This repository contains materials for a beginner’s python image processing course (delivery dates/information about self-taught options below). There is a slight emphasis on bioimage processing; however, those interested in general image processing should also find it useful.
The overall aim of this course it to:
- introduce the handling of multidimensional image data in Python within the comfort of the Jupyter framework
- showcase some of the image processing functionality available in Python by demonstrating (and providing take-home resources) numpy, scipy, scikit-image, matplotlib and napari code
This course does not aim to teach image processing concepts - it is a python course. Participants are pointed to useful resources throughout the course but it is assumed that participants already have a basic understanding of the concepts being used.
- Understanding of image processing. As I said above, this is a python course and not an image processing course.
- Python introductory course: https://github.com/ChasNelson1990/python-zero-to-hero-beginners-course.
- If you've not done much Python in the past, you should work your way through the pre-requisite course (approx. 12-15 hr); however, those comfortable in Python may choose to skips components they are confident about.
Our general philosophy for this course is
- teach in small chunks starting by introducing python concepts, demonstrating an example, working through a simple case and then setting an exercise. Each exercise is then gone through as a group.
- teach through errors, error messages and documentation - so that trainees can debug their own codes after they leave the course
- create a safe environment for asking any and all questions.
If you wish to quickly explore the course, you could use Binder (by clicking the button above). However, this won’t save your progress as you go along so I suggest installing locally as described below.
I have designed this course in such a way that it should be easy to follow and work through on your own. Each notebook stands alone and should provide you with all the information needed to complete the tasks (blue boxes) and exercises (yellow boxes).
In order to aid working through the notebooks I have provided short videos for all tasks and exercises. These videos provide complete answers for every task and should be viewed after attempting each task or exercise.
In order to work through the notebooks please follow the instructions in the prerequisite course (specifically, setup.pdf) for installing Python and Jupyter Lab on your computer, dowload this repository as a .zip file (using the green button at the top of the landing page), unzip the files and navigate to them from within Jupyter Lab.
I suggest you work through each notebook in turn, attempting at least the tasks on your first run-through. You can then use the exercises to revisit and revise topics when you go through the notebooks again in the future. As with all languages, practice makes perfect.
I have run a version of these materials in person once as part of the IAFIG-RMS Bioimage analysis with Python course (2019-12-09).