improved links

Author: tobyhodges

_extras/edge-detection.md

@@ -31,8 +31,8 @@ while ignoring parts of the image that will not help us.
 For example, once we have found the edges of the objects in the image
 (or once we have converted the image to binary using thresholding),
 we can use that information to find the image *contours*,
-which we will learn about in the following
-[connected components]({{ page.root }}/09-connected-components) episode.
+which we will learn about in
+[the _Connected Component Analysis_ episode]({{ page.root }}{% link _episodes/08-connected-components.md %}).
 With the contours,
 we can do things like counting the number of objects in the image,
 measure the size of the objects, classify the shapes of the objects, and so on.
@@ -83,7 +83,7 @@ The skimage `skimage.feature.canny()` function performs the following steps:
 
 1. A Gaussian blur
    (that is characterized by the `sigma` parameter,
-   see [introduction]({{ page.root }}/06-blurring/))
+   see [_Blurring Images_]({{ page.root }}{% link _episodes/06-blurring.md %}))
    is applied to remove noise from the image.
    (So if we are doing edge detection via this function,
    we should not perform our own blurring step.)
@@ -122,7 +122,8 @@ based on the contents of the image(s) to be processed.
 The following program illustrates how the `skimage.feature.canny()` method
 can be used to detect the edges in an image.
 We will execute the program on the `data/junk-01.jpg` image,
-which we used before in the [Thresholding]({{ page.root }}/07-thresholding/) episode:
+which we used before in
+[the _Thresholding_ episode]({{ page.root }}{% link _episodes/07-thresholding.md %}):
 
 ![Colored shapes](../data/shapes-01.jpg)
 
@@ -457,14 +458,14 @@ The image shows the edges in an output file.
 
 Keep this plugin technique in your image processing "toolbox."
 You can use sliders (or other interactive elements,
-see the [skimage documentation](https://scikit-image.org/docs/dev/api/skimage.viewer.widgets.html))
+see [the skimage documentation](https://scikit-image.org/docs/dev/api/skimage.viewer.widgets.html))
 to vary other kinds of parameters, such as sigma for blurring,
 binary thresholding values, and so on.
 A few minutes developing a program to tweak parameters like this can
 save you the hassle of repeatedly running a program from the command line
 with different parameter values.
 Furthermore, skimage already comes with a few viewer plugins that you can
-check out in the [documentation](https://scikit-image.org/docs/dev/api/skimage.viewer.plugins.html).
+check out in [the documentation](https://scikit-image.org/docs/dev/api/skimage.viewer.plugins.html).
 
 ## Other edge detection functions
 

episodes/01-introduction.md

@@ -19,7 +19,7 @@ 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.
+Python, using [the skimage library](https://scikit-image.org/).
 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.
@@ -75,7 +75,7 @@ resulting in an image like this:
 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/).
+the final end-of-workshop [capstone challenge]({{page.root}}{% link _episodes/09-challenges.md %}).
 
 Let's get started,
 by learning some basics about how images are represented and stored digitally.

episodes/02-image-basics.md

@@ -348,8 +348,9 @@ A lot of science went into making this the default due to its robustness
 when it comes to how the human mind interprets relative color values,
 grey-scale printability,
 and color-blind friendliness
-([https://matplotlib.org/stable/tutorials/colors/colormaps.html](https://matplotlib.org/stable/tutorials/colors/colormaps.html),
-[https://bids.github.io/colormap/](https://bids.github.io/colormap/)).
+(You can read more about this default color map in
+[a Matplotlib tutorial](https://matplotlib.org/stable/tutorials/colors/colormaps.html)
+and [an explanatory article by the authors](https://bids.github.io/colormap/)).
 Thus it is a good place to start,
 and you should change it only with purpose and forethought.
 For now, let's see how you can do that using an alternative map

episodes/03-skimage-images.md

@@ -1,5 +1,5 @@
 ---
-title: "Image representation in skimage"
+title: "Image Representation in skimage"
 teaching: 70
 exercises: 50
 questions:
@@ -33,7 +33,7 @@ let us review and expand on the concepts we just learned.
 
 ## Images are represented as NumPy arrays
 
-In the [Image Basics]({{page.root}}/02-image-basics) episode,
+In [the Image Basics episode]({{page.root}}{% link _episodes/02-image-basics.md %}),
 we learned that images are represented as
 rectangular arrays of individually-colored square pixels,
 and that the color of each pixel can be represented as an RGB triplet of numbers.
@@ -87,8 +87,8 @@ blue in layer 2.
 Skimage provides easy-to-use functions for reading, displaying, and saving images.
 All of the popular image formats, such as BMP, PNG, JPEG, and TIFF are supported,
 along with several more esoteric formats.
-See the [skimage documentation](http://scikit-image.org/docs/stable/)
-for more information.
+[The skimage documentation](http://scikit-image.org/docs/stable/)
+has more information about supported file formats.
 
 Let us examine a simple Python program to load, display,
 and save an image to a different format.
@@ -279,7 +279,7 @@ In this case, the `.tif` extension causes the image to be saved as a TIFF.
 
 ## Manipulating pixels
 
-In the [Image Basics]({{page.root}}/02-image-basics) episode,
+In [the _Image Basics_ episode]({{page.root}}{% link _episodes/02-image-basics.md %}),
 we individually manipulated the colors of pixels by changing the numbers stored
 in the image's NumPy array. Let's apply the principles learned there
 along with some new principles to a real world example.
@@ -295,7 +295,7 @@ we can simply look through the array for pixel color 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]({{ page.root }}/07-thresholding/) episode.
+[the _Thresholding_ episode]({{ page.root }}{% link _episodes/07-thresholding.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 color values in an image
 that have value greater than or equal to 128.

episodes/04-drawing.md

@@ -29,7 +29,7 @@ based on changes in color or shape.
 Often we wish to select only a portion of an image to analyze,
 and ignore the rest.
 Creating a rectangular sub-image with slicing,
-as we did in the [skimage Images]({{ page.root }}/03-skimage-images) lesson
+as we did in [the _Image Representation in skimage_ episode]({{ page.root }}{% link _episodes/03-skimage-images.md %})
 is one option for simple cases.
 Another option is to create another special image,
 of the same size as the original,
@@ -133,9 +133,9 @@ The function returns the rectangle as row (`rr`) and column (`cc`) coordinate ar
 > ## Check the documentation!
 >
 > When using an skimage function for the first time - or the fifth time -
-> it is wise to check how the function is used, via the online
-> [skimage documentation](https://scikit-image.org/docs/dev/user_guide)
-> or via other usage examples on programming-related sites such as
+> it is wise to check how the function is used, via
+> [the skimage documentation](https://scikit-image.org/docs/dev/user_guide)
+> or other usage examples on programming-related sites such as
 > [Stack Overflow](https://stackoverflow.com/).
 > Basic information about skimage functions can be found interactively in Python,
 > via commands like `help(skimage)` or `help(skimage.draw.rectangle)`.
@@ -180,8 +180,8 @@ The function returns the rectangle as row (`rr`) and column (`cc`) coordinate ar
 > the (y, x) coordinate of one end of the line,
 > and the (y, x) coordinate of the other end of the line.
 >
-> Other drawing functions supported by skimage can be found in the
-> [skimage reference pages](https://scikit-image.org/docs/dev/api/skimage.draw.html?highlight=draw#module-skimage.draw).
+> Other drawing functions supported by skimage can be found in
+> [the skimage reference pages](https://scikit-image.org/docs/dev/api/skimage.draw.html?highlight=draw#module-skimage.draw).
 >
 > First let's make an empty, black image with a size of 800x600 pixels:
 >

episodes/05-creating-histograms.md

@@ -26,13 +26,14 @@ display histograms for images.
 
 As it pertains to images, a *histogram* is a graphical representation showing
 how frequently various color values occur in the image.
-We saw in the [Image Basics]({{ page.root }}/02-image-basics) episode
+We saw in
+[the _Image Basics_ episode]({{ page.root }}{% link _episodes/02-image-basics.md %})
 that we could use a histogram to visualize
 the differences in uncompressed and compressed image formats.
 If your project involves detecting color changes between images,
 histograms will prove to be very useful,
 and histograms are also quite handy as a preparatory step before performing
-[Thresholding]({{ page.root }}/07-thresholding).
+[thresholding]({{ page.root }}/07-thresholding).
 
 ## Grayscale Histograms
 
@@ -184,9 +185,9 @@ it produces this histogram:
 >
 > First, hover over the plant seedling image with your mouse to determine the
 > *(x, y)* coordinates of a bounding box around the leaf of the seedling.
-> Then, using techniques from the
-> [Drawing and Bitwise Operations]({{ page.root }}/04-drawing/)
-> episode, create a mask with a white rectangle covering that bounding box.
+> Then, using techniques from
+> [the _Drawing and Bitwise Operations_ episode]({{ page.root }}{% link _episodes/04-drawing.md %}),
+> create a mask with a white rectangle covering that bounding box.
 >
 > After you have created the mask, apply it to the input image before passing
 > it to the `np.histogram` function.
@@ -241,7 +242,7 @@ it produces this histogram:
 We can also create histograms for full color images,
 in addition to grayscale histograms.
 We have seen color histograms before,
-in the [Image Basics]({{ page.root }}/02-image-basics) episode.
+in [the _Image Basics_ episode]({{ page.root }}{% link _episodes/02-image-basics.md %}).
 A program to create color histograms starts in a familiar way:
 
 ~~~

episodes/06-blurring.md

@@ -1,5 +1,5 @@
 ---
-title: "Blurring images"
+title: "Blurring Images"
 teaching: 35
 exercises: 25
 questions:
@@ -11,8 +11,7 @@ keypoints:
 - "Applying a low-pass blurring filter smooths edges and removes noise from
 an image."
 - "Blurring is often used as a first step before we perform
-[Thresholding]({{ page.root }}/07-thresholding) or
- [Edge Detection]({{ page.root }}/../_extras/edge_detection.md)."
+thresholding or edge detection."
 - "The Gaussian blur can be applied to an image with the
 `skimage.filters.gaussian()` function."
 - "Larger sigma values may remove more noise, but they will also remove detail
@@ -36,7 +35,7 @@ we make the color transition from one side of an edge in the image to another
 smooth rather than sudden.
 The effect is to average out rapid changes in pixel intensity.
 A blur is a very common operation we need to perform before other tasks such as
-[thresholding]({{ page.root }}/07-thresholding).
+[thresholding]({{ page.root }}{% link _episodes/07-thresholding.md %}).
 There are several different blurring functions in the `skimage.filters` module,
 so we will focus on just one here, the *Gaussian blur*.
 
@@ -216,7 +215,7 @@ next pixel in the image.
 >
 > A similar process would be used to fill in all of the other missing pixels from
 > the kernel. Other *border modes* are available; you can learn more about them
-> in the [skimage documentation](https://scikit-image.org/docs/dev/user_guide).
+> in [the skimage documentation](https://scikit-image.org/docs/dev/user_guide).
 >
 {: .callout}
 
@@ -333,5 +332,5 @@ The Gaussian blur is a way to apply a low-pass filter in skimage.
 It is often used to remove Gaussian (i. e., random) noise from the image.
 For other kinds of noise, e.g. "salt and pepper" or "static" noise, a
 median filter is typically used.
-See the [`skimage.filters` documentation](https://scikit-image.org/docs/dev/api/skimage.filters.html#module-skimage.filters)
+See [the `skimage.filters` documentation](https://scikit-image.org/docs/dev/api/skimage.filters.html#module-skimage.filters)
 for a list of available filters.

episodes/07-thresholding.md

@@ -25,8 +25,7 @@ arrays, since they have only one color value channel. They are boolean, hence th
 the values 0 (off) and 1 (on)."
 - "Thresholding can be used to create masks that select only the interesting
 parts of an image, or as the first step before
-[Edge Detection]({{ page.root }}/08-edge-detection/) or finding
-[Contours]({{ page.root }}/09-contours/)."
+edge detection or finding contours."
 ---
 
 In this episode, we will learn how to use skimage functions to apply
@@ -40,7 +39,7 @@ we use thresholding as a way to select areas of interest of an image,
 while ignoring the parts we are not concerned with.
 We have already done some simple thresholding,
 in the "Manipulating pixels" section of
-the [Skimage Images]({{ page.root }}/03-skimage-images/) episode.
+[the _Image Representation in skimage_ episode]({{ page.root }}{% link _episodes/03-skimage-images.md %}).
 In that case, we used a simple NumPy array manipulation to
 separate the pixels belonging to the root system of a plant from the black background.
 In this episode, we will learn how to use skimage functions to perform thresholding.
@@ -85,7 +84,7 @@ we have to provide a threshold value `t`.
 
 The process works like this.
 First, we will load the original image, convert it to grayscale,
-and de-noise it as in the [Blurring]({{ page.root }}/06-blurring/) episode.
+and de-noise it as in [the _Blurring Images_ episode]({{ page.root }}{% link _episodes/06-blurring.md %}).
 
 ~~~
 # convert the image to grayscale
@@ -117,7 +116,7 @@ and try to identify what grayscale ranges correspond to the shapes in the image
 or the background.
 
 The histogram for the shapes image shown above can be produced as in
-the [Creating Histograms]({{ page.root }}/05-creating-histograms/) episode.
+[the _Creating Histograms_ episode]({{ page.root }}{% link _episodes/05-creating-histograms.md %}).
 
 ~~~
 # create a histogram of the blurred grayscale image
@@ -191,12 +190,12 @@ while the rest of the mask image is black.
 > It is worth noting that the principle for simple and automatic thresholding
 > can also be used for images with pixel ranges other than [0.0, 1.0].
 > For example, we could perform thresholding on pixel intensity values
-> in the range [0, 255] as we have already seen in the
-> [Image representation in skimage]({{ page.root}}/03-skimage-images/) epsiode.
+> in the range [0, 255] as we have already seen in
+> [the _Image Representation in skimage_ episode]({{ page.root}}{% link _episodes/03-skimage-images.md %}).
 {: .callout}
 
 We can now apply the `binary_mask` to the original colored image as we
-have learned in the [Drawing and Bitwise Operations]({{page.root}}/04-drawing/) episode.
+have learned in [the _Drawing and Bitwise Operations_ episode]({{page.root}}{% link _episodes/04-drawing.md %}).
 What we are left with is only the colored shapes from the original.
 
 ~~~
@@ -303,13 +302,13 @@ has two peaks that correspond to background and objects of interest.
 > ## Denoising an image before thresholding
 >
 > In practice, it is often necessary to denoise the image before
-> thresholding, which can be done with one of the methods from the
-> [Blurring]({{ page.root }}/06-blurring/) episode.
+> thresholding, which can be done with one of the methods from
+> [the _Blurring Images_ episode]({{ page.root }}{% link _episodes/06-blurring.md %}).
 {: .callout}
 
 Consider the image `data/maize-root-cluster.jpg` of a maize root system which
 we have seen before in
-the [Skimage Images]({{ page.root }}/03-skimage-images/) episode.
+[the _Image Representation in skimage_ episode]({{ page.root }}{% link _episodes/03-skimage-images.md %}).
 
 ~~~
 image = skimage.io.imread("data/maize-root-cluster.jpg")
@@ -566,7 +565,7 @@ data/trial-293.jpg,0.13607895611702128
 > > If we had coordinates for a rectangular area on the image
 > > that contained the circle and the label,
 > > we could mask the area out easily by using techniques we learned in
-> > the [Drawing and Bitwise Operations]({{ page.root }}/04-drawing/) episode.
+> > [the _Drawing and Bitwise Operations_ episode]({{ page.root }}{% link _episodes/04-drawing.md %}).
 > >
 > > However, a closer inspection of the binary images raises some issues with
 > > that approach.

episodes/08-connected-components.md

@@ -19,8 +19,8 @@ keypoints:
 
 ## Objects
 
-In the [thresholding episode]({{ page.root }}/07-thresholding) we have
-covered dividing an image into foreground and background pixels.
+In [the _Thresholding_ episode]({{ page.root }}{% link _episodes/07-thresholding.md %})
+we have covered dividing an image into foreground and background pixels.
 In the shapes example image,
 we considered the colored shapes as foreground _objects_ on a white background.
 
@@ -236,7 +236,7 @@ def connected_components(filename, sigma=1.0, t=0.5, connectivity=2):
 Note the new import of `skimage.measure` in order to use the
 `skimage.measure.label` function that performs the CCA.
 The first four lines of code are familiar from
-the [Thresholding]({{ page.root }}/07-thresholding) episode.
+[the _Thresholding_ episode]({{ page.root }}{% link _episodes/07-thresholding.md %}).
 
 <!-- Note: shapes image: with sigma=2.0, threshold=0.9 -> 11 objects; with sigma=5 -> 8 objects -->
 
@@ -453,7 +453,7 @@ So we could use a minimum area as a criterion for when an object should be detec
 To apply such a criterion,
 we need a way to calculate the area of objects found by connected components.
 Recall how we determined the root mass in
-the [Thresholding]({{ page.root }}/07-thresholding)
+[the _Thresholding_ episode]({{ page.root }}{% link _episodes/07-thresholding.md %})
 by counting the pixels in the binary mask.
 But here we want to calculate the area of several objects in the labeled image.
 The skimage library provides the function `skimage.measure.regionprops`
@@ -483,7 +483,7 @@ This will produce the output
 > ## Plot a histogram of the object area distribution (10 min)
 >
 > Similar to how we determined a "good" threshold in
-> the [Thresholding]({{ page.root }}/07-thresholding) episode,
+> [the _Thresholding_ episode]({{ page.root }}{% link _episodes/07-thresholding.md %}),
 > it is often helpful to inspect the histogram of an object property.
 > For example, we want to look at the distribution of the object areas.
 >