tassAI is computer vision software that is able to communicate autonomously with applications and devices via the Internet of Things. Our commercial version of tassAI powers a number of our Internet Of Things projects such as retailAI, but an open source version is also available for developers to use with the IoT JumpWay Developer Program.
This repository provides the history of the open source TASS projects, and provides links directly to the relevant projects in our Github repos. Each tutorial provides full instructions and source codes.
CLICK ON THE IMAGES TO BE TAKEN TO THE RELEVANT TUTORIAL
This was the first version of TASS to be open sourced and became quite popular on Hackster.io. The tutorial helps you to build a Raspberry Pi that allows you to train a Haarcascades model, detect recognized/unknown people, optionally monitor the camera in near real-time via a stream, and communicate with the IoT JumpWay sending sensor and warning messages that will allow your device to autonomously communicate with other IoT devices on your IoT JumpWay network.
This was the second version of TASS to be open sourced. The tutorial uses the IoT JumpWay Python MQTT Library for communication, an Intel® NUC DE3815TYKE or any Linux Desktop running Ubuntu, 1 or more IP Cameras, an optional Realsense camera, and our own deep learning neural network based on the popular OpenFace facial recognition toolkit.
This is a Python wrapper based around the third version of TASS and Tensorflow. InceptionFlow is an object & facial recognition Python wrapper for the Tensorflow Imagenet example and integrates IoT connectivity using the TechBubble IoT JumpWay Python MQTT client.
This was the third version of TASS to be open sourced. The tutorial helps you build a Computer Vision security system on Windows using Intel® Computer Vision SDK, an Intel® Realsense camera/web/ip cam and an Intel® Edison connected to the Internet of Things via TechBubble Technologies IoT JumpWay.
This was the fourth version of TASS to be open sourced. The Colfax TASS Trainer replicates the transfer learning side of the original program and is trained on the Intel AI DevCloud HPC Cluster (Colfax Cluster).