YOLOv8 Object Detection with OpenCV and Ultralytics

This Python script uses YOLOv8 from Ultralytics for real-time object detection using OpenCV. The script initializes a camera, loads the YOLOv8 model, and processes frames from the camera, annotating detected objects with bounding boxes.

Prerequisites

Make sure you have the following libraries installed:

• cv2 (OpenCV)
• ultralytics
• supervision

pip install opencv-python
pip install ultralytics
pip install supervision==0.3.0

Getting Started

Clone the repository:

git clone https://github.com/SYED-M-HUSSAIN/YOLOv8-Object-Detection-with-OpenCV-and-Ultralytics

Usage

Run the script:

python yolo_inference.py

Configuration

initialize_camera: Initializes the camera using OpenCV.
load_yolov8_model: Loads the YOLOv8 model from Ultralytics.
process_frame: Processes each frame from the camera using the YOLOv8 model and annotates the detected objects.
main: The main function that captures frames from the camera, processes them, and displays the annotated frames.

Customization

You can customize the camera index, model path, and annotation parameters according to your needs.