Merge pull request #201 from datacarpentry/200-call-attention-to-very-different-implications-for-similar-looking-syntax

200 call attention to very different implications for similar looking syntax

Author: uschille

episodes/07-thresholding.md

@@ -143,8 +143,11 @@ to create a mask. Here, we want to turn "on" all pixels which have
 values smaller than the threshold, so we use the less operator `<` to
 compare the `blurred_image` to the threshold `t`. The operator returns
 a mask, that we capture in the variable `binary_mask`. It has only one
-channel, and each of its values is either 0 or 1. The binary mask
-created by the thresholding operation can be shown with `plt.imshow`.
+channel, and each of its values is either `False` or `True`. The
+binary mask created by the thresholding operation can be shown with
+`plt.imshow`, where the `False` entries are shown as black pixels
+(0-valued) and the `True` entries are shown as white pixels
+(1-valued).
 
 ~~~
 # create a mask based on the threshold
@@ -193,8 +196,8 @@ colored shapes from the original.
 
 ~~~
 # use the binary_mask to select the "interesting" part of the image
-selection = np.zeros_like(image)
-selection[binary_mask] = image[binary_mask]
+selection = image.copy()
+selection[~binary_mask] = 0
 
 fig, ax = plt.subplots()
 plt.imshow(selection)
@@ -268,8 +271,8 @@ plt.show()
 > > And here are the commands to apply the mask and view the thresholded image
 > > ~~~
 > > image = skimage.io.imread("data/shapes-02.jpg")
-> > selection = np.zeros_like(image)
-> > selection[binary_mask] = image[binary_mask]
+> > selection = image.copy()
+> > selection[~binary_mask] = 0
 > >
 > > fig, ax = plt.subplots()
 > > plt.imshow(selection)
@@ -384,8 +387,8 @@ Finally, we use the mask to select the foreground:
 
 ~~~
 # apply the binary mask to select the foreground
-selection = np.zeros_like(image)
-selection[binary_mask] = image[binary_mask]
+selection = image.copy()
+selection[~binary_mask] = 0
 
 fig, ax = plt.subplots()
 plt.imshow(selection)
@@ -605,10 +608,10 @@ data/trial-293.jpg,0.13607895611702128
 > >     # blur before thresholding
 > >     blurred_image = skimage.filters.gaussian(image, sigma=sigma)
 > >
-> >     # perform inverse binary thresholding to mask the white label and circle
-> >     binary_mask = blurred_image > 0.95
+> >     # perform binary thresholding to mask the white label and circle
+> >     binary_mask = blurred_image < 0.95
 > >     # use the mask to remove the circle and label from the blurred image
-> >     blurred_image[binary_mask] = 0
+> >     blurred_image[~binary_mask] = 0
 > >
 > >     # perform automatic thresholding to produce a binary image
 > >     t = skimage.filters.threshold_otsu(blurred_image)

episodes/08-connected-components.md

@@ -1,7 +1,7 @@
 ---
 title: "Connected Component Analysis"
 teaching: 70
-exercises: 45
+exercises: 55
 questions:
 - "How to extract separate objects from an image and describe these objects quantitatively."
 objectives:
@@ -682,7 +682,7 @@ This will produce the output
 > {: .solution}
 {: .challenge}
 
-> ## Color objects by area (10 min)
+> ## Color objects by area (optional, not included in timing)
 >
 > Finally, we would like to display the image with the objects colored
 > according to the magnitude of their area. In practice, this can be
@@ -715,5 +715,22 @@ This will produce the output
 > > {: .language-python}
 > >
 > > ![Objects colored by area](../fig/shapes-01-objects-coloured-by-area.png)
+> >
+> > > You may have noticed that in the solution, we have used the
+> > > `labeled_image` to index the array `object_areas`. This is an
+> > > example of [advanced indexing in
+> > > Numpy](https://numpy.org/doc/stable/user/basics.indexing.html#advanced-indexing)
+> > > The result is an array of the same shape as the `labeled_image`
+> > > whose pixel values are selected from `object_areas` according to
+> > > the object label. Hence the objects will be colored by area when
+> > > the result is displayed. Note that advanced indexing with an
+> > > integer array works slightly different than the indexing with a
+> > > Boolean array that we have used for masking. While Boolean array
+> > > indexing returns only the entries corresponding to the `True`
+> > > values of the index, integer array indexing returns an array
+> > > with the same shape as the index. You can read more about advanced
+> > > indexing in the [Numpy
+> > > documentation](https://numpy.org/doc/stable/user/basics.indexing.html#advanced-indexing).
+> > {: .callout}
 > {: .solution}
 {: .challenge}