https://github.com/mindscope-world/speed-estimation-using-ultralytics-yolo11
https://github.com/mindscope-world/speed-estimation-using-ultralytics-yolo11
Last synced: about 1 year ago
JSON representation
- Host: GitHub
- URL: https://github.com/mindscope-world/speed-estimation-using-ultralytics-yolo11
- Owner: mindscope-world
- License: mit
- Created: 2024-12-29T15:46:23.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-29T19:46:11.000Z (over 1 year ago)
- Last Synced: 2025-03-09T07:18:10.017Z (over 1 year ago)
- Language: Python
- Size: 8.83 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Speed Estimation App
A Streamlit-based web application for real-time speed estimation in uploaded videos. Users can upload videos, view the real-time processing of each frame, and download the processed video once completed.
## Features
- Upload videos via the sidebar.
- Real-time display of processed frames during video analysis.
- Save the processed video in the output folder as `output/processed_video.avi`.
- Display the final processed video after completion.
## Prerequisites
Make sure the following libraries are installed:
- **Streamlit**: For building the user interface.
- **OpenCV**: For video processing.
- **Ultralytics**: For speed estimation using a pre-trained model.
Install the required libraries using pip:
```bash
pip install streamlit opencv-python ultralytics
```
## Installation
1. Clone this repository or download the source code.
2. Navigate to the project directory:
```bash
cd
```
3. Install the dependencies:
```bash
pip install -r requirements.txt
```
## Usage
1. Save the code as `app.py` in the project directory.
2. Run the Streamlit app:
```bash
streamlit run app.py
```
3. Open the app in your browser using the URL provided by Streamlit (usually `http://localhost:8501`).
## How It Works
1. **Upload Video**: Use the sidebar to upload a video file (`.mp4`, `.avi`, `.mov`).
2. **Real-Time Processing**: The app processes the video frame by frame, displaying each processed frame in real-time.
3. **Save & Display**: The processed video is saved as `processed_video.avi` in the root directory and displayed in the app after processing.
## File Structure
```
.
├── app.py # Main application file
├── sample_videos/uploaded_video.mp4 # Temporarily stored uploaded video
├── output/processed_video.avi # Output video after processing
├── requirements.txt # Dependencies
└── README.md # Project documentation
```
## Requirements File (`requirements.txt`)
```text
streamlit
opencv-python
ultralytics
```
## Demo
## License
This project is licensed under the MIT License. See the `LICENSE` file for details.
## Author
Developed by Paul Ndirangu.
## Acknowledgments
- [Ultralytics](https://github.com/ultralytics) for the speed estimation model.
- [Streamlit](https://streamlit.io/) for the easy-to-build web interface.
- [OpenCV](https://opencv.org/) for efficient video processing.
```