🐳 Docker Container for creating Jupyter Notebooks for Computer Vision on ARM64
docker run -v /host/directory:/home/jovyan -it -p 8888:8888 123marvin123/jupyter-computervision
This will create a docker container, running on port 8888. The console will output a corresponding url for visiting the jupyter hub instance, including the access token:
[I 2021-10-16 20:23:14.676 LabApp] JupyterLab extension loaded from /opt/conda/lib/python3.9/site-packages/jupyterlab
[I 2021-10-16 20:23:14.676 LabApp] JupyterLab application directory is /opt/conda/share/jupyter/lab
[I 20:23:14.678 NotebookApp] Serving notebooks from local directory: /home/jovyan
[I 20:23:14.678 NotebookApp] Jupyter Notebook 6.4.4 is running at:
[I 20:23:14.678 NotebookApp] http://5d0088789d01:8888/?token=9c1d58d4bc25694db18405e219e23b86d1857c5e0c66ac5c
[I 20:23:14.678 NotebookApp] or http://127.0.0.1:8888/?token=9c1d58d4bc25694db18405e219e23b86d1857c5e0c66ac5c
[I 20:23:14.678 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
The mandatory system libraries for OpenCV are preinstalled in this image, including the python package for the bindings. After creating a notebook, you can simply
import the cv2 package and use it as usual:
import cv2
cv2.imread('image.png')docker build -t "123marvin123/jupyter-computervision:latest" .