Object Detection and Tracking Package
Project 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
HTML format (only usable if repository is available in local storage): vhh_od_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 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
andsetuptools
(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_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/*```
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