def shear2(x, shear=(0.1, 0.1), is_random=False, row_index=0, col_index=1, channel_index=2,
fill_mode='nearest', cval=0., order=1):
"""Shear an image randomly or non-randomly.
Parameters
-----------
x : numpy array
An image with dimension of [row, col, channel] (default).
shear : tuple of two floats
Percentage of shear for height and width direction (0, 1).
is_random : boolean, default False
If True, randomly shear.
row_index, col_index, channel_index : int
Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0).
fill_mode : string
Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’.
- `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
cval : scalar, optional
Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0.
order : int, optional
The order of interpolation. The order has to be in the range 0-5. See ``apply_transform``.
- `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
References
-----------
- `Affine transformation <https://uk.mathworks.com/discovery/affine-transformation.html>`_
"""
assert len(shear) == 2, "shear should be tuple of 2 floats, or you want to use tl.prepro.shear rather than tl.prepro.shear2 ?"
if is_random:
shear[0] = np.random.uniform(-shear[0], shear[0])
shear[1] = np.random.uniform(-shear[1], shear[1])
shear_matrix = np.array([[1, shear[0], 0],
[shear[1], 1, 0],
[0, 0, 1]])
h, w = x.shape[row_index], x.shape[col_index]
transform_matrix = transform_matrix_offset_center(shear_matrix, h, w)
x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval, order)
return x
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