From 37e678ec181b245b487f69f1c06199168ef42e5e Mon Sep 17 00:00:00 2001
From: Christopher Rhodes <christopher.rhodes@embl.de>
Date: Thu, 28 Sep 2023 14:53:11 +0200
Subject: [PATCH] Tests of zmask results
---
extensions/chaeo/test_zstack.py | 28 ++++++++++++++-------
extensions/chaeo/zstack.py | 43 ++++++++++++++++++---------------
model_server/accessors.py | 8 +++++-
3 files changed, 49 insertions(+), 30 deletions(-)
diff --git a/extensions/chaeo/test_zstack.py b/extensions/chaeo/test_zstack.py
index dd4f279b..ae13cb0f 100644
--- a/extensions/chaeo/test_zstack.py
+++ b/extensions/chaeo/test_zstack.py
@@ -1,5 +1,7 @@
import unittest
+import numpy as np
+
from conf.testing import output_path
from extensions.chaeo.conf.testing import multichannel_zstack, pixel_classifier, pipeline_params
@@ -11,34 +13,42 @@ class TestZStackDerivedDataProducts(unittest.TestCase):
def setUp(self) -> None:
# need test data incl obj map
- self.zstack = generate_file_accessor(multichannel_zstack['path'])
+ self.stack = generate_file_accessor(multichannel_zstack['path'])
pxmodel = IlastikPixelClassifierModel(
{'project_file': pixel_classifier['path']},
)
- mip = InMemoryDataAccessor(self.zstack.get_one_channel_data(channel=0).data.max(axis=-1, keepdims=True))
+ mip = InMemoryDataAccessor(self.stack.get_one_channel_data(channel=0).data.max(axis=-1, keepdims=True))
self.pxmap, result = pxmodel.infer(mip)
write_accessor_data_to_file(output_path / 'pxmap.tif', self.pxmap)
- self.obmap = InMemoryDataAccessor((self.pxmap.data > pipeline_params['threshold']).astype('uint8'))
+ self.obmap = InMemoryDataAccessor(self.pxmap.data > pipeline_params['threshold'])
write_accessor_data_to_file(output_path / 'obmap.tif', self.obmap)
def test_zmask_makes_correct_boxes(self):
zmask, meta = build_stack_mask(
'test_zmask_with boxes',
- self.stack,
- self.obmap,
+ self.obmap.get_one_channel_data(0),
+ self.stack.get_one_channel_data(0),
mask_type='boxes',
)
zmask_acc = InMemoryDataAccessor(zmask)
- self.assertTrue(zmask_acc.is_object_map())
+ self.assertTrue(zmask_acc.is_mask())
# assert dimensionality of zmask
- self.assertEqual(zmask.shape_dict['Z'] > 1)
- self.assertEqual(zmask.shape_dict['C'] == 1)
+ self.assertGreater(zmask_acc.shape_dict['Z'], 1)
+ self.assertEqual(zmask_acc.shape_dict['C'], 1)
+ write_accessor_data_to_file(output_path / 'zmask.tif', zmask_acc)
+
+ # mask values are not just all True or all False
+ self.assertTrue(np.any(zmask))
+ self.assertFalse(np.all(zmask))
# assert non-trivial meta info in boxes
- pass
+ self.assertGreater(len(meta), 1)
+ sh = meta[1]['mask'].shape
+ ar = meta[1]['info'].area
+ self.assertGreaterEqual(sh[0] * sh[1], ar)
def test_zmask_makes_correct_contours(self):
pass
\ No newline at end of file
diff --git a/extensions/chaeo/zstack.py b/extensions/chaeo/zstack.py
index 475f6fd8..71bcd214 100644
--- a/extensions/chaeo/zstack.py
+++ b/extensions/chaeo/zstack.py
@@ -1,10 +1,12 @@
import numpy as np
import pandas as pd
-from skimage.measure import find_contours, regionprops_table
+from skimage.measure import find_contours, label, regionprops_table
+
+from model_server.accessors import GenericImageDataAccessor
# build a single boolean 3d mask (objects v. bboxes) and return bounding boxes
-def build_stack_mask(desc, obmap, stack, filters=None, mask_type='contour', expand_box_by=(0, 0)): # TODO: specify boxes data type
+def build_stack_mask(desc, obmap: GenericImageDataAccessor, stack: GenericImageDataAccessor, filters=None, mask_type='contour', expand_box_by=(0, 0)): # TODO: specify boxes data type
"""
filters: dict of (min, max) tuples
@@ -12,17 +14,17 @@ def build_stack_mask(desc, obmap, stack, filters=None, mask_type='contour', expa
"""
# validate inputs
- assert len(stack.shape) == 3, stack.shape
- assert mask_type in ('contour', 'box'), mask_type # TODO: replace with call to validator
+ # assert len(stack.shape) == 3, stack.shape
+ assert stack.chroma == 1
+ assert stack.shape_dict['Z'] > 1
+ assert mask_type in ('contours', 'boxes'), mask_type # TODO: replace with call to validator
- for k in filters.keys():
- assert k in ('area', 'solidity')
- vmin, vmax = filters[k]
- assert vmin >= 0
+ assert obmap.is_mask()
+ lamap = label(obmap.data[:, :, 0, 0])
# build object query
query_str = 'label > 0' # always true
- if filters:
+ if filters is not None:
for k in filters.keys():
assert k in ('area', 'solidity')
vmin, vmax = filters[k]
@@ -30,11 +32,11 @@ def build_stack_mask(desc, obmap, stack, filters=None, mask_type='contour', expa
query_str = query_str + f' & {k} > {vmin} & {k} < {vmax}'
# build dataframe of objects, assign z index to each object
- argmax = stack.argmax(axis=0)
+ argmax = stack.data.argmax(axis=3, keepdims=True)[:, :, 0, 0]
df = (
pd.DataFrame(
regionprops_table(
- obmap,
+ lamap,
intensity_image=argmax,
properties=('label', 'area', 'intensity_mean', 'solidity', 'bbox')
)
@@ -52,12 +54,12 @@ def build_stack_mask(desc, obmap, stack, filters=None, mask_type='contour', expa
df['zi'] = df['intensity_mean'].round().astype('int')
# make an object map where label is replaced by focus position in stack and background is -1
- lut = np.zeros(obmap.max() + 1) - 1
+ lut = np.zeros(lamap.max() + 1) - 1
lut[df.label] = df.zi
# convert bounding boxes to slices
ebxy, ebz = expand_box_by
- nz, h, w = stack.shape
+ h, w, c, nz = stack.shape
boxes = []
for ob in df.itertuples(name='LabeledObject'):
@@ -76,10 +78,11 @@ def build_stack_mask(desc, obmap, stack, filters=None, mask_type='contour', expa
'x1': ob.x1 - x0,
}
- sl = np.s_[z0: z1 + 1, y0: y1, x0: x1]
+ # sl = np.s_[z0: z1 + 1, y0: y1, x0: x1]
+ sl = np.s_[y0: y1, x0: x1, 0, z0: z1 + 1]
# compute contours
- obmask = (obmap == ob.label)
+ obmask = (lamap == ob.label)
contour = find_contours(obmask)
mask = obmask[ob.y0: ob.y1, ob.x0: ob.x1]
@@ -93,15 +96,15 @@ def build_stack_mask(desc, obmap, stack, filters=None, mask_type='contour', expa
# build mask z-stack
zi_st = np.zeros(stack.shape, dtype='bool')
- if mask_type == 'contour':
- zi_map = (lut[obmap] + 1.0).astype('int')
+ if mask_type == 'contours':
+ zi_map = (lut[lamap] + 1.0).astype('int')
idxs = np.array([zi_map]) - 1
- np.put_along_axis(zi_st, idxs, 1, axis=0)
+ np.put_along_axis(zi_st, idxs, 1, axis=3)
# change background level from to 0 in final frame
- zi_st[-1, :, :][obmap == 0] = 0
+ zi_st[:, :, :, -1][lamap == 0] = 0
- elif mask_type == 'box':
+ elif mask_type == 'boxes':
for bb in boxes:
sl = bb['slice']
zi_st[sl] = 1
diff --git a/model_server/accessors.py b/model_server/accessors.py
index ae5355f0..d9211c48 100644
--- a/model_server/accessors.py
+++ b/model_server/accessors.py
@@ -32,6 +32,9 @@ class GenericImageDataAccessor(ABC):
def is_3d(self):
return True if self.shape_dict['Z'] > 1 else False
+ def is_mask(self):
+ return self._data.dtype == 'bool'
+
def get_one_channel_data (self, channel: int):
c = int(channel)
return InMemoryDataAccessor(self.data[:, :, c:(c+1), :])
@@ -133,7 +136,10 @@ def write_accessor_data_to_file(fpath: Path, accessor: GenericImageDataAccessor)
[3, 2, 0, 1],
[0, 1, 2, 3]
)
- tifffile.imwrite(fpath, zcyx, imagej=True)
+ if accessor.is_mask():
+ tifffile.imwrite(fpath, zcyx.astype('uint8'), imagej=True)
+ else:
+ tifffile.imwrite(fpath, zcyx, imagej=True)
except:
raise FileWriteError(f'Unable to write data to file')
return True
--
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