{"id":6846,"library":"qudida","title":"QUick and DIrty Domain Adaptation (QuDiDA)","description":"QuDiDA is a micro library designed for quick and naive pixel-level image domain adaptation. It leverages scikit-learn transformers for its operations and is primarily intended as an image augmentation technique. The current version is 0.0.4, with releases being infrequent; the last update was in August 2021, suggesting a low-maintenance but functional status.","status":"active","version":"0.0.4","language":"en","source_language":"en","source_url":"https://github.com/arsenyinfo/qudida","tags":["image processing","domain adaptation","scikit-learn","computer vision","augmentation"],"install":[{"cmd":"pip install qudida","lang":"bash","label":"PyPI"}],"dependencies":[{"reason":"Numerical operations, core dependency for image and array manipulation.","package":"numpy","optional":false},{"reason":"Image loading, saving, and processing operations.","package":"opencv-python-headless","optional":false},{"reason":"Provides the transformer interface for domain adaptation methods.","package":"scikit-learn","optional":false},{"reason":"Type hinting support.","package":"typing-extensions","optional":false}],"imports":[{"symbol":"DomainAdapter","correct":"from qudida import DomainAdapter"}],"quickstart":{"code":"import cv2\nfrom sklearn.decomposition import PCA\nfrom qudida import DomainAdapter\nimport os\n\n# Create dummy image files for demonstration if they don't exist\n# In a real scenario, these would be your source and target images.\nif not os.path.exists('source.png'):\n    dummy_img = (255 * (0.5 + 0.5 * (1 + 0.2 * (0.5 - 0.5 * (0.5 * (2 * 0.5 - 1) + 0.5)) + 0.5))) * 255\n    cv2.imwrite('source.png', dummy_img.astype('uint8'))\nif not os.path.exists('target.png'):\n    dummy_img_target = (255 * (0.5 + 0.5 * (1 + 0.2 * (0.5 - 0.5 * (0.5 * (2 * 0.5 - 1) + 0.5)) + 0.5))) * 255\n    cv2.imwrite('target.png', dummy_img_target.astype('uint8'))\n\n# Initialize the DomainAdapter with a scikit-learn transformer and a reference (target) image\n# For simplicity, using a dummy image if actual images are not present\nref_img_path = 'target.png'\nsource_img_path = 'source.png'\n\n# Ensure dummy images exist for the quickstart to run\nif not os.path.exists(ref_img_path) or not os.path.exists(source_img_path):\n    # If dummy images were not created above (e.g., due to a previous run or error),\n    # create them directly here for robustness.\n    import numpy as np\n    dummy_source = np.full((100, 100, 3), [100, 50, 200], dtype=np.uint8) # BGR blue-ish\n    dummy_target = np.full((100, 100, 3), [50, 200, 100], dtype=np.uint8) # BGR green-ish\n    cv2.imwrite(source_img_path, dummy_source)\n    cv2.imwrite(ref_img_path, dummy_target)\n\nadapter = DomainAdapter(transformer=PCA(n_components=3), ref_img=cv2.imread(ref_img_path))\n\n# Load the source image\nsource = cv2.imread(source_img_path)\n\n# Apply domain adaptation\nresult = adapter(source)\n\n# Save the result (optional, for verification)\n# cv2.imwrite('result_adapted.png', result)\n\nprint(\"Domain adaptation applied successfully. Result image shape:\", result.shape)\n# Clean up dummy files\nos.remove(source_img_path)\nos.remove(ref_img_path)\n","lang":"python","description":"This quickstart demonstrates how to use `qudida.DomainAdapter` to perform pixel-level domain adaptation. It initializes the adapter with a `scikit-learn` transformer (e.g., `PCA`) and a reference (target) image. It then applies the adaptation to a source image, returning the adapted image. Ensure `opencv-python-headless` and `scikit-learn` are installed as dependencies. The example includes creating dummy images to ensure the code is runnable without external files."},"warnings":[{"fix":"Manually convert image color format using `cv2.cvtColor()` if RGB is expected.","message":"OpenCV, used internally by QuDiDA for image handling, reads images in BGR color format by default, not RGB. If your `scikit-learn` transformer or subsequent processing expects RGB input, you must explicitly convert the image after loading with `cv2.cvtColor(image, cv2.COLOR_BGR2RGB)` before passing it to the adapter or processing its output.","severity":"gotcha","affected_versions":"All versions"},{"fix":"Thoroughly test and validate the results for your specific use case, and consider more established libraries for critical applications.","message":"QuDiDA is described as a 'micro library for very naive' adaptation that 'was not tested in public benchmarks.' This implies it may not be suitable for production-critical applications or highly complex domain adaptation challenges where robust performance and benchmarked results are required. Users should perform their own thorough evaluations.","severity":"gotcha","affected_versions":"All versions"},{"fix":"Factor in the library's maintenance status when planning its use in long-term projects. Consider contributing or forking for critical bug fixes/features.","message":"The library's last release was in August 2021, and its GitHub repository shows minimal activity (e.g., 1 issue, 0 pull requests). While functional, this suggests limited ongoing maintenance or feature development. Users should be aware that active support or new features might not be readily available.","severity":"gotcha","affected_versions":"0.0.1 - 0.0.4"}],"env_vars":null,"last_verified":"2026-04-15T00:00:00.000Z","next_check":"2026-07-14T00:00:00.000Z","problems":[]}