From 2c7493d6aa2cefcee79a5eb07ac5a802e3ad2bf6 Mon Sep 17 00:00:00 2001
From: Christopher Rhodes <christopher.rhodes@embl.de>
Date: Wed, 17 Jul 2024 13:18:47 +0200
Subject: [PATCH] Removed focal point projection
---
model_server/base/roiset.py | 80 +++++--------------------------------
1 file changed, 10 insertions(+), 70 deletions(-)
diff --git a/model_server/base/roiset.py b/model_server/base/roiset.py
index 74d85ace..ce0157c5 100644
--- a/model_server/base/roiset.py
+++ b/model_server/base/roiset.py
@@ -10,9 +10,7 @@ from pydantic import BaseModel
from scipy.stats import moment
from skimage.filters import sobel
-from skimage.measure import label, regionprops_table, shannon_entropy, find_contours
-from sklearn.preprocessing import PolynomialFeatures
-from sklearn.linear_model import LinearRegression
+from skimage.measure import label, regionprops_table, shannon_entropy
from .accessors import GenericImageDataAccessor, InMemoryDataAccessor, write_accessor_data_to_file
from .models import InstanceSegmentationModel
@@ -170,7 +168,6 @@ def make_df_from_object_ids(acc_raw, acc_obj_ids, expand_box_by) -> pd.DataFrame
df = df_insert_slices(df, acc_raw.shape_dict, expand_box_by)
- # TODO: make this contingent on whether seg is included
def _make_binary_mask(r):
acc = InMemoryDataAccessor(acc_obj_ids.data == r.label)
cropped = acc.get_mono(0, mip=True).crop_hw((r.y0, r.x0, (r.y1 - r.y0), (r.x1 - r.x0))).data[:, :, 0, 0]
@@ -263,7 +260,6 @@ class RoiSet(object):
def __init__(
self,
acc_raw: GenericImageDataAccessor,
- # acc_obj_ids: GenericImageDataAccessor,
df: pd.DataFrame,
params: RoiSetMetaParams = RoiSetMetaParams(),
):
@@ -271,8 +267,7 @@ class RoiSet(object):
A set of regions of interest, referenced by their positions and contours in the YXCZ space of stack acc_raw.
RoiSet contains their internal state, which may be exported as patches, maps, and other products by export methods.
:param acc_raw: accessor to a generally a multichannel z-stack
- :param acc_obj_ids: accessor to a 2D single-channel object identities map, where each pixel's intensity
- labels its membership in a connected object
+ :param df: dataframe containing at minimum bounding box and segmentation mask information
:param params: optional arguments that influence the definition and representation of ROIs
"""
# assert acc_obj_ids.chroma == 1
@@ -281,13 +276,6 @@ class RoiSet(object):
self.accs_derived = []
self.params = params
- # self._df = self.filter_df(
- # self.make_df_from_object_ids(
- # self.acc_raw, self.acc_obj_ids, expand_box_by=params.expand_box_by
- # ),
- # params.filters,
- # )
-
self._df = df
self.count = len(self._df)
self.object_class_maps = {} # classification results
@@ -302,6 +290,14 @@ class RoiSet(object):
acc_obj_ids: GenericImageDataAccessor,
params: RoiSetMetaParams = RoiSetMetaParams(),
):
+ """
+
+ :param acc_raw:
+ :param acc_obj_ids: accessor to a 2D single-channel object identities map, where each pixel's intensity
+ labels its membership in a connected object
+ :param params:
+ :return:
+ """
assert acc_obj_ids.chroma == 1
df = filter_df(
@@ -335,15 +331,6 @@ class RoiSet(object):
return RoiSet.from_object_ids(acc_raw, get_label_ids(acc_seg, allow_3d=allow_3d, connect_3d=connect_3d), params)
- # TODO: generate overlapping RoiSet from multiple masks
- # call e.g. static adder
-
-
- @staticmethod
- def make_df_from_patches():
- pass
-
-
# TODO: get overlapping segments
def get_overlap_seg(self) -> pd.DataFrame:
dfbb = filter_overlap_bbox(self._df)
@@ -797,53 +784,6 @@ class RoiSet(object):
return RoiSet.from_object_ids(acc_raw, id_mask)
-def project_stack_from_focal_points(
- xx: np.ndarray,
- yy: np.ndarray,
- zz: np.ndarray,
- stack: GenericImageDataAccessor,
- degree: int = 2,
-) -> np.ndarray:
- """
- 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
-
- poly = PolynomialFeatures(degree=degree)
- X = np.stack([xx, yy]).T
- features = poly.fit_transform(X, zz)
- model = LinearRegression(fit_intercept=False)
- model.fit(features, zz)
-
- 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(
- stack.hw
- ).round().clip(
- 0, (stack.nz - 1)
- ).astype('uint16')
-
- return np.take_along_axis(
- stack.data,
- np.repeat(
- np.expand_dims(zi_image, (2, 3)),
- stack.chroma,
- axis=2
- ),
- axis=3
- )
-
-
class Error(Exception):
pass
--
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