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Uses nearest-neighbor or bilinear interpolation for mask resampling (avoiding label bleeding from linear interpolation), and automatically handles mask format conversion (single-channel class indices vs multi-channel one-hot encoding).","intents":["I need to augment semantic segmentation datasets while keeping pixel-level class labels synchronized with image transformations","I want to avoid label bleeding artifacts that occur when using linear interpolation on categorical mask data","I need to work with different mask formats (class indices, one-hot, multi-class) without manual conversion"],"best_for":["semantic segmentation engineers working with datasets like Cityscapes or ADE20K","medical imaging teams augmenting organ/tissue segmentation masks","scene understanding teams augmenting panoptic segmentation datasets"],"limitations":["Mask interpolation uses nearest-neighbor by default to preserve labels, which can produce jagged edges on geometric transforms","No built-in support for instance segmentation (per-object masks) — treats all masks as semantic class labels","Multi-class masks require explicit format specification; no auto-detection of mask encoding"],"requires":["Python 3.8+","NumPy","OpenCV","Masks provided as numpy arrays with shape (H, W) for single-class or (H, W, C) for multi-class"],"input_types":["numpy arrays (images, uint8 or float32)","numpy arrays (masks, uint8 or int32 for class indices, or float32 for one-hot)"],"output_types":["augmented images (numpy arrays)","augmented masks (same dtype/shape as input)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-albumentations__cap_4","uri":"capability://data.processing.analysis.3d.volumetric.augmentation.for.medical.imaging","name":"3d volumetric augmentation for medical imaging","description":"Applies geometric and intensity augmentations to 3D medical imaging volumes (CT, MRI, ultrasound) while maintaining spatial consistency across slices. 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