Overview

• What is Mantra?
  
• Downloads
  
• OpenSesame plug-in
  
• Documentation
  
• Accuracy
  
• Citation
  
• Demonstration video
  
• Known issues
  
• Using Mantra from a script (no GUI)
  
• Feedback and support
  

What is Mantra?

Mantra allows you to use object/ movement tracking as a method of response collection in your psychological experiments. Mantra is compatible with popular software for creating experiments, such as OpenSesame, E-Prime, PsychoPy and PyEPL. You can use Mantra with general purpose hardware. Essentially, all you need is a webcam and a computer.

For more information, see the documentation. A peer-reviewed publication is available, which describes several experiments and also provides some accuracy benchmarks. A detailed manual provides a more technical description.

Mantra is free software

Downloads

Mantra 0.41

Mantra can be installed from source, using an automated, distribution-independent installer. For installation instructions, see the documentation or 'doc/mantra-doc.odt' (included in the archive).

Example experiments

These experiments demonstrate how Mantra can be used in combination with E-Prime and Python. They can also be used as templates for creating new experiments. The data that we obtained is included.

• E-Prime experiment (the Mueller-Lyer illusion, Exp. 1 from the paper) 
• Python experiment (additional singleton, Exp. 2 from the paper)
  
• OpenSesame experiment (Spatial accuracy test, Exp. 4 from the paper)
  

Examples that require the OpenSesame Mantra plug-in (recommended):

• Affordances / orientation-effect experiment (Mantra variation)
  
• Mantra inline code example
  

Libraries for using Mantra in your experiments

In order to use Mantra from within E-Prime some code must be placed in the User Scripts section of your experiment. In order to use Mantra from within Python a library must be imported. Please refer to the example experiments, which include all necessary code, for a demonstration.

• E-Basic (E-Prime user script, required for use in E-Prime)
• Libmantra.py (Python library required for use with Python) [Documentation]
  

OpenSesame plug-in

The easiest way to use Mantra in OpenSesame is through the plug-in. You can download the plug-in here and install it per these instructions.

Documentation

Full documentation is available as well as a non-technical paper describing Mantra.

• Documentation [PDF] [HTML] [ODT]
  
• Paper in Behavior Research Methods, describing several experiments and benchmark tests
  

Accuracy

The paper describes four experiments. Experiments 1 and 2 show that Mantra is a viable tool in a realistic experimental setting. Experiments 3 and 4 provide a more quantitative test of Mantra's accuracy/ precision. For details, please refer to the paper, but basically it is feasible to get a spatial precision of up to 0.3°, which will correspond to a about 2mm in most set-ups.

Citation

We would, of course, appreciate it if you cite us when you have used Mantra:

Demonstration video

Download as .avi or .ogv

Known issues

• Under recent versions of Ubuntu, a segmentation fault sometimes occurs. Please refer to this forum post.
• After you have started recording, the network protocol (TCP or UDP) can only be changed by restarting Mantra.
• If you have installed Mantra once and for some reason it failed (e.g., because you installed the .deb on Ubuntu 10.10) you may have to remove the "/usr/local/lib/python2.6/dist-packages/mantra" folder to force a complete reinstallation.
• The distribution independent installer does not create an entry in the applications menu. You can manually add an entry or start Mantra by running "qtmantra" (e.g., from a terminal)

Using Mantra from a script (no GUI)

The following code snippet shows how you can track objects using Mantra from within a Python script (circumventing the GUI).

#!/usr/bin/env python
#-*- coding:utf-8 -*-
from mantra import camera # Import Mantra camera modules
res = 320, 240 # Camera resolution
dev = "/dev/video0" # Camera device
col = 135, 62, 69 # RGB color of the tracked object
fuzz = 50 # Color fuzziness (lower is pickier, higher is more liberal)
# Initialize the camera
camera.camera_init(dev, res[0], res[1])
# Track for ten frames
for i in range(10):
	# Capture a frame
	camera.camera_capture()
	# Track the object in the frame
	# Explanation of parameters: camera.track_object([red], [green], [blue],
	# [fuzz], [predicted x], [predicted y], [highlight object in frame])
	camera.track_object(col[0], col[1], col[2], fuzz,\
		camera.cvar.track_x, camera.cvar.track_y, 0)
	# Print the coordinates
	print camera.cvar.track_x, camera.cvar.track_y 
# Neatly close the camera
camera.camera_close()

Feedback and support

I will gladly answer any questions on the forum.