javabridge.start_vm(class_path=bioformats.JARS)
#path_to_data = os.path.abspath(os.path.join(os.path.dirname( __file__ ), '..', '..', 'Daten', '24h', 'untreated'))
path_to_data = os.path.abspath(os.path.join(os.path.dirname( __file__ ), '..', '..', '..', 'Daten2'))
interpolator = 'bspline'
for directory in os.listdir(path_to_data):
data_dir = os.path.join(path_to_data, directory)
if os.path.exists(data_dir):
for filename in os.listdir(data_dir):
if filename.endswith('.tif'):
path_to_tif = os.path.join(data_dir, filename)
print('Processing image: ', path_to_tif)
# Get a numpy array from the tif stack with the dimension
meta_data, raw_data = bfio.get_tif_stack(filepath=path_to_tif, series=0, depth='z', return_dim_order='XYZC') # XYZC
# Transpose the numpy array from XYZC to CZYX for the use with SimpleITK
raw_data = np.transpose(raw_data, axes=[3,2,1,0]) # CZYX
# Extract the channel -> make for each channel
raw_data = raw_data[0,:,:,:]
# Make a SimpleITK out of the numpy array and set its metadata
image = sitk.GetImageFromArray(raw_data, isVector=False) # XYZ
image.SetOrigin([0.0, 0.0, 0.0])
image.SetDirection(np.identity(3, dtype=np.double).flatten().tolist())
image.SetSpacing((meta_data.get('physical_size_x'),
meta_data.get('physical_size_y'),
meta_data.get('physical_size_z')))
#print(image.GetOrigin())
import bioformats
import SimpleITK as sitk
import numpy as np
import matplotlib.pyplot as plt
import nrrd
from tools import image_io as bfio
# Start the Java VM
javabridge.start_vm(class_path=bioformats.JARS)
filepath = 'test_data/Nucleisegmentedfill.tif'
depth = 't'
# Get a numpy array from the tif stack with the dimension
meta_data, raw_data = bfio.get_tif_stack(filepath=filepath,
series=0,
depth=depth,
return_dim_order='XYZC') # XYZ
# Extract the channel
raw_data = raw_data[:, :, :, 0]
# Transpose the numpy array from XYZC to CZYX for the use with SimpleITK
raw_data = np.transpose(raw_data, axes=[2, 1, 0]) # ZYX
# Make a SimpleITK out of the numpy array
image = sitk.GetImageFromArray(raw_data, isVector=False) # XYZ
print('Dimension: ', image.GetDimension())
print('Width: ', image.GetWidth())
print('Height: ', image.GetHeight())
print('Depth: ', image.GetDepth())
print('XYZ: ', image.GetSize())
#path_to_data = os.path.abspath(os.path.join(os.path.dirname( __file__ ), '..', '..', 'Daten', '24h', 'untreated'))
path_to_data = os.path.abspath(
os.path.join(os.path.dirname(__file__), '..', '..', 'Daten'))
for directory in os.listdir(path_to_data):
data_dir = os.path.join(path_to_data, directory, 'untreated')
if os.path.exists(data_dir):
for item in os.listdir(data_dir):
if item.endswith('.tif'):
tif_name = os.path.splitext(item)
path_to_tif = os.path.join(data_dir, item)
path_to_nrrd = os.path.join(data_dir, tif_name[0] + ".nrrd")
print('Processing image: ', path_to_tif)
orig_meta_data, orig_raw_data = bfio.get_tif_stack(
filepath=path_to_tif,
series=0,
depth='z',
return_dim_order='XYZC') # XYZC
own_raw_data, own_meta_data = nrrd.read(path_to_nrrd)
#print('Original z: ', orig_raw_data.shape[2], 'Own z: ', own_raw_data.shape[2])
abspath = os.path.join(data_dir, item)
if (os.path.isdir(abspath)):
tiffile = os.path.splitext(path_to_tif)
tiffile = tiffile[0].split('\\')
tiffile = tiffile[-1]
if (tiffile in abspath):
path_to_original_stack = os.path.join(
abspath, 'OriginalStack.tif')
#print('Correspondig OpenSegSPIM data: ', path_to_original_stack)
oss_meta_data, oss_raw_data = bfio.get_tif_stack(
filepath=path_to_original_stack,
sys.path.append("..")
import javabridge
import bioformats
import SimpleITK as sitk
import numpy as np
import matplotlib.pyplot as plt
import nrrd
from tools import image_io as bfio
# Start the Java VM
javabridge.start_vm(class_path=bioformats.JARS)
# Get a numpy array from the tif stack with the dimension
meta_data, raw_data = bfio.get_tif_stack(
filepath='test_data/test_nucleid_1ch.tif',
series=0,
depth='z',
return_dim_order='XYZC') # XYZC
# Write the data without header to nrrd-file and view it in Fiji
# -> the z-axis is much shorter
nrrd.write('export\test_nrrd.nrrd',
data=raw_data,
header=None,
index_order='F')
# Transpose the numpy array from XYZC to CZYX for the use with SimpleITK
raw_data = np.transpose(raw_data, axes=[3, 2, 1, 0]) # CZYX
# Extract the channel
raw_data = raw_data[0, :, :, :]
print(directory)
if directory == '24h' or directory == '48h' or directory == '72h':
print('###########################################################')
cultivation_period = directory
data_dir = os.path.join(path_to_data, directory, 'untreated')
if os.path.exists(data_dir):
for filename in os.listdir(data_dir):
if filename.endswith('.nrrd'):
spheroid_name = os.path.splitext(filename)[0]
print('Actual file: ', spheroid_name)
# Read the original data
tif_file = os.path.join(data_dir, spheroid_name + '.tif')
tif_header, tif_data = bfio.get_tif_stack(
filepath=tif_file,
series=0,
depth='z',
return_dim_order='XYZC') # XYZC
tif_data = tif_data[:, :, :, 0]
tif_data = np.transpose(tif_data, axes=(2, 1, 0)) #ZYX
print('TIF Dimension: ', tif_data.shape)
# Read the isotropic data
nrrd_file = os.path.join(data_dir, spheroid_name + '.nrrd')
nrrd_data, nrrd_header = nrrd.read(nrrd_file)
nrrd_data = np.transpose(nrrd_data, axes=(2, 1, 0)) #ZYX
print('NRRD Dimension: ', nrrd_data.shape)
# Save the dimensions in a table
table.append([
cultivation_period, spheroid_name, tif_data.shape[0:3],
for seg_file in seg_files:
if spheroid_name in seg_file and 'NucleiBinary' in seg_file and seg_file.endswith(
'.tif'):
spheroid_file = os.path.abspath(spheroid_file)
spheroid_dir = os.path.abspath(spheroid_dir)
seg_file = os.path.join(spheroid_dir, subdir2,
seg_file)
seg_filename = os.path.splitext(seg_file)[0]
print('Corresponding Segmentation: ', seg_file)
# Load the Spheroid and corresponding segmentation
spheroid_data, spheroid_header = nrrd.read(
spheroid_file)
segmentation_meta_data, segmentation_data = bfio.get_tif_stack(
filepath=seg_file,
series=0,
depth='t',
return_dim_order='XYZC') # XYZC
segmentation_data = segmentation_data[:, :, :, 0]
segmentation_data = np.transpose(segmentation_data,
axes=(2, 1, 0)) # ZYX
# Label the segmentations
labelled_segmentation_data = cc3d.connected_components(
segmentation_data, connectivity=6)
labelled_segmentation_data = np.transpose(
labelled_segmentation_data,
axes=(2, 1, 0)).astype(np.uint16) # XYZ
import sys
sys.path.append("..")
import javabridge
import bioformats
import SimpleITK as sitk
import numpy as np
import matplotlib.pyplot as plt
import nrrd
from tools import image_io as bfio
# Start the Java VM
javabridge.start_vm(class_path=bioformats.JARS)
# Get a numpy array from the tif stack with the dimension
meta_data, raw_data = bfio.get_tif_stack(filepath='labeling_test.tif',
series=0,
depth='z',
return_dim_order='XYZC') # YXZ
# Extract the channel
raw_data = raw_data[:, :, :, 0]
# Plot the image stack through the x-Axis (side view)
for x in range(raw_data.shape[0]):
raw_slice = raw_data[x, :, :]
plt.figure()
plt.imshow(raw_slice)
# Plot the image stack through the y-Axis (frontal view)
for y in range(raw_data.shape[1]):
raw_slice = raw_data[:, y, :]
plt.figure()