prepro.py 文件源码

def shear(x, intensity=0.1, is_random=False, row_index=0, col_index=1, channel_index=2,
                 fill_mode='nearest', cval=0.):
    """Shear an image randomly or non-randomly.

    Parameters
    -----------
    x : numpy array
        An image with dimension of [row, col, channel] (default).
    intensity : float
        Percentage of shear, usually -0.5 ~ 0.5 (is_random==True), 0 ~ 0.5 (is_random==False),
        you can have a quick try by shear(X, 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.

        - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
    """
    if is_random:
        shear = np.random.uniform(-intensity, intensity)
    else:
        shear = intensity
    shear_matrix = np.array([[1, -np.sin(shear), 0],
                             [0, np.cos(shear), 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)
    return x