Python Image.get_data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 get_data [as 别名]
def test_rotate2d():
    # Rotate an image in 2d on a square grid, should result in transposed image
    g = AffineTransform.from_params('ij', 'xy', np.diag([0.7,0.5,1]))
    g2 = AffineTransform.from_params('ij', 'xy', np.diag([0.5,0.7,1]))
    i = Image(np.ones((100,100)), g)
    # This sets the image data by writing into the array
    i.get_data()[50:55,40:55] = 3.
    a = np.array([[0,1,0],
                  [1,0,0],
                  [0,0,1]], np.float)
    ir = resample(i, g2, a, (100, 100))
    assert_array_almost_equal(ir.get_data().T, i.get_data())

# 需要导入模块: from nipy.core.api import Image [as 别名]
# 或者: from nipy.core.api.Image import get_data [as 别名]
def test_rotate3d():
    # Rotate / transpose a 3d image on a non-square grid
    g = AffineTransform.from_params('ijk', 'xyz', np.diag([0.5,0.6,0.7,1]))
    g2 = AffineTransform.from_params('ijk', 'xyz', np.diag([0.5,0.7,0.6,1]))
    shape = (100,90,80)
    i = Image(np.ones(shape), g)
    i.get_data()[50:55,40:55,30:33] = 3.
    a = np.array([[1,0,0,0],
                  [0,0,1,0],
                  [0,1,0,0],
                  [0,0,0,1.]])
    ir = resample(i, g2, a, (100,80,90))
    assert_array_almost_equal(np.transpose(ir.get_data(), (0,2,1)),
                              i.get_data())

# 需要导入模块: from nipy.core.api import Image [as 别名]
# 或者: from nipy.core.api.Image import get_data [as 别名]
def test_rollaxis():
    data = np.random.standard_normal((3,4,7,5))
    im = Image(data, AffineTransform.from_params('ijkl', 'xyzt', np.diag([1,2,3,4,1])))

    # for the inverse we must specify an integer
    yield assert_raises, ValueError, image.rollaxis, im, 'i', True

    # Check that rollaxis preserves diagonal affines, as claimed

    yield assert_almost_equal, image.rollaxis(im, 1).affine, np.diag([2,1,3,4,1])
    yield assert_almost_equal, image.rollaxis(im, 2).affine, np.diag([3,1,2,4,1])
    yield assert_almost_equal, image.rollaxis(im, 3).affine, np.diag([4,1,2,3,1])

    # Check that ambiguous axes raise an exception
    # 'l' appears both as an axis and a reference coord name
    # and in different places

    im_amb = Image(data, AffineTransform.from_params('ijkl', 'xylt', np.diag([1,2,3,4,1])))
    yield assert_raises, ValueError, image.rollaxis, im_amb, 'l'

    # But if it's unambiguous, then
    # 'l' can appear both as an axis and a reference coord name

    im_unamb = Image(data, AffineTransform.from_params('ijkl', 'xyzl', np.diag([1,2,3,4,1])))
    im_rolled = image.rollaxis(im_unamb, 'l')
    yield assert_almost_equal, im_rolled.get_data(), \
        im_unamb.get_data().transpose([3,0,1,2])

    for i, o, n in zip('ijkl', 'xyzt', range(4)):
        im_i = image.rollaxis(im, i)
        im_o = image.rollaxis(im, o)
        im_n = image.rollaxis(im, n)

        yield assert_almost_equal, im_i.get_data(), \
                                  im_o.get_data()

        yield assert_almost_equal, im_i.affine, \
            im_o.affine

        yield assert_almost_equal, im_n.get_data(), \
            im_o.get_data()

        for _im in [im_n, im_o, im_i]:
            im_n_inv = image.rollaxis(_im, n, inverse=True)

            yield assert_almost_equal, im_n_inv.affine, \
                im.affine

            yield assert_almost_equal, im_n_inv.get_data(), \
                im.get_data()

# 需要导入模块: from nipy.core.api import Image [as 别名]
# 或者: from nipy.core.api.Image import get_data [as 别名]
def test_slice_from_3d():
    # Resample a 3d image, returning a zslice, yslice and xslice
    #
    # This example creates a coordmap that coincides with
    # a given z, y, or x slice of an image, and checks that
    # resampling agrees with the data in the given slice.
    shape = (100,90,80)
    g = AffineTransform.from_params('ijk',
                                    'xyz',
                                    np.diag([0.5,0.5,0.5,1]))
    img = Image(np.ones(shape), g)
    img.get_data()[50:55,40:55,30:33] = 3
    I = np.identity(4)
    zsl = slices.zslice(26,
                        ((0,49.5), 100),
                        ((0,44.5), 90),
                        img.reference)
    ir = resample(img, zsl, I, (100, 90))
    assert_array_almost_equal(ir.get_data(), img[:,:,53].get_data())
    ysl = slices.yslice(22,
                        ((0,49.5), 100),
                        ((0,39.5), 80),
                        img.reference)
    ir = resample(img, ysl, I, (100, 80))
    assert_array_almost_equal(ir.get_data(), img[:,45,:].get_data())
    xsl = slices.xslice(15.5,
                        ((0,44.5), 90),
                        ((0,39.5), 80),
                        img.reference)
    ir = resample(img, xsl, I, (90, 80))
    assert_array_almost_equal(ir.get_data(), img[32,:,:].get_data())

# 需要导入模块: from nipy.core.api import Image [as 别名]
# 或者: from nipy.core.api.Image import get_data [as 别名]
def test_nonaffine():
    # resamples an image along a curve through the image.
    #
    # FIXME: use the reference.evaluate.Grid to perform this nicer
    # FIXME: Remove pylab references
    def curve(x): # function accept N by 1, returns N by 2 
        return (np.vstack([5*np.sin(x.T),5*np.cos(x.T)]).T + [52,47])
    for names in (('xy', 'ij', 't', 'u'),('ij', 'xy', 't', 's')):
        in_names, out_names, tin_names, tout_names = names
        g = AffineTransform.from_params(in_names, out_names, np.identity(3))
        img = Image(np.ones((100,90)), g)
        img.get_data()[50:55,40:55] = 3.
        tcoordmap = AffineTransform.from_start_step(
            tin_names,
            tout_names,
            [0],
            [np.pi*1.8/100])
        ir = resample(img, tcoordmap, curve, (100,))
    if gui_review:
        import pylab
        pylab.figure(num=3)
        pylab.imshow(img, interpolation='nearest')
        d = curve(np.linspace(0,1.8*np.pi,100))
        pylab.plot(d[0], d[1])
        pylab.gca().set_ylim([0,99])
        pylab.gca().set_xlim([0,89])
        pylab.figure(num=4)
        pylab.plot(ir.get_data())

# 需要导入模块: from nipy.core.api import Image [as 别名]
# 或者: from nipy.core.api.Image import get_data [as 别名]
def test_synchronized_order():

    data = np.random.standard_normal((3,4,7,5))
    im = Image(data, AffineTransform.from_params('ijkl', 'xyzt', np.diag([1,2,3,4,1])))

    im_scrambled = im.reordered_axes('iljk').reordered_reference('xtyz')
    im_unscrambled = image.synchronized_order(im_scrambled, im)
    
    yield assert_equal, im_unscrambled.coordmap, im.coordmap
    yield assert_almost_equal, im_unscrambled.get_data(), im.get_data()
    yield assert_equal, im_unscrambled, im
    yield assert_true, im_unscrambled == im
    yield assert_false, im_unscrambled != im

    # the images don't have to be the same shape

    data2 = np.random.standard_normal((3,11,9,4))
    im2 = Image(data, AffineTransform.from_params('ijkl', 'xyzt', np.diag([1,2,3,4,1])))

    im_scrambled2 = im2.reordered_axes('iljk').reordered_reference('xtyz')
    im_unscrambled2 = image.synchronized_order(im_scrambled2, im)

    yield assert_equal, im_unscrambled2.coordmap, im.coordmap

    # or the same coordmap

    data3 = np.random.standard_normal((3,11,9,4))
    im3 = Image(data, AffineTransform.from_params('ijkl', 'xyzt', np.diag([1,9,3,-2,1])))

    im_scrambled3 = im3.reordered_axes('iljk').reordered_reference('xtyz')
    im_unscrambled3 = image.synchronized_order(im_scrambled3, im)

    yield assert_equal, im_unscrambled3.axes, im.axes
    yield assert_equal, im_unscrambled3.reference, im.reference

# 需要导入模块: from nipy.core.api import Image [as 别名]
# 或者: from nipy.core.api.Image import get_data [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 get_data [as 别名]
def test_labels1():
    img = load_image(funcfile)
    data = img.get_data()
    parcelmap = Image(img[0].get_data(), AfT("kji", "zyx", np.eye(4)))
    parcelmap = (parcelmap.get_data() * 100).astype(np.int32)
    v = 0
    for i, d in axis0_generator(data, parcels(parcelmap)):
        v += d.shape[1]
    assert_equal(v, parcelmap.size)

# 需要导入模块: from nipy.core.api import Image [as 别名]
# 或者: from nipy.core.api.Image import get_data [as 别名]
def test_resample2d3():
    # Same as test_resample2d, only a different way of specifying
    # the transform: here it is an (A,b) pair
    g = AffineTransform.from_params('ij', 'xy', np.diag([0.5,0.5,1]))
    i = Image(np.ones((100,90)), g)
    i.get_data()[50:55,40:55] = 3.
    a = np.identity(3)
    a[:2,-1] = 4.
    ir = resample(i, i.coordmap, a, (100,90))
    assert_array_almost_equal(ir.get_data()[42:47,32:47], 3.)

# 需要导入模块: from nipy.core.api import Image [as 别名]
# 或者: from nipy.core.api.Image import get_data [as 别名]
def test_resample2d2():
    g = AffineTransform.from_params('ij', 'xy', np.diag([0.5,0.5,1]))
    i = Image(np.ones((100,90)), g)
    i.get_data()[50:55,40:55] = 3.
    a = np.identity(3)
    a[:2,-1] = 4.
    A = np.identity(2)
    b = np.ones(2)*4
    ir = resample(i, i.coordmap, (A, b), (100,90))
    assert_array_almost_equal(ir.get_data()[42:47,32:47], 3.)

# 需要导入模块: from nipy.core.api import Image [as 别名]
# 或者: from nipy.core.api.Image import get_data [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 get_data [as 别名]
def test_rotate2d3():
    # Another way to rotate/transpose the image, similar to
    # test_rotate2d2 and test_rotate2d, except the world of the
    # output coordmap is the same as the world of the
    # original image. That is, the data is transposed on disk, but the
    # output coordinates are still 'x,'y' order, not 'y', 'x' order as
    # above

    # this functionality may or may not be used a lot. if data is to
    # be transposed but one wanted to keep the NIFTI order of output
    # coords this would do the trick
    g = AffineTransform.from_params('xy', 'ij', np.diag([0.5,0.7,1]))
    i = Image(np.ones((100,80)), g)
    # This sets the image data by writing into the array
    i.get_data()[50:55,40:55] = 3.
    a = np.identity(3)
    g2 = AffineTransform.from_params('xy', 'ij', np.array([[0,0.5,0],
                                                  [0.7,0,0],
                                                  [0,0,1]]))
    ir = resample(i, g2, a, (80,100))
    assert_array_almost_equal(ir.get_data().T, i.get_data())

# 需要导入模块: from nipy.core.api import Image [as 别名]
# 或者: from nipy.core.api.Image import get_data [as 别名]
def test_2d_from_3d():
    # Resample a 3d image on a 2d affine grid
    # This example creates a coordmap that coincides with
    # the 10th slice of an image, and checks that
    # resampling agrees with the data in the 10th slice.
    shape = (100,90,80)
    g = AffineTransform.from_params('ijk', 'xyz', np.diag([0.5,0.5,0.5,1]))
    i = Image(np.ones(shape), g)
    i.get_data()[50:55,40:55,30:33] = 3.
    a = np.identity(4)
    g2 = ArrayCoordMap.from_shape(g, shape)[10]
    ir = resample(i, g2.coordmap, a, g2.shape)
    assert_array_almost_equal(ir.get_data(), i[10].get_data())

# 需要导入模块: from nipy.core.api import Image [as 别名]
# 或者: from nipy.core.api.Image import get_data [as 别名]
def test_resample2d1():
    # Tests the same as test_resample2d, only using a callable instead of
    # an AffineTransform instance
    g = AffineTransform.from_params('ij', 'xy', np.diag([0.5,0.5,1]))
    i = Image(np.ones((100,90)), g)
    i.get_data()[50:55,40:55] = 3.
    a = np.identity(3)
    a[:2,-1] = 4.
    A = np.identity(2)
    b = np.ones(2)*4
    def mapper(x):
        return np.dot(x, A.T) + b
    ir = resample(i, i.coordmap, mapper, (100,90))
    assert_array_almost_equal(ir.get_data()[42:47,32:47], 3.)

# 需要导入模块: from nipy.core.api import Image [as 别名]
# 或者: from nipy.core.api.Image import get_data [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