scripts/builtin/img_shear.dml

#-------------------------------------------------------------
#
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# to you under the Apache License, Version 2.0 (the
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#   http://www.apache.org/licenses/LICENSE-2.0
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#-------------------------------------------------------------

# This function applies a shearing transformation to an image.
# Uses nearest neighbor sampling.
#
# .. code-block:: python
#
#   >>> import numpy as np
#   >>> from systemds.context import SystemDSContext
#   >>> from systemds.operator.algorithm import img_shear
#   >>> 
#   >>> with SystemDSContext() as sds:
#   ...     img = sds.from_numpy(
#   ...         np.array([[ 10., 20., 30.],
#   ...                   [ 40., 50., 60.],
#   ...                   [ 70., 80., 90.]], dtype=np.float32)
#   ...     )
#   ...     result_img = img_shear(img, 1., 0., 255).compute()
#   ...     print(result_img)
#   [[ 10.  20.  30.]
#    [255.  40.  50.]
#    [255. 255.  70.]]
#
#
# INPUT:
# ---------------------------------------------------------------------------------------------
# img_in      Input image as 2D matrix with top left corner at [1, 1]
# shear_x     Shearing factor for horizontal shearing
# shear_y     Shearing factor for vertical shearing
# fill_value  The background color revealed by the shearing
# ---------------------------------------------------------------------------------------------
#
# OUTPUT:
# ------------------------------------------------------------------------------------------
# img_out  Output image as 2D matrix with top left corner at [1, 1]
# ------------------------------------------------------------------------------------------

m_img_shear = function(Matrix[Double] img_in, Double shear_x, Double shear_y, Double fill_value) return (Matrix[Double] img_out) {
  img_out = img_transform(img_in, ncol(img_in), nrow(img_in), 1, shear_x, 0, shear_y, 1, 0, fill_value)
}