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Source  : 5224322
Branch  : main
Author  : Toby Hodges <[email protected]>
Time    : 2023-12-19 13:25:09 +0000
Message : Merge pull request #318 from bear-rsg/connected-components-changes

Review changes

08-connected-components.md

@@ -316,14 +316,14 @@ labeled_image, count = connected_components(filename="data/shapes-01.jpg", sigma
 
 fig, ax = plt.subplots()
 plt.imshow(labeled_image)
-plt.axis("off")
+plt.axis("off");
 ```
 
 ::::::::::::::::  spoiler
 
 ## Do you see an empty image?
 
-If you are using an old version of Matplotlib you might get a warning
+If you are using an older version of Matplotlib you might get a warning
 `UserWarning: Low image data range; displaying image with stretched contrast.`
 or just see a visually empty image.
 
@@ -371,6 +371,16 @@ Alternatively we could convert the image to RGB and then display it.
 
 :::::::::::::::::::::::::
 
+:::::::::::::::::::::::::::::::::::::::::  callout
+
+## Suppressing outputs in Jupyter Notebooks
+
+We just used `plt.axis("off");` to hide the axis from the image for a visually cleaner figure. The
+semicolon is added to supress the output(s) of the statement, in this [case](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.axis.html) 
+the axis limits. This is specific to Jupyter Notebooks.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
 We can use the function `ski.color.label2rgb()`
 to convert the 32-bit grayscale labeled image to standard RGB colour
 (recall that we already used the `ski.color.rgb2gray()` function
@@ -385,7 +395,7 @@ colored_label_image = ski.color.label2rgb(labeled_image, bg_label=0)
 
 fig, ax = plt.subplots()
 plt.imshow(colored_label_image)
-plt.axis("off")
+plt.axis("off");
 ```
 
 ![](fig/shapes-01-labeled.png){alt='Labeled objects'}
@@ -407,7 +417,7 @@ How does changing the `sigma` and `threshold` values influence the result?
 ## Solution
 
 As you might have guessed, the return value `count` already
-contains the number of found objects in the image. So it can simply be printed
+contains the number of objects found in the image. So it can simply be printed
 with
 
 ```python
@@ -733,7 +743,7 @@ colored_label_image = ski.color.label2rgb(labeled_image, bg_label=0)
 
 fig, ax = plt.subplots()
 plt.imshow(colored_label_image)
-plt.axis("off")
+plt.axis("off");
 
 print("Found", count, "objects in the image.")
 ```
@@ -786,7 +796,7 @@ fig, ax = plt.subplots()
 im = plt.imshow(colored_area_image)
 cbar = fig.colorbar(im, ax=ax, shrink=0.85)
 cbar.ax.set_title("Area")
-plt.axis("off")
+plt.axis("off");
 ```
 
 ![](fig/shapes-01-objects-coloured-by-area.png){alt='Objects colored by area'}

md5sum.txt

@@ -10,7 +10,7 @@
 "episodes/05-creating-histograms.md" "59c07192c0a6217e8a42d3e7365025f9" "site/built/05-creating-histograms.md" "2023-12-08"
 "episodes/06-blurring.md" "8d109bb4c49f27f54857f6d35b4c6b9a" "site/built/06-blurring.md" "2023-12-08"
 "episodes/07-thresholding.md" "bc0b3a64255ef9dd359d4cf3188aba83" "site/built/07-thresholding.md" "2023-12-15"
-"episodes/08-connected-components.md" "ffea5032d5ede5aa12673c83f9c62e97" "site/built/08-connected-components.md" "2023-12-15"
+"episodes/08-connected-components.md" "c4b727de6f6f5ea4f8ef5467759c85bd" "site/built/08-connected-components.md" "2023-12-19"
 "episodes/09-challenges.md" "655bdca8cab5d28dc6b2c2e9275aaecc" "site/built/09-challenges.md" "2023-12-15"
 "instructors/instructor-notes.md" "b1c166a544eb4b9b91f3ac8f2dafd549" "site/built/instructor-notes.md" "2023-05-12"
 "learners/discuss.md" "ad762c335f99400dc2cd1a8aad36bdbd" "site/built/discuss.md" "2023-07-26"