Python Image.header方法代码示例

# 需要导入模块: from nipy.core.api import Image [as 别名]
# 或者: from nipy.core.api.Image import header [as 别名]
def load(filename):
    """Load an image from the given filename.

    Parameters
    ----------
    filename : string
        Should resolve to a complete filename path.

    Returns
    -------
    image : An `Image` object
        If successful, a new `Image` object is returned.

    See Also
    --------
    save_image : function for saving images
    fromarray : function for creating images from numpy arrays

    Examples
    --------

    >>> from nipy.io.api import load_image
    >>> from nipy.testing import anatfile
    >>> img = load_image(anatfile)
    >>> img.shape
    (33, 41, 25)
    """
    img = formats.load(filename)
    aff = img.get_affine()
    shape = img.get_shape()
    hdr = img.get_header()

    # Get info from NIFTI header, if present, to tell which axes are
    # which.  This is a NIFTI-specific kludge, that might be abstracted
    # out into the image backend in a general way.  Similarly for
    # getting zooms

    # axis_renames is a dictionary: dict([(int, str)])
    # that has keys in range(3)
    # the axes of the Image are renamed from 'ijk'
    # using these names

    try:
        axis_renames = hdr.get_axis_renames()
    except (TypeError, AttributeError):
        axis_renames = {}

    try:
        zooms = hdr.get_zooms()
    except AttributeError:
        zooms = np.ones(len(shape))

    # affine_transform is a 3-d transform

    affine_transform3d, affine_transform = \
        affine_transform_from_array(aff, 'ijk', pixdim=zooms[3:])
    img = Image(img.get_data(), affine_transform.renamed_domain(axis_renames))
    img.header = hdr
    return img

# 需要导入模块: from nipy.core.api import Image [as 别名]
# 或者: from nipy.core.api.Image import header [as 别名]
def load(filename):
    """Load an image from the given filename.

    Parameters
    ----------
    filename : string
        Should resolve to a complete filename path.

    Returns
    -------
    image : An `Image` object
        If successful, a new `Image` object is returned.

    See Also
    --------
    save_image : function for saving images
    fromarray : function for creating images from numpy arrays

    Examples
    --------

    >>> from nipy.io.api import load_image
    >>> from nipy.testing import anatfile
    >>> img = load_image(anatfile)
    >>> img.shape
    (33, 41, 25)
    """
    img = nib.load(filename)
    aff = img.get_affine()
    shape = img.get_shape()
    hdr = img.get_header()
    # If the header implements it, get a list of names, one per axis,
    # and put this into the coordinate map.  In fact, no image format
    # implements this at the moment, so in practice, the following code
    # is not currently called. 
    axis_renames = {}
    try:
        axis_names = hdr.axis_names
    except AttributeError:
        pass
    else:
        # axis_renames is a dictionary: dict([(int, str)]) that has keys
        # in range(3). The axes of the Image are renamed from 'ijk' using
        # these names
        for i in range(min([len(axis_names), 3])):
            name = axis_names[i]
            if not (name is None or name == ''):
                axis_renames[i] = name
    zooms = hdr.get_zooms()
    # affine_transform is a 3-d transform
    affine_transform3d, affine_transform = \
        affine_transform_from_array(aff, 'ijk', pixdim=zooms[3:])
    img = Image(img.get_data(), affine_transform.renamed_domain(axis_renames))
    img.header = hdr
    return img

# 需要导入模块: from nipy.core.api import Image [as 别名]
# 或者: from nipy.core.api.Image import header [as 别名]
def load(filename):
    """Load an image from the given filename.

    Parameters
    ----------
    filename : string
        Should resolve to a complete filename path.

    Returns
    -------
    image : An `Image` object
        If successful, a new `Image` object is returned.

    See Also
    --------
    save_image : function for saving images
    fromarray : function for creating images from numpy arrays

    Examples
    --------

    >>> from nipy.io.api import load_image
    >>> from nipy.testing import anatfile
    >>> img = load_image(anatfile)
    >>> img.shape
    (33, 41, 25)
    """
    img = formats.load(filename)
    aff = img.get_affine()
    shape = img.get_shape()
    hdr = img.get_header()
    # Get info from NIFTI header, if present, to tell which axes are
    # which.  This is a NIFTI-specific kludge, that might be abstracted
    # out into the image backend in a general way.  Similarly for
    # getting zooms
    try:
        fps = hdr.get_dim_info()
    except (TypeError, AttributeError):
        fps = (None, None, None)
    ijk = ijk_from_fps(fps)
    try:
        zooms = hdr.get_zooms()
    except AttributeError:
        zooms = np.ones(len(shape))
    aff = _match_affine(aff, len(shape), zooms)
    coordmap = coordmap_from_affine(aff, ijk)
    img = Image(img.get_data(), coordmap)
    img.header = hdr
    return img