath, '1_track.data')
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# if os.path.exists(fpath_0_track) and os.path.isfile(fpath_0_track):
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# _, _, _, _, tracking_boxes, tracking_feat_dict = extract_data(fpath_0_track)
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# event['back_tracking_boxes'] = tracking_boxes
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# event['back_tracking_feats'] = tracking_feat_dict
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# if os.path.exists(fpath_1_track) and os.path.isfile(fpath_1_track):
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# _, _, _, _, tracking_boxes, tracking_feat_dict = extract_data(fpath_1_track)
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# event['front_tracking_boxes'] = tracking_boxes
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# event['front_tracking_feats'] = tracking_feat_dict
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# =============================================================================
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# '''================1. 直接指定读取，速度快======================================'''
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# '''读取 tracking_output.data 文件中的数据'''
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# fpath_0_tracking = os.path.join(datapath, '0_tracking_output.data')
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# fpath_1_tracking = os.path.join(datapath, '1_tracking_output.data')
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# if os.path.exists(fpath_0_tracking) and os.path.isfile(fpath_0_tracking):
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# tracking_output_boxes, tracking_output_feats = read_tracking_output(fpath_0_tracking)
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# event['back_sole_boxes'] = tracking_output_boxes
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# event['back_sole_feats'] = tracking_output_feats
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#
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# if os.path.exists(fpath_1_tracking) and os.path.isfile(fpath_1_tracking):
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# tracking_output_boxes, tracking_output_feats = read_tracking_output(fpath_1_tracking)
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# event['front_sole_boxes'] = tracking_output_boxes
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# event['front_sole_feats'] = tracking_output_feats
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# =============================================================================
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'''================2. 遍历方式读取（与以上方式二选一）======================================'''
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'''读取当前事件的 data 文件'''
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for filename in os.listdir(datapath):
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# filename = '1_track.data'
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fpath = os.path.join(datapath, filename)
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CamerType = filename.split('_')[0]
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# if os.path.isfile(fpath) and filename.find("track.data")>0:
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# bboxes, ffeats, trackerboxes, tracker_feat_dict, tracking_boxes, tracking_feat_dict = extract_data(fpath)
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# if CamerType == '0':
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# event['back_tracking_boxes'] = tracking_boxes
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# event['back_tracking_feats'] = tracking_feat_dict
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# elif CamerType == '1':
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# event['front_tracking_boxes'] = tracking_boxes
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# event['front_tracking_feats'] = tracking_feat_dict
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if os.path.isfile(fpath) and filename.find("tracking_output.data")>0:
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tracking_output_boxes, tracking_output_feats = read_tracking_output(fpath)
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if CamerType == '0':
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event['back_sole_boxes'] = tracking_output_boxes
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event['back_sole_feats'] = tracking_output_feats
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elif CamerType == '1':
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event['front_sole_boxes'] = tracking_output_boxes
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event['front_sole_feats'] = tracking_output_feats
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'''事件的特征表征方式选择'''
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fs_feats = event['front_sole_feats']
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bs_feats = event['back_sole_feats']
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'''1. 如果前后摄均没有轨迹选择输出，返回'''
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condt1 = len(fs_feats) + len(bs_feats) == 0
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if condt1:
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return event
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'''2. 构造综合特征'''
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feats_compose = np.empty((0, 256), dtype=np.float64)
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if len(fs_feats):
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feats_compose = np.concatenate((feats_compose, fs_feats), axis=0)
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if len(bs_feats):
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feats_compose = np.concatenate((feats_compose, bs_feats), axis=0)
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event['feats_compose'] = feats_compose
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'''3. 构造前摄特征'''
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if len(fs_feats):
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event['feats_select'] = fs_feats
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return event
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'''4. 从前摄输出轨迹中选取特定轨迹对应的特征'''
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# =============================================================================
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# ftrboxes = event['front_tracking_boxes']
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# ftrfeats = event['front_tracking_feats']
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#
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# condt2 = len(ftrboxes) + len(ftrfeats) == 0
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# condt3 = len(ftrfeats) != len(ftrboxes)
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# if condt2 or condt3:
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# return event
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#
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# bprops = []
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# for boxes in ftrboxes:
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# track = Track(boxes)
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# bprops.append(max(track.trajdist))
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#
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# index = bprops.index(max(bprops))
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# box_select = ftrboxes[index]
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# tid = int(box_select[0, 4])
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#
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# feat_select = ftrfeats[f"track_{tid}"]
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# feats_select = np.empty((0, 256), dtype=np.float64)
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# for fid_bid, feat in feat_select['feats'].items():
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# feats_select = np.concatenate((feats_select, feat[None, :]), axis=0)
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# event['feats_select'] = feats_select
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# =============================================================================
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return event
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def creatd_deletedBarcode_front(filepath):
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'''
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生成deletedBarcodeTest.txt
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'''
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# filepath = r'\\192.168.1.28\share\测试_202406\0723\0723_1\deletedBarcode.txt'
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basepath, _ = os.path.split(filepath)
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bcdlist = read_deletedBarcode_file(filepath)
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MatchList = []
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k = 0
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for s_list in bcdlist:
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getout_fold = s_list['SeqDir'].strip()
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getout_path = os.path.join(basepath, getout_fold)
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'''取出事件文件夹不存在，跳出循环'''
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if not os.path.exists(getout_path) and not os.path.isdir(getout_path):
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continue
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day, hms = getout_fold.strip('_').split('-')
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''' 生成取出事件字典 '''
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getout_event = {}
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getout_event['barcode'] = s_list['Deleted'].strip()
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getout_event['path'] = getout_path
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getout_event['feats_compose'] = np.empty((0, 256), dtype=np.float64)
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getout_event['feats_select'] = np.empty((0, 256), dtype=np.float64)
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InputList = []
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barcodes = [s.strip() for s in s_list['barcode']]
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similarity = [float(s.strip()) for s in s_list['similarity']]
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for i, barcode in enumerate(barcodes):
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''' 生成放入事件字典 '''
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input_event = {}
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input_folds, times = [], []
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for pathname in os.listdir(basepath):
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if pathname.endswith('_'): continue
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if os.path.isfile(os.path.join(basepath, pathname)):continue
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infold = pathname.split('_')
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if len(infold)!=2: continue
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if len(infold[0])<=14 or len(infold[1])<=10: continue
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day1, hms1 = infold[0].split('-')
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if day1==day and infold[1]==barcode and int(hms1)<int(hms):
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input_folds.append(pathname)
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times.append(int(hms1))
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if len(input_folds)==0: continue
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''' 根据时间排序，选择离取出操作最近时间的文件夹，作为取出操作应的放入操作所对应的文件夹 '''
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input_fold = input_folds[times.index(max(times))]
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input_path = os.path.join(basepath, input_fold)
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if not os.path.exists(getout_path) and not os.path.isdir(getout_path):
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continue
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input_event['barcode'] = barcode
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input_event['path'] = input_path
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input_event['similarity'] = float(similarity[i])
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input_event['feats_compose'] = np.empty((0, 256), dtype=np.float64)
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input_event['feats_select'] = np.empty((0, 256), dtype=np.float64)
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InputList.append(input_event)
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MatchList.append((getout_event, InputList))
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# k += 1
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# if k==2:
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# break
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print('Step 1: Event init Done!')
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for getout_event, InputList in MatchList:
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getout_path = getout_event['path']
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if os.path.exists(getout_path) and os.path.isdir(getout_path):
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event = update_event(getout_path)
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getout_event.update(event)
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'''====== 放入事件是在取出事件存在的情况下分析 ======'''
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for input_event in InputList:
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input_path = input_event['path']
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if os.path.exists(input_path) and os.path.isdir(input_path):
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event = update_event(input_path)
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input_event.update(event)
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print('Step 2: Event update Done!')
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results = []
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for getout_event, InputList in MatchList:
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getout_barcode = getout_event['barcode']
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getout_feats_compose = getout_event['feats_compose']
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getout_feats_select = getout_event['feats_select']
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if len(getout_feats_select):
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outfeats_select = getout_feats_select.copy()
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else:
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outfeats_select = getout_feats_compose.copy()
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result = {}
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result['SeqDir'] = os.path.split(getout_event['path'])[1]
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result['Deleted'] = getout_barcode
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result['List'] = {}
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for input_event in InputList:
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input_barcode = input_event['barcode']
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input_feats_compose = input_event['feats_compose']
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input_feats_select = input_event['feats_select']
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if len(input_feats_select):
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infeats_select = input_feats_select.copy()
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else:
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infeats_select = input_feats_compose.copy()
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similar_comp = compute_similar(getout_feats_compose, input_feats_compose)
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similar_selt = compute_similar(outfeats_select, infeats_select)
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'''现场测试相似度，组合特征相似度，前摄选择特征相似度'''
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result['List'][f'{input_barcode}'] = (input_event['similarity'], similar_comp, similar_selt)
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# result[f'{input_barcode}'] = (input_event['similarity'], similar_comp, similar_selt)
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results.append(result)
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print('Step 3: Similarity conputation Done!')
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wpath = os.path.split(filepath)[0]
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wfile = os.path.join(wpath, 'deletedBarcodeTest.txt')
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with open(wfile, 'w', encoding='utf-8') as file:
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for result in results:
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SeqDir = result['SeqDir']
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Deleted = result['Deleted']
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file.write('\n')
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file.write(f'SeqDir: {SeqDir}\n')
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file.write(f'Deleted: {Deleted}\n')
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file.write('List:\n')
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for key, value in result['List'].items():
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file.write(f'{key}: ')
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file.write(f'{value[0]}, {value[1]:.3f}, {value[2]:.3f}\n')
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print('Step 4: File writting Done!')
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def precision_compare(filepath, savepath):
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'''
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1. deletedBarcode.txt 中的相似度的计算为现场算法前后摄轨迹特征合并
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2. deletedBarcodeTest.txt 中的 3 个相似度计算方式依次为：
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(1)现场算法前后摄轨迹特征合并;
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(2)本地算法前后摄轨迹特征合并；
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(3)本地算法优先选择前摄
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'''
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fpath = os.path.split(filepath)[0]
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_, basefile = os.path.split(fpath)
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'''1. 综合前后摄特征的相似度比对性能'''
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fpath1 = os.path.join(fpath, 'deletedBarcode.txt')
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blist1 = read_deletedBarcode_file(fpath1)
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errpairs, corrpairs, err_similarity, correct_similarity = performance_evaluate(blist1)
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recall, prec, ths = compute_recall_precision(err_similarity, correct_similarity)
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os.path.split(fpath1)
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plt1 = show_recall_prec(recall, prec, ths)
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# plt1.show()
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plt1.xlabel(f'threshold, Num: {len(blist1)}')
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plt1.title(basefile + ', compose')
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plt1.savefig(os.path.join(savepath, basefile+'_pr.png'))
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plt1.close()
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'''2. 优先选取前摄特征的相似度比对性能'''
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fpath2 = os.path.join(fpath, 'deletedBarcodeTest.txt')
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blist2 = read_deletedBarcode_file(fpath2)
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front_errpairs, front_corrpairs, front_err_similarity, front_correct_similarity = performance_evaluate(blist2)
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front_recall, front_prec, front_ths = compute_recall_precision(front_err_similarity, front_correct_similarity)
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plt2 = show_recall_prec(front_recall, front_prec, front_ths)
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# plt2.show()
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plt2.xlabel(f'threshold, Num: {len(blist2)}')
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plt1.title(basefile + ', front')
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plt2.savefig(os.path.join(savepath, basefile+'_pr_front.png'))
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plt2.close()
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def main():
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'''
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1. 成deletedBarcodeTest.txt
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2. 不同特征选择下的精度比对性能比较
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'''
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fplist = [#r'\\192.168.1.28\share\测试_202406\0723\0723_1\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0723\0723_2\deletedBarcode.txt',
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r'\\192.168.1.28\share\测试_202406\0723\0723_3\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0722\0722_01\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0722\0722_02\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0719\719_1\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0719\719_2\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0719\719_3\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0719\719_4\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0718\0718-1\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0718\0718-2\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0717\0717-1\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0717\0717-2\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0717\0717-3\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0716\0716_1\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0716\0716_2\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0716\0716_3\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0715\0715_1\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0715\0715_2\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0715\0715_3\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0712\0712_1\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0712\0712_2\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\711\images01\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\711\images02\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\710\images_1\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\710\images_2\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\709\deletedBarcode.txt',
|
||
# r'\\192.168.1.28\share\测试_202406\705\deletedBarcode.txt',
|
||
# r'\\192.168.1.28\share\测试_202406\703\deletedBarcode.txt',
|
||
# r'\\192.168.1.28\share\测试_202406\702_pm_1\702_pm_1\deletedBarcode.txt',
|
||
# r'\\192.168.1.28\share\测试_202406\702_pm_3\702_pm_3\deletedBarcode.txt',
|
||
# r'\\192.168.1.28\share\测试_202406\701_am\deletedBarcode.txt',
|
||
# r'\\192.168.1.28\share\测试_202406\701_pm\701_pm\deletedBarcode.txt',
|
||
# r'\\192.168.1.28\share\测试_202406\628\1\deletedBarcode.txt',
|
||
# r'\\192.168.1.28\share\测试_202406\628\2\deletedBarcode.txt',
|
||
# r'\\192.168.1.28\share\测试_202406\627\deletedBarcode.txt',
|
||
]
|
||
|
||
savepath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\illustration'
|
||
for filepath in fplist:
|
||
try:
|
||
#1. 生成deletedBarcodeTest.txt 文件
|
||
creatd_deletedBarcode_front(filepath)
|
||
|
||
#2. 确保该目录下存在deletedBarcode.txt, deletedBarcodeTest.txt 文件
|
||
precision_compare(filepath, savepath)
|
||
except Exception as e:
|
||
print(f'{filepath}, Error: {e}')
|
||
|
||
if __name__ == '__main__':
|
||
|
||
main()
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|