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01-introduction.md

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+---
+title: Introduction
+teaching: 5
+exercises: 0
+---
+
+::::::::::::::::::::::::::::::::::::::: objectives
+
+- Recognise scientific questions that could be solved with image processing / computer vision.
+- Recognise morphometric problems (those dealing with the number, size, or shape of the objects in an image).
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: questions
+
+- What sort of scientific questions can we answer with image processing / computer vision?
+- What are morphometric problems?
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+As computer systems have become faster and more powerful,
+and cameras and other imaging systems have become commonplace
+in many other areas of life,
+the need has grown for researchers to be able to
+process and analyse image data.
+Considering the large volumes of data that can be involved -
+high-resolution images that take up a lot of disk space/virtual memory,
+and/or collections of many images that must be processed together -
+and the time-consuming and error-prone nature of manual processing,
+it can be advantageous or even necessary for this processing and analysis
+to be automated as a computer program.
+
+This lesson introduces an open source toolkit for processing image data:
+the Python programming language
+and [the *scikit-image* (`skimage`) library](https://scikit-image.org/).
+With careful experimental design,
+Python code can be a powerful instrument in answering many different kinds of questions.
+
+## Uses of Image Processing in Research
+
+Automated processing can be used to analyse many different properties of an image,
+including the distribution and change in colours in the image,
+the number, size, position, orientation, and shape of objects in the image,
+and even - when combined with machine learning techniques for object recognition -
+the type of objects in the image.
+
+Some examples of image processing methods applied in research include:
+
+- [Imaging a black hole](https://iopscience.iop.org/article/10.3847/2041-8213/ab0e85)
+- [Segmentation of liver and vessels from CT images](https://doi.org/10.1016/j.cmpb.2017.12.008)
+- [Monitoring wading birds in the Everglades using drones](https://dx.doi.org/10.1002/rse2.421) ([Blog article summarizing the paper](https://jabberwocky.weecology.org/2024/07/29/monitoring-wading-birds-in-near-real-time-using-drones-and-computer-vision/))
+- [Estimating the population of emperor penguins](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3325796/)
+- [Global-scale analysis of marine plankton diversity](https://www.cell.com/cell/fulltext/S0092-8674\(19\)31124-9)
+
+With this lesson,
+we aim to provide a thorough grounding in the fundamental concepts and skills
+of working with image data in Python.
+Most of the examples used in this lesson focus on
+one particular class of image processing technique, *morphometrics*,
+but what you will learn can be used to solve a much wider range of problems.
+
+## Morphometrics
+
+Morphometrics involves counting the number of objects in an image,
+analyzing the size of the objects,
+or analyzing the shape of the objects.
+For example, we might be interested in automatically counting
+the number of bacterial colonies growing in a Petri dish,
+as shown in this image:
+
+![](fig/colonies-01.jpg){alt='Bacteria colony'}
+
+We could use image processing to find the colonies, count them,
+and then highlight their locations on the original image,
+resulting in an image like this:
+
+![](fig/colony-mask.png){alt='Colonies counted'}
+
+:::::::::::::::::::::::::::::::::::::::::  callout
+
+## Why write a program to do that?
+
+Note that you can easily manually count the number of bacteria colonies
+shown in the morphometric example above.
+Why should we learn how to write a Python program to do a task
+we could easily perform with our own eyes?
+There are at least two reasons to learn how to perform tasks like these
+with Python and scikit-image:
+
+1. What if there are many more bacteria colonies in the Petri dish?
+  For example, suppose the image looked like this:
+
+![](fig/colonies-03.jpg){alt='Bacteria colony'}
+
+Manually counting the colonies in that image would present more of a challenge.
+A Python program using scikit-image could count the number of colonies more accurately,
+and much more quickly, than a human could.
+
+2. What if you have hundreds, or thousands, of images to consider?
+  Imagine having to manually count colonies on several thousand images
+  like those above.
+  A Python program using scikit-image could move through all of the images in seconds;
+  how long would a graduate student require to do the task?
+  Which process would be more accurate and repeatable?
+
+As you can see, the simple image processing / computer vision techniques you
+will learn during this workshop can be very valuable tools for scientific
+research.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+As we move through this workshop,
+we will learn image analysis methods useful for many different scientific problems.
+These will be linked together
+and applied to a real problem in the final end-of-workshop
+[capstone challenge](09-challenges.md).
+
+Let's get started,
+by learning some basics about how images are represented and stored digitally.
+
+:::::::::::::::::::::::::::::::::::::::: keypoints
+
+- Simple Python and scikit-image techniques can be used to solve genuine image analysis problems.
+- Morphometric problems involve the number, shape, and / or size of the objects in an image.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::