Skip to main content

QUick and DIrty Domain Adaptation

Project description

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.


pip install qudida


pip install git+


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)


Source image: source Target image (style donor): target Result with various adaptations: result

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

qudida-0.0.4.tar.gz (3.1 kB view hashes)

Uploaded source

Built Distribution

qudida-0.0.4-py3-none-any.whl (3.5 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page