How to Display Grayscale Images Correctly with Matplotlib\'s `imshow()` Function?

Loading and Displaying Grayscale Images with Matplotlib

Converting an image to grayscale involves removing its color information, resulting in an image with shades of gray. While Matplotlib provides the imshow() function for displaying images, it can mistakenly display grayscale images using a color map, which introduces unwanted colors.

To rectify this, it's crucial to specify the cmap argument in imshow() to 'gray'. This instructs Matplotlib to use a grayscale color map, ensuring that the image is displayed in shades of gray. Additionally, setting the vmin and vmax parameters to the minimum and maximum values of the pixel intensities, respectively, ensures that the grayscale values are correctly represented.

Here's an example code that demonstrates this:

<code class="python">import numpy as np
import matplotlib.pyplot as plt
from PIL import Image

fname = 'image.png'
image = Image.open(fname).convert("L")
arr = np.asarray(image)
plt.imshow(arr, cmap='gray', vmin=0, vmax=255)
plt.show()</code>

This code reads an image from a file named 'image.png', converts it to grayscale, and displays the grayscale image using Matplotlib's imshow() function with the correct grayscale color map. By using this approach, you can successfully display grayscale images without encountering color map issues.

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