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Laser Beam Centroid Detection

This project provides a set of techniques for detecting the centroid of a laser beam in images. It leverages superpixel-based segmentation methods (Felzenszwalb, SLIC, Quickshift) to segment an image and then calculates the centroid with different approaches, offering insights into performance and accuracy.

Features

  • Superpixel Segmentation Demonstrates how to apply several segmentation algorithms to help isolate the laser beam region in images.
  • Multiple Centroid Detection Methods Includes implementations using OpenCV morphological operations and Scikit-Image CCL, allowing for easy comparison of computation time and detection accuracy.
  • Performance Evaluation Measures the execution time of each approach for benchmarking purposes.

Installation

  1. Clone the repository
   git clone https://github.com/greg-ogs/Centroid-for-spot-laser.git
  1. Navigate to the project directory
   cd Centroid-for-spot-laser
  1. Install dependencies Make sure you have Python 3.7+ environment, then run:
   pip install --no-cache-dir -r requirements.txt

Alternatively, you can build a dev container using the Dockerfile.

Docker Instructions

  1. Build the Docker image
   docker build -t laser-beam-centroid .
  1. Run the Docker container
   docker run -it --rm laser-beam-centroid

This will create a reproducible environment with all required dependencies installed.

Usage

  1. Prepare your input image Ensure you have all the directories / classes in the image directory
  2. Run the script
   python main.py

This will apply the different segmentation algorithms and centroid methods, then print out the computation times for each approach.

  1. Modify parameters Feel free to customize paths, algorithm parameters, or other settings within the code to suit your needs.