Python nibabel.load方法代碼示例

# 需要導入模塊: import nibabel [as 別名]
# 或者: from nibabel import load [as 別名]
def _run_interface(self, runtime):
        import nibabel as nib
        from nipype.utils.filemanip import fname_presuffix

        bold_img = nib.load(self.inputs.timeseries_file)
        bold_mask_img = nib.load(self.inputs.mask_file)

        bold_data = bold_img.get_fdata()
        bold_mask = bold_mask_img.get_fdata().astype(bool)

        outliers = is_outlier(bold_data[bold_mask].T, thresh=self.inputs.threshold)

        out = fname_presuffix(self.inputs.timeseries_file, suffix='_censored')

        bold_img.__class__(bold_data[..., ~outliers],
                           bold_img.affine, bold_img.header).to_filename(out)

        self._results['censored_file'] = out
        self._results['outliers'] = outliers

        return runtime 

# 需要導入模塊: import nibabel [as 別名]
# 或者: from nibabel import load [as 別名]
def test_censor_volumes(tmp_path, betaseries_file, brainmask_file):
    outlier_file = tmp_path / 'betaseries_outlier.nii.gz'

    # make an outlier volume
    outlier_idx = 6
    beta_img = nib.load(str(betaseries_file))
    beta_data = beta_img.get_fdata()
    beta_data[..., outlier_idx] += 1000

    beta_img.__class__(
        beta_data, beta_img.affine, beta_img.header).to_filename(str(outlier_file))

    censor_volumes = CensorVolumes(timeseries_file=str(outlier_file),
                                   mask_file=str(brainmask_file))

    res = censor_volumes.run()

    assert nib.load(res.outputs.censored_file).shape[-1] == beta_img.shape[-1] - 1
    assert res.outputs.outliers[outlier_idx] 

# 需要導入模塊: import nibabel [as 別名]
# 或者: from nibabel import load [as 別名]
def forget_importer(name):
    '''
    forget_importer(name) yields True if an importer of type name was successfully forgotten from
      the neuropythy importers list and false otherwise. This function must be called before an
      importer can be replaced.
    '''
    global importers
    name = name.lower()
    if name in importers:
        importers = importers.discard(name)
        delattr(load, name)
        return True
    else:
        return False



# The list of exporter types we understand 

# 需要導入模塊: import nibabel [as 別名]
# 或者: from nibabel import load [as 別名]
def load_json(filename, to='auto'):
    '''
    load_json(filename) yields the object represented by the json file or stream object filename.
    
    The optional argument to may be set to None to indicate that the JSON data should be returned
    verbatim rather than parsed by neuropythy's denormalize system.
    '''
    from neuropythy.util import denormalize as denorm
    if pimms.is_str(filename):
        try:
            with gzip.open(filename, 'rt') as fl: dat = json.load(fl)
        except Exception:
            with open(filename, 'rt') as fl: dat = json.load(fl)
    else:
        dat = json.load(filename)
        filename = '<stream>'
    if to is None: return dat
    elif to == 'auto': return denorm(dat)
    else: raise ValueError('unrecognized to option: %s' % to) 

# 需要導入模塊: import nibabel [as 別名]
# 或者: from nibabel import load [as 別名]
def generateImgSlicesFolder(data_folder = '../Data/MS2017a/scans/'):
	scan_folders = glob.glob(data_folder + '*')

	for sf in scan_folders:
		slice_dir_path = os.path.join(sf, 'slices/')
		if not os.path.exists(slice_dir_path):
			print('Creating directory at:' , slice_dir_path)
			os.makedirs(slice_dir_path)

		img = nib.load(os.path.join(sf, 'pre/FLAIR.nii.gz'))
		img_np = img.get_data()
		img_affine = img.affine
		print(sf)
		print('The img shape', img_np.shape[2])
		for i in range(img_np.shape[2]):
			slice_img_np = img_np[:,:,i]
			nft_img = nib.Nifti1Image(slice_img_np, img_affine)
			nib.save(nft_img, slice_dir_path + 'FLAIR_' + str(i) + '.nii.gz')

			if os.path.basename(sf) == '0':
				slice_img = nib.load(slice_dir_path + 'FLAIR_' + str(i) + '.nii.gz').get_data() / 5
				print('DID I GET HERE?')
				print('Writing to', str(i) + '.jpg') 

# 需要導入模塊: import nibabel [as 別名]
# 或者: from nibabel import load [as 別名]
def generateGTSlicesFolder(data_folder = '../Data/MS2017a/scans/'):
	scan_folders = glob.glob(data_folder + '*')

	for sf in scan_folders:
		slice_dir_path = os.path.join(sf, 'gt_slices/')
		if not os.path.exists(slice_dir_path):
			print('Creating directory at:' , slice_dir_path)
			os.makedirs(slice_dir_path)

		img = nib.load(os.path.join(sf, 'wmh.nii.gz'))
		img_np = img.get_data()
		img_affine = img.affine
		print(sf)
		print('The img shape', img_np.shape[2])
		for i in range(img_np.shape[2]):
			slice_img_np = img_np[:,:,i]
			nft_img = nib.Nifti1Image(slice_img_np, img_affine)
			nib.save(nft_img, slice_dir_path + 'wmh_' + str(i) + '.nii.gz')

			if os.path.basename(sf) == '0':
				slice_img = nib.load(slice_dir_path + 'wmh_' + str(i) + '.nii.gz').get_data() * 256
				#cv2.imwrite('temp/' + str(i) + '.jpg', slice_img) 

# 需要導入模塊: import nibabel [as 別名]
# 或者: from nibabel import load [as 別名]
def load_surfaces(filename, suppress_echo = False):
    '''
    separate a cifti file into surfaces,
    then loads the surface data
    '''
    ## separate the cifti file into left and right surfaces
    with TempDir() as tempdir:
        L_data_surf=os.path.join(tempdir, 'Ldata.func.gii')
        R_data_surf=os.path.join(tempdir, 'Rdata.func.gii')
        run(['wb_command','-cifti-separate', filename, 'COLUMN',
            '-metric', 'CORTEX_LEFT', L_data_surf,
            '-metric', 'CORTEX_RIGHT', R_data_surf],
            suppress_echo = suppress_echo)

        ## load both surfaces and concatenate them together
        Ldata = load_gii_data(L_data_surf)
        Rdata = load_gii_data(R_data_surf)

    return Ldata, Rdata 

# 需要導入模塊: import nibabel [as 別名]
# 或者: from nibabel import load [as 別名]
def __get_tr(self, tr_arg):
        '''read tr from func file is not indicated'''
        if tr_arg:
            tr = float(tr_arg)
        else:
            if self.func.type == "nifti":
                tr_ms = nib.load(self.func.path).header.get_zooms()[3]
                if tr_ms > 150:
                    '''nowing that no tr is never between 20 and 200, we assume seconds vs ms'''
                    logger.info('The TR is greater than 150, we beleive this is in ms, dividing by 1000')
                    tr = float(tr_ms) / 1000
                else:
                    tr = tr_ms
            if self.func.type == "cifti":
                tr_out = ciftify.utils.get_stdout(['wb_command','-file-information',
                        '-only-step-interval', self.func.path])
                tr = float(tr_out.strip())
        if tr > 150:
            logger.warning("TR should be specified in seconds, improbable value {} given".format(tr))
        return tr 

# 需要導入模塊: import nibabel [as 別名]
# 或者: from nibabel import load [as 別名]
def _calculate_dadt_nifti(msh, nifti_image, coil_matrix, didt, geo_fn):

    """ auxiliary function that interpolates the dA/dt field from a nifti file """
    if isinstance(nifti_image, str):
        nifti_image = nib.load(nifti_image)
    elif isinstance(nifti_image, nib.nifti1.Nifti1Image):
        pass
    else:
        raise NameError('Failed to parse input volume (not string or nibabel nifti1 volume)')
    coords = msh.nodes.node_coord

    out = _get_field(nifti_image, coords, coil_matrix)
    out = out * didt

    node_data = mesh_io.NodeData(out.T)

    if geo_fn is not None:
        y_axis = np.arange(1, 10, dtype=float)[:, None] * (0, 1, 0)
        z_axis = np.arange(1, 30, dtype=float)[:, None] * (0, 0, 1)
        pos = np.vstack((((0, 0, 0)), y_axis, z_axis))
        pos = (coil_matrix[:3, :3].dot(pos.T) + coil_matrix[:3, 3][:, None]).T
        mesh_io.write_geo_spheres(pos, geo_fn,
                               name='coil_directions')

    return node_data 

# 需要導入模塊: import nibabel [as 別名]
# 或者: from nibabel import load [as 別名]
def _get_vol_info(self):
        if self.anisotropy_vol is not None:
            if self.anisotropy_affine is not None:
                return self.anisotropy_vol, self.anisotropy_affine

        if not self.fn_tensor_nifti:
            raise ValueError('could not get anisotropy information: '
                             'fn_tensor_nifti not set')

        fn_nifti = \
            os.path.abspath(os.path.expanduser(self.fn_tensor_nifti))
        if not os.path.isfile(fn_nifti):
            raise ValueError(
                'Could not find file \'{0}\' to get anisotropy '
                'information'.format(self.fn_tensor_nifti))

        # Load the nifti and interpolate the conductivieis
        image = nibabel.load(fn_nifti)
        affine = image.affine
        return image.dataobj, affine 

# 需要導入模塊: import nibabel [as 別名]
# 或者: from nibabel import load [as 別名]
def read_gifti_surface(fn):
    ''' Reads a gifti surface

    Parameters
    -----------
    fn: str
        File name

    Returns
    ---------
    msh: Msh()
        mesh structure with geometrical information
    '''
    s = nibabel.load(fn)
    faces = s.get_arrays_from_intent('NIFTI_INTENT_TRIANGLE')[0].data
    nodes = s.get_arrays_from_intent('NIFTI_INTENT_POINTSET')[0].data
    msh = Msh()
    msh.elm = Elements(triangles=np.array(faces + 1, dtype=int))
    msh.nodes = Nodes(np.array(nodes, dtype=float))
    return msh 

# 需要導入模塊: import nibabel [as 別名]
# 或者: from nibabel import load [as 別名]
def load_nifti_img(filepath, dtype):
    '''
    NIFTI Image Loader
    :param filepath: path to the input NIFTI image
    :param dtype: dataio type of the nifti numpy array
    :return: return numpy array
    '''
    nim = nib.load(filepath)
    out_nii_array = np.array(nim.get_data(),dtype=dtype)
    out_nii_array = np.squeeze(out_nii_array) # drop singleton dim in case temporal dim exists
    meta = {'affine': nim.get_affine(),
            'dim': nim.header['dim'],
            'pixdim': nim.header['pixdim'],
            'name': os.path.basename(filepath)
            }

    return out_nii_array, meta 

# 需要導入模塊: import nibabel [as 別名]
# 或者: from nibabel import load [as 別名]
def auto_crop_image(input_name, output_name, reserve):
    nim = nib.load(input_name)
    image = nim.get_data()
    X, Y, Z = image.shape[:3]

    # Detect the bounding box of the foreground
    idx = np.nonzero(image > 0)
    x1, x2 = idx[0].min() - reserve, idx[0].max() + reserve + 1
    y1, y2 = idx[1].min() - reserve, idx[1].max() + reserve + 1
    z1, z2 = idx[2].min() - reserve, idx[2].max() + reserve + 1
    x1, x2 = max(x1, 0), min(x2, X)
    y1, y2 = max(y1, 0), min(y2, Y)
    z1, z2 = max(z1, 0), min(z2, Z)
    print('Bounding box')
    print('  bottom-left corner = ({},{},{})'.format(x1, y1, z1))
    print('  top-right corner = ({},{},{})'.format(x2, y2, z2))

    # Crop the image
    image = image[x1:x2, y1:y2, z1:z2]

    # Update the affine matrix
    affine = nim.affine
    affine[:3, 3] = np.dot(affine, np.array([x1, y1, z1, 1]))[:3]
    nim2 = nib.Nifti1Image(image, affine)
    nib.save(nim2, output_name) 

# 需要導入模塊: import nibabel [as 別名]
# 或者: from nibabel import load [as 別名]
def _split_segments(in_file):
    from pathlib import Path
    import numpy as np
    import nibabel as nb

    segimg = nb.load(in_file)
    data = np.int16(segimg.dataobj)
    hdr = segimg.header.copy()
    hdr.set_data_dtype('uint8')

    out_files = []
    for i, label in enumerate(("GM", "WM", "CSF"), 1):
        out_fname = str(Path.cwd() / f"aseg_label-{label}_mask.nii.gz")
        segimg.__class__(data == i, segimg.affine, hdr).to_filename(out_fname)
        out_files.append(out_fname)

    return out_files 

# 需要導入模塊: import nibabel [as 別名]
# 或者: from nibabel import load [as 別名]
def apply_lut(in_dseg, lut, newpath=None):
    """Map the input discrete segmentation to a new label set (lookup table, LUT)."""
    import numpy as np
    import nibabel as nb
    from nipype.utils.filemanip import fname_presuffix

    if newpath is None:
        from os import getcwd
        newpath = getcwd()

    out_file = fname_presuffix(in_dseg, suffix='_dseg', newpath=newpath)
    lut = np.array(lut, dtype='int16')

    segm = nb.load(in_dseg)
    hdr = segm.header.copy()
    hdr.set_data_dtype('int16')
    segm.__class__(lut[np.asanyarray(segm.dataobj, dtype=int)].astype('int16'),
                   segm.affine, hdr).to_filename(out_file)

    return out_file