Training your Watcher

Kenzy has built-in capacity for face detection. This is done using the haarcascade classifiers in conjunction with openCV.

All three haarcascade classifiers are included in the installation package and available under "kenzy_watcher/data/models/watcher". The haarcascade_frontal_default.xml is used if no classifier is explicitly specified.

To train your model the train() method needs to be called. The most simple method is:

python3 -m kenzy --training-source-folder /path/to/faces-directory

You can force the model to be retrained by adding the --force-train option.

Your faces directory should be configured as follows:

/faces-directory
   - /Jane
       - /image1.jpg
       - /image2.jpg
       - /image3.jpg
   - /John
       - /image1.jpg
       - /image2.jpg
       - /image3.jpg

This will create a recognizer.yml file and a names.json file. These files are both used to determine who Kenzy sees when capturing video. If you already have a recognizer and names file built you can specify them with the recognizerFile and namesFile parameters when creating a new Watcher device. View the file ~/.kenzy/config.json to configure specific runtime options.

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Help & Support

Help and additional details is available at https://kenzy.ai