Lane Detection - Readme

This project demonstrates the implementation of a lane detection algorithm using computer vision techniques. It can be used to detect and highlight lane markings in images or videos.

Project Overview

The project consists of the following files:

• main.py: This file contains the Python code for the lane detection algorithm. It utilizes OpenCV and other libraries to perform various image processing and computer vision tasks.

Usage

To use the lane detection algorithm, follow these steps:

1. Make sure you have Python installed on your system. You can download and install Python from the official website: https://www.python.org/downloads/

2. Install the required dependencies by running the following command in your terminal or command prompt:

   pip install opencv-python matplotlib numpy moviepy
   

3. Place the image or video file you want to process in the same directory as the main.py file.

4. Open the main.py file in a text editor or IDE of your choice.

5. Modify the process_image() function if needed. This function defines the pipeline for lane detection and can be customized based on specific requirements.

6. In the main() function, specify the path to your input image or video file. Update the input_file variable accordingly.

7. Save the changes to the main.py file.

8. Open a terminal or command prompt, navigate to the directory containing the main.py file, and run the following command:

   python main.py
   

   The lane detection algorithm will process the input file and display the output. For videos, the processed video will be saved in the same directory with the filename appended with "_output".

9. Review the output and adjust the parameters or code as needed to improve the lane detection results.

Customization

You can customize the lane detection algorithm based on your requirements. Here are some possible modifications:

• Adjust the thresholds or parameters used in the image processing steps, such as color filtering, edge detection, or Hough line detection.
• Modify the region of interest (ROI) vertices to focus on specific areas of the image.
• Experiment with different image enhancement techniques, such as histogram equalization or contrast stretching.
• Implement additional post-processing steps, such as curve fitting or lane tracking.

Remember to test and evaluate the modifications to ensure accurate and reliable lane detection results.

Notes

• This lane detection algorithm is designed for straight or slightly curved roads with visible lane markings. It may not perform well in challenging conditions or on roads without clear lane markings.
• The algorithm relies on computer vision techniques and assumes that the lane markings are distinguishable from the surrounding environment. It may not work accurately in complex scenarios or under varying lighting conditions.

Credits

The lane detection algorithm is implemented using Python and various libraries, including OpenCV, matplotlib, numpy, and moviepy. It draws inspiration from existing computer vision techniques and lane detection research. The project is intended for educational and demonstration purposes.

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Please modify the content according to your specific lane detection project. Include relevant details, instructions, and additional information as needed.