def open_rois(filename=None):
"""
Open an roi.txt file, creates ROI objects and places them in the current Window.
Args:
filename (str): The filename (including full path) of the roi.txt file.
Returns:
list of rois
"""
if filename is None:
filetypes = '*.txt'
prompt = 'Load ROIs from file'
filename = open_file_gui(prompt, filetypes=filetypes)
if filename is None:
return None
text = open(filename, 'r').read()
rois = []
kind = None
pts = None
for text_line in text.split('\n'):
if kind is None:
kind = text_line
pts = []
elif text_line == '':
roi = makeROI(kind, pts)
rois.append(roi)
kind = None
pts = None
else:
pts.append(tuple(int(float(i)) for i in text_line.split()))
return rois
def __call__(self,
inputChoice,
keepOriginalWindow,
keepSourceWindow=False):
g.settings['volumeSlider']['inputChoice'] = inputChoice
g.settings['volumeSlider']['keepOriginalWindow'] = keepOriginalWindow
g.m.statusBar().showMessage("Starting Volume Slider...")
if inputChoice == 'Current Window':
camVolumeSlider.startVolumeSlider(keepWindow=keepOriginalWindow)
elif inputChoice == 'Numpy Array':
A_path = open_file_gui(directory=os.path.expanduser("~/Desktop"),
filetypes='*.npy')
g.m.statusBar().showMessage("Importing Array: " + A_path)
A = np.load(str(A_path))
camVolumeSlider.startVolumeSlider(A=A,
keepWindow=keepOriginalWindow)
elif inputChoice == 'Batch Process':
g.m.statusBar().showMessage("Starting Batch Processing...")
camVolumeSlider.startVolumeSlider(batch=True)
return
def gui(self):
filetypes = 'Image Files (*.tif *.tiff);;All Files (*.*)'
prompt = 'Open File'
self.filename = open_file_gui(prompt, filetypes=filetypes)
if self.filename is None:
return None
self.openTiff(self.filename)
def gui(self):
filetypes = 'Image Files (*.tif *.tiff);;All Files (*.*)'
prompt = 'Open File'
filename = open_file_gui(prompt, filetypes=filetypes)
if filename is None:
return None
self.openTiff(filename)
self.sclicesPerVolume = int(self.nFrames*self.nChannels / self.nVolumes)
return (self.nChannels, self.nVolumes, self.sclicesPerVolume)
def gui(self):
filetypes = 'Image Files (*.tif *.tiff);;All Files (*.*)'
prompt = 'Open File'
filename = open_file_gui(prompt, filetypes=filetypes)
if filename is None:
return None
self.openTiff(filename)
if self.errorFlag:
return None
return (self.nChannels, self.nVolumes, self.sclicesPerVolume,
self.channel1_array, self.channel2_array)
def load_classifications(self, filename=None):
if filename is None:
filename = open_file_gui("Open classifications",
filetypes='*.json')
if filename is None:
return None
obj_text = codecs.open(filename, 'r', encoding='utf-8').read()
data = json.loads(obj_text)
roi_states = np.array(data['states'])
if len(roi_states) != len(self.window_states):
g.alert(
'The number of ROIs in this file does not match the number of ROIs in the image. Cannot import classifications'
)
else:
g.quantimus.roiStates = np.copy(roi_states)
self.window_states = np.copy(roi_states)
self.set_roi_states()
def __call__(self,
inputChoice,
keepOriginalWindow,
preProcess,
slicesPerVolume,
slicesDeletedPerVolume,
slicesDeletedPerMovie,
overlay,
overlayStart,
keepSourceWindow=False):
g.settings['volumeSlider']['inputChoice'] = inputChoice
g.settings['volumeSlider']['keepOriginalWindow'] = keepOriginalWindow
g.settings['volumeSlider']['preProcess'] = preProcess
g.settings['volumeSlider']['slicesPerVolume'] = slicesPerVolume
g.settings['volumeSlider'][
'slicesDeletedPerVolume'] = slicesDeletedPerVolume
g.settings['volumeSlider'][
'slicesDeletedPerMovie'] = slicesDeletedPerMovie
g.settings['volumeSlider']['overlay'] = overlay
g.settings['volumeSlider']['overlayStart'] = overlayStart
g.m.statusBar().showMessage("Starting Volume Slider...")
if inputChoice == 'Current Window':
windowName = g.win.filename
#Get overlay
if overlay:
A = np.array(deepcopy(g.win.image))
print(A.shape)
endFrame = g.win.mt - 1
g.win.close()
#overlayWin = Window(A[overlayStart:endFrame,:,:],'Overlay')
dataWin = Window(A[0:overlayStart, :, :], 'Overlay')
#Trim movie
if preProcess and slicesDeletedPerMovie != 0:
trim(0, slicesDeletedPerMovie, delete=True)
camVolumeSlider.setFileName(windowName)
#start volumeSlider
if overlay:
camVolumeSlider.startVolumeSlider(
keepWindow=keepOriginalWindow,
preProcess=preProcess,
framesPerVol=slicesPerVolume,
framesToDelete=slicesDeletedPerVolume,
overlayEmbeded=True,
A_overlay=A[overlayStart:endFrame, :, :])
else:
camVolumeSlider.startVolumeSlider(
keepWindow=keepOriginalWindow,
preProcess=preProcess,
framesPerVol=slicesPerVolume,
framesToDelete=slicesDeletedPerVolume)
#print('loading:',windowName)
elif inputChoice == 'Numpy Array':
A_path = open_file_gui(directory=os.path.expanduser("~/Desktop"),
filetypes='*.npy')
g.m.statusBar().showMessage("Importing Array: " + A_path)
A = np.load(str(A_path))
camVolumeSlider.setFileName(A_path)
camVolumeSlider.startVolumeSlider(A=A,
keepWindow=keepOriginalWindow)
elif inputChoice == 'Batch Process':
g.m.statusBar().showMessage("Starting Batch Processing...")
camVolumeSlider.startVolumeSlider(batch=True)
elif inputChoice == 'Load file':
g.m.statusBar().showMessage("Loading file...")
#Open file using tiff loader
load_tiff.gui()
#Get overlay
if overlay:
A = np.array(deepcopy(g.win.image))
print(A.shape)
endFrame = g.win.mt - 1
g.win.close()
#overlayWin = Window(A[overlayStart:endFrame,:,:],'Overlay')
dataWin = Window(A[0:overlayStart, :, :], 'Overlay')
#Trim movie
if preProcess and slicesDeletedPerMovie != 0:
trim(0, slicesDeletedPerMovie, delete=True)
#start volumeSlider
camVolumeSlider.setFileName(load_tiff.getFileName())
if overlay:
camVolumeSlider.startVolumeSlider(
keepWindow=keepOriginalWindow,
preProcess=preProcess,
framesPerVol=slicesPerVolume,
framesToDelete=slicesDeletedPerVolume,
overlayEmbeded=True,
A_overlay=A[overlayStart:endFrame, :, :])
else:
camVolumeSlider.startVolumeSlider(
keepWindow=keepOriginalWindow,
preProcess=preProcess,
framesPerVol=slicesPerVolume,
framesToDelete=slicesDeletedPerVolume)
return