Pillow is a powerful Python library for image processing, and while it's not designed for advanced segmentation tasks like deep learning models, it can still be used for basic pixel-level operations. This tutorial will guide you through simple segmentation techniques using Pillow.

What is Image Segmentation? 🔍

Image segmentation involves dividing an image into multiple segments (sets of pixels) to simplify or analyze it. While Pillow doesn't offer built-in machine learning tools, it can help with:

  • Thresholding (e.g., black-and-white conversion)
  • Color-based filtering
  • Object extraction using masks

For advanced segmentation, consider exploring: /en/tutorials/using_tensorflow_for_segmentation

Quick Start Guide 🚀

  1. Install Pillow 

    pip install pillow

  2. Load an Image 

    from PIL import Image
img = Image.open("example.jpg")

  3. Basic Segmentation Example 

    # Convert to grayscale and apply threshold
img_gray = img.convert("L")
threshold = 128
img_segmented = img_gray.point(lambda x: 255 if x > threshold else 0)
    Image_Segmentation_Example

Advanced Techniques 🔧

  • Use ImageFilter for edge detection
  • Apply draw.polygon() to create custom masks
  • Combine with NumPy for pixel array manipulation

Tips for Better Results 📌

  • Always resize images before processing
  • Experiment with different threshold values
  • Use show() to visualize intermediate steps

For deeper insights into segmentation algorithms, check our Advanced Image Processing Tutorial.