Robotic Object Recognition via CNN and Image Processing

Welcome to the Robotic Object Recognition via Convolutional Neural Networks (CNN) and Image Processing repository! This project aims to develop and implement robust object recognition techniques for robotics applications using CNNs and advanced image processing methods.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Examples
• Contributing
• Contact

Introduction

The Robotic Object Recognition via CNN and Image Processing project focuses on integrating state-of-the-art deep learning techniques and image processing algorithms to enable precise and efficient object recognition in robotic systems. This enhances the capability of robots to understand and interact with their environment effectively.

Features

• CNN-Based Object Recognition: Implement CNN models for accurate and real-time object recognition.
• Image Processing Techniques: Utilize advanced image processing methods for preprocessing and enhancing image data.
• Real-Time Recognition: Achieve real-time object recognition suitable for dynamic robotic applications.
• Dataset Integration: Support for various datasets to train and evaluate object recognition models.
• Modular Design: Easily adaptable and extendable codebase for different robotic platforms.

⚙️ Installation

To set up the project, clone the repository and install the required dependencies:

git clone https://github.com/yourusername/Robotic-Object-Recognition-via-CNN-and-Image-Processing.git
cd Robotic-Object-Recognition-via-CNN-and-Image-Processing
pip install -r requirements.txt

Ensure you have the necessary hardware and software configurations as detailed in the documentation.

Usage

The project includes various modules and examples to demonstrate object recognition using CNN and image processing techniques. You can run these examples to see the system in action or customize them to fit your specific needs.

python examples/object_recognition.py
python examples/image_preprocessing.py

For detailed usage instructions, refer to the documentation provided in the docs folder.

Examples

Here are some basic examples to get you started:

• Object Recognition

  from recognition.cnn_model import CNNModel
  cnn_model = CNNModel()
  cnn_model.load_model('models/object_recognition_model.h5')
  result = cnn_model.predict('images/test_image.jpg')
  print(f"Recognized Object: {result}") 
  

• Image Preprocessing

   from processing.image_preprocessor import ImagePreprocessor
   image_preprocessor = ImagePreprocessor()
   preprocessed_image = image_preprocessor.process('images/raw_image.jpg')
   preprocessed_image.show()
  

Check out the examples folder for more detailed examples and use cases.

Contributing

We welcome contributions from the community! Please read our Contributing Guide for more information on how to get involved.

• Fork the repository
• Create a new branch (git checkout -b feature-branch)
• Commit your changes (git commit -m 'Add some feature')
• Push to the branch (git push origin feature-branch)
• Create a new Pull Request

Contact

If you have any questions, suggestions, or feedback, feel free to contact us at abhianantapatil@gmail.com.

Thank you for your interest in the Robotic Object Recognition via CNN and Image Processing project! We hope you find it useful and engaging. Happy coding!