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This two-day, in-person workshop gives researchers a thorough grounding in using Python to process and analyse image data.
Participants will learn the fundamentals of image processing using the skimage library to analyse many different properties of an image, primarily focused on morphometrics, but which are widely applicable to a range of image analysis problems.
Starting with how images are represented in digital format, the pros and cons of different image file formats, metadata and how images are stored in arrays, we will move through various lessons to explore image data, implementing bitwise operations, masks, filtering and thresholding to optimise your analysis.
The workshop will culminate in a hands-on exercise that provides participants with the opportunity to combine the approaches learned throughout all of the previous lessons.
Who is this training for?
Researchers who have a working knowledge of Python and who expect to be producing volumes of image data that would benefit from some automated analysis.
You should be in the early stages of planning your image collection project to maximise the benefit from this course.
Course prerequisites
These requirements can be fulfilled by:
- completing a Software Carpentry Python workshop or
- completing a Data Carpentry Ecology workshop (with Python) and a Data Carpentry Genomics workshop or
- 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 joining the workshop, please complete the data and software setup described in Further Information.
Please ensure this is done well in advance of the course start date, as we will not have time to troubleshoot software issues that arise in the morning.
