Metadata-Version: 2.1
Name: vhh_od
Version: 1.0.1
Summary: Object Detection and Tracking Package
Home-page: https://github.com/dahe-cvl/vhh_od
Author: Daniel Helm
Author-email: daniel.helm@tuwien.ac.at
License: UNKNOWN
Description: # Plugin package: Object Detection and Tracking (ODT)
        
        This package includes all methods to detect and track objects within on image sequence.
        This repo is based and is adapted on the following repositories:
        
            https://github.com/eriklindernoren/PyTorch-YOLOv3
            https://github.com/ZQPei/deep_sort_pytorch
        
        ## Package Description
        
        PDF format: [vhh_od_pdf](https://github.com/dahe-cvl/vhh_od/blob/master/ApiSphinxDocumentation/build/latex/vhhpluginpackageshottypeclassificationvhh_stc.pdf)
            
        HTML format (only usable if repository is available in local storage): [vhh_od_html](https://github.com/dahe-cvl/vhh_od/blob/master/ApiSphinxDocumentation/build/html/index.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_od_env/)
           * python3 -m venv /xxx/vhh_od_env/
        
        **Activate the environment:**
        
           * source /xxx/vhh_od_env/bin/activate
        
        ### 1A Install using Pip
        
        The VHH Object Detection and Tracking package is available on [PyPI](https://pypi.org/project/vhh-stc/) and can be installed via ```pip```.
        
        * Update pip and setuptools (tested using pip\==20.2.3 and setuptools==50.3.0)
        * ```pip install vhh-od```
        
        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_od
        
        **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](https://pytorch.org/get-started/locally/) for detailed instructions.
        
        ### 3 Setup environment variables (optional)
        
           * source /data/dhelm/python_virtenv/vhh_od_env/bin/activate
           * export CUDA_VISIBLE_DEVICES=1
           * export PYTHONPATH=$PYTHONPATH:/XXX/vhh_od/:/XXX/vhh_od/Develop/:/XXX/vhh_od/Demo/
        
        ### 4 Run demo script (optional)
        
            * Make sure to have a video (e.g vid.m4v) stored under /videos and a corresponding shot boundary detection result in /results/sbd/final_results (e.g. vid.csv)
            * make sure that the vhh_od directory is in your Python-Path
            * Settings can be adjusted via config/config_vhh_od_debug.yaml
            * ```cd Demo```
            * ```python run_od_on_single_video.py```
        
        ### 5 Visualization (optional)
        
            * Make sure to have a video (e.g vid.m4v) stored under /videos and a corresponding object detection result in /results/od/final_results (e.g. vid.csv)
            * Make sure that the vhh_od directory is in your Python-Path
            * Settings can be adjusted via config/vis_config.yaml
            * ```cd od```
            * ```python visualize.py vid.m4v```
        
        
        ## 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/*```
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
