diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..93f30b3230e4fda8d8f65da4c3d3c33f6c217552
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+*SJR_old*
+unet_weights_*
+*pycache*
diff --git a/GUI_main.py b/GUI_main.py
index f8ebf561374e816e37464a358618b50c1e3315b3..6732a302fd755097e2872cfaac2316f4bec662da 100644
--- a/GUI_main.py
+++ b/GUI_main.py
@@ -198,7 +198,14 @@ class App(QMainWindow):
# loading the first images of the cells from the nd2 file
self.currentframe = self.reader.LoadOneImage(self.Tindex,self.FOVindex)
- self.nextframe = self.reader.LoadOneImage(self.Tindex+1, self.FOVindex)
+
+ #check if the t+1 time frame exists, avoid failure if there is only
+ #one picture in the folder/nd2 file
+ if self.Tindex+1 < self.reader.sizet:
+ self.nextframe = self.reader.LoadOneImage(self.Tindex+1, self.FOVindex)
+ else:
+ self.nextframe = np.zeros([self.reader.sizey, self.reader.sizex])
+
self.previousframe = np.zeros([self.reader.sizey, self.reader.sizex])
@@ -206,7 +213,14 @@ class App(QMainWindow):
# loading the first masks from the hdf5 file
self.mask_curr = self.reader.LoadMask(self.Tindex, self.FOVindex)
self.mask_previous = np.zeros([self.reader.sizey, self.reader.sizex])
- self.mask_next = self.reader.LoadMask(self.Tindex+1, self.FOVindex)
+
+ #check if the t+1 mask exists, avoid failure if there is only
+ #one mask in the hdf file
+ if self.Tindex+1 < self.reader.sizet:
+ self.mask_next = self.reader.LoadMask(self.Tindex+1, self.FOVindex)
+ else:
+ self.mask_next = np.zeros([self.reader.sizey, self.reader.sizex])
+
# creates a list of all the buttons, which will then be used in order
@@ -1103,19 +1117,24 @@ class App(QMainWindow):
self.m.prevpicture = np.zeros([self.reader.sizey, self.reader.sizex], dtype = np.uint16)
self.m.prevplotmask = np.zeros([self.reader.sizey, self.reader.sizex], dtype = np.uint16)
-# load the image and the mask for the next plot.
- self.m.nextpicture = self.reader.LoadOneImage(self.Tindex+1, self.FOVindex)
- self.m.nextplotmask = self.reader.LoadMask(self.Tindex+1, self.FOVindex)
-
-
+# load the image and the mask for the next plot, check if it exists
+ if self.Tindex+1 < self.reader.sizet:
+ self.m.nextpicture = self.reader.LoadOneImage(self.Tindex+1, self.FOVindex)
+ self.m.nextplotmask = self.reader.LoadMask(self.Tindex+1, self.FOVindex)
+ # enables the next frame button in case it was disabled when the
+ # fov/channel was changed
+ self.button_nextframe.setEnabled(True)
+ else:
+ self.m.nextpicture = np.zeros([self.reader.sizey, self.reader.sizex], dtype = np.uint16)
+ self.m.nextplotmask = np.zeros([self.reader.sizey, self.reader.sizex], dtype = np.uint16)
+ # disables the next frame button if the mask or the picture
+ # does not exist.
+ self.button_nextframe.setEnabled(False)
+
# once the images and masks are loaded into the variables, they are
# displaye in the gui.
self.m.UpdateBckgrndPicture()
-
-# enables the next frame button in case it was disabled when the
-# fov/channel was changed
- self.button_nextframe.setEnabled(True)
# disables the previous frame button in case it was active before
# changing fov/channel.
self.button_previousframe.setEnabled(False)
@@ -1394,8 +1413,8 @@ class App(QMainWindow):
# self.button_nextframe.setShortcut(Qt.Key_Right)
# self.button_nextframe.pressed.connect(self.Test)
# self.button_nextframe.setChecked(False)
-
- self.button_nextframe.setEnabled(True)
+ if self.Tindex + 1 < self.reader.sizet:
+ self.button_nextframe.setEnabled(True)
# self.button_nextframe.pressed.connect(self.Test)
# self.button_nextframe.setChecked(False)
@@ -1424,8 +1443,11 @@ class App(QMainWindow):
# self.button_nextframe.disconnect()
self.Disable(self.button_nextframe)
+ print("self.Tindex",self.Tindex)
+ print("self.reader.sizet",self.reader.sizet)
if self.Tindex + 1 < self.reader.sizet - 1 :
+ print('if')
self.reader.SaveMask(self.Tindex, self.FOVindex, self.m.plotmask)
self.m.prevpicture = self.m.currpicture.copy()
@@ -1446,6 +1468,7 @@ class App(QMainWindow):
#
else:
+ print('else')
self.reader.SaveMask(self.Tindex, self.FOVindex, self.m.plotmask)
self.m.prevpicture = self.m.currpicture.copy()
@@ -1647,7 +1670,7 @@ class App(QMainWindow):
self.m.titlecurr.set_text('Time index {}'.format(self.Tindex))
self.m.titleprev.set_text('No frame {}'.format(''))
- self.m.titlenext.set_text('Next Time index {}'.format(self.Tindex+1))
+ self.m.titlenext.set_text('Next time index {}'.format(self.Tindex+1))
# self.m.ax.set_title('Time index {}'.format(self.Tindex))
@@ -1674,7 +1697,7 @@ class App(QMainWindow):
else:
self.m.titlecurr.set_text('Time index {}'.format(self.Tindex))
self.m.titleprev.set_text('Previous time index {}'.format(self.Tindex-1))
- self.m.titlenext.set_text('Next Time index {}'.format(self.Tindex+1))
+ self.m.titlenext.set_text('Next time index {}'.format(self.Tindex+1))
# self.m.ax.set_title('Time index {}'.format(self.Tindex))
@@ -1976,7 +1999,7 @@ class PlotCanvas(FigureCanvas):
self.titlecurr = self.ax.set_title('Time index {}'.format(parent.Tindex))
self.titleprev = self.ax2.set_title('No frame {}'.format(''))
- self.titlenext = self.ax3.set_title('Next Time index {}'.format(parent.Tindex+1))
+ self.titlenext = self.ax3.set_title('Next time index {}'.format(parent.Tindex+1))
# these variables are just set to test the states of the buttons
@@ -2234,6 +2257,7 @@ class PlotCanvas(FigureCanvas):
background coordinates, setting the background to 0 again.
"""
if flag:
+# print("plotmask",self.plotmask.shape)
self.currmask.set_data((self.plotmask%10+1)*(self.plotmask!=0))
else :
self.currmask.set_data((self.tempmask%10+1)*(self.tempmask!=0))
@@ -2303,9 +2327,13 @@ class PlotCanvas(FigureCanvas):
if self.button_showval_check.isChecked():
vals = np.unique(self.plotmask)
+ vals = np.delete(vals,np.where(vals==0)) #SJR: for some reason are floats and contains background (0)
+ xtemp = []
+ ytemp = []
+ val = []
for k in vals:
- x,y = (self.plotmask==k).nonzero()
- sample = np.random.choice(len(x), size=20, replace=False)
+ y,x = (self.plotmask==k).nonzero() # this was wrong x,y I believe
+ sample = np.random.choice(len(x), size=20, replace=True)
meanx = np.mean(x[sample])
meany = np.mean(y[sample])
xtemp.append(int(round(meanx)))
@@ -2331,7 +2359,7 @@ class PlotCanvas(FigureCanvas):
## val.append(k)
if xtemp:
for i in range(0,len(xtemp)):
- ann = self.ax.annotate(str(val[i]), (xtemp[i], ytemp[i]))
+ ann = self.ax.annotate(str(int(val[i])), (xtemp[i], ytemp[i]))
self.ann_list.append(ann)
@@ -2365,7 +2393,7 @@ class PlotCanvas(FigureCanvas):
if self.button_showval_check.isChecked():
- maxval = np.amax(self.prevplotmask)
+ maxval = int(np.amax(self.prevplotmask))
minval = 1
xtemp = []
ytemp = []
@@ -2422,7 +2450,7 @@ class PlotCanvas(FigureCanvas):
if self.button_showval_check.isChecked():
- maxval = np.amax(self.nextplotmask)
+ maxval = int(np.amax(self.nextplotmask))
minval = 1
xtemp = []
ytemp = []
diff --git a/disk/DialogFileBrowser.py b/disk/DialogFileBrowser.py
index 2cb50bb8e6e1ba354a7ca13eaee7a72c5cdc97f0..e645820909eb79ef2d8eabbb75a50671e2b903a4 100644
--- a/disk/DialogFileBrowser.py
+++ b/disk/DialogFileBrowser.py
@@ -18,26 +18,27 @@ class FileBrowser(QDialog):
self.setGeometry(100,100, 800,200)
- self.button_opennd2 = QPushButton('Open Image File')
+ self.button_opennd2 = QPushButton('Open image file')
self.button_opennd2.setEnabled(True)
self.button_opennd2.clicked.connect(self.getnd2path)
self.button_opennd2.setToolTip("Browse for an image file")
self.button_opennd2.setMaximumWidth(150)
- self.button_openfolder = QPushButton('Open Image Folder')
+ self.button_openfolder = QPushButton('Open image folder')
self.button_openfolder.setEnabled(True)
self.button_openfolder.clicked.connect(self.getfolder)
self.button_openfolder.setToolTip("Browse for folder with images")
self.button_openfolder.setMaximumWidth(150)
- self.button_openhdf = QPushButton('Open hdf file')
+ self.button_openhdf = QPushButton('Open mask file')
self.button_openhdf.setEnabled(True)
self.button_openhdf.clicked.connect(self.gethdfpath)
- self.button_openhdf.setToolTip("Browse for an hdf file containing the masks")
+ self.button_openhdf.setToolTip("Browse for a mask file")
self.button_openhdf.setMaximumWidth(150)
self.newhdfentry = QLineEdit()
# self.newhdfentry(Qt.AlignLeft)
+ self.newhdfentry.setText("newmaskfile")
@@ -56,10 +57,10 @@ class FileBrowser(QDialog):
self.labelnd2 = QLabel()
- self.labelnd2.setText('No nd2 file selected')
+ self.labelnd2.setText('No image file (.nd2, .tif, .tiff) selected')
self.labelhdf = QLabel()
- self.labelhdf.setText('No hdf file selected')
+ self.labelhdf.setText('No mask file (.h5, .tif, .tiff) selected')
self.labelfolder = QLabel()
self.labelfolder.setText('No folder selected')
@@ -78,15 +79,19 @@ class FileBrowser(QDialog):
def getnd2path(self):
- self.nd2name,_ = QFileDialog.getOpenFileName(self, 'Open .nd2 File','', 'Image Files (*.nd2 *.tif *.tiff)')
+ self.nd2name,_ = QFileDialog.getOpenFileName(self, 'Open image file','', 'Image files (*.nd2 *.tif *.tiff)')
# print(self.nd2name)
# print(self.nd2name)
- self.labelnd2.setText(self.nd2name)
+ if self.nd2name != '':
+ self.labelnd2.setText(self.nd2name)
def gethdfpath(self):
- self.hdfname,_ = QFileDialog.getOpenFileName(self,'Open .hdf File','', 'hdf Files (*.h5)')
- self.labelhdf.setText(self.hdfname)
+ self.hdfname,_ = QFileDialog.getOpenFileName(self,'Open mask file','', 'Mask files (*.h5 *.tif *.tiff)')
+ if self.hdfname != '':
+ self.labelhdf.setText(self.hdfname)
+ self.newhdfentry.setText("")
def getfolder(self):
self.nd2name = QFileDialog.getExistingDirectory(self, ("Select Image Folder"))
- self.labelnd2.setText(self.nd2name)
\ No newline at end of file
+ if self.nd2name != '':
+ self.labelnd2.setText(self.nd2name)
diff --git a/disk/InteractionDisk_temp.py b/disk/InteractionDisk_temp.py
index a52672b078190ae1499255f8c516642bd4088f19..2295e451f816f757c36f035265a0a6da64d8c4e1 100644
--- a/disk/InteractionDisk_temp.py
+++ b/disk/InteractionDisk_temp.py
@@ -49,25 +49,33 @@ class Reader:
self.sizey = images.sizes['y']
self.sizec = images.sizes['c']
self.sizet = images.sizes['t']
+ print("Debug: self.sizet",self.sizet)
self.Npos = images.sizes['v']
self.channel_names = images.metadata['channels']
elif self.istiff:
with pytiff.Tiff(self.nd2path) as handle:
- self.sizex, self.sizey = handle.shape
+ self.sizey, self.sizex = handle.shape #SJR: changed by me
self.sizec = 1
self.sizet = handle.number_of_pages
+ print("Debug: handle.number_of_pages",handle.number_of_pages)
self.Npos = 1
self.channel_names = ['Channel1']
elif self.isfolder:
+
filelist = os.listdir(self.nd2path)
+
+ for f in filelist:
+ if f.startswith('.'):
+ filelist.remove(f)
+
im = skimage.io.imread(self.nd2path + '/' + filelist[0])
- self.sizex, self.sizey = im.shape
+ self.sizey, self.sizex = im.shape #SJR: changed by me
self.sizec = 1
self.Npos = 1
self.sizet = len(filelist)
- print(self.sizet)
+ print("Debug: self.sizet",self.sizet)
self.channel_names = ['Channel1']
#create the labels which index the masks with respect to time and
@@ -118,14 +126,57 @@ class Reader:
if not self.hdfpath:
return self.Createhdf()
else:
-#
- temp = self.hdfpath[:-3]
- self.thresholdname = temp + '_thresholded' + '.h5'
- self.segmentname = temp + '_segmented' + '.h5'
- self.predictname = temp + '_predicted' + '.h5'
+ filenamewithpath, extension = os.path.splitext(self.hdfpath)
+
+ if extension == ".h5":
+ temp = self.hdfpath[:-3]
+
+ self.thresholdname = temp + '_thresholded' + '.h5'
+ self.segmentname = temp + '_segmented' + '.h5'
+ self.predictname = temp + '_predicted' + '.h5'
+ #SJR: Mask is a tiff file
+ elif extension == '.tiff' or extension == '.tif':
+ #SJR: Careful, self.hdfpath is a tif file
+ im = skimage.io.imread(self.hdfpath)
+ print('Inithdf',im.shape)
+ imdims = im.shape
+ if len(imdims) == 3 and imdims[2] < imdims[0] and imdims[2] < imdims[1]: # num pages should be smaller than x or y dimension, very unlikely not to be the case
+ im = np.moveaxis(im, -1, 0) # move last axis to first
+
+ with h5py.File(filenamewithpath + '.h5', 'w') as hf:
+ hf.create_group('FOV0')
+
+ if im.ndim==2:
+ hf.create_dataset('/FOV0/T0', data = im, compression = 'gzip')
+ elif im.ndim==3:
+ nim = im.shape[0]
+ for i in range(nim):
+ hf.create_dataset('/FOV0/T{}'.format(i),
+ data = im[i,:,:], compression = 'gzip')
+
+ #SJR addition:
+ self.thresholdname = filenamewithpath + '_thresholded' + '.h5'
+ hf = h5py.File(self.thresholdname,'w')
+ hf.create_group('FOV0')
+ hf.close()
+
+ self.segmentname = filenamewithpath + '_segmented' + '.h5'
+ hf = h5py.File(self.segmentname,'w')
+ hf.create_group('FOV0')
+ hf.close()
+
+ self.predictname = filenamewithpath + '_predicted' + '.h5'
+ hf = h5py.File(self.predictname,'w')
+ hf.create_group('FOV0')
+ hf.close()
+
+ self.hdfpath = filenamewithpath + '.h5'
+
+
+
+
-#
def Createhdf(self):
@@ -206,10 +257,8 @@ class Reader:
mask = np.array(file['/{}/{}'.format(self.fovlabels[currentFOV], self.tlabels[currentT])], dtype = np.uint16)
file.close()
-
return mask
-
else:
# change with Matthias code!
@@ -322,6 +371,8 @@ class Reader:
with ND2Reader(self.nd2path) as images:
images.default_coords['v'] = currentfov
images.default_coords['c'] = self.default_channel
+ print('Debug in InteractionDisk_temp',currentfov)
+ print('Debug in InteractionDisk_temp',self.default_channel)
images.iter_axes = 't'
im = images[currentT]
@@ -329,31 +380,35 @@ class Reader:
with pytiff.Tiff(self.nd2path) as handle:
handle.set_page(currentT)
im = handle[:]
+ print('Debug in InteractionDisk_temp',im.shape)
+# print('Debug in InteractionDisk_temp',im[0,0],np.amin(im),np.amax(im))
elif self.isfolder:
filelist = os.listdir(self.nd2path)
+ for f in filelist:
+ if f.startswith('.'):
+ filelist.remove(f)
im = skimage.io.imread(self.nd2path + '/' + filelist[currentT])
-
- return np.array(im, dtype = np.uint16)
+
+ # be careful here, the output is converted to 16, not sure it's a good idea.
+ outputarray = np.array(im, dtype = np.uint16)
+ print("Time point", currentT, ". Image properties: ", np.mean(outputarray), " (mean), ", np.std(outputarray), " (std), ", np.median(outputarray), " (median).")
+ return outputarray
def LoadSeg(self, currentT, currentFOV):
-
-
+
file = h5py.File(self.segmentname, 'r+')
if self.TestTimeExist(currentT,currentFOV,file):
mask = np.array(file['/{}/{}'.format(self.fovlabels[currentFOV], self.tlabels[currentT])], dtype = np.uint16)
file.close()
-
return mask
-
-
+
else:
zeroarray = np.zeros([self.sizey, self.sizex],dtype = np.uint16)
file.create_dataset('/{}/{}'.format(self.fovlabels[currentFOV], self.tlabels[currentT]), data = zeroarray, compression = 'gzip', compression_opts = 7)
-
file.close()
return zeroarray
@@ -452,6 +507,7 @@ class Reader:
im = self.LoadOneImage(currentT, currentFOV)
+ im = im*1.0; # SJR: for some reason has to be float64
pred = nn.prediction(im)
file.create_dataset('/{}/{}'.format(self.fovlabels[currentFOV],
self.tlabels[currentT]), data = pred, compression = 'gzip',
diff --git a/init/InitButtons.py b/init/InitButtons.py
index 538ff22745ca4af58909be80fcdcc110b76d9b5c..ff5a288d04fa02cc7579a268c793d43e90bd4e30 100644
--- a/init/InitButtons.py
+++ b/init/InitButtons.py
@@ -5,6 +5,8 @@ Initializing all the buttons in this file.
from PyQt5.QtWidgets import QWidget, QPushButton, QShortcut, QComboBox, QCheckBox, QLineEdit, QStatusBar
from PyQt5 import QtGui
from PyQt5.QtCore import pyqtSignal, QObject, Qt
+import numpy as np
+
def Init(parent):
@@ -44,6 +46,9 @@ def Init(parent):
parent.button_nextframe.setToolTip("Use right arrow key for shortcut")
parent.button_nextframe.setMaximumWidth(150)
parent.button_nextframe.setShortcut(Qt.Key_Right)
+ # if there is only one picture than this button is disabled
+ if np.all(parent.nextframe == 0):
+ parent.button_nextframe.setEnabled(False)
# parent.buttonlist.append(parent.button_nextframe)
# parent.button_nextframe.setShortcutAutoRepeat(False)
diff --git a/packages b/packages.txt
similarity index 88%
rename from packages
rename to packages.txt
index 8e27001716ac8e7e3c2dd4c04b7973366962e3e4..656250e1c91619483f60f53fc866116f8a23d048 100644
--- a/packages
+++ b/packages.txt
@@ -7,4 +7,4 @@ install scikit-image
install openpyxl
install Tensorflow==1.9.0
install Keras
-install pytiff
+install opencv-python
diff --git a/tmp/test/image/0.png b/tmp/test/image/0.png
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea70ffa96e99a8336fca5710645b653023abf0
Binary files /dev/null and b/tmp/test/image/0.png differ
diff --git a/to-do.txt b/to-do.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5f5d1b57fb751bd4712748e9fa2868854c197137
--- /dev/null
+++ b/to-do.txt
@@ -0,0 +1 @@
+if user chooses .tif mask, check whether .h5 mask exists and warn that will be overwritten
diff --git a/unet/data.py b/unet/data.py
index 2f37a4a0caa59bbc0b953616f05d9deea7d98951..215a1ecb82f784bda1dfe322a300260318b162d7 100644
--- a/unet/data.py
+++ b/unet/data.py
@@ -22,6 +22,11 @@ Pedestrian = [64,64,0]
Bicyclist = [0,128,192]
Unlabelled = [0,0,0]
+# SJR:
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+
+
COLOR_DICT = np.array([Sky, Building, Pole, Road, Pavement,
Tree, SignSymbol, Fence, Car, Pedestrian, Bicyclist, Unlabelled])
diff --git a/unet/neural_network.py b/unet/neural_network.py
index 5a73bdf28b00371a3c3f481e4bd2a44d525d1211..7d56dfee6bac7e0b6c2f55dbd9ce5ae004eb09e4 100644
--- a/unet/neural_network.py
+++ b/unet/neural_network.py
@@ -14,6 +14,10 @@ import numpy as np
import skimage
from skimage import io
+# SJR:
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+
def create_directory_if_not_exists(path):
"""
Create in the file system a new directory if it doesn't exist yet.
@@ -50,6 +54,12 @@ def prediction(im):
Return:
res: the predicted distribution of probability of the labels (numpy array)
"""
+
+
+ imsize=im.shape
+ im = im[0:2048,0:2048] #crop image if too large
+ im = np.pad(im,((0, 2048 - imsize[0] ),(0, 2048 - imsize[1] )),constant_values=0) # pad with zeros if too small
+
path_test = './tmp/test/image/'
create_directory_if_not_exists(path_test)
@@ -65,61 +75,16 @@ def prediction(im):
model = unet( pretrained_weights = None,
input_size = (2048,2048,1) )
- model.load_weights('./unet_weights_batchsize_25_Nepochs_100_full.hdf5')
+# model.load_weights('unet/unet_weights_batchsize_25_Nepochs_100_full.hdf5')
+ model.load_weights('unet/unet_weights_batchsize_25_Nepochs_100_SJR0_9.hdf5')
results = model.predict_generator(testGene,
1,
verbose=1)
-# res = reconstruct_result(236, results[:,10:246,10:246,0], resized_shape,original_shape)
-# if the size of the image is not 2048x2048, the difference between 2048x2048
-# and the original size is cut off here. (in the prediction it is
-# artificially augmented from the original size to 2048x2048)
-
- index_x = 2048-len(im[:,0])
- index_y = 2048-len(im[0,:])
- #this bolean values are true if index_x(y)/2 else they are false.
- #they are initialized to false.
- flagx = False
- flagy = False
-
- #test if x dimension of im is not already the max size
- if index_x != 0:
- #if index_x not the 0 (im has not max size in x axis) then the
- #the difference is divided by two, index_x is the difference between
- #size of im in x axis and max size 2048
- ind_x = index_x/2
- if index_x%2 == 0:
- ind_x = int(ind_x)
- flagx = True
- else:
- #if already max size, it is set to 0
- ind_x = 0
- flagx = True
-
- #test if y dimension of im is not already the max size
- if index_y != 0:
- #if index_y not the 0 (im has not max size in y axis) then the
- #the difference is divided by two, index_y is the difference between
- #size of im in y axis and max size 2048
- ind_y = index_y/2
- if index_y%2 == 0:
- ind_y = int(ind_y)
- flagy = True
- else:
- ind_y = 0
- flagy = True
-
-
- if flagx and flagy:
- res = results[0,ind_x:2048-ind_x,ind_y:2048-ind_y,0]
- return res
- elif not(flagx) and flagy:
- res = results[0,int(ind_x):2048-(int(ind_x)+1),ind_y:2048-ind_y,0]
- return res
- elif flagx and not(flagy):
- res = results[0,ind_x:2048-ind_x, int(ind_y):2048-(int(ind_y)+1),0]
- return res
- else:
- res = results[0, int(ind_x):2048-(int(ind_x)+1),int(ind_y):2048-(int(ind_y)+1),0]
- return res
+ res = results[0,:,:,0] #get rid of strange 1st and 4th indices
+ res = res[0:imsize[0],0:imsize[1]] #crop if needed, e.g., im was smaller than 2048x2048
+ res = np.pad(res,((0, max(0,imsize[0] - 2048) ),(0, max(0,imsize[0] - 2048) )),constant_values=0) # pad with zeros if too small
+
+ return res
+
diff --git a/unet/segment.py b/unet/segment.py
old mode 100755
new mode 100644
index 8a4c805b522b43f1a0eaba283deaac5550d1cd32..59545e1cb6c158e8cadb4d79c06fcf8e595d3cf4
--- a/unet/segment.py
+++ b/unet/segment.py
@@ -87,10 +87,13 @@ def segment(th, pred, min_distance=10, topology=None): #SJR: added pred to evalu
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 + maskoriobj1*objcounter
#else:
# display(obj1+1,' no longer there.')
return wshclean
+# return wsh