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_extras/edge-detection.md

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episodes/01-introduction.md

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computer vision?"
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- "What are morphometric problems?"
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objectives:
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- "Recognize scientific questions that could be solved with image processing
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- "Recognise scientific questions that could be solved with image processing
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/ computer vision."
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- "Recognize morphometric problems (those dealing with the number, size, or
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- "Recognise morphometric problems (those dealing with the number, size, or
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shape of the objects in an image)."
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keypoints:
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- "Simple Python and skimage (scikit-image) techniques can be used to solve genuine
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objects in an image."
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---
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As computer systems have become faster and more powerful,
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and cameras and other imaging systems have become commonplace
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in many other areas of life,
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## Morphometrics
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Morphometrics involves counting the number of objects in an
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image, analyzing the size of the objects, or analyzing the shape of the
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objects. For example, we might be interested in automatically counting the
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number of bacterial colonies growing in a Petri dish, as shown in this
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image:
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Morphometrics involves counting the number of objects in an image,
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analyzing the size of the objects,
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or analyzing the shape of the objects.
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For example, we might be interested in automatically counting
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the number of bacterial colonies growing in a Petri dish,
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as shown in this image:
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![Bacteria colony](../fig/colonies-01.jpg)
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We could use image processing to find the colonies, count them, and
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then highlight their locations on the original image, resulting in an
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image like this:
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We could use image processing to find the colonies, count them,
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and then highlight their locations on the original image,
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resulting in an image like this:
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![Colonies counted](../fig/colony-mask.png)
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> ## Why write a program to do that?
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>
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> Note that you can easily manually count the number of bacteria colonies shown
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> in the morphometric example above. Why should we learn how to write a Python
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> program to do a task we could easily perform with our own eyes? There are at
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> least two reasons to learn how to perform tasks like these with Python and
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> skimage:
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> Note that you can easily manually count the number of bacteria colonies
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> shown in the morphometric example above.
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> Why should we learn how to write a Python program to do a task
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> we could easily perform with our own eyes?
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> There are at least two reasons to learn how to perform tasks like these
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> with Python and skimage:
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>
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> 1. What if there are many more bacteria colonies in the Petri dish? For
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> example, suppose the image looked like this:
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> 1. What if there are many more bacteria colonies in the Petri dish?
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> For example, suppose the image looked like this:
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>
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> ![Bacteria colony](../fig/colonies-03.jpg)
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>
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> Manually counting the colonies in that image would present more of a
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> challenge. A Python program using skimage could count the number of
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> colonies more accurately, and much more quickly, than a human could.
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> Manually counting the colonies in that image would present more of a challenge.
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> A Python program using skimage could count the number of colonies more accurately,
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> and much more quickly, than a human could.
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>
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> 2. What if you have hundreds, or thousands, of images to consider? Imagine
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> having to manually count colonies on several thousand images like
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> those above. A Python program using skimage could move through all of
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> the images in seconds; how long would a graduate student require to do
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> the task? Which process would be more accurate and repeatable?
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> 2. What if you have hundreds, or thousands, of images to consider?
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> Imagine having to manually count colonies on several thousand images
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> like those above.
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> A Python program using skimage could move through all of the images in seconds;
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> how long would a graduate student require to do the task?
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> Which process would be more accurate and repeatable?
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>
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> As you can see, the simple image processing / computer vision techniques you
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> will learn during this workshop can be very valuable tools for scientific
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> research.
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{: .callout}
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As we move through this workshop, we will learn image analysis methods
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useful for many different scientific problems. These will be linked together
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As we move through this workshop,
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we will learn image analysis methods useful for many different scientific problems.
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These will be linked together
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and applied to a real problem in the final end-of-workshop
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[capstone challenge]({{page.root}}/09-challenges/).
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[capstone challenge]({{ page.root }}{% link _episodes/09-challenges.md %}).
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Let's get started, by learning some basics about how images are represented and
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stored digitally.
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Let's get started,
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by learning some basics about how images are represented and stored digitally.

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