surenzimurenzi/objectdetection

This project demonstrates real-time object detection using the YOLO (You Only Look Once) modelwith a webcam feed. It utilizes OpenCV to capture video, process frames, and apply YOLO to identify and label objects.

Object Detection with YOLOv3

This project implements real-time object detection using the YOLOv3 model. The script captures video from your webcam and detects objects in real-time. Features

-Real-time object detection using YOLOv3.

-Draws bounding boxes around detected objects.

-Displays the class label and confidence score for each detected object.

Preview

Requirements

Before running the script, make sure to install the necessary Python libraries and download the required YOLOv3 files. Dependencies

Create and activate a virtual environment (optional but recommended):

bash

python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate

Install dependencies:

bash

pip install -r requirements.txt

YOLOv3 Files

To get started with YOLOv3, download the following files and save them in your project directory:

1. YOLOv3 Weights: Download yolov3.weights
2. YOLOv3 Configuration: Download yolov3.cfg
3. COCO Names File: Download coco.names

Ensure that these files are located in the same directory as the main.py script.

Usage

Run the script to start the object detection:

bash

python main.py

The script will open a webcam feed and start detecting objects in real-time. Configuration

Confidence Threshold: Adjust the confidence variable in the script to set the minimum confidence level for object detection.

Frame Resolution: The frame is resized to 416x416 to speed up processing. Modify this size in the script if needed.

Troubleshooting

Camera Issues: Ensure your webcam is properly connected and accessible.

Performance: Reducing the frame resolution or processing fewer frames per second can help with performance.