def xtomo_read_images(self,
file_name,
images_start=0,
images_end=0,
images_step=1,
sino_start=0,
sino_end=0,
sino_step=1,
pixels_start=0,
pixels_end=0,
pixels_step=1,
images_digits=-1,
images_zeros=True,
dtype='uint16',
data_type='fabio',
array_type='projections',
flip='false',
log='INFO'):
# Determine file name and extension type.
data_file = os.path.splitext(file_name)[0]
data_extension = os.path.splitext(file_name)[1]
images_file_index = ["" for x in range(images_digits)]
for m in range(images_digits):
if images_zeros is True:
images_file_index[m] = '0' * (images_digits - m - 1)
elif images_zeros is False:
images_file_index[m] = ''
_file_name = ' '
image_exist = False
# Start reading projections one-by-one.
ind = range(images_start, images_end, images_step)
for m in range(len(ind)):
for n in range(images_digits):
if ind[m] < np.power(10, n + 1):
_file_name = data_file + images_file_index[n] + str(
ind[m]) + data_extension
self.logger.info("Generating image file names: [%s]",
_file_name)
break
if os.path.isfile(_file_name):
image_exist = True
self.logger.info("Reading image file: [%s]",
os.path.realpath(_file_name))
self.logger.info("data type: [%s]", data_type)
f = XTomoReader(_file_name)
if (data_type is 'spe'):
tmpdata = f.spe(y_start=sino_start,
y_end=sino_end,
y_step=sino_step)
elif (data_type is 'nc'):
tmpdata = f.netcdf(y_start=sino_start,
y_end=sino_end,
y_step=sino_step)
elif (data_type is 'tiff'):
tmpdata = f.tiff(x_start=sino_start,
x_end=sino_end,
x_step=sino_step,
dtype=dtype,
flip=flip)
elif (data_type is 'compressed_tiff'):
tmpdata = f.tiffc(x_start=sino_start,
x_end=sino_end,
x_step=sino_step,
dtype=dtype)
elif (data_type is 'hdf5'):
tmpdata = f.hdf5_2d(x_start=sino_start,
x_end=sino_end,
x_step=sino_step,
array_name='/entry1/data/data')
elif (data_type is 'edf2'):
tmpdata = f.edf2(x_start=sino_start,
x_end=sino_end,
x_step=sino_step)
elif (data_type is 'fabio'):
tmpdata = f.fabio(x_start=sino_start,
x_end=sino_end,
x_step=sino_step,
flip=flip)
if ((data_type is 'spe') or (data_type is 'nc')):
if m == 0: # Get resolution once.
input_data = np.vstack([tmpdata])
else:
input_data = np.concatenate((input_data, tmpdata),
axis=0)
if ((data_type is 'tiff') or (data_type is 'compressed_tiff')
or (data_type is 'hdf5') or (data_type is 'edf2')
or (data_type is 'fabio')):
if m == 0: # Get resolution once.
input_data = np.empty(
(len(ind), tmpdata.shape[0], tmpdata.shape[1]),
dtype=dtype)
input_data[m, :, :] = tmpdata
# Update data.
if image_exist:
data = input_data
dtype = input_data.dtype
elif (file_name == "does_not_exist"):
self.logger.error("image file [%s]", file_name)
data = None
else:
self.logger.info("Attempt reading images from a single file: [%s]",
file_name)
data = None
if (data_type is 'h5'):
# Read the projections that are all in a single file
if os.path.isfile(file_name):
self.logger.info("Image file: [%s] exists", file_name)
f = XTomoReader(file_name)
array_name = '/'.join([exchange_base, "data"])
tmpdata = f.hdf5(z_start=images_start,
z_end=images_end,
z_step=images_step,
y_start=sino_start,
y_end=sino_end,
y_step=sino_step,
x_start=pixels_start,
x_end=pixels_end,
x_step=pixels_step,
array_name=array_name)
data = tmpdata
elif (data_type is 'nxs'):
# Read the projections that are all in a single file
if os.path.isfile(file_name):
self.logger.info("Image file: [%s] exists", file_name)
f = XTomoReader(file_name)
array_type = 'projections'
tmpdata = f.nxs(z_start=images_start,
z_end=images_end,
z_step=images_step,
y_start=sino_start,
y_end=sino_end,
y_step=sino_step,
x_start=pixels_start,
x_end=pixels_end,
x_step=pixels_step,
array_type=array_type)
data = tmpdata
elif (data_type is 'edf'):
# Read the projections that are all in a single file
if os.path.isfile(file_name):
self.logger.info("Image file: [%s] exists", file_name)
f = XTomoReader(file_name)
tmpdata = f.edf(y_start=sino_start,
y_end=sino_end,
y_step=sino_step)
data = tmpdata
elif (data_type is 'xradia'):
# Read the projections that are all in a single file
if os.path.isfile(file_name):
self.logger.info("Image file: [%s] exists", file_name)
f = XTomoReader(file_name)
tmpdata = f.txrm(y_start=sino_start,
y_end=sino_end,
y_step=sino_step)
data = tmpdata
dtype = tmpdata.dtype
elif (data_type is 'dpt'):
# Read the projections that are all in a single file
if os.path.isfile(file_name):
self.logger.info("Image file: [%s] exists", file_name)
f = XTomoReader(file_name)
tmpdata = f.dpt(y_start=sino_start,
y_end=sino_end,
y_step=sino_step)
data = tmpdata
else:
data = None
self.logger.error("No valid images found")
self.logger.error("Missng white/dark images will be generated")
return data
def xtomo_read_images(self, file_name,
images_start=0,
images_end=0,
images_step=1,
sino_start=0,
sino_end=0,
sino_step=1,
pixels_start=0,
pixels_end=0,
pixels_step=1,
images_digits=-1,
images_zeros=True,
dtype='uint16',
data_type='fabio',
array_type='projections',
flip='false',
log='INFO'):
# Determine file name and extension type.
data_file = os.path.splitext(file_name)[0]
data_extension = os.path.splitext(file_name)[1]
images_file_index = ["" for x in range(images_digits)]
for m in range(images_digits):
if images_zeros is True:
images_file_index[m] = '0' * (images_digits-m-1)
elif images_zeros is False:
images_file_index[m] = ''
_file_name = ' '
image_exist = False
# Start reading projections one-by-one.
ind = range(images_start, images_end, images_step)
for m in range(len(ind)):
for n in range(images_digits):
if ind[m] < np.power(10, n+1):
_file_name = data_file + images_file_index[n] + str(ind[m]) + data_extension
self.logger.info("Generating image file names: [%s]", _file_name)
break
if os.path.isfile(_file_name):
image_exist = True
self.logger.info("Reading image file: [%s]", os.path.realpath(_file_name))
self.logger.info("data type: [%s]", data_type)
f = XTomoReader(_file_name)
if (data_type is 'spe'):
tmpdata = f.spe(y_start = sino_start,
y_end = sino_end,
y_step = sino_step)
elif (data_type is 'nc'):
tmpdata = f.netcdf(y_start = sino_start,
y_end = sino_end,
y_step = sino_step)
elif (data_type is 'tiff'):
tmpdata = f.tiff(x_start=sino_start,
x_end=sino_end,
x_step=sino_step,
dtype=dtype,
flip=flip)
elif (data_type is 'compressed_tiff'):
tmpdata = f.tiffc(x_start=sino_start,
x_end=sino_end,
x_step=sino_step,
dtype=dtype)
elif (data_type is 'hdf5'):
tmpdata = f.hdf5_2d(x_start=sino_start,
x_end=sino_end,
x_step=sino_step,
array_name ='/entry1/data/data')
elif (data_type is 'edf2'):
tmpdata = f.edf2(x_start=sino_start,
x_end=sino_end,
x_step=sino_step)
elif (data_type is 'fabio'):
tmpdata = f.fabio(x_start=sino_start,
x_end=sino_end,
x_step=sino_step,
flip=flip)
if ((data_type is 'spe') or
(data_type is 'nc')):
if m == 0: # Get resolution once.
input_data = np.vstack([tmpdata])
else:
input_data = np.concatenate((input_data, tmpdata), axis=0)
if ((data_type is 'tiff') or
(data_type is 'compressed_tiff') or
(data_type is 'hdf5') or
(data_type is 'edf2') or
(data_type is 'fabio')):
if m == 0: # Get resolution once.
input_data = np.empty((len(ind), tmpdata.shape[0], tmpdata.shape[1]), dtype=dtype)
input_data[m, :, :] = tmpdata
# Update data.
if image_exist:
data = input_data
dtype = input_data.dtype
elif (file_name == "does_not_exist"):
self.logger.error("image file [%s]", file_name)
data = None
else:
self.logger.info("Attempt reading images from a single file: [%s]", file_name)
data = None
if (data_type is 'h5'):
# Read the projections that are all in a single file
if os.path.isfile(file_name):
self.logger.info("Image file: [%s] exists", file_name)
f = XTomoReader(file_name)
array_name = '/'.join([exchange_base, "data"])
tmpdata = f.hdf5(z_start = images_start,
z_end = images_end,
z_step = images_step,
y_start = sino_start,
y_end = sino_end,
y_step = sino_step,
x_start = pixels_start,
x_end = pixels_end,
x_step = pixels_step,
array_name = array_name)
data = tmpdata
elif (data_type is 'nxs'):
# Read the projections that are all in a single file
if os.path.isfile(file_name):
self.logger.info("Image file: [%s] exists", file_name)
f = XTomoReader(file_name)
array_type = 'projections'
tmpdata = f.nxs(z_start = images_start,
z_end = images_end,
z_step = images_step,
y_start = sino_start,
y_end = sino_end,
y_step = sino_step,
x_start = pixels_start,
x_end = pixels_end,
x_step = pixels_step,
array_type = array_type)
data = tmpdata
elif (data_type is 'edf'):
# Read the projections that are all in a single file
if os.path.isfile(file_name):
self.logger.info("Image file: [%s] exists", file_name)
f = XTomoReader(file_name)
tmpdata = f.edf(y_start = sino_start,
y_end = sino_end,
y_step = sino_step)
data = tmpdata
elif (data_type is 'xradia'):
# Read the projections that are all in a single file
if os.path.isfile(file_name):
self.logger.info("Image file: [%s] exists", file_name)
f = XTomoReader(file_name)
tmpdata = f.txrm(y_start = sino_start,
y_end = sino_end,
y_step = sino_step)
data = tmpdata
dtype = tmpdata.dtype
elif (data_type is 'dpt'):
# Read the projections that are all in a single file
if os.path.isfile(file_name):
self.logger.info("Image file: [%s] exists", file_name)
f = XTomoReader(file_name)
tmpdata = f.dpt(y_start = sino_start,
y_end = sino_end,
y_step = sino_step)
data = tmpdata
else:
data = None
self.logger.error("No valid images found")
self.logger.error("Missng white/dark images will be generated")
return data