from hdf5_image_processing import Hdf5ImageProcessingLib as IPL
import os
import numpy as np
__author__ = 'jhennies'
if __name__ == '__main__':
yamlfile = os.path.dirname(os.path.abspath(__file__)) + '/parameters.yml'
ipl = IPL(yaml=yamlfile)
ipl.logging('Parameters: {}', ipl.get_params())
params = ipl.get_params()
ipl.data_from_file(filepath=params['datafolder'] +
'cremi.splA.raw_neurons.crop.h5',
skeys='raw',
tkeys='raw')
ipl.crop_bounding_rect(np.s_[10:110, 200:712, 200:712], keys='raw')
ipl.write(filepath=params['datafolder'] +
'cremi.splA.raw_neurons.crop.crop_10-200-200_110-712-712.h5')
'/mnt/localdata02/jhennies/neuraldata/cremi_2016/cremi.splA.train.probs.crop.h5',
'/mnt/localdata02/jhennies/neuraldata/cremi_2016/cremi.splA.train.raw_neurons.crop.h5'
]
outfiles = [
'/mnt/localdata02/jhennies/neuraldata/cremi_2016/cremi.splA.train.probs.crop.crop_x10_110_y200_712_z200_712.split_xyz.h5',
'/mnt/localdata02/jhennies/neuraldata/cremi_2016/cremi.splA.train.raw_neurons.crop.crop_x10_110_y200_712_z200_712.split_xyz.h5'
]
for i in xrange(0, len(infiles)):
ipl = IPL(
filepath=infiles[i]
)
ipl.logging('Datastructure\n---\n{}', ipl.datastructure2string())
ipl.crop_bounding_rect(bounds=np.s_[10:110, 200:712, 200:712])
def shape(image):
return image.shape
print ipl.datastructure2string(function=shape)
ipl_split = split_in_xyz(ipl)
ipl_split.write(filepath=outfiles[i])
# # Sample A
# sample_a = IPL(
# filepath='/mnt/localdata02/jhennies/neuraldata/cremi_2016/cremi.splA.raw_neurons.crop.h5'
# )
#
# sample_a.logging('Sample A datastructure\n---\n{}', sample_a.datastructure2string())
from hdf5_image_processing import Hdf5ImageProcessingLib as IPL
import os
import numpy as np
__author__ = 'jhennies'
if __name__ == '__main__':
yamlfile = os.path.dirname(os.path.abspath(__file__)) + '/parameters.yml'
ipl = IPL(
yaml=yamlfile
)
ipl.logging('Parameters: {}', ipl.get_params())
params = ipl.get_params()
ipl.data_from_file(filepath=params['datafolder'] + 'cremi.splA.raw_neurons.crop.h5',
skeys='raw',
tkeys='raw')
ipl.crop_bounding_rect(np.s_[10:110, 200:712, 200:712], keys='raw')
ipl.write(filepath=params['datafolder'] + 'cremi.splA.raw_neurons.crop.crop_10-200-200_110-712-712.h5')
infiles = [
'/mnt/localdata02/jhennies/neuraldata/cremi_2016/cremi.splA.train.probs.crop.h5',
'/mnt/localdata02/jhennies/neuraldata/cremi_2016/cremi.splA.train.raw_neurons.crop.h5'
]
outfiles = [
'/mnt/localdata02/jhennies/neuraldata/cremi_2016/cremi.splA.train.probs.crop.crop_x10_110_y200_712_z200_712.split_xyz.h5',
'/mnt/localdata02/jhennies/neuraldata/cremi_2016/cremi.splA.train.raw_neurons.crop.crop_x10_110_y200_712_z200_712.split_xyz.h5'
]
for i in xrange(0, len(infiles)):
ipl = IPL(filepath=infiles[i])
ipl.logging('Datastructure\n---\n{}', ipl.datastructure2string())
ipl.crop_bounding_rect(bounds=np.s_[10:110, 200:712, 200:712])
def shape(image):
return image.shape
print ipl.datastructure2string(function=shape)
ipl_split = split_in_xyz(ipl)
ipl_split.write(filepath=outfiles[i])
# # Sample A
# sample_a = IPL(
# filepath='/mnt/localdata02/jhennies/neuraldata/cremi_2016/cremi.splA.raw_neurons.crop.h5'
# )
#
