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24 changes: 24 additions & 0 deletions
24
tests/unit/mazepa_layer_processing/segmentation/test_watershed.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,24 @@ | ||
| import pytest | ||
| import torch | ||
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| from zetta_utils.mazepa_layer_processing.segmentation import watershed | ||
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| @pytest.mark.parametrize( | ||
| "fragments_in_xy", | ||
| [False, True], | ||
| ) | ||
| def test_ws_dummy_data_lsd(fragments_in_xy): | ||
| affs = torch.zeros(3, 8, 8, 8) | ||
| ret = watershed.watershed_from_affinities(affs, method="lsd", fragments_in_xy=fragments_in_xy) | ||
| assert ret.shape == (1, 8, 8, 8) | ||
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| @pytest.mark.parametrize( | ||
| "size_threshold", | ||
| [0, 200], | ||
| ) | ||
| def test_ws_dummy_data_abiss(size_threshold): | ||
| affs = torch.zeros(3, 8, 8, 8) | ||
| ret = watershed.watershed_from_affinities(affs, method="abiss", size_threshold=size_threshold) | ||
| assert ret.shape == (1, 8, 8, 8) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1 +1,2 @@ | ||
| from . import masks | ||
| from . import masks, watershed | ||
| from zetta_utils.internal import segmentation |
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zetta_utils/mazepa_layer_processing/segmentation/watershed.py
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| Original file line number | Diff line number | Diff line change |
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| from __future__ import annotations | ||
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| from typing import Literal | ||
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| import abiss | ||
| import einops | ||
| import numpy as np | ||
| import torch | ||
| from lsd.post.fragments import watershed_from_affinities as _watershed_lsd | ||
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| from zetta_utils import builder | ||
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| def _run_watershed_abiss( | ||
| affs: torch.Tensor, | ||
| aff_threshold_low: float = 0.01, | ||
| aff_threshold_high: float = 0.99, | ||
| size_threshold: int = 0, | ||
| # agglomeration_threshold: float = 0.0, | ||
| ) -> torch.Tensor: | ||
| """ | ||
| Args: | ||
| affs: | ||
| Affinity tensor in float32 with values [0.0, 1.0]. | ||
| aff_threshold_low, aff_threshold_high: | ||
| Low and high watershed thresholds. | ||
| size_threshold: | ||
| If greater than 0, perform single-linkage merging as a subsequent | ||
| step. | ||
| agglomeration_threshold: | ||
| If greater than 0.0, perform agglomeration as a subsequent | ||
| step with this threshold. | ||
| """ | ||
| affs = torch.nn.functional.pad(affs, (1, 1, 1, 1, 1, 1)) # abiss requires 1px padding | ||
| affs = einops.rearrange(affs, "C X Y Z -> X Y Z C") # channel last | ||
| ret = abiss.watershed( | ||
| affs=affs.numpy(), | ||
| aff_threshold_low=aff_threshold_low, | ||
| aff_threshold_high=aff_threshold_high, | ||
| size_threshold=size_threshold, | ||
| # agglomeration_threshold=agglomeration_threshold, | ||
| ) | ||
| ret = ret[1:-1, 1:-1, 1:-1] | ||
| ret = np.expand_dims(ret, axis=0) | ||
| return ret | ||
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| def _run_watershed_lsd( | ||
| affs: torch.Tensor, | ||
| fragments_in_xy: bool = False, | ||
| min_seed_distance: int = 10, | ||
| affs_in_xyz: bool = True, | ||
| ) -> torch.Tensor: | ||
| """ | ||
| Args: | ||
| affs: | ||
| Affinity tensor in either float32 or uint8. | ||
| fragments_in_xy: | ||
| Produce supervoxels in xy. | ||
| min_seed_distance: | ||
| Controls distance between seeds in voxels. | ||
| """ | ||
| """ | ||
| TODO: | ||
| - add supervoxel filtering based on average aff value | ||
| - add option to also perform agglomeration | ||
| """ | ||
| affs_np = einops.rearrange(affs, "C X Y Z -> C Z Y X").numpy() | ||
| if affs_in_xyz: | ||
| # aff needs to be zyx | ||
| affs_np = np.flip(affs_np, 0) | ||
| if affs_np.dtype == np.uint8: | ||
| max_affinity_value = 255.0 | ||
| affs_np = affs_np.astype(np.float32) | ||
| else: | ||
| max_affinity_value = 1.0 | ||
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| ret, _ = _watershed_lsd( | ||
| affs=affs_np, | ||
| max_affinity_value=max_affinity_value, | ||
| fragments_in_xy=fragments_in_xy, | ||
| min_seed_distance=min_seed_distance, | ||
| ) | ||
| ret = einops.rearrange(ret, "Z Y X -> X Y Z") | ||
| ret = np.expand_dims(ret, axis=0) | ||
| return ret | ||
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| @builder.register("watershed_from_affinities") | ||
| def watershed_from_affinities( | ||
| affs: torch.Tensor, # in CXYZ | ||
| method: Literal["abiss", "lsd"], | ||
| **kwargs, | ||
| ) -> torch.Tensor: | ||
| """ | ||
| Produce supervoxels by running watershed on aff data. Optionally perform | ||
| agglomeration and output segmentation. | ||
| """ | ||
| if method == "lsd": | ||
| seg = _run_watershed_lsd(affs, **kwargs) | ||
| elif method == "abiss": | ||
| seg = _run_watershed_abiss(affs, **kwargs) | ||
| """ | ||
| TODO: write a wrapper for multi-chunk watershed that performs: | ||
| - relabel supervoxels based on chunkid & chunk size | ||
| - add supervoxel filtering based on mask | ||
| - store a list of supervoxels within a chunk to a database | ||
| """ | ||
| return seg |