From 881f1fde6edb6e2c4119c858c1232f5dab6bd494 Mon Sep 17 00:00:00 2001
From: Christopher Rhodes <christopher.rhodes@embl.de>
Date: Wed, 30 Oct 2024 12:06:08 +0100
Subject: [PATCH] Added and covered PatchStack dataframe with summary stats
---
model_server/base/accessors.py | 16 +++++++++++-----
tests/base/test_accessors.py | 15 ++++++++++++++-
2 files changed, 25 insertions(+), 6 deletions(-)
diff --git a/model_server/base/accessors.py b/model_server/base/accessors.py
index 35f6575d..e48103eb 100644
--- a/model_server/base/accessors.py
+++ b/model_server/base/accessors.py
@@ -492,16 +492,22 @@ class PatchStack(InMemoryDataAccessor):
def shape_dict(self):
return dict(zip(('P', 'Y', 'X', 'C', 'Z'), self.data.shape))
- @property
- def df(self):
+ def get_object_df(self, mask) -> pd.DataFrame:
+ """
+ Given a mask patch stack of the same size, return a DataFrame summarizing the area and intensity of objects,
+ assuming the each patch in the patch stack represents a single object.
+ :param mask of the same dimensions
+ """
+ if self.shape != mask.shape or not mask.is_mask():
+ raise DataShapeError(f'Patch stack object dataframe expects a mask of the same dimensions')
df = pd.DataFrame([
{
'label': i,
- 'area': (self.iat(i).data > 0).sum(),
- 'sum': self.iat(i).data.sum()
+ 'area': (mask.iat(i).data > 0).sum(),
+ 'intensity_sum': (self.iat(i).data * (mask.iat(i).data > 0)).sum()
} for i in range(0, self.count)
])
- df['intensity_mean'] = df['sum'] / df['area']
+ df['intensity_mean'] = df['intensity_sum'] / df['area']
return df
diff --git a/tests/base/test_accessors.py b/tests/base/test_accessors.py
index 31d32d7d..05663dd7 100644
--- a/tests/base/test_accessors.py
+++ b/tests/base/test_accessors.py
@@ -303,8 +303,21 @@ class TestPatchStackAccessor(unittest.TestCase):
self.assertEqual(acc.get_mono(channel=0).data_yxz.shape, (n, h, w, nz))
self.assertEqual(acc.get_mono(channel=0, mip=True).data_yx.shape, (n, h, w))
+ def test_object_df(self):
+ w = 30
+ h = 20
+ n = 2
+ nz = 3
+ nc = 3
+ acc = PatchStack(_random_int(n, h, w, nc, nz))
+ mask_data= np.zeros((n, h, w, nc, nz), dtype='uint8')
+ mask_data[0, 0:5, 0:5, :, :] = 255
+ mask_data[1, 0:10, 0:10, :, :] = 255
+ mask = PatchStack(mask_data)
+ df = acc.get_object_df(mask)
# intensity means are centered around half of full range
- self.assertTrue(np.all(((acc.df['intensity_mean'] / acc.dtype_max) - 0.5)**2 < 1e-3))
+ self.assertTrue(np.all(((df['intensity_mean'] / acc.dtype_max) - 0.5)**2 < 1e-3))
+ self.assertTrue(df['area'][1] / df['area'][0] == 4.0)
return acc
def test_get_one_channel(self):
--
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