Based on labelImg, we add many useful annotate tools, in Annoatate-tools and Video-tools menu, including:
TOOL LIST
:- Auto Annotate:anto annotate images using yolov7 detector
- Tracking Annotate:using tracking method in opencv, annotate video data
- Magnifing Lens:helpful when annotating small objects, optional function
- Data Agument:data agument
- Search System:search details info based on your input
- other tools:label selecting/rename/counting, fix annotation, video merge/extract, welcome to try
2022-08-29: Updated to support the latest version of the yolov5 model
2022.01.14:remove Retinanet( matain yolov5 only), and add label selecting when autolabeling
2022.01.11:imporve magnifing lens, more fluent and can be shut
2020.12.28:add video tracking annotate
2020.12.10:autolabelimg,version 1.0
-
clone this repo:
git clone https://github.com/ZayneYe/AutoLabelImg_yolov7.git cd AutoLabelImg_yolov7
-
install requirments:
conda create -n {your_env_name} python=3.7.6 conda activate {your_env_name} pip install -r requirements.txt
-
compile source code:
Ubuntu User:
sudo apt-get install pyqt5-dev-tools make qt5py3
Windows User:
pyrcc5 -o libs/resources.py resources.qrc
-
prepare yolov5 weights file and move them to here: [official model zoo:Yolov5]
mv {your_model_weight.pt} yolov7/weights/
-
open labelimg software
python labelImg.py
Windows User:
create a file:labelImg.bat, open it and type these text(D disk as an example):
D:
cd D:{path to your labelImg folder}
start python labelImg.py
exit
double click labelImg.bat to open the software.
Ubuntu User:
open environment setting file:
vim ~/.bashrc
add this command:
alias labelimg='cd {path to your labelImg folder} && python labelImg.py
source it:
source ~/.bashrc
typing 'labeling' in terminal to open the software.
Updated on the basis of AutoLabelImg, the code of the author is very rigorous