break up into smaller lines

Author: tobyhodges

_extras/edge-detection.md

@@ -12,67 +12,70 @@ objectives:
 - "Recognize morphometric problems (those dealing with the number, size, or
 shape of the objects in an image)."
 keypoints:
-- "Simple Python and skimage (scikit-image) techniques can be used to solve genuine 
+- "Simple Python and skimage (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."
 ---
 
 We can use relatively simple image processing and computer vision techniques in
-Python, using the [skimage](https://scikit-image.org/) library. With careful
-experimental design, a digital camera or a flatbed scanner, in conjunction with
-some Python code, can be a powerful instrument in answering many different
-kinds of problems. Consider the following problem that might be
-of interest to a scientist.
+Python, using the [skimage](https://scikit-image.org/) library.
+With careful experimental design, a digital camera or a flatbed scanner,
+in conjunction with some Python code,
+can be a powerful instrument in answering many different kinds of problems.
+Consider the following problem that might be of interest to a scientist.
 
 ## 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:
+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:
 
 ![Bacteria colony](../fig/colonies-01.jpg)
 
-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:
+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:
 
 ![Colonies counted](../fig/colony-mask.png)
 
 > ## 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
-> skimage:
+> 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 skimage:
 >
-> 1. What if there are many more bacteria colonies in the Petri dish? For
-> 	example, suppose the image looked like this:
+> 1. What if there are many more bacteria colonies in the Petri dish?
+>   For example, suppose the image looked like this:
 >
 > 	![Bacteria colony](../fig/colonies-03.jpg)
 >
-> 	Manually counting the colonies in that image would present more of a
-> 	challenge. A Python program using skimage could count the number of
-> 	colonies more accurately, and much more quickly, than a human could.
+> 	Manually counting the colonies in that image would present more of a challenge.
+>   A Python program using skimage 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 skimage 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?
+> 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 skimage 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.
 {: .callout}
 
-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]({{page.root}}/09-challenges/).
+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]({{page.root}}/09-challenges/).
 
-Let's get started, by learning some basics about how images are represented and
-stored digitally.
+Let's get started,
+by learning some basics about how images are represented and stored digitally.