Applies the given transform(s) to the image(s).

Usage

op_image_affine_transform(
  image,
  transform,
  interpolation = "bilinear",
  fill_mode = "constant",
  fill_value = 0L,
  data_format = "channels_last"
)

Arguments

image

Input image or batch of images. Must be 3D or 4D.

transform

Projective transform matrix/matrices. A vector of length 8 or tensor of size N x 8. If one row of transform is [a0, a1, a2, b0, b1, b2, c0, c1], then it maps the output point (x, y) to a transformed input point (x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k), where k = c0 x + c1 y + 1. The transform is inverted compared to the transform mapping input points to output points. Note that gradients are not backpropagated into transformation parameters. Note that c0 and c1 are only effective when using TensorFlow backend and will be considered as 0 when using other backends.

interpolation

Interpolation method. Available methods are "nearest", and "bilinear". Defaults to "bilinear".

fill_mode

Points outside the boundaries of the input are filled according to the given mode. Available methods are "constant", "nearest", "wrap" and "reflect". Defaults to "constant".

"reflect": (d c b a | a b c d | d c b a) The input is extended by reflecting about the edge of the last pixel.
"constant": (k k k k | a b c d | k k k k) The input is extended by filling all values beyond the edge with the same constant value k specified by fill_value.
"wrap": (a b c d | a b c d | a b c d) The input is extended by wrapping around to the opposite edge.
"nearest": (a a a a | a b c d | d d d d) The input is extended by the nearest pixel.

fill_value

Value used for points outside the boundaries of the input if fill_mode = "constant". Defaults to 0.

data_format

string, either "channels_last" or "channels_first". The ordering of the dimensions in the inputs. "channels_last" corresponds to inputs with shape (batch, height, width, channels) while "channels_first" corresponds to inputs with shape (batch, channels, height, weight). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last".

Value

Applied affine transform image or batch of images.

Examples

x <- random_uniform(c(2, 64, 80, 3)) # batch of 2 RGB images
transform <- op_array(rbind(c(1.5, 0, -20, 0, 1.5, -16, 0, 0),  # zoom
                           c(1, 0, -20, 0, 1, -16, 0, 0)))  # translation))
y <- op_image_affine_transform(x, transform)
shape(y)

## shape(2, 64, 80, 3)

# (2, 64, 80, 3)

x <- random_uniform(c(64, 80, 3)) # single RGB image
transform <- op_array(c(1.0, 0.5, -20, 0.5, 1.0, -16, 0, 0))  # shear
y <- op_image_affine_transform(x, transform)
shape(y)

## shape(64, 80, 3)

# (64, 80, 3)

x <- random_uniform(c(2, 3, 64, 80)) # batch of 2 RGB images
transform <- op_array(rbind(
  c(1.5, 0,-20, 0, 1.5,-16, 0, 0),  # zoom
  c(1, 0,-20, 0, 1,-16, 0, 0)  # translation
))
y <- op_image_affine_transform(x, transform, data_format = "channels_first")
shape(y)

## shape(2, 3, 64, 80)

# (2, 3, 64, 80)

Other ops:
op_abs()
op_add()
op_all()
op_any()
op_append()
op_arange()
op_arccos()
op_arccosh()
op_arcsin()
op_arcsinh()
op_arctan()
op_arctan2()
op_arctanh()
op_argmax()
op_argmin()
op_argsort()
op_array()
op_average()
op_average_pool()
op_batch_normalization()
op_binary_crossentropy()
op_bincount()
op_broadcast_to()
op_cast()
op_categorical_crossentropy()
op_ceil()
op_cholesky()
op_clip()
op_concatenate()
op_cond()
op_conj()
op_conv()
op_conv_transpose()
op_convert_to_numpy()
op_convert_to_tensor()
op_copy()
op_correlate()
op_cos()
op_cosh()
op_count_nonzero()
op_cross()
op_ctc_decode()
op_ctc_loss()
op_cumprod()
op_cumsum()
op_custom_gradient()
op_depthwise_conv()
op_det()
op_diag()
op_diagonal()
op_diff()
op_digitize()
op_divide()
op_divide_no_nan()
op_dot()
op_eig()
op_eigh()
op_einsum()
op_elu()
op_empty()
op_equal()
op_erf()
op_erfinv()
op_exp()
op_expand_dims()
op_expm1()
op_extract_sequences()
op_eye()
op_fft()
op_fft2()
op_flip()
op_floor()
op_floor_divide()
op_fori_loop()
op_full()
op_full_like()
op_gelu()
op_get_item()
op_greater()
op_greater_equal()
op_hard_sigmoid()
op_hard_silu()
op_hstack()
op_identity()
op_imag()
op_image_crop()
op_image_extract_patches()
op_image_map_coordinates()
op_image_pad()
op_image_resize()
op_image_rgb_to_grayscale()
op_in_top_k()
op_inv()
op_irfft()
op_is_tensor()
op_isclose()
op_isfinite()
op_isinf()
op_isnan()
op_istft()
op_leaky_relu()
op_less()
op_less_equal()
op_linspace()
op_log()
op_log10()
op_log1p()
op_log2()
op_log_sigmoid()
op_log_softmax()
op_logaddexp()
op_logical_and()
op_logical_not()
op_logical_or()
op_logical_xor()
op_logspace()
op_logsumexp()
op_lu_factor()
op_matmul()
op_max()
op_max_pool()
op_maximum()
op_mean()
op_median()
op_meshgrid()
op_min()
op_minimum()
op_mod()
op_moments()
op_moveaxis()
op_multi_hot()
op_multiply()
op_nan_to_num()
op_ndim()
op_negative()
op_nonzero()
op_norm()
op_normalize()
op_not_equal()
op_one_hot()
op_ones()
op_ones_like()
op_outer()
op_pad()
op_power()
op_prod()
op_qr()
op_quantile()
op_ravel()
op_real()
op_reciprocal()
op_relu()
op_relu6()
op_repeat()
op_reshape()
op_rfft()
op_roll()
op_round()
op_rsqrt()
op_scatter()
op_scatter_update()
op_segment_max()
op_segment_sum()
op_select()
op_selu()
op_separable_conv()
op_shape()
op_sigmoid()
op_sign()
op_silu()
op_sin()
op_sinh()
op_size()
op_slice()
op_slice_update()
op_softmax()
op_softplus()
op_softsign()
op_solve()
op_solve_triangular()
op_sort()
op_sparse_categorical_crossentropy()
op_split()
op_sqrt()
op_square()
op_squeeze()
op_stack()
op_std()
op_stft()
op_stop_gradient()
op_subtract()
op_sum()
op_svd()
op_swapaxes()
op_take()
op_take_along_axis()
op_tan()
op_tanh()
op_tensordot()
op_tile()
op_top_k()
op_trace()
op_transpose()
op_tri()
op_tril()
op_triu()
op_unstack()
op_var()
op_vdot()
op_vectorize()
op_vectorized_map()
op_vstack()
op_where()
op_while_loop()
op_zeros()
op_zeros_like()

Applies the given transform(s) to the image(s).

Usage

Arguments

Value

Examples

See also