https://github.com/mbalatsko/opencv-rolling-ball

Fully Ported to Python from ImageJ's Background Subtractor. Rolling ball and sliding paraboloid background subtraction algorithms.
https://github.com/mbalatsko/opencv-rolling-ball

background-subtraction image-processing imagej python3 rolling-ball sliding-paraboloid

Last synced: about 2 months ago
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Fully Ported to Python from ImageJ's Background Subtractor. Rolling ball and sliding paraboloid background subtraction algorithms.

• Host: GitHub
• URL: https://github.com/mbalatsko/opencv-rolling-ball
• Owner: mbalatsko
• License: mit
• Created: 2018-03-11T10:35:33.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2022-06-21T21:15:02.000Z (over 2 years ago)
• Last Synced: 2024-11-01T21:42:16.883Z (2 months ago)
• Topics: background-subtraction, image-processing, imagej, python3, rolling-ball, sliding-paraboloid
• Language: Python
• Homepage:
• Size: 1.7 MB
• Stars: 42
• Watchers: 4
• Forks: 6
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
# Rolling ball and sliding paraboloid background subtraction algorithms 

[![PyPI version](https://badge.fury.io/py/opencv-rolling-ball.svg)](https://badge.fury.io/py/opencv-rolling-ball)

[![Downloads](https://pepy.tech/badge/opencv-rolling-ball)](https://pepy.tech/project/pypylon-opencv-viewer)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

Fully Ported to Python from ImageJ's Background Subtractor.

Only works for 8-bit greyscale images currently.

Based on the concept of the rolling ball algorithm described

in Stanley Sternberg's article,

"Biomedical Image Processing", IEEE Computer, January 1983.

Imagine that the 2D grayscale image has a third (height) dimension by the image

value at every point in the image, creating a surface. A ball of given radius

is rolled over the bottom side of this surface; the hull of the volume

reachable by the ball is the background.

http://rsbweb.nih.gov/ij/developer/source/ij/plugin/filter/BackgroundSubtracter.java.html

**This algorithms are perfect for microscope images, to distinguish particles

from background.**

## Installation

```bash

pip install opencv-rolling-ball

```

## Usage

```python

import cv2

from cv2_rolling_ball import subtract_background_rolling_ball

img = cv2.imread(f'path/to/img.tif', 0)

img, background = subtract_background_rolling_ball(img, 30, light_background=True,

                                     use_paraboloid=False, do_presmooth=True)

```

## Example outputs

#### Input

![Input](https://raw.githubusercontent.com/mbalatsko/opencv-rolling-ball/master/outputs/example.png)

#### Subtracted background

![Background](https://raw.githubusercontent.com/mbalatsko/opencv-rolling-ball/master/outputs/example_bg.png)

#### Without background

![Without background](https://raw.githubusercontent.com/mbalatsko/opencv-rolling-ball/master/outputs/example_roll.png)