Matplotlib imread() for Image Arrays

matplotlib.pyplot.imread() reads an image file into a NumPy array. It is useful when you want to display an image with Matplotlib, inspect image dimensions, or use image pixels as array data in a quick plotting workflow.

For current Matplotlib code, there is one important caveat: the official documentation says imread() exists mostly for historical reasons and recommends PIL.Image.open() from Pillow for general image loading. Use plt.imread() when you are already working inside a Matplotlib example; use Pillow when you need broader image I/O control.

Syntax

matplotlib.pyplot.imread(fname, format=None)

Passing URL strings directly is deprecated in current Matplotlib. If you need to read an image from a URL, open it yourself and pass the data to Pillow.

Return value and array shape

imread() returns a NumPy array. The shape depends on the image type:

Matplotlib also documents a dtype difference that often surprises beginners: PNG images are returned as float arrays in the 0 to 1 range, while other formats are commonly returned as integer arrays based on the image file’s bit depth.

Read and display an image

import matplotlib.pyplot as plt

img = plt.imread("sample.png")

fig, ax = plt.subplots()
ax.imshow(img)
ax.axis("off")
plt.show()

imread() loads the pixels into img, and imshow() displays that array. For a deeper look at display options, read our guide to Matplotlib imshow().

Check image shape and dtype

import matplotlib.pyplot as plt

img = plt.imread("sample.jpg")
print(img.shape)
print(img.dtype)
print(img.min(), img.max())

These checks tell you whether the image is grayscale, RGB, or RGBA, and whether values are floats or integers. Always check this before normalizing, converting, or passing the array into another image-processing library.

Read an image with Pillow and display it with Matplotlib

For robust loading, use Pillow first and convert the image to a NumPy array:

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

with Image.open("sample.jpg") as im:
    img = np.array(im.convert("RGB"))

plt.imshow(img)
plt.axis("off")
plt.show()

This pattern gives you more control over image mode conversion. It is also the better approach when opening images from URLs, working with metadata, or handling formats outside a simple Matplotlib demo.

Read an image from a URL

Instead of passing a URL string to plt.imread(), open the URL and let Pillow read the file-like object:

from urllib.request import urlopen
from PIL import Image
import numpy as np

url = "https://example.com/image.png"
with urlopen(url) as response:
    with Image.open(response) as im:
        img = np.array(im.convert("RGB"))

This matches Matplotlib’s current guidance and avoids relying on deprecated URL handling.

Grayscale and color images

If the returned array has two dimensions, Matplotlib treats it as scalar image data and maps it through a colormap. Use cmap="gray" when displaying grayscale data:

import matplotlib.pyplot as plt

img = plt.imread("mask.png")
plt.imshow(img, cmap="gray")
plt.axis("off")
plt.show()

If the image is already RGB or RGBA, imshow() ignores the colormap because the array already contains color channels. For color map examples, see our guide to Matplotlib colormaps.

Matplotlib imread vs cv2.imread

If you read an image with OpenCV and display it with Matplotlib, convert BGR to RGB first. Otherwise, red and blue channels will appear swapped.

Common mistakes

Expecting all images to return the same dtype: Check img.dtype and value range before calculations.

Passing URL strings directly: Current Matplotlib deprecates direct URL strings for imread(). Use urlopen() and Pillow instead.

Using cmap on RGB data: cmap affects 2D scalar arrays, not RGB/RGBA arrays.

Confusing Matplotlib and OpenCV color order: Matplotlib displays RGB; OpenCV loads BGR by default.

Official references

• Matplotlib documentation for pyplot.imread()
• Matplotlib image API documentation for imread()
• Pillow file-handling documentation for Image.open()

Conclusion

matplotlib.pyplot.imread() is still useful for quick plotting examples because it returns image pixels as a NumPy array that imshow() can display directly. For production image loading, URL handling, metadata, or broader file-format support, open images with Pillow and pass the resulting NumPy array to Matplotlib.