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 menpo.io as mio images =  for image in mio.import_images('./images_folder'): images.append(image.crop_to_landmarks())
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.Installation
Please refer to our detailed installation instructions in menpo.org.
To get started, check out the user guide in
menpo.orgfor an explanation of some of the core concepts within Menpo.
Finally, please refer to Menpo’s Changelog for a list of changes per release.
This section attempts to provide a simple browsing experience for the Menpo documentation. In Menpo, we use legible docstrings, and therefore, all documentation should be easily accessible in any sensible IDE (or IPython) via tab completion. However, this section should make most of the core classes available for viewing online.