Python Pil Jupyter Notebook Example

7 min read Oct 14, 2024

Harnessing the Power of Images with Python's PIL in Jupyter Notebooks

Jupyter Notebooks have become an indispensable tool for data scientists and programmers due to their interactive and collaborative nature. Combining this powerful platform with the capabilities of Python's Pillow (PIL) library unlocks a world of possibilities for image manipulation and analysis. This article will guide you through the essential aspects of using PIL within Jupyter Notebooks, showcasing practical examples and techniques to get you started.

Why Choose PIL for Image Processing?

Python Imaging Library (PIL) is a widely used and well-documented library for working with images in Python. It provides a comprehensive set of functionalities for tasks such as:

Opening and Saving Images: PIL allows you to effortlessly open images in various formats, including PNG, JPEG, GIF, and more. You can also save processed images in your desired format.
Image Transformations: Resize, crop, rotate, and flip images to suit your needs.
Color Manipulation: Adjust brightness, contrast, and color balance. Apply filters and effects to enhance or modify image appearance.
Image Analysis: Extract information from images, such as pixel values, color histograms, and edge detection.

Getting Started with PIL in Jupyter Notebook

Installation: Make sure you have PIL installed in your Jupyter Notebook environment. You can do this using the following command in a notebook cell:
```
!pip install Pillow
```
Importing the Library: After installation, import the PIL library into your notebook:
```
from PIL import Image
```

Fundamental PIL Operations

1. Opening and Displaying an Image

from PIL import Image

image_path = 'path/to/your/image.jpg'  # Replace with your image path
image = Image.open(image_path)

image.show()

This code snippet opens an image from the specified path and displays it using your default image viewer.

2. Basic Image Transformations

from PIL import Image

image_path = 'path/to/your/image.jpg' 
image = Image.open(image_path)

# Resize the image
resized_image = image.resize((200, 150))  # Set desired width and height
resized_image.show()

# Crop the image
cropped_image = image.crop((100, 50, 300, 200))  # Specify coordinates for cropping
cropped_image.show()

3. Color Manipulation

from PIL import Image

image_path = 'path/to/your/image.jpg' 
image = Image.open(image_path)

# Convert to grayscale
gray_image = image.convert('L')  # 'L' mode represents grayscale
gray_image.show()

# Adjust brightness (example: increase brightness by 20)
brightened_image = Image.new("RGB", image.size)
for x in range(image.width):
    for y in range(image.height):
        r, g, b = image.getpixel((x, y))
        brightened_image.putpixel((x, y), (min(r + 20, 255), min(g + 20, 255), min(b + 20, 255)))
brightened_image.show()

Illustrative Example: Watermarking an Image

Let's put our PIL skills to work by creating a simple watermarking application.

from PIL import Image, ImageDraw, ImageFont

def watermark_image(image_path, watermark_text, output_path):
   """Adds a watermark to an image.

   Args:
       image_path: Path to the input image.
       watermark_text: Text for the watermark.
       output_path: Path to save the watermarked image.
   """
   image = Image.open(image_path)
   draw = ImageDraw.Draw(image)

   font = ImageFont.truetype("arial.ttf", 20)  # Choose your font
   text_width, text_height = draw.textsize(watermark_text, font)

   # Position watermark in the bottom right corner
   x = image.width - text_width - 10
   y = image.height - text_height - 10

   draw.text((x, y), watermark_text, fill=(255, 255, 255), font=font)  # White text
   image.save(output_path)

# Usage example
watermark_image("input.jpg", "Copyright © 2023", "watermarked_image.jpg")

This function takes an image path, watermark text, and output path as input. It then opens the image, draws the watermark text with a chosen font and color, and saves the watermarked image.

Exploring Advanced PIL Features

PIL offers a wide array of advanced functionalities for image processing, including:

Filters and Effects: Apply various filters, such as blur, sharpen, edge detection, and more.
Image Segmentation: Segment images into different regions based on color, texture, or other characteristics.
Color Quantization: Reduce the number of colors in an image to optimize file size or create interesting artistic effects.
Image Arithmetic and Blending: Perform mathematical operations on images, such as addition, subtraction, and multiplication.

Conclusion

Python's PIL library is a powerful tool for image manipulation and analysis within Jupyter Notebooks. Its user-friendly interface, comprehensive functionality, and integration with the interactive Jupyter environment make it an ideal choice for exploring and experimenting with image data. By mastering the concepts and techniques presented in this article, you can unlock the full potential of PIL to enhance your image processing projects and unlock new creative possibilities.