warp

warp

skimage.transform.warp(image, inverse_map=None, map_args={}, output_shape=None, order=1, mode='constant', cval=0.0, clip=True, preserve_range=False) [source]

Warp an image according to a given coordinate transformation.

Parameters:

image : ndarray Input image. inverse_map : transformation object, callable cr = f(cr, **kwargs), or ndarray Inverse coordinate map, which transforms coordinates in the output images into their corresponding coordinates in the input image. There are a number of different options to define this map, depending on the dimensionality of the input image. A 2-D image can have 2 dimensions for gray-scale images, or 3 dimensions with color information. For 2-D images, you can directly pass a transformation object, e.g. skimage.transform.SimilarityTransform, or its inverse. For 2-D images, you can pass a (3, 3) homogeneous transformation matrix, e.g. skimage.transform.SimilarityTransform.params. For 2-D images, a function that transforms a (M, 2) array of (col, row) coordinates in the output image to their corresponding coordinates in the input image. Extra parameters to the function can be specified through map_args. For N-D images, you can directly pass an array of coordinates. The first dimension specifies the coordinates in the input image, while the subsequent dimensions determine the position in the output image. E.g. in case of 2-D images, you need to pass an array of shape (2, rows, cols), where rows and cols determine the shape of the output image, and the first dimension contains the (row, col) coordinate in the input image. See scipy.ndimage.map_coordinates for further documentation. Note, that a (3, 3) matrix is interpreted as a homogeneous transformation matrix, so you cannot interpolate values from a 3-D input, if the output is of shape (3,). See example section for usage. map_args : dict, optional Keyword arguments passed to inverse_map. output_shape : tuple (rows, cols), optional Shape of the output image generated. By default the shape of the input image is preserved. Note that, even for multi-band images, only rows and columns need to be specified. order : int, optional The order of interpolation. The order has to be in the range 0-5: 0: Nearest-neighbor 1: Bi-linear (default) 2: Bi-quadratic 3: Bi-cubic 4: Bi-quartic 5: Bi-quintic mode : {‘constant’, ‘edge’, ‘symmetric’, ‘reflect’, ‘wrap’}, optional Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of numpy.pad. cval : float, optional Used in conjunction with mode ‘constant’, the value outside the image boundaries. clip : bool, optional Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range. preserve_range : bool, optional Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of img_as_float.

Returns:

warped : double ndarray The warped input image.

Notes

• The input image is converted to a double image.
• In case of a SimilarityTransform, AffineTransform and ProjectiveTransform and order in [0, 3] this function uses the underlying transformation matrix to warp the image with a much faster routine.

Examples

>>> from skimage.transform import warp
>>> from skimage import data
>>> image = data.camera()

The following image warps are all equal but differ substantially in execution time. The image is shifted to the bottom.

Use a geometric transform to warp an image (fast):

>>> from skimage.transform import SimilarityTransform
>>> tform = SimilarityTransform(translation=(0, -10))
>>> warped = warp(image, tform)

Use a callable (slow):

>>> def shift_down(xy):
...     xy[:, 1] -= 10
...     return xy
>>> warped = warp(image, shift_down)

Use a transformation matrix to warp an image (fast):

>>> matrix = np.array([[1, 0, 0], [0, 1, -10], [0, 0, 1]])
>>> warped = warp(image, matrix)
>>> from skimage.transform import ProjectiveTransform
>>> warped = warp(image, ProjectiveTransform(matrix=matrix))

You can also use the inverse of a geometric transformation (fast):

>>> warped = warp(image, tform.inverse)

For N-D images you can pass a coordinate array, that specifies the coordinates in the input image for every element in the output image. E.g. if you want to rescale a 3-D cube, you can do:

>>> cube_shape = np.array([30, 30, 30])
>>> cube = np.random.rand(*cube_shape)

Setup the coordinate array, that defines the scaling:

>>> scale = 0.1
>>> output_shape = (scale * cube_shape).astype(int)
>>> coords0, coords1, coords2 = np.mgrid[:output_shape[0],
...                    :output_shape[1], :output_shape[2]]
>>> coords = np.array([coords0, coords1, coords2])

Assume that the cube contains spatial data, where the first array element center is at coordinate (0.5, 0.5, 0.5) in real space, i.e. we have to account for this extra offset when scaling the image:

>>> coords = (coords + 0.5) / scale - 0.5
>>> warped = warp(cube, coords)