Menpo Documentation Menpo 0.7.7 documentation

Gaussian Markov Random Field. 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. To keep memory usage low. This section attempts to.

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The Menpo Project The Menpo Project

The Python framework for deformable modelling. A playground is a standalone installation of menpo that will get you up and running as quickly as possible. If you are new to menpo or Python in general you may want to start with the playground. Head to our installation page. Conda install -c menpo menpoproject.

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Menpo Documentation Menpo 0.7.7 documentation

DESCRIPTION

Gaussian Markov Random Field. 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. To keep memory usage low. This section attempts to.

CONTENT

This web site had the following in the homepage, "Menpo is a Python package designed to make manipulating annotated data more simple." Our analyzers noticed that the web page stated " In particular, sparse locations on either images or meshes, referred to as landmarks." The Website also said " 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. To keep memory usage low."

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