From 9bce82686ca8a8833c4126cafccff7182b9cd69b Mon Sep 17 00:00:00 2001
From: Christopher Rhodes <christopher.rhodes@embl.de>
Date: Thu, 5 Oct 2023 15:38:40 +0200
Subject: [PATCH] Generalized to flattening multichannel images
---
extensions/chaeo/tests/test_zstack.py | 2 +-
extensions/chaeo/zmask.py | 35 ++++++++++++++-------------
2 files changed, 19 insertions(+), 18 deletions(-)
diff --git a/extensions/chaeo/tests/test_zstack.py b/extensions/chaeo/tests/test_zstack.py
index 39da79b5..1ac0321c 100644
--- a/extensions/chaeo/tests/test_zstack.py
+++ b/extensions/chaeo/tests/test_zstack.py
@@ -106,7 +106,7 @@ class TestZStackDerivedDataProducts(unittest.TestCase):
dff['centroid-0'].to_numpy(),
dff['centroid-1'].to_numpy(),
dff['zi'].to_numpy(),
- self.stack.get_one_channel_data(1),
+ self.stack
)
self.assertEqual(img.shape[0:2], self.stack.shape[0:2])
diff --git a/extensions/chaeo/zmask.py b/extensions/chaeo/zmask.py
index 6206c737..3ef3cd97 100644
--- a/extensions/chaeo/zmask.py
+++ b/extensions/chaeo/zmask.py
@@ -154,20 +154,18 @@ def project_stack_from_focal_points(
zz: np.ndarray,
stack: GenericImageDataAccessor,
degree: int = 2,
-):
- # TODO: add weights
+) -> np.ndarray:
"""
- Given a set of 3D points, project a multichannel z-stack
- :param xx:
- :param yy:
- :param zz:
- :param stack:
- :param degree:
- :return:
+ Given a set of 3D points, project a multichannel z-stack based on a surface fit of the provided points
+ :param xx: vector of point x-coordinates
+ :param yy: vector of point y-coordinates
+ :param zz: vector of point z-coordinates
+ :param stack: z-stack to project
+ :param degree: order of polynomial to fit
+ :return: multichannel 2d projected image array
"""
assert xx.shape == yy.shape
assert xx.shape == zz.shape
- assert stack.chroma == 1
poly = PolynomialFeatures(degree=degree)
X = np.stack([xx, yy]).T
@@ -175,20 +173,23 @@ def project_stack_from_focal_points(
model = LinearRegression(fit_intercept=False)
model.fit(features, zz)
- output_shape = stack.hw
- xy_indices = np.indices(output_shape).reshape(2, -1).T
+ xy_indices = np.indices(stack.hw).reshape(2, -1).T
xy_features = np.dot(
poly.fit_transform(xy_indices, zz),
model.coef_
)
zi_image = xy_features.reshape(
- output_shape
+ stack.hw
).round().clip(
0, stack.nz
).astype('uint16')
return np.take_along_axis(
- stack.data[:, :, 0, :],
- np.expand_dims(zi_image, 2),
- axis=2
- ).reshape(output_shape)
+ stack.data,
+ np.repeat(
+ np.expand_dims(zi_image, (2, 3)),
+ stack.chroma,
+ axis=2
+ ),
+ axis=3
+ )
--
GitLab