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
or
pip install git+https://github.com/arsenyinfo/qudida
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:
Target image (style donor):
Result with various adaptations:
FAQs
QUick and DIrty Domain Adaptation
We found that qudida demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
Research
An advanced npm supply chain attack is leveraging Ethereum smart contracts for decentralized, persistent malware control, evading traditional defenses.
Security News
Research
Attackers are impersonating Sindre Sorhus on npm with a fake 'chalk-node' package containing a malicious backdoor to compromise developers' projects.
Security News
The npm package for the LottieFiles Player web component was hit with a supply chain attack after a software engineer's npmjs credentials were compromised.