Docker enables SnackWatcher to be packaged into distinct containers by running them atop the Docker Daemon with the configured particulars baked in! The daemon exposes and routes the ports between the containers and utilizes aliases to allow them to reference each other where ever they may reside - on a Raspberry Pi or a distributed cluster of daemons.
Jonah Group’s implementation of SnackWatcher uses a Slack bot named Marvin. Marvin is a Hubot, and he offers a fun way for users to interact with the SW system.
This article aims to provide the basic knowledge of how to recognize snacks by
using Python and SimpleCV. Readers will gain practical programming knowledge via
experimentation with the Python scripts included in
the Snack Classifier open source project.
Starting as a fun Jonah Group project,
the Snack Watcher is designed to watch the company’s “Snack Table”. If there are
some new “Snacks” presented on the “Snack Table”, it can be used to report the
event onto chat channels, emails or messages saying “Snack Happened!”, posting
an image and trying to classify the snacks that it observed. It supports both as
web site for interactive snack viewing and RESTful API for programmatic snack querying.
With Raspberry Pi 3, developing a computer vision project is no longer difficult nor expensive. Computer vision is a method of image processing and recognition that is especially useful when applied to Raspberry Pi. You could produce your IoT with computer vision components, to secure your home, to monitor beer in your fridge, to watch your kids. Once you have an initial setup, the possibilities are endless!
This article summarizes how to setup your Raspberry Pi 3, how to install the useful computer vision libraries from OpenCV and SimpleCV, how to install the machine learning framework Orange. Equipping with this software tool suites, plus
Raspberry Pi 3 has Wifi, Bluetooth and optional OpenGL built-in,
your vision project will be on it’s way to reality.