qudida

What it is and what it does

QuDiDA is a lightweight library for adapting image appearance at the pixel level by applying scikit-learn transformers to match a reference image's style. It wraps numpy, scikit-learn, and opencv-python-headless to enable quick, naive domain adaptation—treating it as an image augmentation technique rather than a production-grade solution.

The library works by taking a source image and a reference image, then using a scikit-learn transformer (such as PCA or other decomposition methods) to adjust the source image's pixel values to align with the target's characteristics. It is designed for speed and simplicity over accuracy, and the author notes it has not been tested in public benchmarks.

Use it for:

• Augment training datasets by adapting source images to match target domain appearance before model training.
• Quick style transfer between images using decomposition-based transformers without deep learning.
• Preprocessing step to reduce visual domain shift in computer vision pipelines.
• Rapid prototyping of domain adaptation ideas without heavy dependencies.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

QuDiDA performs pixel-level image domain adaptation using scikit-learn transformers, allowing you to adjust image style or appearance to match a reference image.

Yes, if you need a lightweight, quick pixel-level domain adaptation tool for prototyping or augmentation and are comfortable with dormant maintenance. No, if you require active support, production-grade accuracy, or a clearly licensed library. Verify the license in the repository first.

Install

qudida on PyPI

pip install qudida

uv add qudida

poetry add qudida

Installing qudida

Before you install

Installation is straightforward with low friction. The package is dormant (last commit 2023-12-30) but not archived, so it remains available; however, expect no active maintenance or updates.

License in practice

License status is unclear—no SPDX identifier or raw license text is available. You should verify the actual license terms in the repository before use, particularly if you plan to redistribute or use commercially.

Quickstart

pip install qudida

from qudida import DomainAdapter
from sklearn.decomposition import PCA

adapter = DomainAdapter(transformer=PCA(n_components=1), ref_img=reference_image)
result = adapter(source_image)

Requires opencv-python-headless and scikit-learn; transformer must be compatible with pixel-level data.

Verify before relying

• Whether the package works reliably with modern scikit-learn and opencv-python-headless versions despite dormant status.
• Actual license terms and any redistribution restrictions.
• Real-world performance or accuracy compared to other domain adaptation approaches.

Package facts

Evidence: qudida-0.0.4-py3-none-any.whl

Keywords: Machine Learning, Computer Vision

Tags

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Further reading