From d83a4912763633d64fa85ae9b22c18402d8fff03 Mon Sep 17 00:00:00 2001
From: mattminder <myfiles@vpn-251-030.epfl.ch>
Date: Wed, 13 May 2020 10:49:32 +0200
Subject: [PATCH] cosmetic changes
---
unet/segment.py | 90 +++++++++++++++++++------------------------------
1 file changed, 35 insertions(+), 55 deletions(-)
diff --git a/unet/segment.py b/unet/segment.py
index 43fde00..07f594a 100644
--- a/unet/segment.py
+++ b/unet/segment.py
@@ -3,7 +3,6 @@ from skimage.feature import peak_local_max
from skimage.morphology import watershed, dilation
import numpy as np
-# import cv2
def segment(th, pred, min_distance=10, topology=None):
@@ -72,62 +71,43 @@ def cell_merge(wsh, pred):
print("Processing cell ",obj1+1," of ",wsh.max()," for oversegmentation.")
dil1 = dil_objs[obj1,:,:]
- if np.sum(dil1) > 0: #dil1 can be empty because in the loop, maskobj2 can be deleted if it is joined with a (previous) maskobj1
- objcounter = objcounter + 1
- orig1 = objs[obj1,:,:]
+ # check if mask has been deleted
+ if np.sum(dil1) == 0:
+ continue
+
+ objcounter = objcounter + 1
+ orig1 = objs[obj1,:,:]
- for obj2 in range(obj1+1,wsh.max()):
- dil2 = dil_objs[obj2,:,:]
-
- if (do_box_overlap(obj_coords[obj1,:], obj_coords[obj2,:])
- and np.sum(dil2) > 0):
- border = dil1 * dil2
-
- border_pred = pred[border]
-
- # Border is too small to be considered
- if len(border_pred) < 32:
- continue
-
- # Sum of top 25% of predicted border values
- q75 = np.quantile(border_pred, .75)
- top_border_pred = border_pred[border_pred > q75]
- top_border_height = top_border_pred.sum()
- top_border_area = len(top_border_pred)
+ for obj2 in range(obj1+1,wsh.max()):
+ dil2 = dil_objs[obj2,:,:]
+
+ # only check border if bounding box overlaps, and second mask
+ # is not yet deleted
+ if (do_box_overlap(obj_coords[obj1,:], obj_coords[obj2,:])
+ and np.sum(dil2) > 0):
+
+ border = dil1 * dil2
+ border_pred = pred[border]
+
+ # Border is too small to be considered
+ if len(border_pred) < 32:
+ continue
+
+ # Sum of top 25% of predicted border values
+ q75 = np.quantile(border_pred, .75)
+ top_border_pred = border_pred[border_pred >= q75]
+ top_border_height = top_border_pred.sum()
+ top_border_area = len(top_border_pred)
+
+ # merge cells
+ if top_border_height / top_border_area > .99:
+ orig1 = np.logical_or(orig1, objs[obj2,:,:])
+ dil_objs[obj1,:,:] = np.logical_or(dil1, dil2)
+ dil_objs[obj2,:,:] = np.zeros((wshshape[0], wshshape[1]))
+ obj_coords[obj1,:] = get_bounding_box(dil_objs[obj1,:,:])
+ print("Merged cell ",obj1+1," and ",obj2+1,".")
-# borderprednonzero = pred[np.nonzero(border)] # all the prediction values inside the border area
-# sortborderprednonzero = sorted(borderprednonzero) # sort the values
-# borderprednonzeroarea = len(borderprednonzero) # how many values are there?
-# quartborderarea = round(borderprednonzeroarea/4) # take one fourth of the values. there is some subtlety about how round() rounds but doesn't matter
-# topborderpred = sortborderprednonzero[quartborderarea:] # take top 3/4 of the predictions
-# topborderheight = np.sum(topborderpred) # sum over top 3/4 of the predictions
-# topborderarea = len(topborderpred) # area of 3/4 of predictions. In principle equal to 3/4 of borderprednonzeroarea but because of strange rounding, will just measure again
-
- # merge cells
- if top_border_height / top_border_area > .99:
- orig1 = np.logical_or(orig1, objs[obj2,:,:])
- dil_objs[obj1,:,:] = np.logical_or(dil1, dil2)
- dil_objs[obj2,:,:] = np.zeros((wshshape[0], wshshape[1]))
- obj_coords[obj1,:] = get_bounding_box(dil_objs[obj1,:,:])
- print("Merged cell ",obj1+1," and ",obj2+1,".")
-
-
-# if topborderarea > 8: # SJR: Not only must borderarea be greater than 0 but also have a little bit of border to go on.
-# #print(obj1+1, obj2+1, topborderheight/topborderarea)
-# if topborderheight/topborderarea > 0.99 : # SJR: We are really deep inside a cell, where the prediction is =1. Won't use: borderheight/borderarea > 0.95. Redundant.
-# #print("--")
-# #print(objcounter)
-# #wsh=np.where(wsh==obj2+1, obj1+1, wsh)
-# maskoriobj1 = np.uint8(np.multiply((maskoriobj1 > 0) | (oriobjs[obj2,:,:] > 0),1)) #have to do boolean then integer just to do an 'or'
-# dilobjs[obj1,:,:] = np.uint8(np.multiply((maskobj1 > 0) | (maskobj2 > 0),1)) #have to do boolean then integer just to do an 'or'
-# dilobjs[obj2,:,:] = np.zeros((wshshape[0],wshshape[1]))
-# objcoords[obj1,0] = min(objcoords[obj1,0],objcoords[obj2,0])
-# objcoords[obj1,1] = max(objcoords[obj1,1],objcoords[obj2,1])
-# objcoords[obj1,2] = min(objcoords[obj1,2],objcoords[obj2,2])
-# objcoords[obj1,3] = max(objcoords[obj1,3],objcoords[obj2,3])
-# print("Merged cell ",obj1+1," and ",obj2+1,".")
-
- wshclean = wshclean + orig1*objcounter
+ wshclean = wshclean + orig1*objcounter
return wshclean
--
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