回传数据解析，兼容v5和v10
This commit is contained in:
jiajie555
544
realtime/time_devide.py
Normal file
544
realtime/time_devide.py
Normal file
@ -0,0 +1,544 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed Oct 16 17:37:07 2024
|
||||
|
||||
@author: ym
|
||||
"""
|
||||
|
||||
# import csv
|
||||
import os
|
||||
# import platform
|
||||
# import sys
|
||||
from pathlib import Path
|
||||
import glob
|
||||
import numpy as np
|
||||
import copy
|
||||
import matplotlib.pyplot as plt
|
||||
from collections import OrderedDict
|
||||
|
||||
from event_time_specify import devide_motion_state #, state_measure
|
||||
|
||||
import sys
|
||||
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[1] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
from imgs_inference import run_yolo
|
||||
from tracking.utils.read_data import read_weight_sensor
|
||||
|
||||
# IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm' # include image suffixes
|
||||
# VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv' # include video suffixes
|
||||
|
||||
def filesort(p):
|
||||
'''
|
||||
需将图像文件名标准化
|
||||
'''
|
||||
|
||||
|
||||
files = []
|
||||
files.extend(sorted(glob.glob(os.path.join(p, '*.jpg'))))
|
||||
# images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS]
|
||||
|
||||
tamps_0, tamps_1 = [], []
|
||||
files_0, files_1 = [], []
|
||||
for file in files:
|
||||
basename = os.path.basename(file)
|
||||
# if basename.find('frameId')<0: continue
|
||||
|
||||
|
||||
f, ext = os.path.splitext(basename)
|
||||
_, camer, tamp = f.split('_')
|
||||
|
||||
if camer == '0':
|
||||
tamps_0.append(int(tamp))
|
||||
files_0.append(file)
|
||||
|
||||
if camer == '1':
|
||||
tamps_1.append(int(tamp))
|
||||
files_1.append(file)
|
||||
|
||||
idx0 = sorted(range(len(tamps_0)), key=lambda k: tamps_0[k])
|
||||
files0 = [files_0[i] for i in idx0]
|
||||
|
||||
idx1 = sorted(range(len(tamps_1)), key=lambda k: tamps_1[k])
|
||||
files1 = [files_1[i] for i in idx1]
|
||||
|
||||
|
||||
return (files0, files1)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def rename(filePath, tmin):
|
||||
"""
|
||||
重命名函数fun1
|
||||
输入：文件夹路径
|
||||
功能：对文件夹中的全部文件进行随机命名
|
||||
"""
|
||||
suffix = '.png' # 设置后缀，筛选特定文件以更改名称
|
||||
for file in os.listdir(filePath):
|
||||
if file.endswith(suffix):
|
||||
name = file.split('.')[0]
|
||||
|
||||
tamp = int(name.split('_')[2])-tmin
|
||||
|
||||
suffix = file.split('.')[1]
|
||||
|
||||
newname = name +f'_{int(tamp)}.'+suffix
|
||||
|
||||
os.rename(os.path.join(filePath, file), os.path.join(filePath, newname))
|
||||
|
||||
|
||||
def rerename(filePath=None):
|
||||
"""
|
||||
重命名函数fun1
|
||||
输入：文件夹路径
|
||||
功能：对文件夹中的全部文件进行随机命名
|
||||
"""
|
||||
|
||||
suffix = '.png' # 设置后缀，筛选特定文件以更改名称
|
||||
for file in os.listdir(filePath):
|
||||
if file.endswith(suffix):
|
||||
name = file.split('.')[0]
|
||||
names = name.split('_')
|
||||
if len(names)>=6:
|
||||
newname = "_".join(names[0:5])+'.png'
|
||||
os.rename(os.path.join(filePath, file), os.path.join(filePath, newname))
|
||||
|
||||
|
||||
def state_measure(periods, weights, hands, spath=None):
|
||||
'''
|
||||
数据类型
|
||||
后摄: 0, 前摄: 1, CV综合: 2, 重力: 9
|
||||
frstate_0/1:
|
||||
帧ID 时间戳 单摄状态1 单摄状态2 活动轨迹标记
|
||||
0 1 2 3 4:end
|
||||
|
||||
|
||||
|
||||
time_stream = np.concatenate((tstream, weights))
|
||||
time_stream:
|
||||
序列索引号 数据类型 时间戳 单摄状态1/重力值 单摄状态2/重力(t0, t1) CV状态/重力(t0', t1') 综合数据类型 综合状态
|
||||
0 1 2 3 4 5 6 7
|
||||
|
||||
单摄状态1：基于运动轨迹的起止点确定的运动区间
|
||||
单摄状态2: 基于滑动窗口的起止点(窗口终点)确定的运动区间
|
||||
重力(t0, t1): 重力波动的精确时间区间，基于重力波动的起止点，而不是仅依赖重力稳定时间
|
||||
重力(t0', t1'): 根据加退购对重力波动窗口进行扩展，扩展应该涵盖购物事件的发生过程
|
||||
方案：
|
||||
前后摄状态进行或运算
|
||||
CV与重力状态进行与运算
|
||||
'''
|
||||
|
||||
# BackType = 0 # 后摄数据类型
|
||||
# FrontType = 1 # 前摄数据类型
|
||||
CameraType = 2 # CV数据综合类型
|
||||
HandType = 3 # 手部类型
|
||||
WeightType = 9 # 重力数据类型
|
||||
WeightStableThresh = 7.5 # 单位：g，重力稳定状态下的最大波动范围
|
||||
WeightWinWidth = 10 # 单位：重力数据点数，该值和采样间隔关联，重力稳定时间设定为500ms = WeightWinWidth * 采样间隔
|
||||
CameraTimeInterval = 100 # 单位：ms，前后摄状态进行或运算时的时间间隔阈值
|
||||
InputFrontNum = 10 # 重力增加（放入）向前延拓的重力数据点数
|
||||
InputBackNum = 0 # 重力增加（放入）向后延拓的重力数据点数
|
||||
OutputFrontNum = 2 # 重力减少（取出）向前延拓的重力数据点数
|
||||
OutputBackNum = 10 # 重力减少（取出）向前延拓的重力数据点数
|
||||
CompTimeInterval = 150 # 单位：ms，CV状态和重力状态进行与运算时的时间间隔阈值
|
||||
|
||||
|
||||
'''==================== 1.1 Weight 数据综合并排序 ======================='''
|
||||
nw = len(weights)
|
||||
widx = np.array([k for k in range(0, nw)])[:, None]
|
||||
wtype = WeightType * np.ones((nw, 1))
|
||||
wstate = np.zeros((nw, 4))
|
||||
weights = np.concatenate((widx, wtype, weights, wstate), axis=1).astype(np.int64)
|
||||
weights[:, 6] = WeightType
|
||||
|
||||
weights = weights[np.argsort(weights[:, 2]), :]
|
||||
|
||||
'''=================== 1.2 基确Weight的状态切割 ========================='''
|
||||
w_max = np.max(weights[:, 3])
|
||||
# i0=0
|
||||
for i2 in range(0, nw):
|
||||
i1 = max(i2 - WeightWinWidth, 0)
|
||||
wvalue = weights[i1:i2+1, 3]
|
||||
wi2 = weights[i2, 3]
|
||||
wmin = np.min(wvalue)
|
||||
wmax = np.max(wvalue)
|
||||
|
||||
|
||||
'''对重力波动区间进行标记，并标记最新一次重力稳定值的索引和相应重力值'''
|
||||
if wmax - wmin > WeightStableThresh:
|
||||
weights[i2, 4] = w_max
|
||||
|
||||
if i2==0:
|
||||
i0=0
|
||||
wi0 = weights[i0, 3]
|
||||
elif i2>0 and weights[i2-1, 4]==0:
|
||||
i0 = copy.deepcopy(i2)
|
||||
wi0 = weights[i0, 3]
|
||||
|
||||
|
||||
if i2>0 and weights[i2-1, 4]!=0 and weights[i2, 4]==0:
|
||||
# 当前稳定状态下的重力值和前一次重力稳定值的差值，确定放入还是取出
|
||||
if wi2-wi0 > WeightStableThresh:
|
||||
i00 = max(i0 - InputFrontNum, 0)
|
||||
i22 = min(i2 + InputBackNum, nw)
|
||||
elif wi2-wi0 < -1*WeightStableThresh:
|
||||
i00 = max(i0 - OutputFrontNum, 0)
|
||||
i22 = min(i2 + OutputBackNum, nw)
|
||||
else:
|
||||
i00 = max(i0 - max(InputFrontNum, OutputFrontNum), 0)
|
||||
i22 = min(i2 + max(InputBackNum, OutputBackNum), nw)
|
||||
|
||||
weights[i00:i22, 5] = w_max + 100
|
||||
|
||||
|
||||
'''===================== 2.1 CV 数据综合并排序 =========================='''
|
||||
BackType, frstate_0 = periods[0]
|
||||
FrontType, frstate_1 = periods[1]
|
||||
|
||||
|
||||
n0, n1 = len(frstate_0), len(frstate_1)
|
||||
idx0 = np.array([i for i in range(0, n0)], dtype=np.int64)[:, None]
|
||||
idx1 = np.array([i for i in range(0, n1)], dtype=np.int64)[:, None]
|
||||
ctype0 = BackType * np.ones((n0, 1), dtype=np.int64)
|
||||
ctype1 = FrontType * np.ones((n1, 1), dtype=np.int64)
|
||||
tstamp0 = frstate_0[:,1][:, None]
|
||||
tstamp1 = frstate_1[:,1][:, None]
|
||||
state0 = frstate_0[:,2][:, None]
|
||||
state00 = frstate_0[:,3][:, None]
|
||||
|
||||
state1 = frstate_1[:,2][:, None]
|
||||
state11 = frstate_1[:,3][:, None]
|
||||
|
||||
|
||||
|
||||
'''序列索引号， 相机类型，时间戳, 单摄状态1、单摄状态2、CV综合状态、综合数据类型、综合状态
|
||||
0 1 2 3 4 5 6 7
|
||||
'''
|
||||
tstream0 = np.concatenate((idx0, ctype0, tstamp0, state0, state00), axis=1)
|
||||
tstream1 = np.concatenate((idx1, ctype1, tstamp1, state1, state11), axis=1)
|
||||
tstream = np.concatenate((tstream0, tstream1), axis=0)
|
||||
tstream = np.concatenate((tstream, np.zeros((len(tstream), 3), dtype=np.int64)), axis=1)
|
||||
tstream[:, 6] = CameraType
|
||||
tstream = tstream[np.argsort(tstream[:, 2]), :]
|
||||
|
||||
'''=============== 2.2 基于前后摄运动轨迹起止点确定CV综合状态 ============'''
|
||||
for i in range(0, len(tstream)):
|
||||
idx, ctype, stamp, state = tstream[i, :4]
|
||||
if i==0:
|
||||
tstream[i, 5] = state
|
||||
if i>0:
|
||||
j = i-1
|
||||
idx0, ctype0, stamp0, state0 = tstream[j, :4]
|
||||
while stamp-stamp0 < CameraTimeInterval and ctype == ctype0 and j>0:
|
||||
j -= 1
|
||||
idx0, ctype0, stamp0, state0 = tstream[j, :4]
|
||||
|
||||
'''两摄像头状态的或运算. 由于前后摄图像不同时，如何构造或运算，关键在于选择不同摄像头的对齐点
|
||||
i时刻摄像头(ctype)状态state，另一摄像头(ctype0 != ctype)距 i 最近最近时刻 j 的状态state0
|
||||
'''
|
||||
if ctype != ctype0 and state0==1:
|
||||
tstream[i, 5] = state0
|
||||
else:
|
||||
tstream[i, 5] = state
|
||||
|
||||
|
||||
'''================ 3.1 CV、Wweight 数据综合并排序 ======================'''
|
||||
time_stream = np.concatenate((tstream, weights), axis=0, dtype=np.int64)
|
||||
time_stream = time_stream[np.argsort(time_stream[:, 2]), :]
|
||||
tmin = np.min(time_stream[:, 2])
|
||||
time_stream[:, 2] = time_stream[:, 2] - tmin
|
||||
|
||||
'''============== 3.2 基于 CV 和 Weight 确定 Cart 的综合状态 ============'''
|
||||
for i in range(0, len(time_stream)):
|
||||
idx, _, stamp, value, _, state, ctype = time_stream[i, :7]
|
||||
state = min(state, 1)
|
||||
|
||||
if i==0:
|
||||
time_stream[i, 7] = state
|
||||
|
||||
if i>0:
|
||||
j = i-1
|
||||
idx0, _, stamp0, value0, _, state0, ctype0 = time_stream[j, :7]
|
||||
while stamp-stamp0 < CompTimeInterval and ctype == ctype0 and j>0:
|
||||
j -= 1
|
||||
idx0, _, stamp0, value0, _, state0, ctype0 = time_stream[j, :7]
|
||||
|
||||
'''CV与Weight的与运算. 由于CV与Weight不同时，如何构造与运算，关键在于选择不同数据源的对齐点
|
||||
i时数据类型(ctype)状态state，另一数据类型(ctype0 != ctype)距 i 最近最近时刻 j 的状态state0
|
||||
'''
|
||||
if ctype != ctype0 and state !=0 and state0 !=0:
|
||||
time_stream[i, 7] = 1
|
||||
|
||||
MotionSlice, motion_slice = [], []
|
||||
t0 = time_stream[0, 7]
|
||||
for i in range(1, len(time_stream)):
|
||||
f0 = time_stream[i-1, 7]
|
||||
f1 = time_stream[i, 7]
|
||||
if f0==0 and f1==1:
|
||||
t0 = time_stream[i, 2]
|
||||
elif f0==1 and f1==0:
|
||||
t1 = time_stream[i, 2]
|
||||
if t1-t0>100: #ms
|
||||
MotionSlice.append((t0+tmin, t1+tmin))
|
||||
motion_slice.append((t0, t1))
|
||||
else:
|
||||
print(f"T0: {t0}, T1: {t1}")
|
||||
|
||||
|
||||
|
||||
|
||||
'''===================== 4. Hands数据综合并排序 =========================='''
|
||||
BackType, hdstate_0 = hands[0]
|
||||
FrontType, hdstate_1 = hands[1]
|
||||
n0, n1 = len(hdstate_0), len(hdstate_1)
|
||||
idx0 = np.array([i for i in range(0, n0)], dtype=np.int64)[:, None]
|
||||
idx1 = np.array([i for i in range(0, n1)], dtype=np.int64)[:, None]
|
||||
ctype0 = BackType * np.ones((n0, 1), dtype=np.int64)
|
||||
ctype1 = FrontType * np.ones((n1, 1), dtype=np.int64)
|
||||
hstamp0 = hdstate_0[:,1][:, None]
|
||||
hstamp1 = hdstate_1[:,1][:, None]
|
||||
state0 = hdstate_0[:,2][:, None]
|
||||
state1 = hdstate_1[:,2][:, None]
|
||||
|
||||
'''序列索引号， 相机类型，时间戳, 单摄手部状态、手部综合状态、保留位2、综合数据类型、综合状态
|
||||
0 1 2 3 4 5 6 7
|
||||
'''
|
||||
hstream0 = np.concatenate((idx0, ctype0, hstamp0, state0), axis=1)
|
||||
hstream1 = np.concatenate((idx1, ctype1, hstamp1, state1), axis=1)
|
||||
hstream = np.concatenate((hstream0, hstream1), axis=0)
|
||||
hstream = np.concatenate((hstream, np.zeros((len(hstream), 4), dtype=np.int64)), axis=1)
|
||||
hstream[:, 6] = HandType
|
||||
hstream = hstream[np.argsort(hstream[:, 2]), :]
|
||||
|
||||
for i in range(0, len(hstream)):
|
||||
idx, ctype, stamp, state = hstream[i, :4]
|
||||
if i==0:
|
||||
hstream[i, 4] = state
|
||||
if i>0:
|
||||
j = i-1
|
||||
idx0, ctype0, stamp0, state0 = hstream[j, :4]
|
||||
while stamp-stamp0 < CameraTimeInterval and ctype == ctype0 and j>0:
|
||||
j -= 1
|
||||
idx0, ctype0, stamp0, state0 = hstream[j, :4]
|
||||
|
||||
'''两摄像头状态的或运算. 由于前后摄图像不同时，如何构造或运算，关键在于选择不同摄像头的对齐点
|
||||
i时刻摄像头(ctype)状态state，另一摄像头(ctype0 != ctype)距 i 最近最近时刻 j 的状态state0
|
||||
'''
|
||||
if ctype != ctype0 and state0==2:
|
||||
hstream[i, 4] = state0
|
||||
elif ctype != ctype0 and state0==1:
|
||||
hstream[i, 4] = state0
|
||||
else:
|
||||
hstream[i, 4] = state
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
'''========================== 5 结果显示 ================================'''
|
||||
frstate_0[:, 1] = frstate_0[:, 1]-tmin
|
||||
frstate_1[:, 1] = frstate_1[:, 1]-tmin
|
||||
tstream[:, 2] = tstream[:, 2]-tmin
|
||||
|
||||
fig, (ax1, ax2, ax3, ax4, ax5, ax6) = plt.subplots(6, 1)
|
||||
during = np.max(time_stream[:, 2])
|
||||
|
||||
ax1.plot(weights[:, 2]-tmin, weights[:, 3], 'bo-', linewidth=1, markersize=4)
|
||||
ax1.plot(weights[:, 2]-tmin, weights[:, 4], 'mx-', linewidth=1, markersize=4)
|
||||
ax1.plot(weights[:, 2]-tmin, weights[:, 5], 'gx-', linewidth=1, markersize=4)
|
||||
ax1.set_xlim([0, during])
|
||||
ax1.set_title('Weight (gram)')
|
||||
|
||||
ax2.plot(frstate_0[:, 1], frstate_0[:, 3], 'rx-', linewidth=1, markersize=8)
|
||||
ax2.plot(frstate_0[:, 1], frstate_0[:, 2], 'bo-', linewidth=1, markersize=4)
|
||||
ax2.set_xlim([0, during])
|
||||
ax2.set_title('Back Camera')
|
||||
|
||||
ax3.plot(frstate_1[:, 1], frstate_1[:, 3], 'rx-', linewidth=1, markersize=8)
|
||||
ax3.plot(frstate_1[:, 1], frstate_1[:, 2], 'bo-', linewidth=1, markersize=4)
|
||||
ax3.set_xlim([0, during])
|
||||
ax3.set_title('Front Camera')
|
||||
|
||||
ax4.plot(tstream[:, 2], tstream[:, 5], 'bx-', linewidth=1, markersize=4)
|
||||
ax4.set_xlim([0, during])
|
||||
ax4.set_title('CV State')
|
||||
|
||||
ax5.plot(time_stream[:, 2], time_stream[:, 7], 'gx-', linewidth=1, markersize=4)
|
||||
ax5.set_xlim([0, during])
|
||||
ax5.set_title('Cart State')
|
||||
|
||||
ax6.plot(hstream[:, 2]-tmin, hstream[:, 4], 'gx-', linewidth=1, markersize=4)
|
||||
ax6.set_xlim([0, during])
|
||||
ax6.set_title('Hand State')
|
||||
|
||||
|
||||
plt.show()
|
||||
if spath:
|
||||
plt.savefig(spath)
|
||||
|
||||
return tmin, MotionSlice
|
||||
|
||||
def splitevent(imgpath, MotionSlice):
|
||||
suffix = '.png'
|
||||
|
||||
imgfiles = [f for f in os.listdir(imgpath) if f.endswith(suffix)]
|
||||
timestamp = np.array([int(f.split('_')[2]) for f in imgfiles])
|
||||
|
||||
indexes = []
|
||||
k = 0
|
||||
for t0, t1 in MotionSlice:
|
||||
idx0 = set(np.where(timestamp >= t0)[0])
|
||||
idx1 = set(np.where(timestamp <= t1)[0])
|
||||
idx2 = list(idx0.intersection(idx1))
|
||||
files = [imgfiles[i] for i in idx2]
|
||||
|
||||
for filename in files:
|
||||
file, ext = os.path.splitext(filename)
|
||||
newname = file + f'_{k}.png'
|
||||
os.rename(os.path.join(imgpath, filename), os.path.join(imgpath, newname))
|
||||
k += 1
|
||||
|
||||
print("Done!")
|
||||
|
||||
|
||||
|
||||
def runyolo():
|
||||
eventdirs = r"\\192.168.1.28\share\个人文件\wqg\realtime\eventdata"
|
||||
savedir = r"\\192.168.1.28\share\个人文件\wqg\realtime\result"
|
||||
|
||||
k = 0
|
||||
for edir in os.listdir(eventdirs):
|
||||
edir = "1731316835560"
|
||||
source = os.path.join(eventdirs, edir)
|
||||
files = filesort(source)
|
||||
for flist in files:
|
||||
run_yolo(flist, savedir)
|
||||
|
||||
k += 1
|
||||
if k==1:
|
||||
break
|
||||
|
||||
def run_tracking(trackboxes, MotionSlice):
|
||||
pass
|
||||
|
||||
|
||||
def read_wsensor(filepath):
|
||||
WeightDict = OrderedDict()
|
||||
with open(filepath, 'r', encoding='utf-8') as f:
|
||||
lines = f.readlines()
|
||||
clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]
|
||||
for i, line in enumerate(clean_lines):
|
||||
line = line.strip()
|
||||
|
||||
line = line.strip()
|
||||
|
||||
if line.find(':') < 0: continue
|
||||
# if line.find("Weight") >= 0:
|
||||
# label = "Weight"
|
||||
# continue
|
||||
|
||||
|
||||
keyword = line.split(':')[0]
|
||||
value = line.split(':')[1]
|
||||
|
||||
# if label == "Weight":
|
||||
if len(keyword) and len(value):
|
||||
vdata = [float(s) for s in value.split(',') if len(s)]
|
||||
WeightDict[keyword] = vdata[-1]
|
||||
|
||||
|
||||
weights = [(float(t), w) for t, w in WeightDict.items()]
|
||||
weights = np.array(weights).astype(np.int64)
|
||||
|
||||
return weights
|
||||
|
||||
|
||||
def show_seri():
|
||||
datapath = r"\\192.168.1.28\share\个人文件\wqg\realtime\eventdata\1731316835560"
|
||||
savedir = r"\\192.168.1.28\share\个人文件\wqg\realtime\1"
|
||||
|
||||
|
||||
imgdir = datapath.split('\\')[-2] + "_" + datapath.split('\\')[-1]
|
||||
imgpath = os.path.join(savedir, imgdir)
|
||||
|
||||
eventname = Path(datapath).stem
|
||||
|
||||
datafiles = sorted(glob.glob(os.path.join(datapath, '*.npy')))
|
||||
|
||||
periods, trackboxes, hands = [], [], []
|
||||
win_width = 12
|
||||
for npypath in datafiles:
|
||||
CameraType = Path(npypath).stem.split('_')[-1]
|
||||
tkboxes = np.load(npypath)
|
||||
|
||||
trackboxes.append((CameraType, tkboxes))
|
||||
|
||||
period, handState = devide_motion_state(tkboxes, win_width)
|
||||
periods.append((int(CameraType), period))
|
||||
hands.append((int(CameraType), handState))
|
||||
|
||||
|
||||
|
||||
'''===============读取重力信号数据==================='''
|
||||
seneorfile = os.path.join(datapath, 'sensor.txt')
|
||||
weights = read_wsensor(seneorfile)
|
||||
|
||||
# weights = [(float(t), w) for t, w in WeightDict.items()]
|
||||
# weights = np.array(weights)
|
||||
|
||||
|
||||
'''===============重力、图像信息融合==================='''
|
||||
spath = os.path.join(savedir, f"{eventname}.png" )
|
||||
tmin, MotionSlice = state_measure(periods, weights, hands, spath)
|
||||
|
||||
# 第一次运行时用于更改图像文件名
|
||||
# rerename(imgpath)
|
||||
# rename(imgpath, tmin)
|
||||
|
||||
# splitevent(imgpath, MotionSlice)
|
||||
|
||||
|
||||
|
||||
|
||||
def main():
|
||||
# runyolo()
|
||||
show_seri()
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
||||
# imgpaths = r"\\192.168.1.28\share\realtime\result\eventdata_1728978106733"
|
||||
# rerename(imgpaths)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user