def evaluate_focus(self, obj_position=None):
"""
Evaluate the focus degree of certain objective position.
Parameters
----------
obj_position : float, optional
The target objective position. The default is None.
Returns
-------
degree_of_focus : float
Degree of focus.
"""
if obj_position != None:
self.pi_device_instance.move(obj_position)
# Get the image.
if self.source_of_image == "PMT":
self.galvo_image = self.galvo.run()
plt.figure()
plt.imshow(self.galvo_image)
plt.show()
if False:
with skimtiff.TiffWriter(
os.path.join(
r'M:\tnw\ist\do\projects\Neurophotonics\Brinkslab\Data\Xin\2020-11-17 gaussian fit auto-focus cells\trial_11',
str(obj_position).replace(".", "_") +
'.tif')) as tif:
tif.save(self.galvo_image.astype('float32'), compress=0)
degree_of_focus = ProcessImage.local_entropy(
self.galvo_image.astype('float32'))
time.sleep(0.2)
return degree_of_focus
def PMT_image_processing(self):
"""
Reconstruct the image from np array and save it.
Returns
-------
None.
"""
for imageSequence in range(self.repeatnum):
try:
self.PMT_image_reconstructed_array = self.data_collected_0[np.where(self.PMT_data_index_array == imageSequence+1)]
Dataholder_average = np.mean(self.PMT_image_reconstructed_array.reshape(self.averagenum, -1), axis=0)
Value_yPixels = int(self.lenSample_1/self.ScanArrayXnum)
self.PMT_image_reconstructed = np.reshape(Dataholder_average, (Value_yPixels, self.ScanArrayXnum))
self.PMT_image_reconstructed = self.PMT_image_reconstructed[:, 50:550] # Crop size based on: M:\tnw\ist\do\projects\Neurophotonics\Brinkslab\Data\Xin\2019-12-30 2p beads area test 4um
# self.PMT_image_reconstructed = self.PMT_image_reconstructed[:, 70:326] # for 256*256 images
# Evaluate the focus degree of re-constructed image.
self.FocusDegree_img_reconstructed = ProcessImage.local_entropy(self.PMT_image_reconstructed.astype('float32'))
print('FocusDegree_img_reconstructed is {}'.format(self.FocusDegree_img_reconstructed))
# Save the individual file.
with skimtiff.TiffWriter(os.path.join(self.scansavedirectory, 'Round'+str(self.RoundWaveformIndex[0]) + '_Grid' + str(self.Grid_index) +'_Coords'+str(self.currentCoordsSeq)+'_R'+str(self.CurrentPosIndex[0])+'C'+str(self.CurrentPosIndex[1])+'_PMT_'+str(imageSequence)+'Zpos'+str(self.ZStackOrder)+'.tif'), imagej = True) as tif:
tif.save(self.PMT_image_reconstructed.astype('float32'), compress=0, metadata = {"FocusPos: " : str(self.FocusPos)})
plt.figure()
plt.imshow(self.PMT_image_reconstructed, cmap = plt.cm.gray) # For reconstructed image we pull out the first layer, getting 2d img.
plt.show()
#---------------------------------------------For multiple images in one z pos, Stack the arrays into a 3d array--------------------------------------------------------------------------
# if imageSequence == 0:
# self.PMT_image_reconstructed_stack = self.PMT_image_reconstructed[np.newaxis, :, :] # Turns into 3d array
# else:
# self.PMT_image_reconstructed_stack = np.concatenate((self.PMT_image_reconstructed_stack, self.PMT_image_reconstructed[np.newaxis, :, :]), axis=0)
# print(self.PMT_image_reconstructed_stack.shape)
#---------------------------------------------Calculate the z max projection-----------------------------------------------------------------------
if self.repeatnum == 1: # Consider one repeat image situlation
if self.ZStackNum > 1:
if self.ZStackOrder == 1:
self.PMT_image_maxprojection_stack = self.PMT_image_reconstructed[np.newaxis, :, :]
else:
self.PMT_image_maxprojection_stack = np.concatenate((self.PMT_image_maxprojection_stack, self.PMT_image_reconstructed[np.newaxis, :, :]), axis=0)
else:
self.PMT_image_maxprojection_stack = self.PMT_image_reconstructed[np.newaxis, :, :]
# Save the max projection image
if self.ZStackOrder == self.ZStackNum:
self.PMT_image_maxprojection = np.max(self.PMT_image_maxprojection_stack, axis=0)
# Save the zmax file.
with skimtiff.TiffWriter(os.path.join(self.scansavedirectory, 'Round'+str(self.RoundWaveformIndex[0])+ '_Grid' + str(self.Grid_index) + '_Coords'+str(self.currentCoordsSeq)+'_R'+str(self.CurrentPosIndex[0])+'C'+str(self.CurrentPosIndex[1])+'_PMT_'+str(imageSequence)+'Zmax'+'.tif'), imagej = True) as tif:
tif.save(self.PMT_image_maxprojection.astype('float32'), compress=0, metadata = {"FocusPos: " : str(self.FocusPos)})
except:
print('No.{} image failed to generate.'.format(imageSequence))
def evaluate_focus(self, obj_position = None):
"""
Evaluate the focus degree of certain objective position.
Parameters
----------
obj_position : float, optional
The target objective position. The default is None.
Returns
-------
degree_of_focus : float
Degree of focus.
"""
if obj_position != None:
self.pi_device_instance.move(obj_position)
# Get the image.
if self.source_of_image == "PMT":
self.galvo_image = self.galvo.run()
plt.figure()
plt.imshow(self.galvo_image)
plt.show()
if False:
with skimtiff.TiffWriter(os.path.join(r'M:\tnw\ist\do\projects\Neurophotonics\Brinkslab\Data\Xin\2020-11-17 gaussian fit auto-focus cells\trial_11', str(obj_position).replace(".", "_")+ '.tif')) as tif:
tif.save(self.galvo_image.astype('float32'), compress=0)
degree_of_focus = ProcessImage.local_entropy(self.galvo_image.astype('float32'))
elif self.source_of_image == "Camera":
# First configure the AOTF.
self.AOTF_runner = DAQmission()
# Find the AOTF channel key
for key in self.imaging_conditions:
if 'AO' in key:
# like '488AO'
AOTF_channel_key = key
# Set the AOTF first.
self.AOTF_runner.sendSingleDigital('blankingall', True)
self.AOTF_runner.sendSingleAnalog(AOTF_channel_key, self.imaging_conditions[AOTF_channel_key])
# Snap an image from camera
self.camera_image = self.HamamatsuCam_ins.SnapImage(self.imaging_conditions['exposure_time'])
time.sleep(0.5)
# Set back AOTF
self.AOTF_runner.sendSingleDigital('blankingall', False)
self.AOTF_runner.sendSingleAnalog(AOTF_channel_key, 0)
plt.figure()
plt.imshow(self.camera_image)
plt.show()
if False:
with skimtiff.TiffWriter(os.path.join(r'M:\tnw\ist\do\projects\Neurophotonics\Brinkslab\Data\Xin\2021-03-06 Camera AF\beads', str(obj_position).replace(".", "_")+ '.tif')) as tif:
tif.save(self.camera_image.astype('float32'), compress=0)
degree_of_focus = ProcessImage.variance_of_laplacian(self.camera_image.astype('float32'))
time.sleep(0.2)
return degree_of_focus