kojix2/covid-net
:couple: COVID-Net - An open source project to find COVID-19 from chest x-ray images using deep learning.
:orange: This docker image contains a pre-trained model. There is no need for additional downloads.
docker pull kojix2/covid-net
git clone https://github.com/kojix2/covid-net-docker
cd covid-net-docker
sudo docker build -t kojix2/covid-net .
Mount the current directory as a volume in Docker so that you can access the chest x-ray images placed in the current directory.
sudo docker run -it -v $(pwd):/tmp/share kojix2/covid-net bash
Then, you will see the message below.
________ _______________
___ __/__________________________________ ____/__ /________ __
__ / _ _ \_ __ \_ ___/ __ \_ ___/_ /_ __ /_ __ \_ | /| / /
_ / / __/ / / /(__ )/ /_/ / / _ __/ _ / / /_/ /_ |/ |/ /
/_/ \___//_/ /_//____/ \____//_/ /_/ /_/ \____/____/|__/
WARNING: You are running this container as root, which can cause new files in
mounted volumes to be created as the root user on your host machine.
To avoid this, run the container by specifying your user's userid:
$ docker run -u $(id -u):$(id -g) args...
python inference.py \
--weightspath models/COVIDNet-CXR4-A \
--metaname model.meta \
--ckptname model-18540 \
--imagepath assets/ex-covid.jpeg
Or
python inference.py \
--weightspath models/COVIDNet-CXR4-A \
--metaname model.meta \
--ckptname model-18540 \
--imagepath /tmp/share/your-chest-image.jpg
Result:
assets/ex-covid.jpeg
Prediction: COVID-19
Confidence
Normal: 0.031, Pneumonia: 0.189, COVID-19: 0.780
python inference_severity.py \
--weightspath_geo models/COVIDNet-SEV-GEO \
--weightspath_opc models/COVIDNet-SEV-OPC \
--metaname model.meta \
--ckptname model \
--imagepath assets/ex-covid.jpeg
Or
python inference_severity.py \
--weightspath_geo models/COVIDNet-SEV-GEO \
--weightspath_opc models/COVIDNet-SEV-OPC \
--metaname model.meta \
--ckptname model \
--imagepath /tmp/share/your-chest-image.jpg
Result:
assets/ex-covid.jpeg
Geographic severity: 0.519
Geographic extent score for right + left lung (0 - 8): 4.155
For each lung: 0 = no involvement; 1 = <25%; 2 = 25-50%; 3 = 50-75%; 4 = >75% involvement.
Opacity severity: 0.388
Opacity extent score for right + left lung (0 - 6): 2.329
For each lung: 0 = no opacity; 1 = ground glass opacity; 2 =consolidation; 3 = white-out.
https://github.com/lindawangg/COVID-Net/blob/master/docs/models.md
@sue445, @hareudon, @inductor and ruby-jp slack members.
COVID-NET is not a production-ready solution.
docker pull kojix2/covid-net