Vladimir is my co-pilot (project for last year of HighSchool engineering sciences)

A Yolov2 neural network used to pilot rbpi from SL-5 (a squash ball launcher) over a network using sockets.

a very comprehensive drawing to explain the thingy

All of this is done just to run the neural network on another computer because on the rbpi, he burn 🔥 btw the goal is to follow a person with the cam/cannon so we send x and y pos to the rbpi and he turn the motor to always face the person 🔫. (code not out yet; Iam working on it)

so far so good 🍇

So I just made the thing running and I got very good result in terms of delay/latency wich was making the software unusable on the pi, you can see it with this very scientist data graph 🧪: (these times values are ΔT=t2-t1 with t1 = person in the field of view and t2 = x and y data reaching the pi)

I don't have data for [RBPI + MacBookAir with Yolo + Picamera Video Stream] because I don't have the Picamera (csi) at home
As you can see, the all thing is much faster ⚡️ and can be use to follow a person using the motor.

gotta go fast

mid state 🐥

The code uploaded is a middle state one, it does not handle an video stream on the network due to the fact that I dont currently have a PiCamera
* Server.py run on the rbpi
* Vision.py run on the computer that will handle the neural network and image detection
(port 9093, and need the yolov2-tiny weights and cfg files)

License

Quantum-Vladimir is Free Software: You can use, study share and improve it at your will. Specifically you can redistribute and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.