import NanoImagingPack as nip
# Optionally, tweak styles.
mpl.rc('figure', figsize=(8, 6))
mpl.rc('image', cmap='gray')
if (1):
if (1):
var_tvc = 1e-11
var_tv = 1e-1
experiments.regularizer = 'L1'
#for var_tv in (1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1e-0, 1e1, 1e2):
#for var_tvc in (1e-10, 1e-8, 1e-6, 1e-4, 1e-2):
np_meas_file = './Data/PHANTOM/HeLa_cell_mat_obj_100.mat'
matname = 'HeLa_cell_mat'
obj_real = data.import_realdata_h5(filename=np_meas_file,
matname=matname)
obj_absorption = obj_real * 0
obj_guess = (obj_real + 1j * obj_absorption)
#%%
'''Define some stuff related to infrastructure'''
mytimestamp = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
basepath = ""
is_aberration = False
is_aberation_iterstart = 0 # When to start optimizing for aberration?
is_padding = False
is_optimization = True
is_absorption = 0
is_obj_init_tikhonov = False # intialize the
is_norm = False # Want to have a floating value for the background?
is_recomputemodel = True # TODO: Make it automatic!
is_estimatepsf = False
savepath = basepath + experiments.resultpath + mytimestamp + '_' + '_PSFmodel_' + psf_model + experiments.regularizer + '_' + str(
experiments.lambda_reg) + '_eps_' + str(
experiments.myepstvval) + '_' + 'Shift_x-' + str(
experiments.shiftIcX) + 'Shift_y-' + str(experiments.shiftIcY)
tf_helper.mkdir(savepath)
print('My path is: ' + savepath)
''' MODELLING StARTS HERE'''
if is_recomputemodel:
tf.reset_default_graph()
# need to figure out why this holds somehow true - at least produces reasonable results
''' 1.) Read in the parameters of the dataset '''
if (experiments.obj_meas_filename.find('mat') == -1):
obj_meas = np.load(experiments.obj_meas_filename)
else:
obj_meas = data.import_realdata_h5(
filename=experiments.obj_meas_filename,
matname=experiments.matlab_val_name,
is_complex=True)
# If Z-is odd numbered
if (np.mod(obj_meas.shape[0], 2) == 1):
obj_meas = obj_meas[0:obj_meas.shape[0] - 1, :, :]
obj_meas = obj_meas[:, :, :, ]
# Eventually add an imaginary background to synchronize to methods
if (psf_model == 'BORN'):
obj_meas = obj_meas + experiments.mybackgroundval
''' 2.) Read (optional) referecne object for PSF calibration '''
if (is_estimatepsf
): # if we know the object already, we only need to update the PSF
obj_meas = data.import_realdata_h5(
filename=experiments.matlab_obj_file,
dn = .105 #(1.437-1.3326)#/np.pi
myfac = 1 #- 1e-6
#muscat.NAo = .95
#muscat.dz = 0.1625*2#muscat.lambda0/4
#muscat.dy = .2; muscat.dx = muscat.dy#muscat.lambda0/4
#muscat.dx = 0.1560#muscat.lambda0/4
#muscat.Nx = 50; muscat.Ny = 50; muscat.Nz = 50
#muscat.Nx = 64; muscat.Ny = 64; muscat.Nz = 70
#muscat.dz = muscat.lambda0/4
#muscat.Nz = 36
mymatfile = '/Users/bene/Dropbox/Dokumente/Promotion/PROJECTS/BOSTON/MUSCAT/MATLAB/MuScat/mtfs.mat'
matatf = data.import_realdata_h5(filename = mymatfile, matname='myatf', is_complex=True)
matasf = data.import_realdata_h5(filename = mymatfile, matname='myasf', is_complex=True)
''' Adjust some parameters to fit it in the memory '''
muscat.mysize = (muscat.Nz,muscat.Nx,muscat.Ny) # ordering is (Nillu, Nz, Nx, Ny)
''' Create a 3D Refractive Index Distributaton as a artificial sample'''
mydiameter = 5
if(1):
obj_real= tf_go.generateObject(mysize=muscat.mysize, obj_dim=1, obj_type ='sphere', diameter = mydiameter, dn = dn, nEmbb = muscat.nEmbb)#)dn)
obj_absorption = tf_go.generateObject(mysize=muscat.mysize, obj_dim=1, obj_type ='sphere', diameter = mydiameter, dn = .0, nEmbb = 0.01)
elif(1):
obj_real = tf_go.generateObject(mysize=muscat.mysize, obj_dim=1, obj_type ='hollowsphere', diameter = mydiameter, dn = dn, nEmbb = muscat.nEmbb)#)dn)
obj_absorption = tf_go.generateObject(mysize=muscat.mysize, obj_dim=muscat.dx, obj_type ='sphere', diameter = mydiameter, dn = .0)
elif(0):
np.abs(zernikefactors) > 0
) * 1 #!= np.array((0, 0, 0, 0, 0, 0, , 1, 1, 1, 1))# mask which factors should be updated
'''START CODE'''
tf.reset_default_graph() # just in case there was an open session
# Generate Test-Object
''' File which stores the experimental parameters from the Q-PHASE setup
1.) Read in the parameters of the dataset '''
matlab_pars = data.import_parameters_mat(filename=matlab_par_file,
matname=matlab_par_name)
''' 2.) Read in the parameters of the dataset '''
if (matlab_val_file.find('mat') == -1):
matlab_val = np.load(matlab_val_file)
else:
matlab_val = data.import_realdata_h5(filename=matlab_val_file,
matname=matlab_val_name,
is_complex=True)
if (np.mod(matlab_val.shape[0], 2) == 1):
matlab_val = matlab_val[0:matlab_val.shape[0] - 1, :, :]
#roisize=50
#roicenter = np.array((215,201))
#matlab_val = np.flip(matlab_val[0:100,roicenter[0]-roisize:roicenter[0]+roisize,roicenter[1]-roisize:roicenter[1]+roisize],0)
''' Create the Model'''
muscat = mus.MuScatModel(matlab_pars, is_optimization=is_optimization)
# Correct some values - just for the puprose of fitting in the RAM
muscat.Nx, muscat.Ny, muscat.Nz = matlab_val.shape[1], matlab_val.shape[
2], matlab_val.shape[0]
muscat.shiftIcY = shiftIcY
muscat.shiftIcX = shiftIcX
muscat.dn = dn
Niter = 5000
Ndisplay = 10
'''START CODE'''
tf.reset_default_graph() # just in case there was an open session
''' File which stores the experimental parameters from the Q-PHASE setup
1.) Read in the parameters of the dataset '''
matlab_pars = data.import_parameters_mat(filename = matlab_par_file, matname='myParameterNew')
''' 2.) Read in the parameters of the dataset '''
if(matlab_val_file.find('mat')==-1):
matlab_val = np.load(matlab_val_file)
else:
matlab_val = data.import_realdata_h5(filename = matlab_val_file, matname='allAmp_red', is_complex=True)
if(is_flip):
np_meas = np.flip(matlab_val,0)
print('Attention: We are flipping the data!')
else:
np_meas = matlab_val
print('do we need to flip the data?! -> Observe FFT!!')
''' Create the Model'''
muscat = mus.MuScatModel(matlab_pars, is_optimization=is_optimization, is_optimization_psf = is_optimization_psf)
muscat.Nx,muscat.Ny = int(np.squeeze(matlab_pars['Nx'].value)), int(np.squeeze(matlab_pars['Ny'].value))
muscat.shiftIcY=0
muscat.shiftIcX=0
muscat.dn = dn
# need to figure out why this holds somehow true - at least produces reasonable results
'''START CODE'''
tf.reset_default_graph() # just in case there was an open session
# Generate Test-Object
''' File which stores the experimental parameters from the Q-PHASE setup
1.) Read in the parameters of the dataset '''
matlab_pars = data.import_parameters_mat(filename=configs.matlab_par_file,
matname=configs.matlab_par_name)
''' 2.) Read in the parameters of the dataset '''
if (configs.matlab_val_file.find('mat') == -1):
matlab_val = np.load(configs.matlab_val_file)
else:
matlab_val = data.import_realdata_h5(filename=configs.matlab_val_file,
matname=configs.matlab_val_name,
is_complex=True)
if (np.mod(matlab_val.shape[0], 2) == 1):
matlab_val = matlab_val[0:matlab_val.shape[0] - 1, :, :]
matlab_val = matlab_val + configs.mybackgroundval
#roisize=50
#roicenter = np.array((215,201))
#matlab_val = np.flip(matlab_val,0 )#[0:100,roicenter[0]-roisize:roicenter[0]+roisize,roicenter[1]-roisize:roicenter[1]+roisize],0)
''' Create the Model'''
muscat = mus.MuScatModel(matlab_pars, is_optimization=configs.is_display)
# Correct some values - just for the puprose of fitting in the RAM
muscat.Nx, muscat.Ny, muscat.Nz = matlab_val.shape[1], matlab_val.shape[
2], matlab_val.shape[0]
#tf.reset_default_graph()
''' File which stores the experimental parameters from the Q-PHASE setup
1.) Read in the parameters of the dataset '''
matlab_par_name = 'myParameter' #'./Data/DROPLETS/myParameterNew.mat';matname='myParameterNew' #'./Data/DROPLETS/myParameterNew.mat'
matlab_par_file = './Data/DROPLETS/S19_multiple/Parameter.mat'; matname='myParameter'
matlab_pars = data.import_parameters_mat(filename = matlab_par_file, matname=matlab_par_name)
''' Create a 3D Refractive Index Distributaton as a artificial sample
2.) Read in the virtual sample '''
# Fake Cheek-Cell
matlab_val_file = './Data/PHANTOM/HeLa_cell_mat_obj.mat'; matname='HeLa_cell_mat'
obj_real = data.import_realdata_h5(filename = matlab_val_file, matname=matname)
if(0):
mysize = np.array((128,128,128))
mydiameter=1
obj_real= tf_go.generateObject(mysize=mysize, obj_dim=1, obj_type ='twosphere', diameter = mydiameter, dn = .02, nEmbb = 1.33)#)dn)
#TF_obj = tf.cast(tf.complex(obj_real,obj_real*0), tf.complex64)
TF_obj = tf.cast(tf.placeholder_with_default(obj_real, obj_real.shape), tf.complex64)
# etract scattering subroi-region
mysize = obj_real.shape
mysize_sub = ((32,32,32)); mycenter = ((mysize[0]//2,mysize[1]//2,mysize[1]//2)) # Region around the nuclei
TF_obj_sub = tf_helper.extract((TF_obj-1.33)*2*np.pi, mysize_sub, mycenter)
except (FileExistsError):
print('Folder exists already')
#%% optimize over the hyperparameters
for i_epoch in range(Nepoch):
for i_file in range(len(my_meas_files)):
mylambdatv = lambda_tv
myepstvval = eps_tv
# for eps_tv in eps_tv:
print('Evtl unwrap it!')
# this is the initial guess of the reconstruction
''' 2.) Read in the parameters of the dataset '''
print('Now feeding file: ' + my_meas_files[i_file])
print('Now feeding file: ' + my_gt_files[i_file])
np_meas = data.import_realdata_h5(filename=my_meas_files[i_file],
matname='allAmp_red',
is_complex=True)
np_obj = data.import_realdata_h5(filename=my_gt_files[i_file],
matname='mysphere',
is_complex=False)
''' Create a 3D Refractive Index Distributaton as a artificial sample'''
obj = np_obj * dn
obj_absorption = np_obj * .001
obj = obj + 1j * obj_absorption
init_guess = obj
if (False):
# This won't make much sense here!
# run the fwd model once
'''Numpy to Tensorflow'''
my_fwd = sess.run(tf_fwd)
