Python Pillow - ImageOps.solarize() Function

The PIL.ImageOps.solarize function is used to invert pixel values above a specified threshold in a greyscale image.

Syntax

Following is the syntax of the function −

PIL.ImageOps.solarize(image, threshold=128)

Parameters

Here are the details of this function parameters −

• image − The image to solarize.

• threshold − All pixels above this greyscale level are inverted. The default threshold is set to 128, which means that pixels with intensity values greater than 128 will be inverted.

Return Value

The function returns a new image object where pixel values above the specified threshold have been inverted.

Examples

Example 1

In this example, the ImageOps.solarize function is used to invert pixel values above the default threshold of 128 in the input image.

from PIL import Image, ImageOps

# Open an image file
input_image = Image.open("Images/butterfly.jpg")

# Solarize the image with the default threshold
solarized_image = ImageOps.solarize(input_image)

# Display the original and solarized images
input_image.show()
solarized_image.show()

Output

Input Image

Output Image

Example 2

Here's another example using the PIL.ImageOps.solarize() function with a different image and different threshold.

from PIL import Image, ImageOps

# Open an image file
input_image = Image.open("Images/Tajmahal_2.jpg")

# Solarize the image with a specific threshold
solarized_image = ImageOps.solarize(input_image, threshold=100)

# Display the original and solarized images
input_image.show()
solarized_image.show()

Output

Input Image

Output Image

Example 3

In this example, the code performs solarization with different threshold values of an image, and the results are displayed using Matplotlib.

from PIL import Image, ImageOps
import matplotlib.pyplot as plt

# Open an image file
input_image = Image.open("Images/flowers_1.jpg")

# Define three different thresholds
thresholds = [0, 50, 100]

# Create subplots for original and solarized images
num_thresholds = len(thresholds) + 1 
fig, axes = plt.subplots(2, 2, figsize=(10, 8))
ax = axes.ravel()

# Display the original image
ax[0].imshow(input_image)
ax[0].set_title('Original Image')
ax[0].axis('off')

# Solarize the image with different thresholds and display the results
for idx, threshold in enumerate(thresholds, start=1):
   solarized_image = ImageOps.solarize(input_image, threshold=threshold)
   
   # Display the solarized images
   ax[idx].imshow(solarized_image)
   ax[idx].set_title(f'Solarized (Threshold = {threshold})')
   ax[idx].axis('off')

plt.tight_layout()
plt.show()

Output