556 lines
19 KiB
Python
556 lines
19 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Fri Jul 5 13:59:21 2024
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函数 读取文件
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extract_data() 0/1_track.data
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read_tracking_output() 0/1_tracking_output.data
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read_similar() process.data
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@author: ym
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"""
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import numpy as np
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import re
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import os
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from collections import OrderedDict
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import warnings
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import matplotlib.pyplot as plt
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def str_to_float_arr(s):
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# 移除字符串末尾的逗号（如果存在）
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if s.endswith(','):
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s = s[:-1]
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# 使用split()方法分割字符串，然后将每个元素转化为float
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float_array = [float(x) for x in s.split(",")]
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return float_array
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def find_samebox_in_array(arr, target):
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for i, st in enumerate(arr):
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if st[:4] == target[:4]:
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return i
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return -1
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def extract_data(datapath):
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'''
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0/1_track.data 数据读取
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对于特征，需要构造两个对象：
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(1) tracking输出 boxes，依据 (fid, bid) 找到对应的 feats, tracker_feat_dict 实现较为方便
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(2) frameDictList 中元素为 frameDict，输出同一接口
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'''
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bboxes, ffeats = [], []
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trackerboxes = np.empty((0, 9), dtype=np.float64)
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trackerfeats = np.empty((0, 256), dtype=np.float64)
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boxes, feats, tboxes, tfeats = [], [], [], []
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timestamps, frameIds = [], []
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with open(datapath, 'r', encoding='utf-8') as lines:
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for line in lines:
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line = line.strip() # 去除行尾的换行符和可能的空白字符
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if not line: # 跳过空行
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continue
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if line.find("CameraId")>=0:
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if len(boxes): bboxes.append(np.array(boxes))
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if len(feats): ffeats.append(np.array(feats))
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if len(tboxes):
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trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)))
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if len(tfeats):
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trackerfeats = np.concatenate((trackerfeats, np.array(tfeats)))
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timestamp, frameId = [int(ln.split(":")[1]) for ln in line.split(",")[1:]]
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timestamps.append(timestamp)
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frameIds.append(frameId)
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boxes, feats, tboxes, tfeats = [], [], [], []
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if line.find("box:") >= 0 and line.find("output_box:")<0 and line.find("out_boxes")<0:
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box = line[line.find("box:") + 4:].strip()
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# if len(box)==6:
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boxes.append(str_to_float_arr(box))
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if line.find("feat:") >= 0:
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feat = line[line.find("feat:") + 5:].strip()
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# if len(feat)==256:
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feats.append(str_to_float_arr(feat))
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if line.find("output_box:") >= 0:
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assert(len(boxes)>=0 and len(boxes)==len(feats)), f"{datapath}, {datapath}, len(boxes)!=len(feats)"
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box = str_to_float_arr(line[line.find("output_box:") + 11:].strip())
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index = find_samebox_in_array(boxes, box)
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if index >= 0:
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tboxes.append(box) # 去掉'output_box:'并去除可能的空白字符
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# feat_f = str_to_float_arr(input_feats[index])
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feat_f = feats[index]
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norm_f = np.linalg.norm(feat_f)
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feat_f = feat_f / norm_f
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tfeats.append(feat_f)
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if len(boxes): bboxes.append(np.array(boxes))
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if len(feats): ffeats.append(np.array(feats))
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if len(tboxes): trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)))
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if len(tfeats): trackerfeats = np.concatenate((trackerfeats, np.array(tfeats)))
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assert(len(bboxes)==len(ffeats)), "Error at Yolo output!"
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assert(len(trackerboxes)==len(trackerfeats)), "Error at tracker output!"
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## 生成帧为单位的特征列表
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tracker_feats = {}
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frmIDs = np.sort(np.unique(trackerboxes[:, 7].astype(int)))
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for fid in frmIDs:
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idx = np.where(trackerboxes[:, 7] == fid)[0]
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boxes = trackerboxes[idx, :]
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feats = trackerfeats[idx, :]
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for i in range(len(boxes)):
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f, b = int(boxes[i, 7]), int(boxes[i, 8])
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tracker_feats.update({f"{f}_{b}": feats[i, :]})
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boxes, trackingboxes= [], []
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tracking_flag = False
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with open(datapath, 'r', encoding='utf-8') as lines:
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for line in lines:
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line = line.strip() # 去除行尾的换行符和可能的空白字符
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if not line: # 跳过空行
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tracking_flag = False
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continue
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if tracking_flag:
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if line.find("tracking_") >= 0:
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tracking_flag = False
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else:
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box = str_to_float_arr(line)
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boxes.append(box)
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if line.find("tracking_") >= 0:
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tracking_flag = True
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if len(boxes):
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trackingboxes.append(np.array(boxes))
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boxes = []
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if len(boxes):
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trackingboxes.append(np.array(boxes))
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# tracking_feat_dict = {}
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# try:
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# for i, boxes in enumerate(trackingboxes):
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# for box in boxes:
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# tid, fid, bid = int(box[4]), int(box[7]), int(box[8])
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# if f"track_{tid}" not in tracking_feat_dict:
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# tracking_feat_dict[f"track_{tid}"]= {"feats": {}}
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# tracking_feat_dict[f"track_{tid}"]["feats"].update({f"{fid}_{bid}": tracker_feat_dict[f"frame_{fid}"]["feats"][bid]})
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# except Exception as e:
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# print(f'Path: {datapath}, tracking_feat_dict can not be structured correcttly, Error: {e}')
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# tracker_feat_dict = {}
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# tracker_feat_dict["timestamps"] = timestamps
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# tracker_feat_dict["frameIds"] = frameIds
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# for i in range(len(trackerboxes)):
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# fid, bid = int(trackerboxes[i, 7]), int(trackerboxes[i, 8])
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# if f"frame_{fid}" not in tracker_feat_dict:
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# tracker_feat_dict[f"frame_{fid}"]= {"feats": {}}
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# tracker_feat_dict[f"frame_{fid}"]["feats"].update({bid: trackerfeats[i, :]})
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trackingfeats = []
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try:
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for i, boxes in enumerate(trackingboxes):
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feats = []
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for box in boxes:
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fid, bid = int(box[7]), int(box[8])
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feat = tracker_feats[f"{fid}_{bid}"]
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feats.append(feat)
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trackingfeats.append(np.array(feats))
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except Exception as e:
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print(f'Path: {datapath}, trackingfeats can not be structured correcttly, Error: {e}')
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# return bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, trackingfeats
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return bboxes, ffeats, trackerboxes, tracker_feats, trackingboxes, trackingfeats
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def read_tracking_output(filepath):
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'''
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0/1_tracking_output.data 数据读取
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'''
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boxes = []
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feats = []
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if not os.path.isfile(filepath):
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return np.array(boxes), np.array(feats)
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with open(filepath, 'r', encoding='utf-8') as file:
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for line in file:
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line = line.strip() # 去除行尾的换行符和可能的空白字符
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if not line:
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continue
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if line.endswith(','):
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line = line[:-1]
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data = np.array([float(x) for x in line.split(",")])
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if data.size == 9:
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boxes.append(data)
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if data.size == 256:
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feats.append(data)
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if len(feats) != len(boxes):
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return [np.array([])], [np.array([])]
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return [np.array(boxes)], [np.array(feats)]
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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 read_returnGoods_file(filePath):
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'''
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20241030开始，原 deletedBarcode.txt 中数据格式修改为 returnGoods.txt，读数方式随之变化
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'''
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with open(filePath, 'r', encoding='utf-8') as f:
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lines = f.readlines()
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clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]
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all_list = []
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split_flag, dict = False, {}
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barcode_list, similarity_list = [], []
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event_list, type_list = [], []
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for i, line in enumerate(clean_lines):
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stripped_line = line.strip()
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if line.endswith(','):
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line = line[:-1]
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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(event_list): dict['event'] = event_list
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if len(type_list): dict['type'] = type_list
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if len(dict) and dict['SeqDir'].find('*')<0:
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all_list.append(dict)
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split_flag, dict = False, {}
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barcode_list, similarity_list = [], []
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event_list, type_list = [], []
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continue
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if line.find(':')<0: continue
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if line.find('1:n')==0: continue
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label = line.split(':')[0].strip()
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value = line.split(':')[1].strip()
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if label == 'SeqDir':
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dict['SeqDir'] = value
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dict['Deleted'] = value.split('_')[-1]
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dict['filetype'] = "returnGoods"
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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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bcd = label.split('_')[-1]
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if len(bcd)<8: continue
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# event_list.append(label + '_' + bcd)
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event_list.append(label)
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barcode_list.append(bcd)
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similarity_list.append(value.split(',')[0])
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type_list.append(value.split('=')[-1])
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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(event_list): dict['event'] = event_list
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if len(type_list): dict['type'] = type_list
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if len(dict) and dict['SeqDir'].find('*')<0:
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all_list.append(dict)
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return all_list
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def read_seneor(filepath):
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WeightDict = OrderedDict()
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with open(filepath, 'r', encoding='utf-8') as f:
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lines = f.readlines()
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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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line = line.strip()
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keyword = line.split(':')[0]
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value = line.split(':')[1]
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vdata = [float(s) for s in value.split(',') if len(s)]
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WeightDict[keyword] = vdata[-1]
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return WeightDict
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def read_similar(filePath):
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SimiDict = {}
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SimiDict['one2one'] = []
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SimiDict['one2SN'] = []
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SimiDict['one2n'] = []
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with open(filePath, 'r', encoding='utf-8') as f:
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lines = f.readlines()
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clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]
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one2one_list, one2SN_list, one2n_list = [], [], []
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Flag_1to1, Flag_1toSN, Flag_1ton = False, False, False
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for i, line in enumerate(clean_lines):
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line = line.strip()
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if line.endswith(','):
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line = line[:-1]
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Dict = {}
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if not line:
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if len(one2one_list): SimiDict['one2one'] = one2one_list
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if len(one2SN_list): SimiDict['one2SN'] = one2SN_list
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if len(one2n_list): SimiDict['one2n'] = one2n_list
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one2one_list, one2SN_list, one2n_list = [], [], []
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Flag_1to1, Flag_1toSN, Flag_1ton = False, False, False
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continue
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if line.find('oneToOne')>=0:
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Flag_1to1, Flag_1toSN, Flag_1ton = True, False,False
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continue
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if line.find('oneToSN')>=0:
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Flag_1to1, Flag_1toSN, Flag_1ton = False, True, False
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continue
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if line.find('oneTon')>=0:
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Flag_1to1, Flag_1toSN, Flag_1ton = False, False, True
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continue
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if Flag_1to1:
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barcode = line.split(',')[0].strip()
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value = line.split(',')[1].split(':')[1].strip()
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Dict['barcode'] = barcode
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Dict['similar'] = float(value)
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one2one_list.append(Dict)
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continue
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if Flag_1toSN:
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barcode = line.split(',')[0].strip()
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value = line.split(',')[1].split(':')[1].strip()
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Dict['barcode'] = barcode
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Dict['similar'] = float(value)
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one2SN_list.append(Dict)
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continue
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if Flag_1ton:
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label = line.split(':')[0].strip()
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value = line.split(':')[1].strip()
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bcd = label.split('_')[-1]
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if len(bcd)<8: continue
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Dict['event'] = label
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Dict['barcode'] = bcd
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Dict['similar'] = float(value.split(',')[0])
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Dict['type'] = value.split('=')[-1]
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one2n_list.append(Dict)
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if len(one2one_list): SimiDict['one2one'] = one2one_list
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if len(one2n_list): SimiDict['one2n'] = one2n_list
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if len(one2SN_list): SimiDict['one2SN'] = one2SN_list
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return SimiDict
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def read_weight_timeConsuming(filePth):
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WeightDict, SensorDict, ProcessTimeDict = OrderedDict(), OrderedDict(), OrderedDict()
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with open(filePth, 'r', encoding='utf-8') as f:
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lines = f.readlines()
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# label = ''
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for i, line in enumerate(lines):
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line = line.strip()
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if line.find(':') < 0: continue
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if line.find("Weight") >= 0:
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label = "Weight"
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continue
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if line.find("Sensor") >= 0:
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label = "Sensor"
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continue
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if line.find("processTime") >= 0:
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label = "ProcessTime"
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continue
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keyword = line.split(':')[0]
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value = line.split(':')[1]
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if label == "Weight":
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WeightDict[keyword] = float(value.strip(','))
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if label == "Sensor":
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SensorDict[keyword] = [float(s) for s in value.split(',') if len(s)]
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if label == "ProcessTime":
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ProcessTimeDict[keyword] = float(value.strip(','))
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# print("Done!")
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return WeightDict, SensorDict, ProcessTimeDict
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def plot_sensor_curve(WeightDict, SensorDict, ProcessTimeDict):
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wtime, wdata = [], []
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stime, sdata = [], []
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for key, value in WeightDict.items():
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wtime.append(int(key))
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wdata.append(value)
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for key, value in SensorDict.items():
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if len(value) != 9: continue
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stime.append(int(key))
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sdata.append(np.array(value))
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static_range = []
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dynamic_range = []
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windth = 8
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nw = len(wdata)
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assert(nw) >= 8, "The num of weight data is less than 8!"
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# i1, i2 = 0, 7
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# while i2 < nw:
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# data = wdata[i1:(i2+1)]
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# max(data) - min(data)
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# if i2<7:
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# i1 = 0
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# else:
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# i1 = i2-windth
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min_t = min(wtime + stime)
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wtime = [t-min_t for t in wtime]
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stime = [t-min_t for t in stime]
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max_t = max(wtime + stime)
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fig = plt.figure(figsize=(16, 12))
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gs = fig.add_gridspec(2, 1, left=0.1, right=0.9, bottom=0.1, top=0.9,
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wspace=0.05, hspace=0.15)
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||
# ax1, ax2 = axs
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||
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||
ax1 = fig.add_subplot(gs[0,0])
|
||
ax2 = fig.add_subplot(gs[1,0])
|
||
|
||
ax1.plot(wtime, wdata, 'b--', linewidth=2 )
|
||
for i in range(9):
|
||
ydata = [s[i] for s in sdata]
|
||
ax2.plot(stime, ydata, linewidth=2 )
|
||
|
||
ax1.grid(True), ax1.set_xlim(0, max_t), ax1.set_title('Weight')
|
||
ax1.set_label("(Time: ms)")
|
||
# ax1.legend()
|
||
|
||
ax2.grid(True), ax2.set_xlim(0, max_t), ax2.set_title('IMU')
|
||
# ax2.legend()
|
||
|
||
plt.show()
|
||
|
||
|
||
|
||
|
||
|
||
|
||
def test_process(file_path):
|
||
WeightDict, SensorDict, ProcessTimeDict = read_weight_timeConsuming(file_path)
|
||
plot_sensor_curve(WeightDict, SensorDict, ProcessTimeDict)
|
||
|
||
|
||
def main():
|
||
files_path = r'\\192.168.1.28\share\测试_202406\0814\0814\20240814-102227-62264578-a720-4eb9-b95e-cb8be009aa98_null'
|
||
k = 0
|
||
for filename in os.listdir(files_path):
|
||
filename = 'process.data'
|
||
|
||
file_path = os.path.join(files_path, filename)
|
||
if os.path.isfile(file_path) and filename.find("track.data")>0:
|
||
extract_data(file_path)
|
||
|
||
if os.path.isfile(file_path) and filename.find("process.data")>=0:
|
||
test_process(file_path)
|
||
|
||
k += 1
|
||
if k == 1:
|
||
break
|
||
|
||
def main1():
|
||
fpath = r'\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\比对测试\1209永辉超市测试\20241209-155924-117e1941-70f8-4287-8de1-4866868548a6_6926475209967\process.data'
|
||
simidct = read_similar(fpath)
|
||
print(simidct)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main1()
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
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