Menpo Documentation

Menpo is a Python package designed to make manipulating annotated data more simple. In particular, sparse locations on either images or meshes, referred to as landmarks within Menpo, are tightly coupled with their reference objects. For areas such as Computer Vision that involve learning models based on prior knowledge of object location (such as object detection and landmark localisation), Menpo is a very powerful toolkit.

A short example is often more illustrative than a verbose explanation. Let’s assume that you want to load a set of images that have been annotated with bounding boxes, and that these bounding box locations live in text files next to the images. Here’s how we would load the images and extract the areas within the bounding boxes using Menpo:

import as mio

images = []
for image in mio.import_images('./images_folder'):

Where import_images returns a LazyList to keep memory usage low.

Although the above is a very simple example, we believe that being able to easily manipulate and couple landmarks with images and meshes, is an important problem for building powerful models in areas such as facial point localisation.

Please refer to our detailed installation instructions in

User Guide
To get started, check out the user guide in for an explanation of some of the core concepts within Menpo.

Finally, please refer to Menpo’s Changelog for a list of changes per release.