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

Getting started in Computer Vision with the Raspberry Pi

By Francois Dion

Getting started in Computer Vision with the Raspberry Pi

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