def test_read_as_AcquisitionData1(self):
ag = AcquisitionGeometry.create_Parallel3D()
ag.set_panel([10, 11])
ag.set_angles([i for i in range(12)])
ag.set_channels(3)
print(ag.shape)
# print (glob.glob(os.path.join(self.data_dir, '*')))
reader = TIFFStackReader(file_name=self.data_dir)
acq = reader.read_as_AcquisitionData(ag)
np.testing.assert_array_equal(acq.as_array(), self.data.as_array())
def test_read_as_AcquisitionData2(self):
# with this data will be scrambled but reshape is possible
ag = AcquisitionGeometry.create_Parallel3D()
ag.set_panel([11, 10])
ag.set_angles([i for i in range(12)])
ag.set_channels(3)
reader = TIFFStackReader(file_name=self.data_dir)
acq = reader.read_as_AcquisitionData(ag)
np.testing.assert_array_equal(acq.as_array().flatten(),
self.data.as_array().flatten())
def test_read_as_ImageData_Exceptions(self):
igs = [ImageGeometry(10, 11, 12, channels=5)]
igs.append(ImageGeometry(12, 32))
reader = TIFFStackReader(file_name=self.data_dir)
for geom in igs:
try:
img = reader.read_as_ImageData(geom)
assert False
except ValueError as ve:
print(ve)
assert True
def test_read_as_AcquisitionData_Exceptions1(self):
ag = AcquisitionGeometry.create_Parallel3D()
ag.set_panel([11, 12])
ag.set_angles([i for i in range(12)])
ag.set_channels(3)
reader = TIFFStackReader(file_name=self.data_dir)
try:
acq = reader.read_as_AcquisitionData(ag)
assert False
except ValueError as ve:
print(ve)
assert True
def test_read_as_AcquisitionData_Exceptions2(self):
ag = AcquisitionGeometry.create_Parallel3D()
ag.set_panel([11, 12])
ag.set_angles([i for i in range(12)])
ag.set_channels(3)
reader = TIFFStackReader(file_name=self.data_dir)
try:
class Fake(object):
pass
fake = Fake()
fake.shape = (36, 11, 10)
acq = reader.read_as_ImageData(fake)
assert False
except TypeError as te:
print(te)
assert True
def test_read_as_ImageData1(self):
reader = TIFFStackReader(file_name=self.data_dir)
img = reader.read_as_ImageData(self.ig)
np.testing.assert_array_equal(img.as_array(), self.data.as_array())
def test_read1(self):
data = self.data
reader = TIFFStackReader(file_name=self.data_dir)
read = reader.read()
np.testing.assert_array_equal(read.flatten(),
data.as_array().flatten())
if __name__ == '__main__':
ig = ImageGeometry(10, 11, 12, channels=3)
data = ig.allocate(0)
arr = data.as_array()
k = 0
for i in range(data.shape[0]):
for j in range(data.shape[1]):
arr[i][j] += k
k += 1
data.fill(arr)
os.mkdir("test_tiff")
cwd = os.getcwd()
fname = os.path.join(cwd, 'test_tiff', 'myfile.tif')
data_dir = os.path.dirname(fname)
writer = TIFFWriter(data=data, file_name=fname, counter_offset=0)
writer.write()
ag = AcquisitionGeometry.create_Parallel3D()
ag.set_panel([10, 11])
ag.set_angles([i for i in range(12)])
ag.set_channels(3)
print(ag.shape)
print(glob.glob(os.path.join(data_dir, '*')))
reader = TIFFStackReader(file_name=os.path.dirname(fname))
acq = reader.read_as_AcquisitionData(ag)
print(type(acq), type(data))
np.testing.assert_array_equal(acq.as_array(), data.as_array())
path = "/mnt/data/CCPi/Dataset/SophiaBeads_64_averaged/CentreSlice"
# Create a 2D fan beam Geometry
source_position=(0, -80.6392412185669)
detector_position=(0, 1007.006 - source_position[1])
angles = np.asarray([- 5.71428571428571 * i for i in range(63)], dtype=np.float32) * np.pi / 180.
panel = 2000
panel_pixel_size = 0.2
ag_cs = AcquisitionGeometry.create_Cone2D(source_position, detector_position)\
.set_angles(angles, angle_unit='radian')\
.set_panel(panel, pixel_size=panel_pixel_size, origin='top-right')
#%%
reader = TIFFStackReader()
reader.set_up(file_name=os.path.join(path, 'Sinograms', 'SophiaBeads_64_averaged_0001.tif'))
data = reader.read_as_AcquisitionData(ag_cs)
white_level = 60000.0
data_raw = data.subset(dimensions=['angle','horizontal'])
data_raw = data / white_level
# negative log
ldata = data_raw.log()
ldata *= -1
shift_mm = 0.0024
from cil.io import TIFFStackReader
import numpy as np
import matplotlib.pyplot as plt
import math
import os
#%% read in all Tiff stack from directory
path_common = '/mnt/data/CCPi/LegoLaminography'
path = 'Lego_Lamino30deg_XTH/'
bins = 2
roi = {'axis_1': (None, None, bins), 'axis_2': (None, None, bins)}
reader = TIFFStackReader(file_name=os.path.join(path_common, path), roi=roi)
data = reader.read()
#%% Read in the flat-field and dark-field radiographs and apply shading correction to the data
tiffs = [
os.path.join(path_common,
'Lego_Lamino30deg_ShadingCorrection_XTH/Dark_80kV85uA.tif'),
os.path.join(path_common,
'Lego_Lamino30deg_ShadingCorrection_XTH/Flat_80kV85uA.tif')
]
reader = TIFFStackReader(file_name=tiffs, roi=roi)
SC = reader.read()
data = (data - SC[0]) / (SC[1] - SC[0])
#%% set up the geometry of the AcquisitionData
#%%
# save output to TIFF and NeXuS
from cil.io import TIFFWriter
path = os.path.abspath('/home/edo/scratch/Dataset/')
out_file = os.path.join(path, 'edo', 'recon')
writer = TIFFWriter(data=recon, file_name=out_file)
writer.write()
#%%
from cil.io import TIFFStackReader
path = os.path.abspath('/home/edo/scratch/Dataset/')
fname = os.path.join(path, 'edo')
treader = TIFFStackReader()
treader.set_up(file_name=fname)
# treader.read()
#%%
recon = treader.read_as_ImageData(ig)
#%%
from cil.utilities.jupyter import islicer
islicer(recon, direction='vertical', cmap='inferno', origin='upper-right')
#%%
from cil.io import NikonDataReader
reader = NikonDataReader()
path = os.path.abspath('/home/edo/scratch/Dataset/')
