The Menpo-powered landmarker.io server

About

landmarkerio server is an implementation of the landmarker.io server API in Python. It uses Menpo to load images and meshes and serves them to landmarker.io web clients for annotation. When the annotator is done, it is landmarkerio server that will actually persist the landmarks to disk.

The Python package for landmarkerio server is unambigously landmarkerio.

Purpose

landmarker.io knowns how to talk to landmarkerio server instances over a RESTful API. The server holds all assets and landmarks, and annotators can be constrained in what they can and can't do. For instance - you can limit the choice of templates that users are able to use, or the assets that they see. Upon saving, landmarks are saved back to the server instance, so at all times you remain in control of the annotation experiement.

Serving assets locally

landmarkerio server can be used for annotation jobs on a local machine, but it is not recommend. Consider using Landmarker.app instead.

If you do want to do local annoations with landmarkerio-server, just run the server (called lmio) from the command line. Your browser will automatically open to insecure.landmarker.io and you can start landmarking. See #17 for an indepth discussion on why this is on the 'insecure' subdomain.

You can get as clever as you want to enable remote serving of landmarks - SSH port tunnelling is an easy secure solution.

Installation

landmarkerio server requires Menpo Menpo3d to run. As these have somewhat complex dependencies, by far the easiest way to install landmarkerio, is with conda. On OS X, Linux or Windows, just install conda and then

>> conda install -c menpo landmarkerio

Important concepts

landmarkerio server handles three different forms of data

• assets - meshes, textures and images
• landmarks - annotations on assets
• templates - specifications of landmarks

Usage

The landmarker.io server can be started in one of two modes: 'image' and 'mesh'. To begin annotating a folder of meshes, just run

>> lmio mesh ./path_to_meshes

To begin annotating a folder of images, run

>> lmio image ./path_to_images

You get help on the tool just as you would expect

>> lmio --help

Templates

Syntax

Templates restrict the set of allowed annotations and give the annotations semantic meaning. The user of the server has full control over what annotations the user of landmarker.io should complete by declaring templates. A template file is simple a .yml file. The filename is the name of the template.

An example template is provided below, a more detailed specification is also available on the landmarker.io wiki.

face.yaml

# groups key marks the template itself, anything else is metadata
groups:
  # The first landmark group is called 'mouth'
  # it is made up of six points
  - label: mouth
    points: 6
  - label: nose
    points: 3
    # Pairs of numbers immediately following a declaration
    # of a group specify connectivity information. Here,
    # The first entry of the nose group is joined to the second
    # (0-based indexing) and the second to the third. This will
    # be visualized in the landmarker.
    connectivity:
      - 0 1
      - 1 2
  - label: left_eye
    points: 8
    connectivity:
      # Slice notation is abused to construct straight chains
      # of connectivity. This is expanded out into
      # 0 1
      # 1 2
      # ...
      # 6 7
      - 0:7
      - 7 0
  - label: right_eye
    points: 8
    # The cycle shortcut
    connectivity: cycle
  - label: chin
    points: 1

Storing templates

A collection of template files can be placed in a templates folder. A path to a folder can be provided as the -t argument to landmarkerio. If no argument is provided, lmio looks for the folder ~/.lmiotempates. This provides a convenient place to store frequently used templates.