Data Carpentry Workshop: Image Processing with Python

University of Strasbourg

January 11-12, 2024

9:00 am - 5:00 pm CET

Instructors: Carlos Brandt, Giordano Lipari

Helpers: Sebastien Geiger, Greg Henning, Manon Marchand

General Information

Data Carpentry develops and teaches workshops on the fundamental data skills needed to conduct research. Its target audience is researchers who have little to no prior computational experience, and its lessons are domain specific, building on learners' existing knowledge to enable them to quickly apply skills learned to their own research. Participants will be encouraged to help one another and to apply what they have learned to their own research problems.

For more information on what we teach and why, please see our paper "Good Enough Practices for Scientific Computing".

Who: 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.

Where: Strasbourg Institute of Material Physics and Chemistry, 23 rue du Loess, Strasbourg, France. Get directions with OpenStreetMap or Google Maps.

When: January 11-12, 2024. Add to your Google Calendar.

Requirements: Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed below).

Accessibility: We are committed to making this workshop accessible to everybody. For workshops at a physical location, the workshop organizers have checked that:

Materials will be provided in advance of the workshop and large-print handouts are available if needed by notifying the organizers in advance. If we can help making learning easier for you (e.g. sign-language interpreters, lactation facilities) please get in touch (using contact details below) and we will attempt to provide them.

Contact: Please email or for more information.

Roles: To learn more about the roles at the workshop (who will be doing what), refer to our Workshop FAQ.

Code of Conduct

Everyone who participates in Carpentries activities is required to conform to the Code of Conduct. This document also outlines how to report an incident if needed.

Collaborative Notes

We will use this collaborative document for chatting, taking notes, and sharing URLs and bits of code.


Please be sure to complete these surveys before and after the workshop.

Pre-workshop Survey

Post-workshop Survey


Day 1: Thu 11 January

Before the start Pre-workshop survey
09:00 Introduction
Image Basics
10:45 Morning break
11:00Working with skimage
13:00 Lunch break
14:00 Drawing and Bitwise Operations
15:15 Afternoon break
15:30 Creating Histograms

Day 2: Fri 12 January

09:00Blurring Images
10:45 Morning break
11:00 Thresholding
13:00 Lunch break
14:00Connected Component Analysis
15:15 Afternoon break
15:30Capstone Challenge
16:45Post-workshop survey


To participate in a Data Carpentry workshop, you will need access to software as described below. In addition, you will need an up-to-date web browser.

We maintain a list of common issues that occur during installation as a reference for instructors that may be useful on the Configuration Problems and Solutions wiki page.

To do before the workshop

Please download the dataset we work on as explained in the Data section of this curriculum page.

Please ensure that you have the following software installed on your computer:

Users of Conda/Anaconda can find specific installation information in the Software section of this curriculum page. You are also good to go using other package managers than Conda/Anaconda, like pip and suchlike.
For troubleshooting please refer to the System Administrator of your Lab.