How to perform image thresholding in Python?

Table of Contents

• Introduction
• Common Thresholding Techniques
  • 1. Global Thresholding
    • Using OpenCV
  • 2. Adaptive Thresholding
    • Using OpenCV
  • 3. Otsu's Thresholding
    • Using OpenCV
  • 4. Thresholding Using Pillow
    • Using Pillow
• Practical Examples
  • Example 1: Simple Object Detection
  • Example 2: Document Scanning
• Conclusion

Introduction

Image thresholding is a crucial technique in image processing that converts a grayscale image into a binary image, making it easier to analyze. It works by setting a threshold value that separates pixel intensities into two categories: one for foreground and another for background. This technique is widely used in various applications, such as object detection and image segmentation. This guide will cover different thresholding methods in Python using OpenCV and Pillow.

Common Thresholding Techniques

1. Global Thresholding

Global thresholding applies a single threshold value across the entire image, making it simple and effective for images with good contrast between the foreground and background.

Using OpenCV

2. Adaptive Thresholding

Adaptive thresholding calculates the threshold for smaller regions of the image, which is useful for images with varying lighting conditions.

Using OpenCV

3. Otsu's Thresholding

Otsu's method automatically calculates the optimal threshold value from the image histogram, making it effective for bimodal images.

Using OpenCV

4. Thresholding Using Pillow

Pillow also provides methods to perform thresholding on images.

Using Pillow

Practical Examples

Example 1: Simple Object Detection

You can use thresholding for detecting simple objects in images.

Example 2: Document Scanning

Thresholding can enhance scanned documents by separating the text from the background.

Conclusion

Image thresholding is a fundamental technique in image processing that converts grayscale images into binary formats, facilitating easier analysis. By using libraries such as OpenCV and Pillow, you can apply various thresholding techniques like global, adaptive, and Otsu's thresholding effectively. Understanding and g these techniques allows you to enhance image processing tasks in your Python projects.