Removing calls to skimage.viewer and switching to "lazy importing" module import

_episodes/03-skimage-images.md

@@ -112,18 +112,11 @@ named `image`.
 Next, we will do something with the image:
 
 ~~~
-import skimage.viewer
-
-# display image
-viewer = skimage.viewer.ImageViewer(image)
-viewer.show()
+skimage.io.imshow(image)
 ~~~
 {: .python}
 
-Once we have the image in the program, we next import the `viewer` module of skimage
-(`skimage.viewer`) and display it using `skimage.viewer.ImageViewer()`, which
-returns a `ImageViewer` object we store in the `viewer` variable.
-We then call `viewer.show()` in order to display the image.
+Once we have the image in the program, we next call `skimage.io.imshow()` in order to display the image.
 
 Next, we will save the image in another format:
 
@@ -259,14 +252,12 @@ We will start by reading the image and displaying it.
 """
 import sys
 import skimage.io
-import skimage.viewer
 
 # read input image, based on filename parameter
 image = skimage.io.imread(fname=sys.argv[1])
 
 # display original image
-viewer = skimage.viewer.ImageViewer(image)
-viewer.show()
+skimage.io.imshow(image)
 ~~~
 {: .python}
 
@@ -304,8 +295,7 @@ Now we can threshold the image and display the result.
 image[image < 128] = 0
 
 # display modified image
-viewer = skimage.viewer.ImageViewer(image)
-viewer.show()
+skimage.io.imshow(image)
 ~~~
 {: .python}
 
@@ -347,21 +337,18 @@ extraneous background detail has been removed.
 > > """
 > > import sys
 > > import skimage.io
-> > import skimage.viewer
 > >
 > > # read input image, based on filename parameter
 > > image = skimage.io.imread(fname=sys.argv[1])
 > >
 > > # display original image
-> > viewer = skimage.viewer.ImageViewer(image)
-> > viewer.view()
+> > skimage.io.imshow(img)
 > >
 > > # change high intensity pixels to gray
 > > image[image > 200] = 64
 > >
 > > # display modified image
-> > viewer = skimage.viewer.ImageViewer(image)
-> > viewer.view()
+> > skimage.io.imshow(img)
 > > ~~~
 > > {: .python}
 > {: .solution}
@@ -389,20 +376,17 @@ because using floating point numbers is numerically more stable.
 """
 import sys
 import skimage.io
-import skimage.viewer
 import skimage.color
 
 # read input image, based on filename parameter
 image = skimage.io.imread(fname=sys.argv[1])
 
 # display original image
-viewer = skimage.viewer.ImageViewer(image)
-viewer.show()
+skimage.io.imshow(image)
 
 # convert to grayscale and display
 gray_image = skimage.color.rgb2gray(image)
-viewer = skimage.viewer.ImageViewer(gray_image)
-viewer.show()
+skimage.io.imshow(gray_image)
 ~~~
 {: .python}
 
@@ -417,15 +401,13 @@ We can also load color images as grayscale directly by passing the argument `as_
 """
 import sys
 import skimage.io
-import skimage.viewer
 import skimage.color
 
 # read input image, based on filename parameter
 image = skimage.io.imread(fname=sys.argv[1], as_gray=True)
 
 # display grayscale image
-viewer = skimage.viewer.ImageViewer(image)
-viewer.show()
+skimage.io.imshow(image)
 ~~~
 {: .python}
 
@@ -472,12 +454,10 @@ A program to create the subimage would start by loading the image:
  * NumPy array slicing.
 """
 import skimage.io
-import skimage.viewer
 
 # load and display original image
 image = skimage.io.imread(fname="board.jpg")
-viewer = skimage.viewer.ImageViewer(image)
-viewer.show()
+skimage.io.imshow(image)
 ~~~
 {: .python}
 
@@ -487,8 +467,7 @@ create a new image with our selected area and then display the new image.
 ~~~
 # extract, display, and save sub-image
 clip = image[60:151, 135:481, :]
-viewer = skimage.viewer.ImageViewer(clip)
-viewer.show()
+skimage.io.imshow(clip)
 skimage.io.imsave(fname="clip.tif", arr=clip)
 ~~~
 {: .python}
@@ -499,8 +478,7 @@ We can also change the values in an image, as shown next.
 # replace clipped area with sampled color
 color = image[330, 90]
 image[60:151, 135:481] = color
-viewer = skimage.viewer.ImageViewer(image)
-viewer.show()
+skimage.io.imshow(image)
 ~~~
 {: .python}
 
@@ -535,18 +513,15 @@ the program:
 > >  * roots in an existing image.
 > > """
 > > import skimage.io
-> > import skimage.viewer
 > >
 > > # load and display original image
 > > image = skimage.io.imread(fname="roots.jpg")
-> > viewer = skimage.viewer.ImageViewer(image)
-> > viewer.show()
+> > skimage.io.imshow(image)
 > >
 > > # extract, display, and save sub-image
 > > # WRITE YOUR CODE TO SELECT THE SUBIMAGE NAME clip HERE:
 > > clip = image[0:1999, 1410:2765, :]
-> > viewer = skimage.viewer.ImageViewer(clip)
-> > viewer.show()
+> > skimage.io.imshow(clip)
 > >
 > >
 > > # WRITE YOUR CODE TO SAVE clip HERE

_episodes/04-drawing.md

@@ -56,20 +56,17 @@ image. (Note that the display portion is used here for pedagogical purposes; it
  * Python program to use skimage drawing tools to create a mask.
  *
 """
-import skimage
-from skimage.viewer import ImageViewer
+import skimage.io
+import skimage.draw
 import numpy as np
 
 # Load and display the original image
 image = skimage.io.imread("maize-roots.tif")
-viewer = ImageViewer(image)
-viewer.show()
+skimage.io.imshow(image)
 ~~~
 {: .python}
 
-As before, we first import `skimage`. We also import the NumPy library, and give
-it an alias of `np`. NumPy is necessary when we create the initial mask image,
-and the alias saves us a little typing. Then, we load and display the initial
+As before, we first import the `io`submodule of `skimage` (`skimage.io`). This time, we will also import the `draw` submodule. We also import the NumPy library, and give it an alias of `np`. NumPy is necessary when we create the initial mask image, and the alias saves us a little typing. Then, we load and display the initial
 image in the same way we have done before.
 
 NumPy allows indexing of images/arrays with "boolean" arrays of the same size.
@@ -142,8 +139,7 @@ The final section of the program displays the mask we just created:
 
 ~~~
 # Display constructed mask
-viewer = ImageViewer(mask)
-viewer.show()
+skimage.io.imshow(mask)
 ~~~
 {: .python}
 
@@ -190,8 +186,8 @@ Here is what our constructed mask looks like:
 > > """
 > > import random
 > > import numpy as np
-> > import skimage
-> > from skimage.viewer import ImageViewer
+> > import skimage.draw
+> > import skimage.io
 > >
 > > # create the black canvas
 > > image = np.zeros(shape=(600, 800, 3), dtype="uint8")
@@ -200,9 +196,9 @@ Here is what our constructed mask looks like:
 > > for i in range(15):
 > >     x = random.random()
 > >     if x < 0.33:
-> >         rr, cc = skimage.draw.circle(
+> >         rr, cc = skimage.draw.disk((
 > >             random.randrange(600),
-> >             random.randrange(800),
+> >             random.randrange(800)),
 > >             radius=50,
 > >             shape=image.shape[0:2],
 > >         )
@@ -226,8 +222,7 @@ Here is what our constructed mask looks like:
 > >     image[rr, cc] = color
 > >
 > > # display the results
-> > viewer = ImageViewer(image)
-> > viewer.show()
+> > skimage.io.imshow(image)
 > > ~~~
 > > {: .python}
 > {: .solution}
@@ -266,8 +261,8 @@ original image and create the mask in the same way as before:
  *
 """
 import numpy as np
-import skimage
-from skimage.viewer import ImageViewer
+import skimage.io
+import skimage.draw
 
 # Load the original image
 image = skimage.io.imread("maize-roots.tif")
@@ -293,8 +288,7 @@ image[mask] = 0
 Then, we display the masked image.
 
 ~~~
-viewer = ImageViewer(image)
-viewer.show()
+skimage.io.imshow(image)
 ~~~
 {: .python}
 
@@ -330,8 +324,8 @@ The resulting masked image should look like this:
 > >  *
 > > """
 > > import numpy as np
-> > import skimage
-> > from skimage.viewer import ImageViewer
+> > import skimage.io
+> > import skimage.draw
 > >
 > > # Load the original image
 > > image = skimage.io.imread("./fig/03-remote-control.jpg")
@@ -345,8 +339,7 @@ The resulting masked image should look like this:
 > >
 > > # Apply the mask and display the result
 > > image[mask] = 0
-> > viewer = ImageViewer(image)
-> > viewer.show()
+> > skimage.io.imshow(image)
 > > ~~~
 > > {: .python}
 > {: .solution}
@@ -388,8 +381,8 @@ The resulting masked image should look like this:
 > >  * in a standardized scanned 96-well plate image.
 > > """
 > > import numpy as np
-> > import skimage
-> > from skimage.viewer import ImageViewer
+> > import skimage.io
+> > import skimage.draw
 > > import sys
 > >
 > > # read in original image
@@ -449,8 +442,8 @@ The resulting masked image should look like this:
 > >  * using a file with well center location.
 > > """
 > > import numpy as np
-> > import skimage
-> > from skimage.viewer import ImageViewer
+> > import skimage.io
+> > import skimage.draw
 > > import sys
 > >
 > > # read in original image

_episodes/05-creating-histograms.md

@@ -51,16 +51,14 @@ import sys
 import numpy as np
 import skimage.color
 import skimage.io
-import skimage.viewer
 from matplotlib import pyplot as plt
 
 # read image, based on command line filename argument;
 # read the image as grayscale from the outset
 image = skimage.io.imread(fname=sys.argv[1], as_gray=True)
 
 # display the image
-viewer = skimage.viewer.ImageViewer(image)
-viewer.show()
+skimage.io.imshow(image)
 ~~~
 {: .python}
 
@@ -195,16 +193,14 @@ the program produces this histogram:
 > > import numpy as np
 > > import skimage.draw
 > > import skimage.io
-> > import skimage.viewer
 > > from matplotlib import pyplot as plt
 > >
 > > # read image, based on command line filename argument;
 > > # read the image as grayscale from the outset
 > > img = skimage.io.imread(fname=sys.argv[1], as_gray=True)
 > >
 > > # display the image
-> > viewer = skimage.viewer.ImageViewer(img)
-> > viewer.show()
+> > skimage.io.imshow(img)
 > >
 > > # create mask here, using np.zeros() and skimage.draw.rectangle()
 > > mask = np.zeros(shape=img.shape, dtype="bool")
@@ -249,16 +245,14 @@ color histograms starts in a familiar way:
 """
 import sys
 import skimage.io
-import skimage.viewer
 from matplotlib import pyplot as plt
 
 # read original image, in full color, based on command
 # line argument
 image = skimage.io.imread(fname=sys.argv[1])
 
 # display the image
-viewer = skimage.viewer.Viewer(image)
-viewer.show()
+skimage.io.imshow(image)
 ~~~
 {: .python}
 
@@ -413,7 +407,6 @@ Finally we label our axes and display the histogram, shown here:
 > > """
 > > import sys
 > > import skimage.io
-> > import skimage.viewer
 > > import skimage.draw
 > > import numpy as np
 > > from matplotlib import pyplot as plt
@@ -423,13 +416,12 @@ Finally we label our axes and display the histogram, shown here:
 > > image = skimage.io.imread(fname=sys.argv[1])
 > >
 > > # display the original image
-> > viewer = skimage.viewer.ImageViewer(image)
-> > viewer.show()
+> > skimage.io.imshow(image)
 > >
 > > # create a circular mask to select the 7th well in the first row
 > > # WRITE YOUR CODE HERE
 > > mask = np.zeros(shape=image.shape[0:2], dtype="bool")
-> > circle = skimage.draw.circle(240, 1053, radius=49, shape=image.shape[:2])
+> > circle = skimage.draw.disk((240, 1053), radius=49, shape=image.shape[:2])
 > > mask[circle] = 1
 > >
 > > # just for display:
@@ -442,8 +434,7 @@ Finally we label our axes and display the histogram, shown here:
 > > # create a new window and display maskedImg, to verify the
 > > # validity of your mask
 > > # WRITE YOUR CODE HERE
-> > viewer = skimage.viewer.ImageViewer(masked_img)
-> > viewer.show()
+> > skimage.io.imshow(masked_img)
 > >
 > > # list to select colors of each channel line
 > > colors = ("r", "g", "b")