From aca2c2581a42a421c99cbf2deee7e3c08bc6c66e Mon Sep 17 00:00:00 2001
From: Peter Swain <peter.swain@ed.ac.uk>
Date: Sat, 23 Jul 2022 17:42:27 +0100
Subject: [PATCH] general intro to tiler
---
aliby/tile/tiler.py | 61 ++++++++++++++++++++++++++-------------------
1 file changed, 35 insertions(+), 26 deletions(-)
diff --git a/aliby/tile/tiler.py b/aliby/tile/tiler.py
index 39e2eaf0..a5b5f4fb 100644
--- a/aliby/tile/tiler.py
+++ b/aliby/tile/tiler.py
@@ -1,9 +1,21 @@
"""
-Segment/segmented pipelines.
-Includes splitting the image into traps/parts,
-cell segmentation, nucleus segmentation
+Tiler: Tiles and tracks traps.
-Standard order is (T, C, Z, Y, X)
+The tasks of the Tiler are selecting regions of interest, or tiles, of an image - with one tile per trap, tracking and correcting for the drift of the microscope stage over time, and handling errors and bridging between the image data and ALIBY’s image-processing steps.
+
+Tiler subclasses deal with either network connections or local files.
+
+To find traps, we use a two-step process: we analyse the bright-field image to produce the template of a trap, and we fit this template to the image to find the traps' centres.
+
+We use texture-based segmentation (entropy) to split the image into foreground -- cells and traps -- and background, which we then identify with an Otsu filter. Two methods are used to produce a template trap from these regions: pick the trap with the smallest minor axis length and average over all validated traps.
+
+A peak-identifying algorithm recovers the x and y-axis location of traps in the original image, and we choose the templating approach that identifies the most traps
+
+One key method is Tiler.run.
+
+The image-processing is performed by traps/segment_traps.
+
+The experiment is stored as an array wuth a standard indexing order of (Time, Channels, Z-stack, Y, X).
"""
import warnings
from functools import lru_cache
@@ -306,27 +318,26 @@ class Tiler(ProcessABC):
@lru_cache(maxsize=2)
def get_tc(self, t, c):
"""
- Load image using dask.
- Assumes the image is arranged as
- no of time points
- no of channels
- no of z stacks
- no of pixels in y direction
- no of pixels in z direction
-
- Parameters
- ----------
- t: integer
- An index for a time point
- c: integer
- An index for a channel
-
- def _initialise_traps(self, tile_size: int):
- -------
- full: an array of images
+ Load image using dask.
+ Assumes the image is arranged as
+ no of time points
+ no of channels
+ no of z stacks
+ no of pixels in y direction
+ no of pixels in z direction
+
+ Parameters
+ ----------
+ t: integer
+ An index for a time point
+ c: integer
+ An index for a channel
+
+ Retruns
+ -------
+ full: an array of images
"""
full = self.image[t, c].compute(scheduler="synchronous")
-
return full
@property
@@ -401,7 +412,7 @@ class Tiler(ProcessABC):
def find_drift(self, tp):
"""
- Find any translational drifts between two images at consecutive
+ Find any translational drift between two images at consecutive
time points using cross correlation.
Arguments
@@ -499,7 +510,6 @@ class Tiler(ProcessABC):
time_dim = 0
for t in range(self.image.shape[time_dim]):
self.run_tp(t)
-
return None
# The next set of functions are necessary for the extraction object
@@ -510,7 +520,6 @@ class Tiler(ProcessABC):
Get a multidimensional array with all tiles for a set of channels
and z-stacks.
"""
-
# FIXME add support for subtiling trap
# FIXME can we ignore z(always give)
if channels is None:
--
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