This app is designed to let you use your own custom gesture model to activate voice-activated technology, such as Alexa, Google Home, or Siri, using computer vision. After training your own gesture-detection model, using hand signals of your choice, you can use these signals to play audio files that give commands to smart tech.
You'll need an alwaysAI account and to have alwayAI installed:
This app is intended to work on a model you've trained yourself! Follow the steps below before running your app. The alwaysAI support team is available on Discord to help you if you get stuck: https://discord.gg/alwaysai.
To get you up and running, we've prepared a dataset that includes a few hundred images of an open hand, a hand showing a thumbs up, and a hand showing a peace sign. This app will work best using a model that has been trained on your own hand gestures, so we encourage you to add to this dataset, including images in the same gesture, and adding additional gestures (note that for new gestures, more images will be needed for a balanced dataset; see this doc for data collection tips). For speedy data collection, you can use this image capture app available on the alwaysAI GitHub.
Then you can annotate your data, using this guide.
Then, follow the training section of our quickstart guide to train your own model. You'll find links to tips for data collection and annotation on that page as well.
This step is needed if you want to use your gestures for interacting with 'smart' tech that responds to voice commands instead of gestures. However, you could use your gesture commands to turn on devices that are hooked up to an edge device, like a Raspberry Pi. You could also have it send an alert, by integrating your app with Twilio. You can check out the alwaysAI blog for other tutorials and Discord is a great place to collaborate with users and the alwaysAI team for more ideas.
To make audio files on a Mac:
Open QuickTime Player, select File, then New Audio Recording. Record your message and then save it with a name you choose and without altering the default suffix. Then use ffmpeg to create a .wav file. Ffmpeg is a popular library for altering media files and we recommend users install it for annotation and other projects. Once you have it installed, open your command line interface tool and navigate to the directory with your audio file. Then execute the command ffmpeg -i old_file.m4a new_file.wav, substituting the appropriate name and suffix of your old file and whatever name you choose for your new file (as long as it ends in .wav!). You can delete the old file if you'd like. Make sure the name you choose is specified in the self.actions dictionary in the sign_monitor.py file.
Clone this repo into a local directory. Then cd into new folder and run aai app configure and make the following selections:
- When prompted to choose a project, use the down arrow and select
Create new project, choosing any name you like. - Choose to run either locally or on an edge device.
The app.py and alwaysai.app.json files should be automatically detected and you should not need to create them.
You can find details on working with projects here.
You can either publish your model and add it to your project using aai app models add, or test out an unpublished version using the --local-version flag with this command. See this documentation for full details.
Finally, you'll need to replace the model that is used to create signal_detector in app.py with the name of your own model!
You must make sure each key in the self.actions dictionary is a label in your gesture model, and each value is a desired audio file (you could also put method calls here, if you don't want to go the audio file route). Also make sure to set your initial_audio variable if using audio sounds! The skip value should be reserved for your label that is your start_signal.
Run the project as you would any alwaysAI app! See our docs pages if you need help runnig your program.
Engine: Engine.DNN Accelerator: Accelerator.GPU
Model: alwaysai/hand_detection
Labels: ['???', 'hand']
Engine: Engine.DNN Accelerator: Accelerator.GPU
Model: username/modelname
Labels: ['???', 'peace', 'five', 'thumbs_up']
[INFO] Streamer started at http://localhost:5000 [INFO] Client connected: 3d4cf024f42a49179a13ce56f0a33dd0 recieved start signal, listening for action signal playing initializer length of labels is 6 most common was thumbs_up with count of 6 playing timer.wav stopping listening until recieve start signal recieved start signal, listening for action signal playing initializer length of labels is 21 most common was peace with count of 21 playing weather.wav stopping listening until recieve start signal
Docs: https://dashboard.alwaysai.co/docs/getting_started/introduction.html
Community Discord: https://discord.gg/alwaysai
Email: [email protected] co