Commit 01307ffc authored by Chris Evenhuis's avatar Chris Evenhuis
Browse files

Initial commit

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%% Cell type:code id: tags:
``` python
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
```
%% Cell type:code id: tags:
``` python
cont = pd.read_csv("20171214_Control_FM_48h_100_GFP-int.csv")
treat = pd.read_csv("20171214_FLB0.5MIC_FM_24h_089_GFP-int.csv")
```
%% Cell type:code id: tags:
``` python
cont.head()
```
%%%% Output: execute_result
Area Mean Mode X Y Perim. BX BY Width Height Major \
0 20 255 255 46.400 64.200 18.142 44 61 5 7 6.687
1 66 255 255 65.803 23.333 30.042 60 19 11 9 10.926
2 589 255 255 24.188 46.638 106.225 7 31 33 32 38.092
3 127 255 255 84.059 100.854 50.527 75 94 18 13 19.987
4 816 255 255 82.501 75.407 163.823 63 43 35 61 61.641
Minor Angle Circ. AR Round Solidity cell
0 3.808 73.002 0.764 1.756 0.570 0.784 1
1 7.691 154.351 0.919 1.421 0.704 0.874 2
2 19.687 42.696 0.656 1.935 0.517 0.879 2
3 8.090 34.094 0.625 2.470 0.405 0.867 2
4 16.855 72.893 0.382 3.657 0.273 0.670 3
%% Cell type:code id: tags:
``` python
bins=np.linspace(0,1,11)
fig,axs = plt.subplots(2,1,sharex=True,sharey=True)
axs[0].hist(cont["Round"],bins=bins, density=False, color='blue',alpha=0.5)
axs[1].hist(treat["Round"],bins=bins,density=False,color='red',alpha=0.5);
```
%%%% Output: display_data
![](data:image/png;base64,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)
%% Cell type:code id: tags:
``` python
fig,axs = plt.subplots(2,1,sharex=True,sharey=True,figsize=(4,8))
sns.kdeplot( cont.Round, cont.Area,ax=axs[0])
sns.kdeplot( treat.Round,treat.Area,ax=axs[1])
```
%%%% Output: execute_result
<matplotlib.axes._subplots.AxesSubplot at 0x1a19005588>
%%%% Output: display_data
![](data:image/png;base64,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)
%% Cell type:code id: tags:
``` python
cont = pd.read_csv("20171214_Control_FM_48h_100_cell-stats.csv")
treat = pd.read_csv("20171214_FLB0.5MIC_FM_24h_089_cell-stats.csv")
```
%% Cell type:code id: tags:
``` python
bins=np.arange(0,8)
plt.hist(cont.Cluster,bins=bins)
plt.hist(treat.Cluster,bins=bins)
```
%%%% Output: execute_result
(array([2., 1., 4., 2., 2., 3., 0.]),
array([0, 1, 2, 3, 4, 5, 6, 7]),
<a list of 7 Patch objects>)
%%%% Output: display_data
![](data:image/png;base64,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)
%% Cell type:code id: tags:
``` python
def get_clust_hist( series):
uniq = series.unique()
clusts=[0]*20
for u in uniq:
nu = sum(series==u)
clusts[nu]=clusts[nu]+1
return range(len(clusts)),clusts
```
%% Cell type:code id: tags:
``` python
plt.bar(*get_clust_hist(cont.Cluster))
plt.bar(*get_clust_hist(treat.Cluster))
plt.xlim(0,8)
```
%%%% Output: execute_result
(0, 8)
%%%% Output: display_data
![](data:image/png;base64,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)
%% Cell type:code id: tags:
``` python
```
Area,Mean,Mode,X,Y,Perim.,BX,BY,Width,Height,Major,Minor,Angle,Circ.,AR,Round,Solidity,cell
20,255,255,46.400,64.200,18.142,44,61,5,7,6.687,3.808,73.002,0.764,1.756,0.570,0.784,1
66,255,255,65.803,23.333,30.042,60,19,11,9,10.926,7.691,154.351,0.919,1.421,0.704,0.874,2
589,255,255,24.188,46.638,106.225,7,31,33,32,38.092,19.687,42.696,0.656,1.935,0.517,0.879,2
127,255,255,84.059,100.854,50.527,75,94,18,13,19.987,8.090,34.094,0.625,2.470,0.405,0.867,2
816,255,255,82.501,75.407,163.823,63,43,35,61,61.641,16.855,72.893,0.382,3.657,0.273,0.670,3
398,255,255,58.882,49.407,119.983,40,29,32,33,33.352,15.194,38.726,0.347,2.195,0.456,0.578,4
128,255,255,73.211,69.750,42.627,67,63,12,14,14.510,11.232,71.824,0.885,1.292,0.774,0.905,4
252,255,255,37.492,22.687,65.255,26,15,23,19,19.755,16.242,174.987,0.744,1.216,0.822,0.847,5
35,255,255,58.786,30.329,21.556,55,27,7,7,7.385,6.034,130.996,0.947,1.224,0.817,0.875,5
190,255,255,72.026,44.395,67.012,64,31,14,27,25.786,9.382,107.203,0.532,2.749,0.364,0.793,5
37,255,255,16.311,48.500,24.142,13,44,6,9,9.264,5.085,111.622,0.798,1.822,0.549,0.851,5
279,255,255,21.253,45.654,127.296,12,17,20,47,31.961,11.115,90.129,0.216,2.876,0.348,0.447,6
56,255,255,58.393,32.554,51.456,53,23,9,22,20.487,3.480,112.321,0.266,5.886,0.170,0.626,6
25,255,255,37.420,64.140,25.799,33,59,8,9,10.843,2.936,51.813,0.472,3.693,0.271,0.725,6
435,255,255,26.645,33.638,101.054,12,18,27,33,30.608,18.095,66.362,0.535,1.692,0.591,0.730,7
225,255,255,33.264,25.553,70.184,20,18,29,14,29.076,9.853,164.141,0.574,2.951,0.339,0.867,8
713,255,255,54.811,55.479,182.894,34,41,44,38,40.232,22.564,24.995,0.268,1.783,0.561,0.606,11
596,255,255,58.653,53.579,151.238,32,33,48,48,54.797,13.848,134.182,0.327,3.957,0.253,0.641,12
596,255,255,69.629,56.732,171.338,39,41,50,35,40.934,18.539,153.320,0.255,2.208,0.453,0.569,13
50,255,255,12.920,61.620,29.556,10,55,6,12,11.794,5.398,90.383,0.719,2.185,0.458,0.855,13
116,255,255,98.621,80.483,39.456,92,75,13,12,14.294,10.333,138.672,0.936,1.383,0.723,0.924,13
636,255,255,42.627,56.355,113.640,32,34,25,45,38.517,21.024,97.803,0.619,1.832,0.546,0.853,14
140,255,255,34.750,32.079,70.426,26,21,16,27,28.274,6.304,64.549,0.355,4.485,0.223,0.662,15
279,255,255,57.056,46.134,93.497,39,32,28,25,28.972,12.261,50.682,0.401,2.363,0.423,0.667,15
821,255,255,59.629,58.247,162.652,36,42,53,34,41.335,25.290,6.122,0.390,1.634,0.612,0.679,16
57,255,255,18.149,35.149,45.799,14,24,6,20,19.374,3.746,82.847,0.341,5.172,0.193,0.699,17
604,255,255,49.583,56.778,224.693,36,31,37,53,33.964,22.643,84.471,0.150,1.500,0.667,0.453,18
434,255,255,93.071,42.719,160.995,62,30,63,37,58.739,9.407,153.386,0.210,6.244,0.160,0.549,19
63,255,255,42.897,35.325,30.385,37,32,12,7,11.830,6.781,18.270,0.858,1.745,0.573,0.913,19
68,255,255,30.147,78.647,30.627,25,74,10,10,10.840,7.987,146.408,0.911,1.357,0.737,0.895,19
53,255,255,23.349,104.877,34.042,19,99,9,13,13.575,4.971,115.480,0.575,2.731,0.366,0.779,19
738,255,255,51.932,123.573,168.652,30,99,56,42,53.623,17.523,144.940,0.326,3.060,0.327,0.644,19
417,255,255,53.344,40.054,122.912,22,33,53,15,47.966,11.069,3.710,0.347,4.333,0.231,0.741,21
72,255,255,61.931,56.597,33.799,55,53,13,8,13.229,6.930,171.171,0.792,1.909,0.524,0.873,21
275,255,255,28.995,50.551,72.083,15,42,24,20,24.466,14.312,146.658,0.665,1.709,0.585,0.858,22
48,255,255,74.458,53.229,39.456,72,44,5,16,15.159,4.032,89.758,0.387,3.760,0.266,0.727,22
118,255,255,59.034,54.339,46.284,51,49,17,12,17.158,8.757,28.447,0.692,1.959,0.510,0.843,22
34,255,255,68.353,69.235,29.213,65,63,7,11,10.787,4.013,69.421,0.501,2.688,0.372,0.701,22
Area,Mean,Cluster
19.650,5015.609,0
64.816,7482.604,1
66.330,7929.968,2
42.207,7889.641,3
38.143,7393.616,4
28.487,8985.354,5
36.203,10472.895,6
16.159,5644.049,4
0.315,5449.217,4
1.162,5397.394,4
57.309,8922.654,7
49.204,10193.781,8
62.472,9179.455,9
52.252,8112.991,10
33.160,8713.942,11
66.966,8034.936,12
4.484,5963.760,13
49.866,9164.539,14
104.536,9233.992,15
2.381,6028.442,13
40.724,8633.886,16
38.269,8479.494,17
Area,Mean,Mode,X,Y,Perim.,BX,BY,Width,Height,Major,Minor,Angle,Circ.,AR,Round,Solidity,cell
231,255,255,21.950,20.180,68.426,12,11,20,21,24.856,11.833,45.750,0.620,2.101,0.476,0.872,1
160,255,255,37.844,67.744,65.012,24,60,23,14,19.865,10.255,10.503,0.476,1.937,0.516,0.751,1
54,255,255,32.667,14.370,27.799,28,10,9,9,10.200,6.741,132.958,0.878,1.513,0.661,0.864,2
128,255,255,28.641,29.430,41.698,22,23,14,13,13.207,12.340,159.131,0.925,1.070,0.934,0.911,2
254,255,255,28.539,33.894,95.397,19,20,19,31,28.263,11.443,69.660,0.351,2.470,0.405,0.660,4
192,255,255,40.531,67.062,59.598,32,58,18,18,21.652,11.290,139.861,0.679,1.918,0.521,0.844,4
308,255,255,30.237,35.958,94.811,21,17,18,35,28.626,13.699,79.201,0.431,2.090,0.479,0.744,5
51,255,255,3.441,74.069,42.385,0,67,7,12,10.952,5.929,76.127,0.357,1.847,0.541,0.703,5
23,255,255,113.152,1.804,17.899,110,0,7,4,6.825,4.290,6.084,0.902,1.591,0.629,0.920,6
46,255,255,66.478,30.152,23.556,63,26,7,8,8.311,7.047,58.332,1.000,1.179,0.848,0.920,6
662,255,255,73.502,72.921,133.782,56,51,37,39,44.672,18.868,46.565,0.465,2.368,0.422,0.773,6
299,255,255,40.470,47.159,63.355,31,37,20,19,19.857,19.172,21.881,0.936,1.036,0.966,0.946,7
440,255,255,44.486,23.784,95.882,31,12,28,26,28.049,19.973,44.034,0.601,1.404,0.712,0.826,8
54,255,255,23.370,24.611,36.284,17,20,13,11,15.784,4.356,38.541,0.515,3.624,0.276,0.777,8
171,255,255,18.746,59.944,64.669,11,48,15,22,19.609,11.103,117.560,0.514,1.766,0.566,0.784,8
82,255,255,42.780,38.463,53.698,30,34,21,10,15.139,6.897,177.228,0.357,2.195,0.456,0.590,9
308,255,255,59.315,52.032,70.184,49,39,19,24,22.838,17.171,68.255,0.786,1.330,0.752,0.890,9
392,255,255,30.033,77.630,76.083,19,65,22,25,24.961,19.996,76.485,0.851,1.248,0.801,0.919,10
402,255,255,67.965,51.316,82.083,54,41,25,23,24.665,20.752,157.598,0.750,1.189,0.841,0.901,11
75,255,255,58.460,84.300,45.941,51,77,15,13,18.656,5.119,34.292,0.447,3.645,0.274,0.701,11
409,255,255,59.928,36.630,80.326,48,22,23,28,28.253,18.432,61.510,0.797,1.533,0.652,0.914,12
259,255,255,33.782,20.844,60.184,24,12,20,18,19.092,17.273,7.292,0.899,1.105,0.905,0.918,13
751,255,255,92.681,74.677,132.610,72,50,36,46,49.599,19.279,55.606,0.537,2.573,0.389,0.830,14
Area,Mean,Cluster
29.801,10108.765,0
13.888,9176.174,0
2.203,6571.635,1
34.437,7614.156,2
27.924,7557.855,2
53.730,7933.916,2
21.579,8340.960,2
49.035,11167.323,3
27.871,11600.379,3
32.166,10224.539,4
38.764,11045.961,4
30.989,10429.847,5
18.094,9076.155,5
56.726,9853.288,5
from __future__ import print_function, division
from ij import IJ, ImagePlus, WindowManager, ImageStack,CompositeImage
from ij.plugin.frame import RoiManager
from ij.gui import Overlay, Line
from inra.ijpb.measure import RegionAdjacencyGraph
from inra.ijpb.label import LabelImages
import copy
from ij.plugin import Colors
from java.awt import Color
import random
imp=IJ.getImage()
roim = RoiManager.getRoiManager()
roim.reset()
if( True ):
# load the regions into the Roi manager
ip=imp.getProcessor()
from ij.plugin.filter import ThresholdToSelection
for i in range(1,int(ip.getMax())+1):
ip.setThreshold(i,i,0)
roi = ThresholdToSelection().convert( ip )
if( roi is not None):
ip.setRoi(roi)
stats = ip.getStats()
roim.addRoi(roi)
ncell = roim.getCount()
print(ncell)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
def get_adj_matrix( imp ):
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
ip=imp.getProcessor()
labels=LabelImages.findAllLabels(imp)
lab2i = dict([ (il,i) for i,il in enumerate(labels) ])
ncell = len(labels)-1
print("Here")
A_dict=dict( [ (i, set()) for i in range(len(labels)) ] )
print(A_dict)
adj=RegionAdjacencyGraph.computeAdjacencies(imp.getProcessor())
for kv in adj:
l1 = lab2i[int(kv.label1)]
l2 = lab2i[int(kv.label2)]
A_dict[l1].add(l2)
A_dict[l2].add(l1)
return A_dict
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
def extract_clusters( imp ):
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
''' calculate the clustering for labelling image, such as one produced
by morpholib watershedding.
This is an image with
background pixels = 0
roi[i] = i
This program returns clusters of touching rois
clusters = list of sets. 0-nclust
sets contains the rois in the cluster
'''
labels =LabelImages.findAllLabels(imp)
# create an diction of sets to sote the adjacency matrix
A_dict = get_adj_matrix( imp )
print(A_dict)
# now grow the clusters
clusters=[]
unassigned=set(range(len(labels)))
while( len(unassigned)>0 ):
clust=set([unassigned.pop()])
n0 = 0
while( len(clust)>n0 ):
n0=len(clust)
for n in list(clust):
clust = clust.union(A_dict[n])
clusters.append(clust)
unassigned = unassigned.difference(clust)
cluster_member={}
cluster_member=[0]*len(labels)
for i,c in enumerate(clusters):
#print(i,cluster_member[i])
for j in c:
cluster_member[j]=i
#for i,c in enumerate(clusters):
#for j in c:
# cluster_member[j]=i
return clusters, cluster_member
ncell = int(imp.getProcessor().getMax())
A_dict = get_adj_matrix( imp )
clusters, cluster_member = extract_clusters(imp)
#cluster_member=[0]*ncell
#for i,c in enumerate(clusters):
# print(i,cluster_member[i])
# for j in c:
# cluster_member[j]=i
print(cluster_member)
labels=LabelImages.findAllLabels(imp)
print(labels)
centers = dict()
for i,l in enumerate(labels):
roi = roim.getRoi(i)
if( roi is not None ):
stats=roi.getStatistics()
centers[i]=[stats.xCentroid,stats.yCentroid]
colors = [ Color.GREEN, Color.RED, Color.BLUE,Color.YELLOW,Color.MAGENTA]
ov = Overlay()
for k,c in enumerate(clusters):
#col = colors[ random.randint(0,len(colors)-1) ]
print("cluster",c)
for n0 in c:
print(" ",n0)
print(" ",A_dict[n0])
for n1 in A_dict[n0]:
print(n0,n1)
line = Line( centers[n0][0], centers[n0][1],centers[n1][0],centers[n1][1])
line.setStrokeColor(Color.RED)
ov.add(line)
imp.setOverlay(ov)
imp.setOverlay(ov)
imp.show()
#for n1 in A_dict[n]:
from __future__ import print_function
from ij import IJ, ImagePlus, WindowManager,Prefs
from ij.gui import Overlay,OvalRoi
from java.awt import Color
from ij.plugin.frame import RoiManager
import os,time
import math
# open a 100X image
# open the Roi file
# select the ones in the stack
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
def get_percentile( hist, per):
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
for i in range(1,len(hist)):
hist[i] = hist[i]+hist[i-1]
y = ( i for i,v in enumerate(hist) if per*hist[-1]<v )
return next(y)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
def translate_roi( roi, dx, dy ):
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
roi_t = roi.clone()
fp = roi_t.getInterpolatedPolygon(2,False)
fp.xpoints = [ x+dx for x in fp.xpoints]
fp.ypoints = [ y+dy for y in fp.ypoints]
roi_t = PolygonRoi( fp, roi.getType() )
roi_t.setPosition(0,roi.getPosition(),0)
return roi_t
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
def check_files_exist( nlist ):
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
print(nlist)
dir_n = nlist[0]
for name in nlist[1:]:
exists = os.path.isfile(os.path.join(dir_n,name))
if( not exists ):
IJ.showMessage("File not found {}".format(name))
RaiseException
print( "All files found")
return
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
def select_file():
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
'''
Prompt user to select a file
returns the
[stack,map,roi]
error if not found
'''
import os
from ij.io import OpenDialog
od= OpenDialog("Select a file")
dirn = od.getDirectory()
name = od.getFileName()
[base,ext] = os.path.splitext(name)
print(base)
if( base[-4:] =='_map' ):
base = base[:-4]
# check is all files are present
rnames = [base+ext for ext in ['.nd2','_map.nd2','.zip'] ]
rnames.insert(0,dirn)
check_files_exist(rnames)
return rnames
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
def load_rois( rname, path=None ):
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
'''Loads rois from a zip file'''
roim = RoiManager().getInstance()
if( roim is None ):
roim = RoiMangager()
else:
roim.reset()
if( path is None ):
IJ.open(roi_n)
else:
IJ.open(os.path.join(path,roi_n))
return
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
def convert_cluster_roi(roi, maxtol=200):
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
''' Splits up a compound ROI into cells using marker based watersheed with
heights : Euclidean Distance Map
seed : MaxFinder
'''
from ij.plugin.filter import EDM
from ij.plugin.filter import MaximumFinder
from inra.ijpb.watershed import MarkerControlledWatershedTransform2D
from inra.ijpb.binary import BinaryImages
from inra.ijpb.label import LabelImages
from ij.process import ImageProcessor
ip_mask = roi.getMask()
#ImagePlus("mask",ip_mask).show()
ip_valley = EDM().make16bitEDM( ip_mask)
#ImagePlus("valley",ip_valley).show()
MF = MaximumFinder()
ip_seed = MF.findMaxima(ip_valley,maxtol, MF.IN_TOLERANCE, False)
ip_seed = BinaryImages.componentsLabeling( ip_seed, 8, 32 )
ip_valley.invert()
MCWT=MarkerControlledWatershedTransform2D( ip_valley, ip_seed, ip_mask)
ipw = MCWT.applyWithPriorityQueueAndDams()
#ImagePlus("seed",ip_seed).show()
#ImagePlus("seg",ipw).show()
from ij.plugin.filter import ThresholdToSelection
labels = LabelImages.findAllLabels(ipw)
# colors to label the cells with
col_list=[ getattr(Color,col) for col in ['RED','BLUE','GREEN','ORANGE'] ]
ncol = len(col_list)
roim_t = RoiManager(True)
for i in labels:
ipw.setThreshold(i,i,ImageProcessor.NO_LUT_UPDATE)
roi = ThresholdToSelection().convert( ipw )
col = col_list[i%ncol]
roi.setStrokeColor(col)
roim_t.addRoi(roi)
return roim_t
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
def ridge_detection_for_string(ip):
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
rm = RoiManager().getInstance()
if( rm is not None ):
rmb = rm.clone()
rm.reset()
imp2 = ImagePlus( "ridge", ip)
ridge_opt1="line_width=3 high_contrast=80 low_contrast=150 "
ridge_opt2="minimum_line_length=10 maximum=20000 correct_position "
ridge_opt3="estimate_width extend_line add_to_manager method_for_overlap_resolution=NONE"
#'''sigma=1.37 lower_threshold=5.61 upper_threshold=10.88 '''
ridge_opt="{} {}".format(ridge_opt1,ridge_opt2)
IJ.run(imp2,"Ridge Detection",ridge_opt1+ridge_opt2+ridge_opt3)
return rm
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
def analyse_gfp(imp, roim, ch=2):
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
from ij.plugin.filter import RankFilters,GaussianBlur
from inra.ijpb.morphology import Morphology, Strel
from ij.measure import Measurements, ResultsTable
from ij.gui import Roi,PolygonRoi
from java.lang import Double
from ij.process import ImageProcessor,FloatProcessor,ByteProcessor
from ij.plugin.filter import ParticleAnalyzer
from java.awt import Color
imp.setC(ch)
#ip = imp.getProcessor().duplicate()
cell_rois = roim.getRoisAsArray()
#rti = ResultsTable(True)
#rti.reset()
ov = Overlay()
for ic,roi in enumerate(cell_rois):