Sunday, July 5, 2009

digipy 0.1.1 - Hand Digit Real Time Demo is available

At Montreal-Python6, I have presented a real-time hand digit real-time demo.
This demo allows you to do real-time digit recognition from your digital camera. It allows you to load any trained neural network and apply in real time the same features extraction. This demo allows you to train, extract features, used trained neural networks inside real-time demo, visualize features in 2D and their frequency distribution and get feature discriminant weight.

The packaging 0.1.1 of the demo is now available on pypi:
(unfortunately, some dependency packages aren't supported by easy_install so you have to do 4 steps instead of 1)
  • install opencv (sudo aptitude install python2.5-opencv)
  • install PyQt (sudo aptitude install pyqt4-dev-tools)*
  • instal matplotlib (sudo aptitude install python2.5-matplotlib)*
  • sudo easy_install digipy

* unfortunatly, this package isn't supported with easy_install

Here is the noise robustness comparison of the trained neural network on the raw pixels vs extracted features (digit surface + image convolution with train digits means (0-9)):

If you aren't convinced that Feature Extraction is absolutely required now, I have failed.

Once installed, you will get access to those command line tools:

  1. digipy: Real-time hand digit recognition demo application (ex: digipy --test)
  2. digipy-features2D : demo of feature 2D visualization to see possible clusters
  3. digipy-train: demo training of a Neural Network using mlboost
  4. digipy-compare: compare noise effect on test error on raw inputs and feature extracted datasets
  5. digipy-freq-analysis: demo feature analysis (frequency distributions)
  6. digipy-extract-features: demo features extraction
  7. digipy-see-data: show dataset train and test samples



If you have any trouble using it, just let me know (fpieraut at gmail).
Source code is available here http://bitbucket.org/fraka6/digipy.

(note: now digipy use mlboost.nn module for its NeuralNetwork instead of mlboost.flayers swig wrapper)
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