Line Detection and Occupancy Grid Mapping

This project detects a black line in a video and represents it as an occupancy grid, similar to a ROS-style mapping system. It processes video frames in real time using OpenCV, applies image processing techniques, and generates an adjustable occupancy grid using NumPy.

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Features

✅ Detects a black line from video frames
✅ Generates an occupancy grid without using ROS
✅ Adjustable grid size for different use cases
✅ Real-time processing with OpenCV
✅ Highlights line direction and contours for visualization

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How It Works

1. Preprocessing:

   • Convert each video frame to grayscale.
   • Apply Gaussian blur to reduce noise.
   • Use binary thresholding to highlight the black line.

2. Line Detection:

   • Identify contours in the image.
   • Select the largest contour as the black line.
   • Fit a direction line using cv2.fitLine().

3. Grid Mapping:

   • Resize the detected line into a 100x100 (or custom) occupancy grid.
   • Use NumPy to assign grid values:
     • 100 for occupied (line detected)
     • 0 for free space

4. Visualization:

   • Display the original video frame with detected contours.
   • Generate a zoomed-in occupancy grid with grid lines and labels.
   • Highlight the detected path’s direction using a red guiding line.

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Installation

Requirements

• Python 3.x
• OpenCV (pip install opencv-python)
• NumPy (pip install numpy)

Running the Project

1. Clone the repository:

   git clone https://github.com/Line_detection-camera-.git
   cd Line_detection-camera-

2. Place your video file in the project directory.
3. Update the video path in line.py:

   video_path = "vid.mp4"  # Change this to your video file path

4. Run the script:

   python line.py

5. Press 'q' to exit the visualization.

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Code Overview

line.py

🔹 Reads video frames and applies preprocessing.
🔹 Detects the largest black contour as the main path.
🔹 Fits a directional line for better visualization.
🔹 Generates and displays an occupancy grid.

Adjustable Parameters

• grid_size: Changes the occupancy grid resolution.
• line_length: Adjusts the detected path’s guide line length.

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Example Output

🖼 Real-time Frame with Line Detection

🖼 Generated Occupancy Grid

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Challenges & Solutions

🔸 No ROS Support: Used NumPy to manually create an occupancy grid.
🔸 Noisy Image Data: Applied Gaussian blur and thresholding.
🔸 Processing Speed: Resized frames for faster performance.
🔸 Unwanted Contours: Selected only the largest detected contour.

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Future Improvements

🚀 Integrate with robot navigation for path-following applications.
🎯 Enhance contour filtering to handle complex environments.
📡 Expand to multi-line detection for lane tracking.

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Contributors

👤 Manish Kumar