def load_tif(self,paths):
""" read tifs found at paths (list with paths) """
# reading
x,y,t = io.read_tiffstack(paths[0]).shape
n = len(paths)
self.Main.Data.raw = sp.zeros((x,y,t,n),dtype='uint16')
self.Main.Data.dFF = sp.zeros((x,y,t,n),dtype='float32')
for n,path in enumerate(paths):
print "loading dataset from " + path
print "Dataset size: " + str(os.stat(path).st_size / 1000000.0) + ' MB'
self.Main.MainWindow.statusBar().showMessage("loading dataset: " + path)
self.Main.Data.raw[:,:,:,n] = io.read_tiffstack(path)
self.Main.MainWindow.statusBar().clearMessage()
def load_tif(self, paths):
""" read tifs found at paths (list with paths) """
# reading
x, y, t = io.read_tiffstack(paths[0]).shape
n = len(paths)
self.Main.Data.raw = sp.zeros((x, y, t, n), dtype='uint16')
self.Main.Data.dFF = sp.zeros((x, y, t, n), dtype='float32')
for n, path in enumerate(paths):
print "loading dataset from " + path
print "Dataset size: " + str(
os.stat(path).st_size / 1000000.0) + ' MB'
self.Main.MainWindow.statusBar().showMessage("loading dataset: " +
path)
self.Main.Data.raw[:, :, :, n] = io.read_tiffstack(path)
self.Main.MainWindow.statusBar().clearMessage()
def load_nonparametric_ROIs(self, size_filter=None, thresh=0.5):
""" reads a *_mask.tif
size_filter can be a tuple and segments with an area smaller than
size_filter[0] or larger size_filter[1] are considered to be valid ROIs
"""
self.Main.ROIs.reset()
# get mask data
file_path = self.OpenFileDialog(
title='load nonparametric ROIs',
default_dir=self.Main.Options.general['cwd'],
extension='*.tif')[0]
masks = io.read_tiffstack(file_path)
# skimage based segmentation
masks_thresh = masks > thresh
masks_label = sp.zeros(masks_thresh.shape)
for i in range(masks_thresh.shape[2]):
masks_label[:, :, i] = sklabel(masks_thresh[:, :, i])
# split into a array of submasks
Nsubmasks = [
int(masks_label[:, :, i].max())
for i in range(masks_label.shape[2])
]
submasks = sp.zeros((masks.shape[0], masks.shape[1], sum(Nsubmasks)),
dtype='bool')
i = 0
for j in range(masks_label.shape[2]):
mask = masks_label[:, :, j]
for l in range(1, Nsubmasks[j] + 1):
submasks[mask == l, i] = True
i += 1
# for each mask, calculate a contour
contours = []
for i in range(submasks.shape[2]):
contour = find_contours(submasks[:, :, i], level=0.5)
contours.append(contour)
# add PolyLineROI
for i in range(submasks.shape[2]):
self.Main.ROIs.add_ROI(kind='nonparametric',
label=str(i),
contour=contours[i],
mask=submasks[:, :, i])
pass
def load_nonparametric_ROIs(self,size_filter=None,thresh=0.5):
""" reads a *_mask.tif
size_filter can be a tuple and segments with an area smaller than
size_filter[0] or larger size_filter[1] are considered to be valid ROIs
"""
self.Main.ROIs.reset()
# get mask data
file_path = self.OpenFileDialog(title='load nonparametric ROIs',default_dir = self.Main.Options.general['cwd'], extension='*.tif')[0]
masks = io.read_tiffstack(file_path)
# skimage based segmentation
masks_thresh = masks > thresh
masks_label = sp.zeros(masks_thresh.shape)
for i in range(masks_thresh.shape[2]):
masks_label[:,:,i] = sklabel(masks_thresh[:,:,i])
# split into a array of submasks
Nsubmasks = [int(masks_label[:,:,i].max()) for i in range(masks_label.shape[2])]
submasks = sp.zeros((masks.shape[0],masks.shape[1],sum(Nsubmasks)),dtype='bool')
i = 0
for j in range(masks_label.shape[2]):
mask = masks_label[:,:,j]
for l in range(1,Nsubmasks[j]+1):
submasks[mask == l,i] = True
i += 1
# for each mask, calculate a contour
contours = []
for i in range(submasks.shape[2]):
contour = find_contours(submasks[:,:,i],level=0.5)
contours.append(contour)
# add PolyLineROI
for i in range(submasks.shape[2]):
self.Main.ROIs.add_ROI(kind='nonparametric',label=str(i),contour=contours[i],mask=submasks[:,:,i])
pass
