Metadata-Version: 2.1
Name: qudida
Version: 0.0.4
Summary: QUick and DIrty Domain Adaptation
Home-page: https://github.com/arsenyinfo/qudida
License: UNKNOWN
Keywords: Machine Learning,Computer Vision
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.5.0
Description-Content-Type: text/markdown
Requires-Dist: numpy (>=0.18.0)
Requires-Dist: scikit-learn (>=0.19.1)
Requires-Dist: typing-extensions
Requires-Dist: opencv-python-headless (>=4.0.1)


# QuDiDA (QUick and DIrty Domain Adaptation)

QuDiDA is a micro library for very naive though quick pixel level image domain adaptation via `scikit-learn` transformers.
Is assumed to be used as image augmentation technique, while was not tested in public benchmarks. 

## Installation
```
pip install qudida
```
or
```
pip install git+https://github.com/arsenyinfo/qudida
```

## Usage 
```
import cv2

from sklearn.decomposition import PCA
from qudida import DomainAdapter

adapter = DomainAdapter(transformer=PCA(n_components=1), ref_img=cv2.imread('target.png'))
source = cv2.imread('source.png')
result = adapter(source)
cv2.imwrite('../result.png', result)
```

## Example 
Source image: 
![source](source.png)
Target image (style donor):
![target](target.png)
Result with various adaptations:
![result](result.gif)


