From c14c2ad98292e8683f66e892d66a7b9bd0b0df32 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Al=C3=A1n=20Mu=C3=B1oz?= <alan.munoz@ed.ac.uk>
Date: Thu, 2 Mar 2023 11:16:50 +0000
Subject: [PATCH] change(vis_tools): refactor _overlay_mask_tile
---
src/aliby/utils/vis_tools.py | 96 ++++++++++++++++++++++--------------
1 file changed, 60 insertions(+), 36 deletions(-)
diff --git a/src/aliby/utils/vis_tools.py b/src/aliby/utils/vis_tools.py
index 2436b421..602e8868 100644
--- a/src/aliby/utils/vis_tools.py
+++ b/src/aliby/utils/vis_tools.py
@@ -41,7 +41,7 @@ def get_tiles_at_times(
Parameters
----------
image_path : str
- hdf5 location
+ hdf5 index
timepoints : t.List[int]
list of timepoints to fetch
tile_reduction : t.Union[int, t.List[int], str, t.Callable]
@@ -164,60 +164,84 @@ def crop_mask(img: np.ndarray, mask: np.ndarray):
return img
-def overlay_masks_tiles(
+def _sample_n_tiles_masks(
image_path: str,
results_path: str,
- masks: np.ndarray,
- locations: t.Tuple[t.Tuple[int], t.Tuple[int], t.Tuple[int]],
+ n: int,
+ seed: int = 0,
+ interval=None,
+ as_generator=False,
+) -> t.Tuple[t.Tuple, t.Tuple[np.ndarray, np.ndarray]]:
+
+ cells = Cells(results_path)
+ indices, masks = cells._sample_masks(n, seed=seed, interval=interval)
+
+ processed_tiles, cropped_masks = _overlay_masks_tiles(
+ image_path,
+ results_path,
+ masks,
+ [indices[i] for i in (0, 2)],
+ as_generator=as_generator,
+ )
+ return indices, (processed_tiles, cropped_masks)
+
+
+def _overlay_mask_tile(
+ image_path: str,
+ results_path: str,
+ mask: np.ndarray,
+ index: t.Tuple[int, int, int],
bg_channel: int = 0,
fg_channel: int = 1,
reduce_z: t.Union[None, t.Callable] = np.max,
+ as_generator: bool = False,
) -> t.Tuple[np.ndarray, np.ndarray]:
+ """
+ Return a tuplw with two channels
+ """
- tcs = np.stack(
+ tc = np.stack(
[
- [
- fetch_tc(image_path, results_path, tp, i)
- for i in (bg_channel, fg_channel)
- ]
- for tp in locations[1]
+ fetch_tc(image_path, results_path, index[1], i)
+ for i in (bg_channel, fg_channel)
]
- ) # Returns TC(tile)ZYX
+ ) # Returns C(tile)ZYX
- tiles = np.stack(
- [tcs[i, :, tile].astype(float) for i, tile in enumerate(locations[0])]
- )
+ tiles = tc[:, index[0]].astype(float)
reduced_z = (
- reduce_z(tiles, axis=2) if reduce_z else concatenate_dims(tiles, 2, -2)
+ reduce_z(tiles, axis=1) if reduce_z else concatenate_dims(tiles, 1, -2)
)
- repeated_mask = np.stack(
- [tile_like(mask, reduced_z[0, 0]) for mask in masks]
- )
+ repeated_mask = tile_like(mask, reduced_z[0])
- cropped_fg = np.stack(
- [crop_mask(c, mask) for mask, c in zip(repeated_mask, reduced_z[:, 1])]
- )
+ cropped_fg = crop_mask(reduced_z[1], repeated_mask)
- return reduced_z[:, 0], cropped_fg
+ return reduced_z[0], cropped_fg
-def _sample_n_tiles_masks(
+def _overlay_masks_tiles(
image_path: str,
results_path: str,
- n: int,
- seed: int = 0,
- interval=None,
-) -> t.Tuple[t.Tuple, t.Tuple[np.ndarray, np.ndarray]]:
+ masks: np.ndarray,
+ indices: t.Tuple[t.Tuple[int], t.Tuple[int], t.Tuple[int]],
+ bg_channel: int = 0,
+ fg_channel: int = 1,
+ reduce_z: t.Union[None, t.Callable] = np.max,
+ as_generator: bool = False,
+) -> t.Tuple[np.ndarray, np.ndarray]:
- cells = Cells(results_path)
- locations, masks = cells._sample_masks(n, seed=seed, interval=interval)
+ tmp = [
+ _overlay_mask_tile(
+ image_path,
+ results_path,
+ mask,
+ index,
+ bg_channel,
+ fg_channel,
+ reduce_z,
+ )
+ for mask, index in zip(masks, zip(*indices))
+ ]
- processed_tiles, cropped_masks = overlay_masks_tiles(
- image_path,
- results_path,
- masks,
- [locations[i] for i in (0, 2)],
- )
- return locations, (processed_tiles, cropped_masks)
+ return [np.stack(x) for x in zip(*tmp)]
--
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