Plugin package: Shot Type Classification

This package includes all methods to classify a given shot/or image sequence in one of the categories Extreme Long Shot (ELS), Long Shot (LS), Medium Shot (MS) or Close-Up Shot (CU).

Package Description

PDF format: vhh_stc_pdf

HTML format (only usable if repository is available in local storage): vhh_stc_html

Quick Setup

This package includes a setup.py script and a requirements.txt file which are needed to install this package for custom applications. The following instructions have to be done to used this library in your own application:

Requirements:

• Ubuntu 18.04 LTS
• CUDA 10.1 + cuDNN
• python version 3.6.x

0 Environment Setup (optional)

Create a virtual environment:

• create a folder to a specified path (e.g. /xxx/vhh_stc/)
• python3 -m venv /xxx/vhh_stc/

Activate the environment:

• source /xxx/vhh_stc/bin/activate

1A Install using Pip

The VHH Shot Boundary Detection package is available on PyPI and can be installed via pip.

• Update pip and setuptools (tested using pip==20.2.3 and setuptools==50.3.0)
• pip install vhh-stc

Alternatively, you can also build the package from source.

1B Install by building from Source

Checkout vhh_stc repository to a specified folder:

• git clone https://github.com/dahe-cvl/vhh_stc

Install the stc package and all dependencies:

• Update pip and setuptools (tested using pip==20.2.3 and setuptools==50.3.0)
• Install the wheel package: pip install wheel
• change to the root directory of the repository (includes setup.py)
• python setup.py bdist_wheel
• The aforementioned command should create a /dist directory containing a wheel. Install the package using python -m pip install dist/xxx.whl

NOTE: You can check the success of the installation by using the commend pip list. This command should give you a list with all installed python packages and it should include vhh-stc.

2 Install PyTorch

Install a Version of PyTorch depending on your setup. Consult the PyTorch website for detailed instructions.

3 Setup environment variables (optional)

• source /data/dhelm/python_virtenv/vhh_sbd_env/bin/activate
• export CUDA_VISIBLE_DEVICES=1
• export PYTHONPATH=$PYTHONPATH:/XXX/vhh_stc/:/XXX/vhh_stc/Develop/:/XXX/vhh_stc/Demo/

4 Run demo script (optional)

• change to root directory of the repository
• python Demo/vhh_stc_run_on_single_video.py

Release Generation

• Create and checkout release branch: (e.g. v1.1.0): git checkout -b v1.1.0
• Update version number in setup.py
• Update Sphinx documentation and release version
• Make sure that pip and setuptools are up to date
• Install wheel and twine
• Build Source Archive and Built Distribution using python setup.py sdist bdist_wheel
• Upload package to PyPI using twine upload dist/*