Resize images to size using the specified interpolation method.
Source:R/ops-image.R
op_image_resize.Rd
Resize images to size using the specified interpolation method.
Usage
op_image_resize(
image,
size,
interpolation = "bilinear",
antialias = FALSE,
crop_to_aspect_ratio = FALSE,
pad_to_aspect_ratio = FALSE,
fill_mode = "constant",
fill_value = 0,
data_format = "channels_last"
)
Arguments
- image
Input image or batch of images. Must be 3D or 4D.
- size
Size of output image in
(height, width)
format.- interpolation
Interpolation method. Available methods are
"nearest"
,"bilinear"
, and"bicubic"
. Defaults to"bilinear"
.- antialias
Whether to use an antialiasing filter when downsampling an image. Defaults to
FALSE
.- crop_to_aspect_ratio
If
TRUE
, resize the images without aspect ratio distortion. When the original aspect ratio differs from the target aspect ratio, the output image will be cropped so as to return the largest possible window in the image (of size(height, width)
) that matches the target aspect ratio. By default (crop_to_aspect_ratio=FALSE
), aspect ratio may not be preserved.- pad_to_aspect_ratio
If
TRUE
, pad the images without aspect ratio distortion. When the original aspect ratio differs from the target aspect ratio, the output image will be evenly padded on the short side.- fill_mode
When using
pad_to_aspect_ratio=TRUE
, padded areas are filled according to the given mode. Only"constant"
is supported at this time (fill with constant value, equal tofill_value
).- fill_value
Float. Padding value to use when
pad_to_aspect_ratio=TRUE
.- 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 theimage_data_format
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be"channels_last"
.
Examples
x <- random_uniform(c(2, 4, 4, 3)) # batch of 2 RGB images
y <- op_image_resize(x, c(2, 2))
shape(y)
x <- random_uniform(c(4, 4, 3)) # single RGB image
y <- op_image_resize(x, c(2, 2))
shape(y)
x <- random_uniform(c(2, 3, 4, 4)) # batch of 2 RGB images
y <- op_image_resize(x, c(2, 2), data_format = "channels_first")
shape(y)
See also
Other image ops: op_image_affine_transform()
op_image_crop()
op_image_extract_patches()
op_image_map_coordinates()
op_image_pad()
op_image_rgb_to_grayscale()
Other image utils: image_array_save()
image_from_array()
image_load()
image_smart_resize()
image_to_array()
op_image_affine_transform()
op_image_crop()
op_image_extract_patches()
op_image_map_coordinates()
op_image_pad()
op_image_rgb_to_grayscale()
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_affine_transform()
op_image_crop()
op_image_extract_patches()
op_image_map_coordinates()
op_image_pad()
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()