EVA is a web-based tool for efficient annotation of videos and image sequences. It is a re-design of BeaverDam with additional tracking capabilities. The annotation is done on a bounding box level and the labels can be exported in YOLO or Pascal VOC format.
Requirements:
Install:
git clone https://github.com/Ericsson/eva.git
cd eva
python -m pip install --upgrade pip
pip install virtualenv
virtualenv venv
venv\Scripts\activate.bat
pip install -r requirements.txt
python manage.py preparetracker
python manage.py migrate
python manage.py collectstatic
Extract ffmpeg archive and copy ffmpeg.exe
, from the bin folder, to the root
of the tool\
folder e.g. if you clone the repository as eva
place the ffmpeg.exe inside
eva\
.
Start the app by running the start.bat
file.
In Chrome or Firefox go to http://127.0.0.1:8000/.
Requirements:
Install:
git clone https://github.com/Ericsson/eva.git
cd eva
pip3 install virtualenv
python3 -m virtualenv venv
. venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
python manage.py preparetracker
python manage.py migrate
python manage.py collectstatic
Start the app by running the start.sh
file.
In Chrome or Firefox go to http://127.0.0.1:8000/.
First run the following commands to initialize the tool. These only have to be run once, but if the tool is updated they should be repeated.
docker-compose build
docker-compose run eva python3 manage.py migrate
docker-compose run eva python3 manage.py collectstatic
Start the app by running:
docker-compose up
In Chrome or Firefox open the http link: http://127.0.0.1:8000/
You can change the parameters of the tracker by editing cfg/KCF_config.yml
.
Thanks to Ludwig Thaung for his contribution in building the EVA tool.