semvec/pypi-packages huggingface dataset:
ggb 1.1.5
GGB Color Space in Python
License: MIT

# GGB Color Space

[![PyPI](https://badge.fury.io/py/ggb.svg)](https://badge.fury.io/py/ggb)
[![license](https://img.shields.io/pypi/l/ggb.svg)](https://github.com/reshalfahsi/ggb/blob/master/LICENSE)
[![Travis CI](https://api.travis-ci.com/reshalfahsi/ggb.svg?branch=master)](https://travis-ci.com/github/reshalfahsi/ggb)
[![docker](https://img.shields.io/docker/pulls/reshalfahsi/ggb)](https://hub.docker.com/r/reshalfahsi/ggb)
[![codecov](https://codecov.io/github/reshalfahsi/ggb/coverage.svg?branch=master&precision=2)](https://codecov.io/gh/reshalfahsi/ggb)


This package is implementation of GGB color space from [Development of a Robust Algorithm for Detection of Nuclei and Classification of White Blood Cells in Peripheral Blood Smear Image](https://link.springer.com/content/pdf/10.1007%2Fs10916-018-0962-1.pdf).


## Installation

### Install GGB

This package could be installed via [PyPI](https://pypi.org/project/ggb/).

    pip3 install ggb

or manually:

    python3 setup.py install


### Building Docker Image

`Dockerfile` is also provided in this project. To build the image:

```bash
cd docker/
bash docker.build.sh
```


or pull it directly from [Docker Hub](https://hub.docker.com/r/reshalfahsi/ggb):

    docker pull reshalfahsi/ggb


### Building the Documentation

The documentations to this package could be built using [Sphinx](www.sphinx-doc.org).

```bash
cd docs/
pip3 install -r requirements.txt
make html
```

The HTML pages are in docs/build/html.


## Quick Demo

This package supports various computer vision libraries such as OpenCV and PIL. Complete examples for these computer vision libraries are provided in [here](https://github.com/reshalfahsi/ggb/tree/master/examples). For the short example in Python3:


```python
# import the package and its necessary components
from ggb import GGB, ColorSpace

# we are using OpenCV
import cv2

import urllib.request as urllib
import numpy as np

# load image from internet
req = urllib.urlopen('https://github.com/reshalfahsi/GGB/raw/master/docs/img/leukocytes.png')
arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
img = cv2.imdecode(arr, -1)
    
# convert to GGB Color
ggb_image = GGB(image=img, input_color=ColorSpace.BGR).process()

# show the result    
ggb_image.show()

# save the image to OpenCV format
img = ggb_image.write()
```

This package also could be run through CLI:

    ggb /path/to/image --output /path/to/output


## Result

### Leukocytes
![alt text](https://github.com/reshalfahsi/GGB/raw/master/docs/img/GGB_RGB_LEUKOCYTES.jpg)

### Fundus
![alt text](https://github.com/reshalfahsi/GGB/raw/master/docs/img/GGB_RGB_FUNDUS.jpg)

### Car
![alt text](https://github.com/reshalfahsi/GGB/raw/master/docs/img/GGB_RGB_TESLA.jpg)