示例#1
0
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())
示例#3
0
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')
示例#4
0
    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'
    # )
    #