tf.keras.ops.image.resize

Resize images to size using the specified interpolation method.

tf.keras.ops.image.resize(
    image,
    size,
    interpolation='bilinear',
    antialias=False,
    crop_to_aspect_ratio=False,
    pad_to_aspect_ratio=False,
    fill_mode='constant',
    fill_value=0.0,
    data_format='channels_last'
)

imageInput image or batch of images. Must be 3D or 4D.
sizeSize of output image in (height, width) format.
interpolationInterpolation method. Available methods are "nearest", "bilinear", and "bicubic". Defaults to "bilinear".
antialiasWhether to use an antialiasing filter when downsampling an image. Defaults to False.
crop_to_aspect_ratioIf 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_ratioIf 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_modeWhen 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_valueFloat. Padding value to use when pad_to_aspect_ratio=True.
data_formatstring, 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".

Resized image or batch of images.

Examples:

x = np.random.random((2, 4, 4, 3)) # batch of 2 RGB images
y = keras.ops.image.resize(x, (2, 2))
y.shape
(2, 2, 2, 3)
x = np.random.random((4, 4, 3)) # single RGB image
y = keras.ops.image.resize(x, (2, 2))
y.shape
(2, 2, 3)
x = np.random.random((2, 3, 4, 4)) # batch of 2 RGB images
y = keras.ops.image.resize(x, (2, 2),
    data_format="channels_first")
y.shape
(2, 3, 2, 2)