How do I Display a Grayscale Image Correctly with Matplotlib.pyplot.imshow()?

Achieving Grayscale Image Display with Matplotlib

When attempting to display a grayscale image using matplotlib.pyplot.imshow(), users may encounter difficulties resulting in the image being displayed as a colormap. To address this issue, it's crucial to understand the correct steps for grayscale image conversion.

In this case, the user has loaded the image and converted it to grayscale using PIL's Image.open().convert("L") function. However, the subsequent conversion to a matrix using scipy.misc.fromimage() introduced an unnecessary step and potentially corrupted the image's grayscale representation.

To display the grayscale image correctly, follow these steps:

1. Import the necessary libraries: numpy for numerical operations and matplotlib.pyplot for image plotting.
2. Load the image using PIL's Image.open().
3. Convert the image to grayscale using convert("L").
4. Convert the grayscale image to a NumPy array using np.asarray().
5. Use matplotlib.pyplot.imshow() to display the grayscale image. Ensure to specify cmap='gray' to enforce the grayscale representation.

Here's the sample code:

<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, for displaying the inverse grayscale, switch the cmap argument to 'gray_r'.

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