Hybrid Testbed for Image Segmentation Control

This repository contains a hybrid testbed project for image segmentation control, utilizing various tools and techniques for open loop control and ROS implementation.

Project Structure

• Hybrid-Testbed
  • Open Loop Control
    • Contains scripts and resources for open loop control implementation.
  • ROS Implementation
    • Contains scripts and resources for ROS (Robot Operating System) implementation.

Features

• Live Video Feed: Start and stop live video feed from a Basler camera.
• Display Options: Choose between different image segmentation techniques including lines, blobs, color, edges, contours, shapes, or both lines and blobs.
• Graphical Display: Real-time plots of line and blob detection counts.
• Save Data: Save the processed image data (timestamp, line count, blob count) to a CSV file.

Requirements

• Python 3.x
• tkinter
• pypylon
• opencv-python
• numpy
• matplotlib
• Pillow
• ROS (for ROS implementation)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/hybrid-testbed.git
   cd hybrid-testbed

2. Install dependencies:

   pip install tkinter pypylon opencv-python numpy matplotlib Pillow

3. ROS Installation: Follow the official ROS installation guide for your operating system. ROS Installation Guide

Usage

Open Loop Control

1. Navigate to the Open Loop Control directory:

   cd Hybrid-Testbed/Open Loop Control

2. Run the application:

   python open_loop_control.py

3. Control the application through the GUI:

   • Click "Start Video Feed" to begin the live video feed.
   • Select the desired image segmentation technique from the display options.
   • Click "Stop Video Feed" to stop the live video feed.
   • Click "Save Data" to open a file dialog and choose where to save the CSV file with the processed image data.

ROS Implementation

1. Navigate to the ROS Implementation directory:

   cd Hybrid-Testbed/ROS Implementation

2. Follow the instructions in the README within the ROS Implementation directory to set up and run the ROS nodes.

GUI Elements

• Video Feed Label: Displays the live video feed from the camera.
• Start/Stop Video Buttons: Control the live video feed.
• Display Options: Radio buttons to choose the type of image segmentation.
• Save Data Button: Opens a file dialog to save the CSV file.
• Real-time Plots: Displays plots of line and blob detection counts.

Code Overview

• initialize_gui(): Sets up the GUI elements.
• setup_camera(): Configures the Basler camera.
• start_video_feed() / stop_video_feed(): Starts and stops the live video feed.
• update_camera_feed(): Continuously retrieves images from the camera.
• process_image(): Processes each frame from the camera according to the selected display option.
• save_data(): Saves the collected image data to a CSV file.
• detect_lines() / detect_blobs() / color_segmentation() / edge_detection() / contour_detection() / shape_detection(): Image segmentation methods.

Contributing

Contributions are welcome! Please fork this repository and submit pull requests for any enhancements or bug fixes.

License

This project is licensed under the MIT License.

Acknowledgments

• pypylon - Python bindings for the pylon Camera Software Suite.
• OpenCV - Open Source Computer Vision Library.
• tkinter - Python's de-facto standard GUI package.
• matplotlib - Comprehensive library for creating static, animated, and interactive visualizations in Python.
• Pillow - Python Imaging Library (PIL) fork.
• ROS - Robot Operating System.