added label transform class encorporating min_size of labels #322
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torch_em/transform/label.py
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| def __call__(self, labels): | ||
| components = connected_components(labels, ndim=self.ndim, ensure_zero=self.ensure_zero) | ||
| if self.min_size is not None: | ||
| for component in np.unique(components)[1:]: # Skip background (label 0) |
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This is inefficient and your labels are not consecutively labeled anymore after filtering. You can do the following instead:
ids, sizes = np.unique(components, return_counts=True)
filter_ids = ids[sizes < self.min_size]
components[np.isin(components, filter_ids)] = 0
components, _, _ = skimage.segmentation.relabel_sequential(components)
constantinpape
approved these changes
Jul 11, 2024
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