Facelift

Latest version: v0.2.1

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0.2.1

======================================================================================

Bug Fixes
---------

- Fixing the release task and some inconsistencies that were causing the
:func:`~._data.download_data` function to raise a :class:`ValueError`.

0.2.0

======================================================================================

Miscellaneous
-------------

- Due to PyPi's upload limit of ~100MB, we cannot bundle pre-trained models along with the
built package.
We are now building a process for acquiring these models around an included function
that will attempt to fetch the latest released models from GitHub releases.

The method :func:`~facelift._data.download_data` should be a quick initial setup task
when attempting to use this module.
This task will download the necessary data to use the included detectors and encoders.

More details about what is necessary for this release process is and should (in the
future) be documented in the :mod:`facelift._data` module.

0.1.0

======================================================================================

Miscellaneous
-------------

- The initial release doesn't have a super detailed list of introduced features or
bugfixes as this project was pulled together from other side projects I've had in the
past.
Below I'll list the important features that we are starting out with.
Future additions should result in a history of news fragments that get aggregated into
this changelog.

Starting features:

1. Face feature detection with a few bundled models.
* Basic face feature detection (eyes and nose)
* Partial face feature detection (trained model produced by ``dlib``)
* Full face feature detection (third party trained model)
2. Face recognition with a bundled ResNet produced by ``dlib`` to produce face encoding.
* Includes basic Euclidean distance scoring to find similar faces.
3. Wrappers for OpenCV frame capturing.
* Generators for frames from written media files.
* Generators for frames from streaming devices (webcams).
4. Wrappers for OpenCV windows.
* Context managers for named window management.
5. Wrappers for OpenCV common frame transformations
* Scaling, resizing, rotating, cutting, copying, etc...
6. Wrappers for OpenCV canvas drawing features
* Helper functions for drawing points, lines, polygons, text, etc...
7. Example :mod:`~.helpers` module for basic face normalization.
* Gives a basic re-implementation of ``dlib``'s ``get_face_chip()`` method.

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