remove titration challenge

Author: K-Meech

data/titration.tiff

@@ -6,35 +6,30 @@ questions:
 - "What sort of scientific questions can we answer with image processing /
 computer vision?"
 - "What are morphometric problems?"
-- "What are colorimetric problems?"
 objectives:
 - "Recognize scientific questions that could be solved with image processing
  / computer vision."
 - "Recognize morphometric problems (those dealing with the number, size, or
 shape of the objects in an image)."
-- "Recognize colorimetric problems (those dealing with the analysis of the
-color or the objects in an image)."
 keypoints:
-- "Simple Python and skimage (scikit-image) techniques can be used to solve genuine
-morphometric and colorimetric problems."
+- "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."
-- "Colorimetric problems involve analyzing the color 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 two types of problems that might be
+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 automatically counting 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:
 
@@ -46,21 +41,6 @@ image like this:
 
 ![Colonies counted](../fig/colony-mask.png)
 
-## Colorimetrics
-
-Colorimetrics involves analyzing the color of objects in an
-image. For example, consider this video of a titrant being added to an
-analyte (click on the image to see the video):
-
-[![Titration video](../fig/titration.jpg)](https://youtu.be/NLSY5S8CABk?t=554)
-
-We could use image processing to look at the color of the solution, and
-determine when the titration is complete. This graph shows how the three
-component colors (red, green, and blue) of the solution change over time;
-the change in the solution's color is obvious.
-
-![Titration colors](../fig/colorimetric.png)
-
 > ## Why write a program to do that?
 >
 > Note that you can easily manually count the number of bacteria colonies shown
@@ -89,9 +69,10 @@ the change in the solution's color is obvious.
 > research.
 {: .callout}
 
-As we move through this workshop, we will return to these sample problems
-several times, and you will solve each of these problems during the
-end-of-workshop [challenges]({{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.

data/titration.wmv

@@ -530,34 +530,3 @@ the program:
 > > {: .language-python}
 > {: .solution}
 {: .challenge}
-
-
-> ## Slicing and the colorimetric challenge (20 min)
->
-> In [the introductory episode]({{page.root}}/01-introduction/), we were
-> introduced to a colorimetric challenge, namely, graphing the color values of
-> a solution in a titration, to see when the color change takes place. Let's
-> start thinking about how to solve that problem.
->
-> One part of our ultimate solution will be sampling the color channel values
-> from an image of the solution. To make our graph more reliable, we will want
-> to calculate a mean channel value over several pixels, rather than simply
-> focusing on one pixel from the image.
->
-> Open the `data/titration.tiff` image and display it.
->
-> ![Titration image](../fig/titration.jpg)
->
-> Find the *(x, y)* coordinates of an area of the image you think would be good
-> to sample in order to find the average channel values. Then, write a small
-> Python script that computes the mean channel values for a 10 × 10 pixel
-> region centered around the coordinates you chose. Print the results to the
-> screen, in a format like this:
->
-> ~~~
-> Avg. red value: 193.7778
-> Avg. green value: 189.1481
-> Avg. blue value: 178.6049
-> ~~~
-> {: .output}
-{: .challenge}

episodes/01-introduction.md

@@ -1,19 +1,22 @@
 ---
-title: "Challenges"
+title: "Capstone Challenge"
 teaching: 10
 exercises: 40
 questions:
-- "What are the questions?"
+- "How can we automatically count bacterial colonies with image analysis?"
 objectives:
-- "What are the objectives?"
+- "Bring together everything you've learnt so far to count bacterial colonies
+in 3 images."
 keypoints:
-- "What are the key points?"
+- "Using thresholding, connected component analysis and other tools we can automatically segment
+images of bacterial colonies."
+- "These methods are useful for many scientific problems, especially those involving
+morphometrics."
 ---
 
-In this episode, we will provide two different challenges for you to attempt,
-based on the skills you have acquired so far. One of the challenges will be
-related to the shape of objects in images (*morphometrics*), while the other
-will be related to colors of objects in images (*colorimetrics*).
+In this episode, we will provide a final challenge for you to attempt,
+based on all the skills you have acquired so far. This challenge will be
+related to the shape of objects in images (*morphometrics*).
 
 ## Morphometrics: Bacteria Colony Counting
 
@@ -43,6 +46,8 @@ The image files can be found at `data/colonies-01.tif`,
 > Use what you learnt in the [histograms]({{ page.root }}/05-creating-histograms),
 > [thresholding]({{ page.root }}/07-thresholding) and
 > [Connected Component]({{ page.root }}/08-connected-components) episodes.
+> Try to put your code into a re-usable function, so that it can be applied easily
+> to any image file.
 >
 > > ## Solution
 > >
@@ -164,26 +169,3 @@ The image files can be found at `data/colonies-01.tif`,
 > > like [watershed](https://scikit-image.org/docs/dev/auto_examples/segmentation/plot_watershed.html).
 > {: .solution}
 {: .challenge}
-
-
-## Colorimetrics: titration color analysis
-
-The video showing the titration process first mentioned in the workshop
-[introduction]({{ page.root }}/01-introduction/) episode can be found at
-`data/titration.wmv`.
-Write a Python program that uses skimage to analyze the video on a
-frame-by-frame basis. Your program should do the following:
-
-1. Sample a region from the same location on each frame, and determine the
-	average red, green, and blue channel value.
-
-2. Display a graph plotting the average color channel values as a function of
-	the frame number, similar to this image:
-
-	![Titration colors](../fig/colorimetric.png)
-
-3. Save the graph as an image named `titration.png`.
-
-4. Output a CSV file named `titration.csv`, with each line containing
-	the frame number, average red value, average green value, and average
-	blue value.