Computer Vision

Common Applications

Computer vision is used everywhere and in every industry; think any company that uses humans to visually analyze anything, whether that be traffic, crops, livestock, healthcare diagnostics, security, and much more. Take a look at a few of the ways computer vision is used today.

Code Examples

All of the code examples are written in Python, unless otherwise noted.

Containers

These are code examples in the form of Jupyter notebooks running in a container that come with all the data, libraries, and code you’ll need to run it. Click here to learn why you should be using containers, along with how to do so.
Quickstart: Download Docker, then run the commands below in a terminal.

Computer Vision Image Toolkit

An introductory notebook showing how to implement many different types of image processing.

#pull container, only needs to be run once
docker pull ghcr.io/thedatamine/starter-guides:cv-image-toolkit

#run container
docker run -p 8888:8888 -it ghcr.io/thedatamine/starter-guides:cv-image-toolkit

Neural Nets Intro: Handwritten Digit Image Classification

The neural networks introductory example uses computer vision to classify the handwritten digits.

#pull container, only needs to be run once
docker pull ghcr.io/thedatamine/starter-guides:neural-nets-intro

#run container
docker run -p 8888:8888 -it ghcr.io/thedatamine/starter-guides:neural-nets-intro

Convolutional Neural Networks, Anomaly Identification, and Reading TFRecords

Here we explore using Convolutional Neural Networks (CNN’s) to classify images of concrete by their anomaly status (that is, whether they have a crack or not). Our data is supplied in the form of TFRecords, so we look at how to read those in for training and testing.

#pull container, only needs to be run once
docker pull ghcr.io/thedatamine/starter-guides:cnn-anomaly-reading-tfrecords

#run container
docker run -p 8888:8888 -it ghcr.io/thedatamine/starter-guides:cnn-anomaly-reading-tfrecords

Need help implementing any of this code? Feel free to reach out to datamine-help@purdue.edu and we can help!

Resources

All resources are chosen by Data Mine staff to be of decent quality, and most if not all content is free.