项目中需要对数据集进行标记
首先安装环境，https://github.com/wkentaro/labelme有教程。
本机是window环境已经装好了anaconda，然后在此基础上创建一个新的labelme环境
命令：
conda create --name=labelme python=3.6(base) C:\Users\Administrator>conda create --name=labelme python=3.6
Solving environment: done
==> WARNING: A newer version of conda exists. <==
current version: 4.5.11
latest version: 4.7.12
Please update conda by running
$ conda update -n base -c defaults conda
## Package Plan ##
environment location: D:\Anaconda3\envs\labelme
added / updated specs:
- python=3.6
The following packages will be downloaded:
package | build
---------------------------|-----------------
pip-19.3.1 | py36_0 1.9 MB
vs2015_runtime-14.16.27012 | hf0eaf9b_0 2.4 MB
wincertstore-0.2 | py36h7fe50ca_0 13 KB
python-3.6.9 | h5500b2f_0 20.4 MB
certifi-2019.9.11 | py36_0 155 KB
sqlite-3.30.1 | he774522_0 962 KB
setuptools-41.6.0 | py36_0 677 KB
wheel-0.33.6 | py36_0 58 KB
------------------------------------------------------------
Total: 26.5 MB
The following NEW packages will be INSTALLED:
certifi: 2019.9.11-py36_0
pip: 19.3.1-py36_0
python: 3.6.9-h5500b2f_0
setuptools: 41.6.0-py36_0
sqlite: 3.30.1-he774522_0
vc: 14.1-h0510ff6_4
vs2015_runtime: 14.16.27012-hf0eaf9b_0
wheel: 0.33.6-py36_0
wincertstore: 0.2-py36h7fe50ca_0
Proceed ([y]/n)? y
Downloading and Extracting Packages
pip-19.3.1 | 1.9 MB | #################################### | 100%
vs2015_runtime-14.16 | 2.4 MB | #################################### | 100%
wincertstore-0.2 | 13 KB | #################################### | 100%
python-3.6.9 | 20.4 MB | #################################### | 100%
certifi-2019.9.11 | 155 KB | #################################### | 100%
sqlite-3.30.1 | 962 KB | #################################### | 100%
setuptools-41.6.0 | 677 KB | #################################### | 100%
wheel-0.33.6 | 58 KB | #################################### | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
# $ conda activate labelme
#
# To deactivate an active environment, use
#
# $ conda deactivate
(base) C:\Users\Administrator>
激活环境：使用
conda activate labelme安装
pip install labelmeSuccessfully built labelme termcolor
Installing collected packages: pyparsing, numpy, six, python-dateutil, kiwisolve
r, cycler, matplotlib, Pillow, PyYAML, qtpy, termcolor, PyQt5-sip, PyQt5, labelm
e
Successfully installed Pillow-6.2.1 PyQt5-5.13.1 PyQt5-sip-12.7.0 PyYAML-5.1.2 c
ycler-0.10.0 kiwisolver-1.1.0 labelme-3.16.7 matplotlib-3.1.1 numpy-1.17.3 pypar
sing-2.4.2 python-dateutil-2.8.0 qtpy-1.9.0 six-1.12.0 termcolor-1.1.0有一个问题：
Successfully built labelme imgviz termcolor
ERROR: imgviz 0.12.0 has requirement Pillow<7,>=5.3.0, but you'll have pillow 7.0.0 which is incompatible.
Installing collected packages: kiwisolver, pyparsing, numpy, six, python-dateutil, cycler, matplotlib, Pillow, imgviz, PyYAML, qtpy, termcolor, PyQt5-sip, PyQt5, labelme
Successfully installed Pillow-7.0.0 PyQt5-5.14.1 PyQt5-sip-12.7.1 PyYAML-5.3 cycler-0.10.0 imgviz-0.12.0 kiwisolver-1.1.0 labelme-4.2.7 matplotlib-3.1.3 numpy-1.18.1 pyparsing-2.4.6 python-dateutil-2.8.1 qtpy-1.9.0 six-1.14.0 termcolor-1.1.0看一下指令
(labelme) C:\Users\Administrator>labelme -h
usage: labelme [-h] [--version] [--reset-config]
[--logger-level {debug,info,warning,fatal,error}]
[--output OUTPUT] [--config CONFIG_FILE] [--nodata]
[--autosave] [--nosortlabels] [--flags FLAGS]
[--labelflags LABEL_FLAGS] [--labels LABELS]
[--validatelabel {exact,instance}] [--keep-prev]
[--epsilon EPSILON]
[filename]
positional arguments:
filename image or label filename
optional arguments:
-h, --help show this help message and exit
--version, -V show version
--reset-config reset qt config
--logger-level {debug,info,warning,fatal,error}
logger level
--output OUTPUT, -O OUTPUT, -o OUTPUT
output file or directory (if it ends with .json it is
recognized as file, else as directory)
--config CONFIG_FILE config file (default:
C:\Users\Administrator\.labelmerc)
--nodata stop storing image data to JSON file
--autosave auto save
--nosortlabels stop sorting labels
--flags FLAGS comma separated list of flags OR file containing flags
--labelflags LABEL_FLAGS
yaml string of label specific flags OR file containing
json string of label specific flags (ex. {person-\d+:
[male, tall], dog-\d+: [black, brown, white], .*:
[occluded]})
--labels LABELS comma separated list of labels OR file containing
labels
--validatelabel {exact,instance}
label validation types
--keep-prev keep annotation of previous frame
--epsilon EPSILON epsilon to find nearest vertex on canvas直接输入labelme运行gui（logo是lena！）
到这一步想起来还需要用matlab提取视频的关键帧。。
obj = VideoReader('F://ds//8//ncf5f8001.avi');%输入视频位置
numFrames = obj.NumberOfFrames;% 帧的总数
for k = 1 : numFrames% 读取前15帧
frame=read(obj,k);%读取第几帧
% imshow(frame);%显示
k=k+30;
imwrite(frame,strcat('F:\ds\8\',num2str(k),'.jpg'),'jpg');% 保存帧
end出现了错误：
搜索一下发现可能是本机不支持avi的播放所导致的，因此下载一个media player codec pack http://www.mediaplayercodecpack.com/，安装到电脑上试试。（基本上一路next就可以了）仍然不行，改用python好了
参考：https://blog.csdn.net/qq_38214193/article/details/80997924
先安装一下opencv，一句话：pip install opencv-python
Requirement already satisfied: numpy>=1.11.3 in d:\anaconda3\envs\labelme\lib\s
ite-packages (from opencv-python) (1.17.3)
Installing collected packages: opencv-python
Successfully installed opencv-python-4.1.1.26
说明已经安装好了
import cv2
# 使用opencv按一定间隔截取视频帧，并保存为图片
vc = cv2.VideoCapture('F:/ds/8/ncf5f8001.avi') # 读取视频文件
c = 1
if vc.isOpened():# 判断是否正常打开
rval,frame = vc.read()
else:
rval = False
timeF = 15 #视频帧计数间隔频率
while rval: #循环读取视频帧
rval,frame = vc.read()
if (c%timeF == 0): #每隔timeF帧进行存储操作
cv2.imwrite('F:/ds/8'+str(c)+'.jpg',frame) # 存储为图像
c = c + 1
cv2.waitKey(1)
vc.release()进入当前目录 cd /f f:\ds\8\ 运行python frame.py提取关键帧如下：
把它变成一个工具
第一个参数为视频所在目录，第二个为视频文件名，第三个为提取的帧数。
python frame.py F:\ds\10\ ncf5f10001.avi 10
import sys
import cv2
dir = sys.argv[1]
file = sys.argv[2]
time = sys.argv[3]
vc = cv2.VideoCapture(dir+file)
c = 1
if vc.isOpened():
rval,frame = vc.read()
else:
rval = False
timeF = int(time)
while rval:
rval,frame = vc.read()
if (c%timeF == 0):
cv2.imwrite(dir+str(c)+'.jpg',frame)
c = c + 1
cv2.waitKey(1)
vc.release()继续打开labelme进行提取。
open打开一张图片，点击Create Polygons开始标定。鼠标定点，框住生成区域打标签就可以。
就这样搞定一张图
然后保存为json，注意json名应当与图片名一致。
