How to Display Grayscale Images Correctly in Matplotlib?

Displaying Images as Grayscale

Many image manipulation tasks often require grayscale images for ease of processing. Displaying grayscale images using Matplotlib's imshow() function can be challenging when the image is accidentally rendered as a colormap.

To resolve this issue and display a grayscale image correctly, follow these steps:

1. Import necessary libraries: Load important libraries such as NumPy for image processing, Matplotlib for plotting, and PIL for image manipulation.
2. Read and convert image to grayscale: Use PIL's Image.open() function to read the image from a file. Subsequently, convert the image to grayscale using convert("L").
3. Convert image to matrix: Transform the grayscale image into a numerical matrix using SciPy's scipy.misc.fromimage().
4. Specify grayscale display: When displaying the matrix using matplotlib.pyplot.imshow(), explicitly specify the colormap as 'gray' to render the image in grayscale. Adjust the 'vmin' and 'vmax' parameters to set the value range for grayscale intensities.

<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>

Alternatively, to display the inverse grayscale, simply change the cmap parameter to 'gray_r'.

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