This lesson is being piloted (Beta version)

Image Processing with Python

This lesson shows how to use Python and skimage to do basic image processing.

Prerequisites

This lesson assumes you have a working knowledge of Python and some previous exposure to the Bash shell. These requirements can be fulfilled by: a) completing a Software Carpentry Python workshop or b) completing a Data Carpentry Ecology workshop (with Python) and a Data Carpentry Genomics workshop or c) independent exposure to both Python and the Bash shell.

If you’re unsure whether you have enough experience to participate in this workshop, please read over this detailed list, which gives all of the functions, operators, and other concepts you will need to be familiar with.

Before following the lesson, please make sure you have the software and data required.

Schedule

Setup Download files required for the lesson
00:00 1. Introduction What sort of scientific questions can we answer with image processing / computer vision?
What are morphometric problems?
00:05 2. Image Basics How are images represented in digital format?
00:30 3. Working with skimage How can the skimage Python computer vision library be used to work with images?
02:30 4. Drawing and Bitwise Operations How can we draw on skimage images and use bitwise operations and masks to select certain parts of an image?
04:00 5. Creating Histograms How can we create grayscale and colour histograms to understand the distribution of colour values in an image?
05:20 6. Blurring Images How can we apply a low-pass blurring filter to an image?
06:20 7. Thresholding How can we use thresholding to produce a binary image?
08:10 8. Connected Component Analysis How to extract separate objects from an image and describe these objects quantitatively.
10:15 9. Capstone Challenge How can we automatically count bacterial colonies with image analysis?
11:05 Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.