syedrz/yolov8-detection-segmentation-tracking

Real-time Object Detection and Tracking with YOLOv8 and Streamlit

This repository is a comprehensive open-source project that demonstrates the integration of object detection and tracking using the YOLOv8 object detection algorithm and Streamlit, a popular Python web application framework for building interactive web applications. This project provides a user-friendly and customizable interface that can detect and track objects in real-time video streams.

Tracking With Object Detection Demo

Tracking-With_object-Detection-MOV.mov

Demo Pics

Home page

Page after uploading an image and object detection

Segmentation task on image

Requirements

Python 3.6+ YOLOv8 Streamlit

pip install ultralytics streamlit pafy

Installation

• Clone the repository: git clone https://github.com/syedrz/yolov8-detection-segmentation-tracking.git
• Change to the repository directory: cd yolov8-detection-segmentation-tracking
• Create weights, videos, and images directories inside the project.
• Download the pre-trained YOLOv8 weights from (https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) and save them to the weights directory in the same project.

Usage

• Run the app with the following command: streamlit run app.py
• The app should open in a new browser window.

ML Model Config

• Select task (Detection, Segmentation)
• Select model confidence
• Use the slider to adjust the confidence threshold (25-100) for the model.

One the model config is done, select a source.

Detection on images

• The default image with its objects-detected image is displayed on the main page.
• Select a source. (radio button selection Image).
• Upload an image by clicking on the "Browse files" button.
• Click the "Detect Objects" button to run the object detection algorithm on the uploaded image with the selected confidence threshold.
• The resulting image with objects detected will be displayed on the page. Click the "Download Image" button to download the image.("If save image to download" is selected)

Detection in Videos

• Drag and drop video file
• Click on Detect Video Objects button and the selected task (detection/segmentation) will start on the selected video.

Detection on RTSP

• Select the RTSP stream button
• Enter the rtsp url inside the textbox and hit Detect Objects button

Detection on YouTube Video URL

• Select the source as YouTube
• Copy paste the url inside the text box.
• The detection/segmentation task will start on the YouTube video url

movobjdetyoutubeurl.mov

Acknowledgements

This app is based on the YOLOv8(https://github.com/ultralytics/ultralytics) object detection algorithm. The app uses the Streamlit(https://github.com/streamlit/streamlit) library for the user interface.

Disclaimer

Please note that this project is intended for educational purposes only and should not be used in production environments.

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