Installation Birdwatcher package

Birdwatcher officially supports Python 3.13 or higher, but older Python 3 versions may also work.

User installation

1. We recommend using Anaconda for installation. Install Anaconda from https://www.anaconda.com/ .

2. Open Anaconda prompt in terminal.

3. Create new environment for Birdwatcher (name is up to you, in example here ‘mybirdwatcher’). We install Jupyter lab and ffmpeg at the same time:

   $ conda create -n mybirdwatcher python=3.12 jupyterlab ffmpeg=4.2.2 git

4. Switch to this new environment:

   $ conda activate mybirdwatcher

5. Install Birdwatcher:

   Stable latest official release from PyPi:

   $ pip install Birdwatcher

   If instead you want the latest version of the git master branch from, use:

   $ pip install git+https://github.com/gbeckers/birdwatcher@master

Dependencies

The following dependencies are automatically taken care of when you install Birdwatcher using the pip method above:

• numpy

• pandas

• matplotlib

• seaborn

• darr

• opencv-python

• opencv-contrib-python

It further depends on:

• ffmpeg (including ffprobe)

If you do not use the conda way above to install ffmpeg, you need to install it yourself (https://www.ffmpeg.org/).

Run notebooks tutorial

To quickly learn the fundamentals of Birdwatcher, please walk through our notebooks. First, you need to download the notebooks and example videos from github. Then, navigate to the directory of the notebooks and activate the ‘mybirdwatcher’ environment. Type jupyter lab which opens in your browser. You can now open the notebooks and run the tutorial.

Test

To run the test suite:

>>>import birdwatcher as bw
>>>bw.test()
..................................................
----------------------------------------------------------------------
Ran 50 tests in 75.858s

OK

<unittest.runner.TextTestResult run=50 errors=0 failures=0>

Documentation

https://birdwatcher.readthedocs.io

Examples

See jupyter notebook directory.

Contributions

Sita ter Haar and Dylan Minekus helped exploring the application of movement detection algorithms.