Why Does My Grayscale Image Appear With a Colormap When Using Matplotlib?

Displaying Images as Grayscale with Matplotlib

When working with images using matplotlib.pyplot.imshow(), converting them to grayscale is essential for overlaying elements with color. To facilitate this conversion, PIL's Image.open().convert("L") function is commonly employed.

Problem

Despite using PIL to convert an image to grayscale, displaying it with matplotlib.pyplot.imshow() results in the image appearing with a colormap instead of true grayscale.

Solution

To resolve this issue, it is crucial to specify the colormap argument when calling matplotlib.pyplot.imshow(). By default, matplotlib selects a colormap that may introduce color into the image. To ensure grayscale representation, set cmap='gray' and explicitly define the gray value range using vmin=0 and vmax=255.

Example Code

The following code snippet demonstrates how to load an image, convert it to grayscale, and display it:

<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, if you prefer the inverse grayscale representation, modify the cmap argument to 'gray_r'.

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