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Improve inverse_map description
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@@ -1006,14 +1006,23 @@ def warp(image, inverse_map=None, map_args={}, output_shape=None, order=1,
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e.g. `skimage.transform.SimilarityTransform`, or its inverse.
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- For 2-D images, you can pass a (3, 3) homogeneous transformation
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matrix, e.g. `skimage.transform.SimilarityTransform.params`
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- For M-D images, a function that transforms a (N, M) coordinates.
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In case of 2-D images this means a function that transforms a
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(N, 2) array of ``(x, y)`` coordinates in the *output image* into
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their corresponding coordinates in the *source image*. Extra
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- For M-D images, a function that transforms a (N, M) coordinate
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matrix in the output image to their corresponding coordinates in
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the source image, where N is the total number of pixels in the
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output image. In case of 2-D images this means a function that
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transforms a (N, 2) array of ``(x, y)`` coordinates. Extra
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parameters to the function can be specified through `map_args`.
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- For M-D images, you can directly pass an array of coordinates.
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See `scipy.ndimage.map_coordinates`. Note, that a (3, 3) matrix
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is interpreted as a homogeneous transformation matrix.
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The first dimension specifies the coordinates in the source image,
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while the subsequent dimensions determine the position in the
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output image. In case of 2-D images, you need to pass an array of
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shape ``(2, rows, cols)``, where `rows` and `cols` determine the
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shape of the output image, and the first dimension contains the
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``(row, col)`` coordinate in the source image. Note, that a
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``(3, 3)`` matrix is interpreted as a homogeneous transformation
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matrix, so you cannot interpolate values from a 3-D input, if the
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output is of shape ``(3, )``. See `scipy.ndimage.map_coordinates`
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for further documentation.
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See example section for usage.
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map_args : dict, optional
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