Update image tutorial.

anntzer · anntzer · commit 2e9fe2d3c353 · 2022-09-14T18:38:24.000+02:00
- Use Pillow to load the image rather than the discouraged plt.imread.
- Remove discussion about rescaling to 0-1 as dropping the use of
  plt.imread also gets rid of that (not so nice) behavior.  Also drop
  the lengthy discussion about uint8 and float32, which seems out of
  place here (if anything it should be in the Pillow docs).  Make
  corresponding fixes to code to use 0-255 instead of 0-1.
- Fix subplot management to go full pyplot (the local switch to
  OO-style is a bit weird).
- Fix interpolation discussion as the default is now "nearest" (actually
  it's now "antialiased", but let's sweep that under the rug for this
  tutorial for now).
diff --git a/tutorials/introductory/images.py b/tutorials/introductory/images.py
@@ -49,7 +49,8 @@
 """
 
 import matplotlib.pyplot as plt
-import matplotlib.image as mpimg
+import numpy as np
+from PIL import Image
 
 ###############################################################################
 # .. _importing_data:
@@ -74,20 +75,13 @@
 #
 # And here we go...
 
-img = mpimg.imread('../../doc/_static/stinkbug.png')
-print(img)
+img = np.asarray(Image.open('../../doc/_static/stinkbug.png'))
+print(repr(img))
 
 ###############################################################################
-# Note the dtype there - float32.  Matplotlib has rescaled the 8 bit
-# data from each channel to floating point data between 0.0 and 1.0.  As
-# a side note, the only datatype that Pillow can work with is uint8.
-# Matplotlib plotting can handle float32 and uint8, but image
-# reading/writing for any format other than PNG is limited to uint8
-# data.  Why 8 bits? Most displays can only render 8 bits per channel
-# worth of color gradation.  Why can they only render 8 bits/channel?
-# Because that's about all the human eye can see.  More here (from a
-# photography standpoint): `Luminous Landscape bit depth tutorial
-# <https://luminous-landscape.com/bit-depth/>`_.
+# Here, we used Pillow to open an image (with `PIL.Image.open`), and
+# immediately converted the `PIL.Image.Image` object into a 8-bit
+# (``dtype=uint8``) numpy array.
 #
 # Each inner list represents a pixel.  Here, with an RGB image, there
 # are 3 values.  Since it's a black and white image, R, G, and B are all
@@ -188,7 +182,7 @@
 # interesting regions is the histogram.  To create a histogram of our
 # image data, we use the :func:`~matplotlib.pyplot.hist` function.
 
-plt.hist(lum_img.ravel(), bins=256, range=(0.0, 1.0), fc='k', ec='k')
+plt.hist(lum_img.ravel(), bins=range(256), fc='k', ec='k')
 
 ###############################################################################
 # Most often, the "interesting" part of the image is around the peak,
@@ -205,20 +199,19 @@
 #
 # You can specify the clim in the call to ``plot``.
 
-imgplot = plt.imshow(lum_img, clim=(0.0, 0.7))
+imgplot = plt.imshow(lum_img, clim=(0, 175))
 
 ###############################################################################
 # You can also specify the clim using the returned object
-fig = plt.figure()
-ax = fig.add_subplot(1, 2, 1)
+plt.subplot(1, 2, 1)
 imgplot = plt.imshow(lum_img)
-ax.set_title('Before')
-plt.colorbar(ticks=[0.1, 0.3, 0.5, 0.7], orientation='horizontal')
-ax = fig.add_subplot(1, 2, 2)
+plt.title('Before')
+plt.colorbar(orientation='horizontal')
+plt.subplot(1, 2, 2)
 imgplot = plt.imshow(lum_img)
-imgplot.set_clim(0.0, 0.7)
-ax.set_title('After')
-plt.colorbar(ticks=[0.1, 0.3, 0.5, 0.7], orientation='horizontal')
+imgplot.set_clim(0, 175)
+plt.title('After')
+plt.colorbar(orientation='horizontal')
 
 ###############################################################################
 # .. _Interpolation:
@@ -242,19 +235,17 @@
 # We'll use the Pillow library that we used to load the image also to resize
 # the image.
 
-from PIL import Image
-
 img = Image.open('../../doc/_static/stinkbug.png')
 img.thumbnail((64, 64), Image.ANTIALIAS)  # resizes image in-place
 imgplot = plt.imshow(img)
 
 ###############################################################################
-# Here we have the default interpolation, bilinear, since we did not
+# Here we have the default interpolation ("nearest"), since we did not
 # give :func:`~matplotlib.pyplot.imshow` any interpolation argument.
 #
-# Let's try some others. Here's "nearest", which does no interpolation.
+# Let's try some others. Here's "bilinear":
 
-imgplot = plt.imshow(img, interpolation="nearest")
+imgplot = plt.imshow(img, interpolation="bilinear")
 
 ###############################################################################
 # and bicubic:
