tensorflow 2.13, opencv 4.8.1 python binding along with object detection module research tree installation

Dockerfile

FROM tensorflow/tensorflow:2.13.0

RUN mkdir -p /var/lib/apt/lists/partial && \
    apt-get update
RUN yes yes | apt-get install wget unzip git

RUN useradd -m -s /bin/bash -G sudo od
RUN usermod -aG sudo od
RUN chmod ug+rw /etc/ld.so.cache

RUN mkdir /tf-train
RUN chown od /tf-train

# Set the user as the default user for the container
USER od

WORKDIR /tf-train

# Lines below seem redundant for tf 2.14.0
RUN wget https://github.com/protocolbuffers/protobuf/releases/download/v24.3/protoc-24.3-linux-x86_64.zip 
RUN mkdir protoc && unzip protoc-24.3-linux-x86_64.zip -d ./protoc
ENV PATH="/tf-train/protoc/bin:/home/od/.local/bin:$PATH"
RUN rm *.zip

RUN python3 -m pip install --upgrade pip
RUN pip3 install opencv-python-headless==4.8.1.78
RUN pip3 install pillow==10.0.0
RUN pip install -U -q "tensorflow==2.13.0" "tf-models-official==2.13.0"

#RUN wget https://github.com/tensorflow/models/archive/refs/heads/r2.13.0.zip 
#RUN unzip r2.13.0.zip && mv models-r2.13.0 models && rm *.zip

#added for more consistency going forward. Master changed constantly and research folder might vanish soon
RUN git clone https://github.com/ainosoft/models.git 
WORKDIR /tf-train/models/research/

#COPY protoc-gen.sh .
# This statement below doesn't run within docker due to weird constraints. So python proto files are built manually and committed. 

ENV PATH_TO_TF_ROOT="/tf-train"
ENV PYTHONPATH="$PYTHONPATH:$PATH_TO_TF_ROOT/models:$PATH_TO_TF_ROOT/models/research"

RUN protoc object_detection/protos/*.proto --python_out=.

RUN cp object_detection/packages/tf2/setup.py .
RUN python -m pip install .
RUN python object_detection/builders/model_builder_tf2_test.py
WORKDIR /tf-train