Is the warp function in ImageTransformation.jl same as _warp_fast in python?

tosalonijain · 2018-03-11 08:45:21 UTC

JuliaImages/ImageTransformations.jl/blob/master/src/warp.jl

"""
    warp(img, tform, [indices], [degree = Linear()], [fill = NaN]) -> imgw

Transform the coordinates of `img`, returning a new `imgw`
satisfying `imgw[I] = img[tform(I)]`. This approach is known as
backward mode warping. The transformation `tform` must accept a
`SVector` as input. A useful package to create a wide variety of
such transformations is
[CoordinateTransformations.jl](https://github.com/FugroRoames/CoordinateTransformations.jl).

# Reconstruction scheme

During warping, values for `img` must be reconstructed at
arbitrary locations `tform(I)` which do not lie on to the lattice
of pixels. How this reconstruction is done depends on the type of
`img` and the optional parameter `degree`.

When `img` is a plain array, then on-grid b-spline interpolation
will be used. It is possible to configure what degree of b-spline
to use with the parameter `degree`. For example one can use

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This is the link for warp in julia.

github.com

scikit-image/scikit-image/blob/master/skimage/transform/_warps_cy.pyx#L37


cdef double xx, yy, zz


xx = H[0] * x + H[1] * y + H[2]
yy = H[3] * x + H[4] * y + H[5]
zz =  H[6] * x + H[7] * y + H[8]


x_[0] = xx / zz
y_[0] = yy / zz




def _warp_fast(cnp.ndarray image, cnp.ndarray H, output_shape=None,
           int order=1, mode='constant', double cval=0):
"""Projective transformation (homography).


Perform a projective transformation (homography) of a
floating point image, using interpolation.


For each pixel, given its homogeneous coordinate :math:`\mathbf{x}
= [x, y, 1]^T`, its target position is calculated by multiplying
with the given matrix, :math:`H`, to give :math:`H \mathbf{x}`.
E.g., to rotate by theta degrees clockwise, the matrix should be::

This is the link for _warp_fast in python.

If not please suggest an alternative for _warp_fast in julia.
Thanks.