Getting started in Computer Vision with Raspberry Pi

Francois Dion

Chief Data Scientist

Dion Research LLC

PyData Triangle

About me

Francois Dion

Chief Data Scientist

Dion Research LLC

fdion@dionresearch.com

linkedIn                @f_dion

github

Perceptron

In 1958, Frank Rosenblatt built the Perceptron Mark 1, a machine designed for image recognition. The machine simulated simplified biological neurons. It used an array of 400 photocells, to simulate the retina.

Modern Photocell

Raspberry Pi

Fundamentally, we still need a “machine” to do the work of the neurons, and a camera to do the work of the eye and retina.

 

Meet the Raspberry Pi family

Getting started

Python Modules

  • SimpleCV
  • OpenCV
  • PyTorch + TorchVision
  • Tensorflow 2 with Keras
  • MXNet

Limited Power

  • no conda, use virtualenv
  • pip from piwheels.org/simple
  • develop on desktop with GPU
  • (retrain) deploy on Raspberry Pi
  • consider acceleration:
    • Tensorflow Lite
    • TPU

Books

  • 2011, Computer Vision by Richard Szelisky, Springer
  • 2012, Practical Computer Vision with SimpleCV, O’Reilly
  • 2012, Mastering OpenCV with Practical Computer Vision Projects, PacktPub
  • For PyTorch or Tensorflow, start with the tutorials
  • Get a cookbook and a deep learning book covering the package you prefer

Thank you!

fdion@dionresearch.com

 

https://slides.com/fdion

Right now, available for short to long term engagements, in advisory capacity or full time