Extracts patches from the image(s).
tf.keras.ops.image.extract_patches(
image,
size,
strides=None,
dilation_rate=1,
padding='valid',
data_format='channels_last'
)
Args |
image
|
Input image or batch of images. Must be 3D or 4D.
|
size
|
Patch size int or tuple (patch_height, patch_widht)
|
strides
|
strides along height and width. If not specified, or
if None , it defaults to the same value as size .
|
dilation_rate
|
This is the input stride, specifying how far two
consecutive patch samples are in the input. For value other than 1,
strides must be 1. NOTE: strides > 1 is not supported in
conjunction with dilation_rate > 1
|
padding
|
The type of padding algorithm to use: "same" or "valid" .
|
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 |
Extracted patches 3D (if not batched) or 4D (if batched)
|
Examples:
image = np.random.random(
(2, 20, 20, 3)
).astype("float32") # batch of 2 RGB images
patches = keras.ops.image.extract_patches(image, (5, 5))
patches.shape
(2, 4, 4, 75)
image = np.random.random((20, 20, 3)).astype("float32") # 1 RGB image
patches = keras.ops.image.extract_patches(image, (3, 3), (1, 1))
patches.shape
(18, 18, 27)