Pull updates from original datacarpentry/image-processing repo

CONTRIBUTING.md

@@ -14,6 +14,10 @@ your change proposal as promptly as we can, and help you become a member of our
 community. Everyone involved in [The Carpentries][cp-site] agrees to abide by
 our [code of conduct](CODE_OF_CONDUCT.md).
 
+### Who Should Contribute?
+
+Contributions to this lesson are welcome from anyone with an interest in the project.
+
 ### How to Contribute
 
 The easiest way to get started is to file an issue to tell us about a spelling
@@ -43,7 +47,7 @@ documentation][template-doc].
    to [The Workbench documentation][template-doc].
 
 
-### What to Contribute
+### What to Contribute (General)
 
 There are many ways to contribute, from writing new exercises and improving
 existing ones to updating or filling in the documentation and submitting [bug
@@ -58,7 +62,66 @@ are particularly valuable**: it's easy for people who have been using these
 lessons for a while to forget how impenetrable some of this material can be, so
 fresh eyes are always welcome.
 
-### What *Not* to Contribute
+### What to Contribute (This Lesson)
+
+Any contributions are welcome, particularly ideas for how the existing content could be
+improved or updated, and/or errors that need to be corrected. Comments on existing issues
+and reviews of pull requests are similarly welcome.
+
+If you plan to submit a pull request, please open an issue
+(or comment on an existing thread) first to ensure that effort is not duplicated
+or spent making a change that will not be accepted by the Maintainers.
+
+#### Content / style guidelines
+
+- If you add an image / figure that was generated from Python code, please include this
+  code in your PR under `episodes/fig/source`.
+
+- If you add a new image or figure, verify that it displays correctly in the lesson’s dark mode. A color-inversion filter is applied [by Varnish to all images by default in dark mode](https://github.com/carpentries/varnish/issues/181), which may cause some images to appear incorrectly or become unreadable. If your image is affected, include an additional version of the same image with a `-dark` suffix in its filename.
+
+- Use the terms in the table below, when referring to Python libraries within the lesson.
+  The table gives two terms for each library: `Term for descriptive text` which should be
+  used when discussing the library in plain English / full sentences and `Term for code`
+  which should be used when referring to code (and within code).
+
+   | Python library  | Term for descriptive text | Term for code  |
+   | :-------------  | :-------------            | :------------- |
+   | [scikit-image](https://scikit-image.org/)  | scikit-image  | `skimage` |
+   | [NumPy](https://numpy.org/)  | NumPy | `numpy` |
+   | [Matplotlib](https://matplotlib.org/) | Matplotlib | `matplotlib` |
+   | [imageio](https://imageio.readthedocs.io/en/stable/index.html) | imageio | `imageio` |
+
+
+- When importing scikit-image use:
+   ```python
+   import skimage as ski
+   ```
+   Therefore, to access specific functions, you need to use their submodule name. For example:
+
+   ```python
+   import skimage as ski
+
+   rr, cc = ski.draw.rectangle(start=(357, 44), end=(740, 720))
+   ```
+
+- For reading and writing images, use the [imageio](https://imageio.readthedocs.io/en/stable/index.html)
+  library and avoid use of `skimage.io`. For example:
+   ```python
+   import imageio.v3 as iio
+
+   chair = iio.imread(uri="data/chair.jpg")  # read an image
+   iio.imwrite(uri="data/chair.tif", image=chair)  # write an image
+   ```
+
+- Comments providing an overall description of a code snippet should use triple quotes `"""`, e.g.,
+   ```python
+   """Python script to load a colour image in grayscale"""
+
+   chair = iio.imread(uri="data/chair.jpg")
+   gray_chair = ski.color.rgb2gray(chair)
+   ```
+
+### What *Not* to Contribute (General)
 
 Our lessons already contain more material than we can cover in a typical
 workshop, so we are usually *not* looking for more concepts or tools to add to
@@ -72,6 +135,23 @@ platform. Our workshops typically contain a mixture of Windows, macOS, and
 Linux users; in order to be usable, our lessons must run equally well on all
 three.
 
+### What *Not* to Contribute (This Lesson)
+
+Although most contributions will be welcome at this stage of the curriculum's development,
+the time available to deliver the content in a training event is strictly limited
+and needs to be accounted for when considering the addition of any new content.
+If you want to suggest the addition of new content, especially whole new sections or episodes,
+please open an issue to discuss this with the Maintainers first and provide the following
+information alongside a summary of the content to be added:
+
+1. A suggested location for the new content.
+2. An estimate of how much time you estimate the new content would require in training
+   (teaching + exercises).
+3. The [learning objective(s)][cldt-lo] of this new content.
+4. (optional, but strongly preferred)
+   A suggestion of which of the currently-used learning objectives could be
+   removed from the curriculum to make space for the new content.
+
 ### Using GitHub
 
 If you choose to contribute via GitHub, you may want to look at [How to
@@ -92,6 +172,14 @@ Each lesson has a team of maintainers who review issues and pull requests or
 encourage others to do so. The maintainers are community volunteers, and have
 final say over what gets merged into the lesson.
 
+#### Merging Policy
+
+Pull requests made to the default branch of this repository
+(from which the lesson site is built)
+can only be merged after at least one approving review from a Maintainer.
+Any Maintainer can merge a pull request that has received at least one approval,
+but they may prefer to wait for further input from others before merging.
+
 ### Other Resources
 
 The Carpentries is a global organisation with volunteers and learners all over
@@ -102,6 +190,7 @@ media, slack, newsletters, and email lists. You can also [reach us by
 email][contact].
 
 [repo]: https://github.com/datacarpentry/image-processing
+[cldt-lo]: https://carpentries.github.io/lesson-development-training/05-objectives.html#learning-objectives
 [contact]: mailto:team@carpentries.org
 [cp-site]: https://carpentries.org/
 [dc-issues]: https://github.com/issues?q=user%3Adatacarpentry

README.md

@@ -14,6 +14,10 @@ The original ideas and discussions in our first in-person workshop has been arch
 
 Make a suggestion or correct an error by [raising an Issue](https://github.com/carpentries-incubator/bioimage-analysis-python/issues).
 
+Please see the [CONTRIBUTING.md file](CONTRIBUTING.md) for contributing guidelines and details on how to get involved with 
+this project. Some specific guidelines for content / style are provided in the 
+['What to Contribute (This Lesson)' section](CONTRIBUTING.md#what-to-contribute-this-lesson).
+
 ## Code of Conduct
 
 All participants should agree to abide by the [The Carpentries Code of Conduct](https://carpentrieshandbook.readthedocs.io/en/latest/topic_folders/policies/code-of-conduct.html).

config.yaml

@@ -71,7 +71,12 @@ episodes:
 - 10-challenges.md
 
 # Information for Learners
-learners:
+learners: 
+- prereqs.md
+- discuss.md
+- reference.md
+- further-reading.md
+- edge-detection.md
 
 # Information for Instructors
 instructors:

episodes/01-introduction.md

@@ -114,7 +114,7 @@ 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).
+[capstone challenge](10-challenges.md).
 
 Let's get started,
 by learning some basics about how images are represented and stored digitally.

episodes/03-reading-images.md

@@ -240,14 +240,14 @@ Suppose we are interested in this maize root cluster image.
 We want to be able to focus our program's attention on the roots themselves,
 while ignoring the black background.
 
-![](data/maize-root-cluster.jpg){alt='Root cluster image'}
+![](fig/maize-root-cluster.jpg){alt='Root cluster image'}
 
 Since the image is stored as an array of numbers,
 we can simply look through the array for pixel colour values that are
 less than some threshold value.
 This process is called *thresholding*,
 and we will see more powerful methods to perform the thresholding task in
-[the *Thresholding* episode](07-thresholding.md).
+[the *Thresholding* episode](06-processing-segmentation.md).
 Here, though, we will look at a simple and elegant NumPy method for thresholding.
 Let us develop a program that keeps only the pixel colour values in an image
 that have value greater than or equal to 128.
@@ -379,7 +379,7 @@ the low intensity pixels while changing the high intensity ones.
 
 The file `data/sudoku.png` is an RGB image of a sudoku puzzle:
 
-![](data/sudoku.png){alt='Su-Do-Ku puzzle'}
+![](fig/sudoku.png){alt='Su-Do-Ku puzzle'}
 
 Your task is to load the image in grayscale format and turn all of
 the bright pixels in the image to a
@@ -465,7 +465,7 @@ Consider this image of a whiteboard, and suppose that we want to create a
 sub-image with just the portion that says "odd + even = odd," along with the
 red box that is drawn around the words.
 
-![](data/board.jpg){alt='Whiteboard image'}
+![](fig/board.jpg){alt='Whiteboard image'}
 
 Using `matplotlib.pyplot.imshow`
 we can determine the coordinates of the corners of the area we wish to extract

episodes/04-drawing.md

@@ -393,7 +393,7 @@ Copy that image to your computer, write some code to make a mask,
 and apply it to select the part of the image containing your object.
 For example, here is an image of a remote control:
 
-![](data/remote-control.jpg){alt='Remote control image'}
+![](fig/remote-control.jpg){alt='Remote control image'}
 
 And, here is the end result of a program masking out everything but the remote:
 
@@ -446,7 +446,7 @@ fig, ax = plt.subplots()
 ax.imshow(wellplate)
 ```
 
-![](data/wellplate-01.jpg){alt='96-well plate'}
+![](fig/wellplate-01.jpg){alt='96-well plate'}
 
 Suppose that we are interested in the colours of the solutions in each of the wells.
 We *do not* care about the colour of the rest of the image,

episodes/05-creating-histograms.md

@@ -52,7 +52,7 @@ and histograms are also quite handy as a preparatory step before performing
 We will start with grayscale images,
 and then move on to colour images.
 We will use this image of a plant seedling as an example:
-![](data/plant-seedling.jpg){alt='Plant seedling'}
+![](fig/plant-seedling.jpg){alt='Plant seedling'}
 
 Here we load the image in grayscale instead of full colour, and display it:
 

episodes/06-blurring.md

@@ -276,7 +276,7 @@ fig, ax = plt.subplots()
 ax.imshow(image)
 ```
 
-![](data/gaussian-original.png){alt='Original image'}
+![](fig/gaussian-original.png){alt='Original image'}
 
 Next, we apply the gaussian blur:
 
@@ -331,8 +331,8 @@ ax.imshow(blurred)
 
 ## Visualising Blurring
 
-Somebody said once "an image is worth a thousand words". 
-What is actually happening to the image pixels when we apply blurring may be 
+Somebody said once "an image is worth a thousand words".
+What is actually happening to the image pixels when we apply blurring may be
 difficult to grasp. Let's now visualise the effects of blurring from a different
 perspective.
 
@@ -430,18 +430,18 @@ but can be viewed by following the links in the captions.
 
 
 ![
-A 3D plot of pixel intensities across the whole Petri dish image before blurring. 
-[Explore how this plot was created with matplotlib](https://gist.github.com/chbrandt/63ba38142630a0586ba2a13eabedf94b). 
+A 3D plot of pixel intensities across the whole Petri dish image before blurring.
+[Explore how this plot was created with matplotlib](https://gist.github.com/chbrandt/63ba38142630a0586ba2a13eabedf94b).
 Image credit: [Carlos H Brandt](https://github.com/chbrandt/).
 ](fig/3D_petri_before_blurring.png){
 alt='3D surface plot showing pixel intensities across the whole example Petri dish image before blurring'
 }
 
 ![
-A 3D plot of pixel intensities after Gaussian blurring of the Petri dish image. 
-Note the 'smoothing' effect on the pixel intensities of the colonies in the image, 
-and the 'flattening' of the background noise at relatively low pixel intensities throughout the image. 
-[Explore how this plot was created with matplotlib](https://gist.github.com/chbrandt/63ba38142630a0586ba2a13eabedf94b). 
+A 3D plot of pixel intensities after Gaussian blurring of the Petri dish image.
+Note the 'smoothing' effect on the pixel intensities of the colonies in the image,
+and the 'flattening' of the background noise at relatively low pixel intensities throughout the image.
+[Explore how this plot was created with matplotlib](https://gist.github.com/chbrandt/63ba38142630a0586ba2a13eabedf94b).
 Image credit: [Carlos H Brandt](https://github.com/chbrandt/).
 ](fig/3D_petri_after_blurring.png){
 alt='3D surface plot illustrating the smoothing effect on pixel intensities across the whole example Petri dish image after blurring'