/Road_lane_line_detection

Primary LanguagePython

Road_lane_line_detection Using OpenCV

Overview

This project utilizes the OpenCV library in Python to detect lane lines in images or videos. Lane detection is a crucial component of various applications, including self-driving cars and advanced driver-assistance systems (ADAS). The system identifies lane boundaries and overlays them on the original image or video, providing a visual representation of the road lanes.

Features

  • Lane Detection: Identifies and highlights the lane lines on the road.
  • Region of Interest (ROI): Defines a region of interest within the image to focus on lane detection.
  • Canny Edge Detection: Utilizes Canny edge detection to find potential lane edges.
  • Hough Transform: Applies the Hough line transform to detect lines in the image.
  • Averaging Lines: Averages and extrapolates detected line segments to obtain full lane lines.
  • Visual Output: Generates a visual output showing detected lane lines overlaid on the original image or video.

Usage

  1. Ensure you have OpenCV and NumPy installed. You can install them using pip:

  2. Place your input images or videos in the project directory.

  3. Open the Python script (lane_detection.py) in your preferred code editor.

  4. Modify the input_path variable to specify the path to your input video or image.

  5. Optionally, adjust parameters such as the region of interest or Canny edge detection thresholds to fine-tune lane detection.

  6. Run the script:

  7. The program will process the input and display the output with lane lines highlighted.

Files

  • lane_detection.py: The main Python script for lane detection.
  • test_image.jpg: Sample test image for lane detection.
  • test.mp4: Sample test video for lane detection.

Dependencies

  • Python 3.x
  • OpenCV (Open Source Computer Vision Library)
  • NumPy (Numerical Python)

Acknowledgments

Contact

If you have any questions or issues, please feel free to contact Aditi Gupta