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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 to fill_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 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

Resized image or batch of images.

Examples

x <- random_uniform(c(2, 4, 4, 3)) # batch of 2 RGB images
y <- op_image_resize(x, c(2, 2))
shape(y)

## shape(2, 2, 2, 3)

x <- random_uniform(c(4, 4, 3)) # single RGB image
y <- op_image_resize(x, c(2, 2))
shape(y)

## shape(2, 2, 3)

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)

## shape(2, 3, 2, 2)

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_psnr()
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_slogdet()
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()