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'
)
Args |
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" .
|
Returns |
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)