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* colors expression by KMean algorithm * object detection by imageai * object detection by cvlib * add encapsulation of object detection * remove encapsulation of objdetect v0 * objects expression to dict * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * added imageai to requirements * add objects to dictionary * update for AnalysisMethod baseline * add objects dection support explore_analysis display * extend python version of misinf to allow imageai * account for older python * use global functionality for dict to csv convert * update for docker build * docker will build now but ipywidgets still not working * test code * include test data folder in repo * add some sample images * load cvs labels to dict * add test data * retrigger checks * add map to human coding * get orders from dict, missing dep * add module to test accuracy * retrigger checks * retrigger checks * now removing imageai * removed imageai * move labelmanager to analyse * multiple faces in mydict * fix pre-commit issues * map mydict * hide imageai * objects default using cvlib, isolate and disable imageai * correct python version * refactor faces tests * refactor objects tests * sonarcloud issues * refactor utils tests * address code smells * update readme * update notebook without imageai Co-authored-by: Ma Xianghe <825074348@qq.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: iulusoy <inga.ulusoy@uni-heidelberg.de>
33 строки
1022 B
Docker
33 строки
1022 B
Docker
FROM jupyter/base-notebook
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# Install system dependencies for computer vision packages
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USER root
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RUN apt update && apt install -y build-essential libgl1 libglib2.0-0 libsm6 libxrender1 libxext6
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USER $NB_USER
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# Copy the repository into the container
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COPY --chown=${NB_UID} . /opt/misinformation
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# Install the Python package
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RUN python -m pip install /opt/misinformation
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# Install additional dependencies for running the notebooks
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RUN python -m pip install -r /opt/misinformation/requirements.txt
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# Make JupyterLab the default for this application
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ENV JUPYTER_ENABLE_LAB=yes
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# Export where the data is located
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ENV XDG_DATA_HOME=/opt/misinformation/data
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# Copy notebooks into the home directory
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RUN rm -rf $HOME/work
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RUN cp /opt/misinformation/notebooks/*.ipynb $HOME
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ARG GOOGLE_CREDS
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ENV GOOGLE_APPLICATION_CREDENTIALS=credentials.json
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RUN echo ${GOOGLE_CREDS} > $GOOGLE_APPLICATION_CREDENTIALS
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# Bundle the pre-built models (that are downloaded on demand) into the
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# Docker image.
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RUN misinformation_prefetch_models
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