This repository is an extensive open-source project showcasing the seamless integration of object detection and tracking using YOLOv8 (object detection algorithm), along with Streamlit (a popular Python web application framework for creating interactive web apps). The project offers a user-friendly and customizable interface designed to detect and track objects in real-time video streams from sources such as RTSP, UDP, and YouTube URLs, as well as static videos and images.

For a deeper dive into the implementation, check out my three-part blog series on Medium, where I detail the step-by-step process of creating this web application.

Thank you team Streamlit for the community support for the cloud upload.

This app is up and running on Streamlit cloud server!!! You can check the demo of this web application on this link yolov8-streamlit-detection-tracking-webapp

Note: In the demo, Due to non-availability of GPUs, you may encounter slow video inferencing.

Python 3.6+ YOLOv8 Streamlit

pip install ultralytics streamlit pytube

• Clone the repository: git clone https://github.com/CodingMantras/yolov8-streamlit-detection-tracking.git
• Change to the repository directory: cd yolov8-streamlit-detection-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.

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

• 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.

• 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)

• Create a folder with name videos in the same directory
• Dump your videos in this folder
• In settings.py edit the following lines.

# video
VIDEO_DIR = ROOT / 'videos' # After creating the videos folder

# Suppose you have four videos inside videos folder
# Edit the name of video_1, 2, 3, 4 (with the names of your video files) 
VIDEO_1_PATH = VIDEO_DIR / 'video_1.mp4' 
VIDEO_2_PATH = VIDEO_DIR / 'video_2.mp4'
VIDEO_3_PATH = VIDEO_DIR / 'video_3.mp4'
VIDEO_4_PATH = VIDEO_DIR / 'video_4.mp4'

# Edit the same names here also.
VIDEOS_DICT = {
    'video_1': VIDEO_1_PATH,
    'video_2': VIDEO_2_PATH,
    'video_3': VIDEO_3_PATH,
    'video_4': VIDEO_4_PATH,
}

# Your videos will start appearing inside streamlit webapp 'Choose a video'.

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

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

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

This app uses YOLOv8 for object detection algorithm and Streamlit library for the user interface.

This project is intended as a learning exercise and demonstration of integrating various technologies, including:

• Streamlit
• YoloV8
• Object-Detection on Images And Live Video Streams
• Python-OpenCV

Please note that this application is not designed or tested for production use. It serves as an educational resource and a showcase of technology integration rather than a production-ready web application.

Contributors and users are welcome to explore, learn from, and build upon this project for educational purposes.