Matplotlib - Images

What are Images in Matplotlib?

In Matplotlib library displaying and manipulating images involves using the imshow() function. This function visualizes 2D arrays or images. This function is particularly useful for showing images in various formats such as arrays representing pixel values or actual image files.

Images in Matplotlib provide a way to visualize gridded data, facilitating the interpretation and analysis of information represented in 2D arrays. This capability is crucial for various scientific, engineering and machine learning applications that deal with image data.

Use Cases for Images in Matplotlib

The following are the use cases of Images in Matplotlib library.

Visualizing Gridded Data

The matplotlib library can be used for displaying scientific data such as heatmaps, terrain maps, satellite images etc.

Image Processing

Analyzing and manipulating image data in applications such as computer vision or image recognition.

Artificial Intelligence and Machine Learning

Handling and processing image data in training and evaluation of models.

Loading and Displaying Images

To load and display an image using Matplotlib library we can use the following lines of code.

Example

import matplotlib.pyplot as plt
import matplotlib.image as mpimg

# Load the image
img = mpimg.imread('Images/flowers.jpg')  # Load image file

# Display the image
plt.imshow(img)
plt.axis('off')  # Turn off axis labels and ticks (optional)
plt.show()

Key Points of the above code

matplotlib.image.imread() − Loads an image file and returns it as an array. The file path ('image_path') should be specified.
plt.imshow() − Displays the image represented by the array.
plt.axis('off') − Turns off axis labels and ticks, which is optional for purely displaying the image without axes.

Output

Customizing Image Display

We can customize the image as per the requirement by thebelow mentioned functions.

Colormap − We can apply a colormap to enhance image visualization by specifying the cmap parameter in imshow().
Colorbar − To add a colorbar indicating the intensity mapping we can use plt.colorbar() after imshow().

Example

import matplotlib.pyplot as plt
import matplotlib.image as mpimg

# Load the image
img = mpimg.imread('Images/flowers.jpg')  # Load image file

# Display the image
plt.imshow(img, cmap = 'Oranges')
plt.colorbar()

# Turn on axis labels and ticks (optional)
plt.axis('on')  
plt.show()

Output

Image Manipulation

We can perform manipulation for our images by using the below mentioned functions.

Cropping − Select a specific portion of the image by slicing the array before passing it to imshow().
Resizing − Use various image processing libraries such as Pillow, OpenCV to resize images before displaying them.

Example

In this example we are manipulating the image and displaying the image by using the above mentioned functions.

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2

# Load the image
img = mpimg.imread('Images/flowers.jpg')

# Display the image with grayscale colormap and colorbar
plt.imshow(img, cmap='gray')
plt.colorbar()

# Display only a portion of the image (cropping)
plt.imshow(img[100:300, 200:400])

# Display a resized version of the image
resized_img = cv2.resize(img, (new_width, new_height))
plt.imshow(resized_img)
plt.show()

Output

Remember Matplotlib's imshow() is suitable for basic image display and visualization. For more advanced image processing tasks such as resizing, filtering, etc. using dedicated image processing libraries like OpenCV or Pillow is recommended.

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