Lane Detection Using Sliding Windows in Python with OpenCV

This project demonstrates lane detection using the sliding windows technique in Python, leveraging OpenCV for image processing. The video tutorial provides a step-by-step explanation, covering essential techniques like perspective transformation, thresholding, and histogram analysis.

YouTube Video Tutorial

Follow along with the detailed tutorial:
Lane Detection Using Sliding Windows | Image Processing Project

Project Overview

Steps Covered:

1. Reading Video: Load a video file for processing.
2. Region of Interest (ROI): Define the area of interest for lane detection.
3. Perspective Transformation: Transform the perspective to align road lanes properly.
4. Image Thresholding: Highlight lane lines using color and gradient thresholding.
5. Histogram & Sliding Windows: Detect lane lines based on a histogram and sliding window approach.

Chapters

• 00:00 Introduction
• 01:48 Step-1: Reading Video
• 02:20 Step-2: Region of Interest
• 04:29 Step-3: Perspective Transformation
• 05:36 Step-4: Image Thresholding
• 06:59 Final Step: Histogram
• 14:10 Final Step: Sliding Windows

Resources and Further Reading

This project is supported by various resources to enhance understanding:

1. Reading Video File: Video Link
2. Perspective Transformation - Coding Aspect: Video Link
3. Perspective Transformation - Logic & Math: Video Link
4. Image Thresholding - Coding Aspect: Video Link
5. Image Thresholding - Logic Aspect: Video Link
6. Lane Detection Using Sliding Windows In Python Using OpenCV | Tutorial Video Link
7. Lane Detection with Sliding Windows | Map Lanes to Original Video Frame Video Link
8. Lane Detection to Autonomous Driving Video Link

Additional reference:

• Ross Kippenbrock - Finding Lane Lines for Self-Driving Cars

Code

The complete code for the project is provided in this repository.

Requirements

• Python 3.x
• OpenCV2
• Numpy

License

This project is open-source and available under the MIT License.