Our project 4 implements hand gesture recognition using energy images and SVM classification.

There were several prototypes leading to final implementation, but the final code can be found in trainer.py.

There are several important functions that implement the bulk of the interactive recognition program:

_create_energy_images: Allows user to view energy images in real-time and to save them on demand by pression a number key from 1 to 5.

_svm_train: Reads energy images from a directory and uses them to train the SVM classifier. Also does post training testing and outputs accuracy results.

_recognize: Allows user to view real-time camera input, do hand gestures, and see the classifier output on the screen.

No unit tests were created/required for this program as it is interactive with self contained functions.

To run the program in training mode:

python trainer.py svm_output energy_path

where:
  svm_output = filename of SVM classifier output file
  energy_path = path to energy images

To run the program in recognition mode:

python trainer.py svm_input

where:
  svm_input = filename of SVM classifier input file  

NOTE:

Deprecated code from previous iterations is included in the repository. These include preprocessor.py, recognizer.py, tracking_hand.py, Tracking_hand_bgsubtraction.py, and vidutils.py.