GestureRecognition

A Code Network Gesture Recognition Software project implemented in Python for recognizing and classifying hand gestures using computer vision and machine learning techniques.

📌 Features

• Real-time hand gesture detection using OpenCV.
• Machine learning model for gesture classification.
• Custom dataset creation for training.
• Live visualization of recognized gestures.
• Modular and extensible architecture.

✋ Gestures Recognised

Gesture	Example	Gesture	Example
Thumbs up		Thumbs down
Horns Sign		Vulcan Salute
Palm/Stop		Fist Bump
Fist		Peace
Heart Fingers		Heart Hands
Chef's Kiss		Okay

🚀 Installation

1. Clone the repository:

   git clone https://github.com/codenetwork/gestureRecognition.git

2. Create a virtual environment and install dependencies:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   pip install -r requirements.txt

🖥️ Usage

Running Gesture Recognition

Run the main script to start recognizing gestures in real-time:

python gesture_recog.py

🛠️ Technologies Used

• Python (Programming Language)
• OpenCV (Computer Vision)
• YOLO (Machine Learning Model)
• Pytorch (Machine Learning Model)
• NumPy & Pandas (Data Handling)
• Seaborn (Data Visualization)
• Scikit-learn (Machine Learning Model)

🧪 Methodology

Two different models will be trained. One will be a YOLO model and the other will be a CNN model

1. Data Collection: Capturing hand gestures using photos on phones.
2. Data Annotation/Preprocessing: Extracting key hand landmarks/Processing data for training.
3. Model Training: Using a neural network to identify/classify gestures.
4. Real-time Prediction: Integrating the trained model for live recognition.

🌟 If You Are Interested

If you have the following skills or if you are simply looking to learn, here's how you can contribute:

• Python Basics: If you're learning Python, start by looking at simple scripts and trying to understand how they work. You can help by cleaning up code, adding comments, or fixing small issues.
• Data Collection: If you're interested in data science, try capturing different gestures in different environments and use them to train the model.
• Machine Learning: Learn about leveraging certain machine learning models. Help improve the model accuracy, experiment with the model architecture, optimize performance, change hyperparameters or identify alternative methodologies.
• Testing & Debugging: Run the project, see if you encounter any issues, and report them. Even better, try to find small bugs and suggest fixes.
• Implementation: Implementing machine learning into a real-world context.
• Documentation: Improving explanations in the README, adding beginner-friendly guides, or fixing typos can be a huge help.

Feel free to contribute and enhance this project!