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Update image tutorial. #23889

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Merged
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Sep 15, 2022
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Update image tutorial. #23889

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62 changes: 23 additions & 39 deletions tutorials/introductory/images.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,8 @@
"""

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
from PIL import Image

###############################################################################
# .. _importing_data:
Expand All @@ -72,23 +73,14 @@
# <https://raw.githubusercontent.com/matplotlib/matplotlib/main/doc/_static/stinkbug.png>`_
# to your computer for the rest of this tutorial.
#
# And here we go...
# We use Pillow to open an image (with `PIL.Image.open`), and immediately
# convert the `PIL.Image.Image` object into an 8-bit (``dtype=uint8``) numpy
# array.

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/>`_.
#
# 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
# similar. An RGBA (where A is alpha, or transparency), has 4 values
Expand Down Expand Up @@ -188,37 +180,31 @@
# 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,
# and you can get extra contrast by clipping the regions above and/or
# below the peak. In our histogram, it looks like there's not much
# useful information in the high end (not many white things in the
# image). Let's adjust the upper limit, so that we effectively "zoom in
# on" part of the histogram. We do this by passing the clim argument to
# imshow. You could also do this by calling the
# :meth:`~matplotlib.cm.ScalarMappable.set_clim` method of the image plot
# object, but make sure that you do so in the same cell as your plot
# command when working with the Jupyter Notebook - it will not change
# plots from earlier cells.
# on" part of the histogram. We do this by setting *clim*, the colormap
# limits.
#
# You can specify the clim in the call to ``plot``.
# This can be done by passing a *clim* keyword argument in the call to
# ``imshow``.

imgplot = plt.imshow(lum_img, clim=(0.0, 0.7))
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)
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)
# This can also be done by calling the
# :meth:`~matplotlib.cm.ScalarMappable.set_clim` method of the returned image
# plot object, but make sure that you do so in the same cell as your plot
# command when working with the Jupyter Notebook - it will not change
# plots from earlier cells.

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)

###############################################################################
# .. _Interpolation:
Expand All @@ -242,19 +228,17 @@
# 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
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Suggested change
img.thumbnail((64, 64), Image.ANTIALIAS) # resizes image in-place
img.thumbnail((64, 64)) # resizes image in-place

May just as well put that here instead of #23881 (which can be closed then).

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I merged yours.

imgplot = plt.imshow(img)

###############################################################################
# Here we have the default interpolation, bilinear, since we did not
# Here we use 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:
Expand Down