https://github.com/tayyabwaqar/webcam-image-object-detector
This app, built with Streamlit and YOLOv8, is designed for real-time object detection in images and video streams. It can identify and label various objects in both uploaded images and live webcam feeds.
https://github.com/tayyabwaqar/webcam-image-object-detector
computer-vision image image-classification object-detection webcam
Last synced: 27 days ago
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This app, built with Streamlit and YOLOv8, is designed for real-time object detection in images and video streams. It can identify and label various objects in both uploaded images and live webcam feeds.
- Host: GitHub
- URL: https://github.com/tayyabwaqar/webcam-image-object-detector
- Owner: tayyabwaqar
- Created: 2024-08-30T17:14:54.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-12T19:22:16.000Z (over 1 year ago)
- Last Synced: 2025-03-05T02:42:56.956Z (over 1 year ago)
- Topics: computer-vision, image, image-classification, object-detection, webcam
- Language: Python
- Homepage: https://detectifyai.streamlit.app/
- Size: 5.61 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# AI-Powered Object Detection App
## Overview
The **AI-Powered Object Detection App** is a web application built with Streamlit and YOLO, designed for real-time object detection in images and video streams. This app leverages AI technology to identify and label various objects in both uploaded images and live webcam feeds.
## Live Demo
You can try the live demo of the application at the following link:
[Live Demo - Detectify](https://detectifyai.streamlit.app/)
## Key Features
- **Real-Time Detection**: Utilize your webcam to detect and classify objects in real-time, with instant feedback on detected items.
- **Image Upload**: Easily upload images to perform object detection, with results displayed alongside the original image.
- **Customizable Settings**: Adjust the confidence threshold and select specific object classes to tailor detection to your needs.
- **Interactive Interface**: A clean and intuitive user interface that allows for seamless interaction and visualization of detection results.
- **Detection Summary Table**: View a summary of detected objects, including counts for each type, presented in a neatly formatted table.
## Technologies Used
- **Streamlit**: For creating the interactive web interface.
- **OpenCV**: For image processing and handling webcam input.
- **YOLOv8**: For advanced object detection capabilities.
- **Pandas**: For creating and manipulating the detection summary dataframe.
- **streamlit-webrtc**: For handling real-time video streaming from the webcam.
## Installation
To run the app locally, clone the repository and install the required dependencies:
```bash
git clone https://github.com/tayyabwaqar/webcam-image-object-detector
cd
pip install -r requirements.txt
streamlit run app.py