tf.config.set_visible_devices([], 'GPU')
start = time.time()
refX = tf.cast(tf.linspace(-2., 2., 5), tf.float32)
refY = tf.cast(tf.linspace(-2., 2., 5), tf.float32)
refXX, refYY = tf.meshgrid(refX, refY, indexing='ij')
refShifts = tf.stack([tf.reshape(refXX, [-1]), tf.reshape(refYY, [-1])], 1)
#refShifts = tf.cast(tf.constant([[0., 0.]]), tf.float32)
#%% generate USAF
parametersUSAF = MuScatParameters(
0.650, # wavelength in vacuum
[1, 90, 90], # gridSize [z, x ,y]
0.3, # dx
0.3, # dy
0.25, # dz
1.4881, # refractive index in medium
0.5, # NAc
0.5) # NAo
USAFObject = MuScatObject(parametersUSAF)
usaf = np.loadtxt('USAF.csv', delimiter=',')
usafPart = usaf[-100:-10, -91:-1]
USAFObject.RIDistrib = tf.cast(
tf.reshape(tf.constant(usafPart[::1, ::1] * (1 - USAFObject.refrIndexM)),
USAFObject.gridSize), tf.float32)
#%% generate random thick layer
parametersRandLay = MuScatParameters(
0.650, # wavelength in vacuum
[1, 90, 90], # gridSize [z, x ,y]
#%% Load data
start = time.time()
zStackMeasured = np.array(scipy.io.loadmat(r'twoBeads1.mat')['croppedBeads'])
zStackMeasured *= 1 / 6000
zStackMeasured = np.transpose(zStackMeasured, [2, 0, 1])
zStackMeasured = zStackMeasured[:, ::3, ::3]
zStckShape = zStackMeasured.shape
zStackMeasured = tf.reshape(tf.constant(zStackMeasured),
[1, zStckShape[0], zStckShape[1], zStckShape[2]])
#%%
parameters = MuScatParameters(
0.650, # wavelength in vacuum
[305, zStckShape[1], zStckShape[2]], # gridSize [z, x ,y]
0.47, # dx
0.47, # dy
0.3, # dz
1.5, # refractive index in medium
0.5, # NAc
0.95) # NAo
regLambdaL1 = 0.001
regLambdaL2 = 0.0000 #1
learnRate = 0.01
refShifts = tf.cast(tf.constant([[0., 0.]]), tf.float32)
imagingSim = MuScatMicroscopeSim(parameters)
imagingSim.Illumination()
imagingSim.Detection()
print(time.time() - start)
from MuScatObject import MuScatObject
from MuScatParameters import MuScatParameters
from MuScatMicroscopeSim import MuScatMicroscopeSim
import tensorflow as tf
import time
import numpy as np
import matplotlib.pyplot as plt
tf.config.set_visible_devices([], 'GPU') # remove in Google Colab
start = time.time()
parameters = MuScatParameters(
0.650, # wavelength in vacuum
[50, 32, 32], # gridSize [z, x ,y]
0.3, # dx
0.3, # dy
0.3, # dz
1.5, # refractive index in medium
0.9, # NAc
0.95) # NAo
regLambdaL1 = 0.0001
regLambdaL2 = 0.001
learnRate = 0.01
imagedObject = MuScatObject(parameters)
imagedObject.GenerateTwoBeads(2., 6., 1.52)
imagingSim = MuScatMicroscopeSim(parameters)
imagingSim.Illumination()
imagingSim.Detection()
zPositions = imagedObject.realzzz[:, 0, 0] - imagedObject.realzzz[0, 0, 0]
from MuScatObject import MuScatObject
from MuScatField import MuScatField
from MuScatParameters import MuScatParameters
from MuScatMicroscopeSim import MuScatMicroscopeSim
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow_addons as tfa
import time
import numpy as np
tf.config.set_visible_devices([], 'GPU')
# %%
start = time.time()
parameters = MuScatParameters(lambda0=0.650, # wavelength in vacuum
gridSize=[50, 32, 32], # gridSize [z, x ,y]
dx=0.45, # dx
dy=0.45, # dy
dz=0.3, # dz
refrIndexM=1.5, # refr index in medium
NAc=0.5, # NAc
NAo=0.5) # NAo
centerInd = np.int32(parameters.gridSize[1] / 2)
regLambdaL1 = 0.0001
regLambdaL2 = 0.000
learnRate = 0.01
refShifts = tf.cast(tf.constant([[0., 0.]]), tf.float32)
imagedObject = MuScatObject(parameters)
# imagedObject.GenerateBox(4., 4., 4., 1.52)
imagedObject.GenerateBead(3., 1.52)
# imagedObject.GenerateSheppLogan(1.55)
imagingSim = MuScatMicroscopeSim(parameters)