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https://github.com/agastyahukoo/optical-reference-stabiliser
A minimal Optical Stabilisation System that processes video files (or streams) to detect circles, providing a web interface for uploading, viewing live-processed frames, and adjusting detection/recording settings on the fly.
https://github.com/agastyahukoo/optical-reference-stabiliser
opencv python
Last synced: 16 days ago
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A minimal Optical Stabilisation System that processes video files (or streams) to detect circles, providing a web interface for uploading, viewing live-processed frames, and adjusting detection/recording settings on the fly.
- Host: GitHub
- URL: https://github.com/agastyahukoo/optical-reference-stabiliser
- Owner: agastyahukoo
- License: mit
- Created: 2025-01-12T14:48:50.000Z (29 days ago)
- Default Branch: main
- Last Pushed: 2025-01-24T21:22:16.000Z (17 days ago)
- Last Synced: 2025-01-24T22:21:34.466Z (16 days ago)
- Topics: opencv, python
- Language: HTML
- Homepage: https://agastyahukoo.github.io/Optical-Reference-Stabiliser/
- Size: 35.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Optical Stabilisation System
A web-based application for **circle detection** and **reference image stabilization**, featuring:
- **Video Upload** and live-processed playback in the browser
- **Circle Detection** with configurable Hough Transform parameters
- **Settings Panel** to adjust and persist detection or output configurations
- **Dark-Themed UI** for a modern and minimal look
## Features
- **Upload & Detect**: Users can upload video files, which are processed frame-by-frame to detect circles.
- **Live Preview**: The processed video is streamed back to the browser in real time using Flask’s `Response` streaming.
- **Configurable Parameters**: The built-in **Settings** panel lets you modify Hough Circle detection parameters (`dp`, `minDist`, `param1`, `param2`, `minRadius`, `maxRadius`) and adjust output formats (`video_format`, `image_format`) — updates are saved to `config.json`.
- **Toggleable Settings Panel**: Quickly pause (hide) the video to tweak parameters, then resume processing with new settings.
## Requirements
- **Python 3.7+**
- **Flask 2.0+**
- **OpenCV 4.x**
- **NumPy**
*(See `requirements.txt` or install via `pip install flask opencv-python numpy`.)*
## Getting Started
1. **Clone** this repository:
```bash
git clone https://github.com/agastyahukoo/Optical-Reference-Stabiliser.git
cd Optical-Reference-Stabiliser
```
2. **Install dependencies**:
```bash
pip install -r requirements.txt
```
*(Or manually install the required Python packages.)*
3. **Configure `config.json`**:
```json
{
"circle_params": {
"dp": 1.2,
"minDist": 1000,
"param1": 100,
"param2": 30,
"minRadius": 150,
"maxRadius": 300
},
"output_params": {
"video_format": "avi",
"image_format": "jpg"
}
}
```
Adjust initial detection or output parameters here.
4. **Run the Flask server**:
```bash
python app.py
```
By default, the app runs at `http://127.0.0.1:5000`.
5. **Open Your Browser** at `http://127.0.0.1:5000`:
- Click **Choose File**, select a video, and **Upload**.
- The processed video stream will appear.
- **Settings** lets you pause the preview and adjust circle detection or output settings. Click **Save & Close** to apply changes.
## How It Works
1. Uploaded videos are saved in the `uploads/` folder.
2. Frames are processed with Hough Circle detection (CLAHE > Gaussian Blur > HoughCircles).
3. The **Settings** panel updates `config.json` in real time, so new frames use the updated parameters.
## Contributing
Feel free to **open issues** or **create pull requests** for improvements, bug fixes, or new features. Any help is appreciated!
## License
This project is made available under the [MIT License](LICENSE).