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https://github.com/rampal-punia/yolov8-streamlit-detection-tracking

Object detection and tracking algorithm implemented for Real-Time video streams and static images.
https://github.com/rampal-punia/yolov8-streamlit-detection-tracking

machine-learning ml object-detection object-tracker streamlit streamlit-application tracking-algorithm tracking-by-detection yolo yolov8

Last synced: 4 days ago
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Object detection and tracking algorithm implemented for Real-Time video streams and static images.

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README 

        
#  Real-time Object Detection and Tracking with YOLOv8 & Streamlit 



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.



## Explore Implementation Details on Medium (3 parts blog series) 

For a deeper dive into the implementation, check out my three-part blog series on [Medium](https://medium.com/@mycodingmantras), where I detail the [step-by-step process of creating this web application](https://medium.com/@mycodingmantras/building-a-real-time-object-detection-and-tracking-app-with-yolov8-and-streamlit-part-1-30c56f5eb956).



## WebApp Demo on Streamlit Server



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](https://yolov8-object-detection-and-tracking-app.streamlit.app/)



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



##  Tracking With Object Detection Demo



## Overview



## Demo Pics



### Home page






### Page after uploading an image and object detection






### Segmentation task on image






## Requirements



Python 3.6+

YOLOv8

Streamlit



```bash

pip install ultralytics streamlit pytube

```



## Installation



- Clone the repository: git clone 

- 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 () 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



- Create a folder with name `videos` in the same directory

- Dump your videos in this folder

- In `settings.py` edit the following lines.



```python

# 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.



### 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



## Acknowledgements



This app uses [YOLOv8]() for object detection algorithm and [Streamlit]() library for the user interface.



### Disclaimer



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.



### Hit star ⭐ if you like this repo!!!