kojix2/covid-net

By kojix2

Updated over 4 years ago

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covid-net-docker

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: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 or build

pull
docker pull kojix2/covid-net
Or build it yourself...
git clone https://github.com/kojix2/covid-net-docker
cd covid-net-docker
sudo docker build -t kojix2/covid-net .

Docker run

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...

Inference

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

Inference Severity

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.

Download pre trained models

https://github.com/lindawangg/COVID-Net/blob/master/docs/models.md

Want to make covid-net-docker better?

  • Report bugs
  • Fix bugs and submit pull requests

Contributors

@sue445, @hareudon, @inductor and ruby-jp slack members.

Disclaimer

COVID-NET is not a production-ready solution.

Docker Pull Command

docker pull kojix2/covid-net