Object Classification using OpenCV and CVZone

This repository contains code for real-time object classification using OpenCV and CVZone. It uses a pre-trained Keras model for classification and displays FPS (Frames Per Second) on the captured video stream.

Overview

Object classification involves identifying objects in images or video frames using a trained model. This project utilizes OpenCV for capturing video from a webcam and CVZone for real-time classification and FPS calculation.

Dependencies

• Python 3.6+
• OpenCV
• CVZone
• Keras

Installation

To run this project, follow these steps:

1. Clone the repository:

   git clone https://github.com/SlimenFellah/Object-Classification.git
   cd Object-Classification

Install dependencies:

pip install -r requirements.txt

Usage Ensure your webcam is connected and accessible.

Run the main.py script:

python main.py

The script will open a window showing the webcam feed with real-time object classification predictions and FPS displayed.

Customization Model: Replace ./myModel/keras_model.h5 with your own trained Keras model path. Labels: Update ./myModel/labels.txt with your labels file corresponding to the model. Settings: Modify the code to adjust settings such as FPS display position or scale.

Instructions:

• Dependencies: Update the requirements.txt file with the actual dependencies used in your project.
• Customization: Add any additional customization or configuration instructions specific to your project, such as model paths, label files, or settings adjustments.
• License: Ensure the LICENSE file in your repository reflects the appropriate license for your project.