tf.keras.ops.image.extract_patches_3d

Extracts patches from the volume(s).

tf.keras.ops.image.extract_patches_3d(
    volumes,
    size,
    strides=None,
    dilation_rate=1,
    padding='valid',
    data_format=None
)

volumes Input volume or batch of volumes. Must be 4D or 5D.
size Patch size int or tuple (patch_depth, patch_height, patch_width)
strides strides along depth, 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. Note that using dilation_rate > 1 is not supported in conjunction with strides > 1 on the TensorFlow backend.
padding The type of padding algorithm to use: "same" or "valid".
data_format A string specifying the data format of the input tensor. It can be either "channels_last" or "channels_first". "channels_last" corresponds to inputs with shape (batch, depth, height, width, channels), while "channels_first" corresponds to inputs with shape (batch, channels, depth, height, width). If not specified, the value will default to keras.config.image_data_format().

Extracted patches 4D (if not batched) or 5D (if batched)

Examples:

import numpy as np
import keras
# Batched case
volumes = np.random.random(
    (2, 10, 10, 10, 3)
).astype("float32") # batch of 2 volumes
patches = keras.ops.image.extract_patches_3d(volumes, (3, 3, 3))
patches.shape
(2, 3, 3, 3, 81)
# Unbatched case
volume = np.random.random((10, 10, 10, 3)).astype("float32") # 1 volume
patches = keras.ops.image.extract_patches_3d(volume, (3, 3, 3))
patches.shape
(3, 3, 3, 81)