Hand Sign Detection and Classification

This project uses computer vision to detect and classify hand gestures captured via a webcam. The project consists of two main components: data collection for creating a custom dataset of hand gestures and real-time classification of these gestures using a pre-trained model.

---

Table of Contents

• Overview
• Features
• Dependencies
• Setup Instructions
• Usage
  • Data Collection
  • Hand Gesture Classification

---

Overview

This project provides tools to:

1. Collect images of hand gestures for training a machine learning model.
2. Detect hand gestures in real-time and classify them into predefined categories using a deep learning model.

---

Features

• Hand Detection: Detects hands in the video feed using OpenCV and cvzone's HandDetector.
• Dataset Creation: Saves cropped hand images for creating a custom dataset.
• Gesture Classification: Classifies gestures into categories (e.g., "A", "B", "C", "OK") using a pre-trained Keras model.
• Real-Time Feedback: Displays predictions and bounding boxes around detected hands on the live feed.

---

Dependencies

The project requires the following Python libraries:

• cv2 (OpenCV)
• cvzone
• numpy
• math

Install these dependencies using pip:

pip install opencv-python cvzone numpy

---

Setup Instructions

1. Clone the repository:

   git clone https://github.com/VRP-github/HandSign-Detection.git

2. Ensure the following folders and files are present:

   • Images/ for storing collected gesture images.
   • Model/keras_model.h5 for the pre-trained classification model.
   • Model/labels.txt for gesture labels.

3. Connect a webcam to your system.

---

Usage

Data Collection

1. Run the dataCollection.py script to collect gesture images:

   python dataCollection.py

2. Make gestures in front of the camera. Press s to save images of your gesture into the Images/ folder.
3. Use the saved images to train your custom gesture classification model.

Hand Gesture Classification

1. Ensure the pre-trained model and labels file are present in the Model/ folder.
2. Run the test.py script to start gesture classification:

   python test.py

3. Wave your hand in front of the camera to see real-time predictions displayed on the screen.

---