Quest Coffee Machine

Requirements:

• Raspberry Pi 3 Model B
• One sound sensor connected to GPIO pins
• One coffee machine

How to run:

Assuming you have clean RPi system image installed

Clone this repo first:

git clone https://github.com/wkpn/quest-coffee-machine

First thing you need to do is to place your training images inside ./train_data folder. Each image should be in label.jpg format. One image per person

train_data
├── Label1.jpg
├── Label2.jpg
├── Label3.jpg
├── ...

Next you need to run some scripts

$ cd ./libs
$ ./download_libs.sh

This script will download all the necessary libraries for the whole thing to function. When it's done you need to run script which is going to download face recognition model along with shape predictor model

$ cd ./models
$ ./download_models.sh

At the very first run you can execute setup.py script. This script will go through all your images in ./train_data folder and will compute vectors for them. It will save all data about labels in ./labels folder. If you do this, you need to provide unpickle=True at main.py#L14. It means that the recognizer will load data about labels from setup.py results. You can delete your ./train_data folder after running this script

It is not necessary to run setup.py. If you won't run it before running main.py you need to provide unpickle=False at main.py#L14. By doing so the recognizer class will load all training images from ./train_data folder and will compute vectors for them. Labels will be saved to ./labels folder. You can delete these images now as you don't need them anymore. Later you need to provide unpickle=True at main.py#L14. All labels data will be loaded from ./saved_data folder

Don't forget to change ANONYMOUS_UPN and REST_API_URL in settings.py according to your needs!

Recognition logic:

Labels data is stored in dict format. This is how it is going to look like after first run (or running setup.py):

{
  'Label1': [Label1Vector1],
  'Label2': [Label2Vector1],
  ...
}

Model is learning after each successful face recognition. For example, if we successfully recognized a person we are going to add his vector to labels dictionary. After some time it will be looking like that:

{
  'Label1': [Label1Vector1, Label1Vector2, Label1Vector3],
  'Label2': [Label2Vector1], Label2Vector2 ...]
  ...
}

It means that model is learning and each time recognition will be more accurate. We save our labels after each successful face recognition for two reasons:

• We can stop the whole thing and run it next time with new data
• Backup

That's it. You're ready to go!