The course teaches how to build a distributed computer vision pipeline using Python, OpenCV, and ZMQ. The learning outcomes include understanding the hub and spoke design for processing distribution, pros and cons of PyZMQ messaging for image transfer, and OpenCV techniques for image processing. The course covers tools such as OpenCV, ZMQ, and Python bindings for fast processing. The teaching method involves a talk describing the speaker's experience in developing an IOT network with multiple cameras and sensors. The intended audience includes individuals interested in computer vision, Python programming, and building IOT networks.
Yin Yang Ranch - Building a Distributed Computer Vision Pipeline Using Python
Overview
Syllabus
Intro
Yin Yang Ranch: A Distributed Computer Vision System
Building a Small Permaculture Farm in Suburbia
Jeff Bass Bio Bullets (College, Career, Retirement)
How Computer Vision helps manage my small permaculture farm
What is a Distributed Computer Vision Pipeline?
My Tools for Computer Vision in Python
Image Computing Toolset: OpenCV
imagenode image ZMQ imagehub Pseudocode for imagenode
DCVP Example: Reading my Water Meter
Imagenode Code Snippet (Runs on a RPI)
There are many imagenode CV settings... Parameter Tuning to optimize detection
For each image captured, imagenode: • Applies Transformations
Image Transport: ZMQ
Imagehub Code Snippet (Runs on a Mac)
DCVP end to end example: the Barn Coyote Cam
Communicating to End Users (me :-)
Yin Yang Ranch DCVP: Lessons Learned So Far
Hardware: Example Light Types
Hardware Enclosures: Use Existing Light Fixtures!
Learnings: Some Hardware Tips and Tricks
Yin Yang Ranch DCVP projects are Open Source & on GitHub
Questions about this talk? Get Answers!
Taught by
PyCon US
