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:
Target image (style donor):
Result with various adaptations:
Metadata
Release files for qudida 0.0.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| qudida-0.0.4.tar.gz | 3.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| qudida-0.0.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 6.6 kB
Release files / qudida-0.0.4.tar.gz
| Download URL | qudida-0.0.4.tar.gz |
|---|---|
| Size | 3.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / qudida-0.0.4-py3-none-any.whl
| Download URL | qudida-0.0.4-py3-none-any.whl |
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| Size | 3.5 kB |
| Tags | Python 3 |
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twine/3.4.2 importlib_metadata/4.6.3 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.0 CPython/3.7.11
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