373 lines
11 KiB
Python
373 lines
11 KiB
Python
# -*- coding: utf-8 -*-
|
||
"""
|
||
Created on Fri Jul 5 13:59:21 2024
|
||
func: extract_data()
|
||
读取 Pipeline 各模块的数据，在 read_pipeline_data.py（马晓慧）的基础上完成接口改造
|
||
|
||
@author: ym
|
||
"""
|
||
import numpy as np
|
||
import re
|
||
import os
|
||
from collections import OrderedDict
|
||
import matplotlib.pyplot as plt
|
||
|
||
|
||
|
||
def str_to_float_arr(s):
|
||
# 移除字符串末尾的逗号（如果存在）
|
||
if s.endswith(','):
|
||
s = s[:-1]
|
||
|
||
# 使用split()方法分割字符串，然后将每个元素转化为float
|
||
float_array = [float(x) for x in s.split(",")]
|
||
return float_array
|
||
|
||
|
||
def find_samebox_in_array(arr, target):
|
||
|
||
for i, st in enumerate(arr):
|
||
if st[:4] == target[:4]:
|
||
return i
|
||
return -1
|
||
|
||
import warnings
|
||
|
||
|
||
def extract_data(datapath):
|
||
bboxes, ffeats = [], []
|
||
|
||
trackerboxes = np.empty((0, 9), dtype=np.float64)
|
||
trackerfeats = np.empty((0, 256), dtype=np.float64)
|
||
|
||
boxes, feats, tboxes, tfeats = [], [], [], []
|
||
with open(datapath, 'r', encoding='utf-8') as lines:
|
||
for line in lines:
|
||
line = line.strip() # 去除行尾的换行符和可能的空白字符
|
||
if not line: # 跳过空行
|
||
continue
|
||
|
||
if line.find("CameraId")>=0:
|
||
if len(boxes): bboxes.append(np.array(boxes))
|
||
if len(feats): ffeats.append(np.array(feats))
|
||
|
||
# with warnings.catch_warnings(record=True) as w:
|
||
# if len(boxes): bboxes.append(np.array(boxes))
|
||
# if len(feats): ffeats.append(np.array(feats))
|
||
# if w:
|
||
# print(f"捕获到 {len(w)} 个警告:")
|
||
# for warning in w:
|
||
# print(f"警告类型: {warning.category}")
|
||
# print(f"警告消息: {warning.message}")
|
||
# print(f"警告发生的地方: {warning.filename}:{warning.lineno}")
|
||
|
||
if len(tboxes):
|
||
trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)))
|
||
if len(tfeats):
|
||
trackerfeats = np.concatenate((trackerfeats, np.array(tfeats)))
|
||
|
||
boxes, feats, tboxes, tfeats = [], [], [], []
|
||
|
||
if line.find("box:") >= 0 and line.find("output_box:") < 0:
|
||
box = line[line.find("box:") + 4:].strip()
|
||
# if len(box)==6:
|
||
boxes.append(str_to_float_arr(box))
|
||
|
||
if line.find("feat:") >= 0:
|
||
feat = line[line.find("feat:") + 5:].strip()
|
||
# if len(feat)==256:
|
||
feats.append(str_to_float_arr(feat))
|
||
|
||
if line.find("output_box:") >= 0:
|
||
box = str_to_float_arr(line[line.find("output_box:") + 11:].strip())
|
||
tboxes.append(box) # 去掉'output_box:'并去除可能的空白字符
|
||
index = find_samebox_in_array(boxes, box)
|
||
|
||
assert(len(boxes)==len(feats)), f"{datapath}, {datapath}, len(boxes)!=len(feats)"
|
||
if index >= 0:
|
||
# feat_f = str_to_float_arr(input_feats[index])
|
||
feat_f = feats[index]
|
||
norm_f = np.linalg.norm(feat_f)
|
||
feat_f = feat_f / norm_f
|
||
tfeats.append(feat_f)
|
||
|
||
if len(boxes): bboxes.append(np.array(boxes))
|
||
if len(feats): ffeats.append(np.array(feats))
|
||
if len(tboxes): trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)))
|
||
if len(tfeats): trackerfeats = np.concatenate((trackerfeats, np.array(tfeats)))
|
||
|
||
assert(len(bboxes)==len(ffeats)), "Error at Yolo output!"
|
||
assert(len(trackerboxes)==len(trackerfeats)), "Error at tracker output!"
|
||
|
||
tracker_feat_dict = {}
|
||
for i in range(len(trackerboxes)):
|
||
tid, fid, bid = int(trackerboxes[i, 4]), int(trackerboxes[i, 7]), int(trackerboxes[i, 8])
|
||
if f"frame_{fid}" not in tracker_feat_dict:
|
||
tracker_feat_dict[f"frame_{fid}"]= {"feats": {}}
|
||
tracker_feat_dict[f"frame_{fid}"]["feats"].update({bid: trackerfeats[i, :]})
|
||
|
||
|
||
boxes, trackingboxes= [], []
|
||
tracking_flag = False
|
||
with open(datapath, 'r', encoding='utf-8') as lines:
|
||
for line in lines:
|
||
line = line.strip() # 去除行尾的换行符和可能的空白字符
|
||
if not line: # 跳过空行
|
||
continue
|
||
if tracking_flag:
|
||
if line.find("tracking_") >= 0:
|
||
tracking_flag = False
|
||
else:
|
||
box = str_to_float_arr(line)
|
||
boxes.append(box)
|
||
if line.find("tracking_") >= 0:
|
||
tracking_flag = True
|
||
if len(boxes):
|
||
trackingboxes.append(np.array(boxes))
|
||
boxes = []
|
||
|
||
if len(boxes):
|
||
trackingboxes.append(np.array(boxes))
|
||
|
||
tracking_feat_dict = {}
|
||
try:
|
||
for i, boxes in enumerate(trackingboxes):
|
||
for box in boxes:
|
||
tid, fid, bid = int(box[4]), int(box[7]), int(box[8])
|
||
if f"track_{tid}" not in tracking_feat_dict:
|
||
tracking_feat_dict[f"track_{tid}"]= {"feats": {}}
|
||
tracking_feat_dict[f"track_{tid}"]["feats"].update({f"{fid}_{bid}": tracker_feat_dict[f"frame_{fid}"]["feats"][bid]})
|
||
except Exception as e:
|
||
print(f'Path: {datapath}, tracking_feat_dict can not be structured correcttly, Error: {e}')
|
||
|
||
return bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict
|
||
|
||
def read_tracking_output(filepath):
|
||
boxes = []
|
||
feats = []
|
||
with open(filepath, 'r', encoding='utf-8') as file:
|
||
for line in file:
|
||
line = line.strip() # 去除行尾的换行符和可能的空白字符
|
||
|
||
if not line:
|
||
continue
|
||
|
||
if line.endswith(','):
|
||
line = line[:-1]
|
||
|
||
data = np.array([float(x) for x in line.split(",")])
|
||
if data.size == 9:
|
||
boxes.append(data)
|
||
if data.size == 256:
|
||
feats.append(data)
|
||
|
||
assert(len(feats)==len(boxes)), f"{filepath}, len(feats)!=len(boxes)"
|
||
|
||
return np.array(boxes), np.array(feats)
|
||
|
||
|
||
def read_deletedBarcode_file(filePth):
|
||
with open(filePth, 'r', encoding='utf-8') as f:
|
||
lines = f.readlines()
|
||
|
||
split_flag, all_list = False, []
|
||
dict, barcode_list, similarity_list = {}, [], []
|
||
|
||
clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]
|
||
|
||
for i, line in enumerate(clean_lines):
|
||
stripped_line = line.strip()
|
||
if not stripped_line:
|
||
if len(barcode_list): dict['barcode'] = barcode_list
|
||
if len(similarity_list): dict['similarity'] = similarity_list
|
||
if len(dict): all_list.append(dict)
|
||
|
||
split_flag = False
|
||
dict, barcode_list, similarity_list = {}, [], []
|
||
continue
|
||
|
||
if line.find(':')<0: continue
|
||
label = line.split(':')[0]
|
||
value = line.split(':')[1]
|
||
|
||
if label == 'SeqDir':
|
||
dict['SeqDir'] = value
|
||
if label == 'Deleted':
|
||
dict['Deleted'] = value
|
||
if label == 'List':
|
||
split_flag = True
|
||
continue
|
||
if split_flag:
|
||
barcode_list.append(label)
|
||
similarity_list.append(value)
|
||
|
||
if len(barcode_list): dict['barcode'] = barcode_list
|
||
if len(similarity_list): dict['similarity'] = similarity_list
|
||
if len(dict): all_list.append(dict)
|
||
return all_list
|
||
|
||
|
||
def read_weight_timeConsuming(filePth):
|
||
WeightDict, SensorDict, ProcessTimeDict = OrderedDict(), OrderedDict(), OrderedDict()
|
||
|
||
with open(filePth, 'r', encoding='utf-8') as f:
|
||
lines = f.readlines()
|
||
for i, line in enumerate(lines):
|
||
line = line.strip()
|
||
|
||
if line.find(':') < 0: continue
|
||
if line.find("Weight") >= 0:
|
||
label = "Weight"
|
||
continue
|
||
if line.find("Sensor") >= 0:
|
||
label = "Sensor"
|
||
continue
|
||
if line.find("processTime") >= 0:
|
||
label = "ProcessTime"
|
||
continue
|
||
|
||
keyword = line.split(':')[0]
|
||
value = line.split(':')[1]
|
||
|
||
if label == "Weight":
|
||
WeightDict[keyword] = float(value.strip(','))
|
||
if label == "Sensor":
|
||
SensorDict[keyword] = [float(s) for s in value.split(',') if len(s)]
|
||
if label == "ProcessTime":
|
||
ProcessTimeDict[keyword] = float(value.strip(','))
|
||
|
||
# print("Done!")
|
||
return WeightDict, SensorDict, ProcessTimeDict
|
||
|
||
|
||
def plot_sensor_curve(WeightDict, SensorDict, ProcessTimeDict):
|
||
|
||
wtime, wdata = [], []
|
||
stime, sdata = [], []
|
||
for key, value in WeightDict.items():
|
||
wtime.append(int(key))
|
||
wdata.append(value)
|
||
|
||
for key, value in SensorDict.items():
|
||
if len(value) != 9: continue
|
||
|
||
stime.append(int(key))
|
||
sdata.append(np.array(value))
|
||
|
||
static_range = []
|
||
dynamic_range = []
|
||
windth = 8
|
||
nw = len(wdata)
|
||
assert(nw) >= 8, "The num of weight data is less than 8!"
|
||
|
||
i1, i2 = 0, 7
|
||
while i2 < nw:
|
||
data = wdata[i1:(i2+1)]
|
||
max(data) - min(data)
|
||
|
||
if i2<7:
|
||
i1 = 0
|
||
else:
|
||
i1 = i2-windth
|
||
|
||
min_t = min(wtime + stime)
|
||
wtime = [t-min_t for t in wtime]
|
||
stime = [t-min_t for t in stime]
|
||
|
||
max_t = max(wtime + stime)
|
||
|
||
fig = plt.figure(figsize=(16, 12))
|
||
gs = fig.add_gridspec(2, 1, left=0.1, right=0.9, bottom=0.1, top=0.9,
|
||
wspace=0.05, hspace=0.15)
|
||
# ax1, ax2 = axs
|
||
|
||
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 main(file_path):
|
||
WeightDict, SensorDict, ProcessTimeDict = read_weight_timeConsuming(file_path)
|
||
plot_sensor_curve(WeightDict, SensorDict, ProcessTimeDict)
|
||
|
||
|
||
|
||
|
||
if __name__ == "__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:
|
||
main(file_path)
|
||
|
||
k += 1
|
||
if k == 1:
|
||
break
|
||
|
||
|
||
|
||
# print("Done")
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|