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tf.image.extract_image_patches

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Extract patches from images and put them in the "depth" output dimension.

Aliases:

  • tf.compat.v1.extract_image_patches
  • tf.compat.v1.image.extract_image_patches
  • tf.extract_image_patches
tf.image.extract_image_patches(
    images,
    ksizes=None,
    strides=None,
    rates=None,
    padding=None,
    name=None,
    sizes=None
)

Args:

  • images: A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. 4-D Tensor with shape [batch, in_rows, in_cols, depth].
  • ksizes: A list of ints that has length >= 4. The size of the sliding window for each dimension of images.
  • strides: A list of ints that has length >= 4. 1-D of length 4. How far the centers of two consecutive patches are in the images. Must be: [1, stride_rows, stride_cols, 1].
  • rates: A list of ints that has length >= 4. 1-D of length 4. Must be: [1, rate_rows, rate_cols, 1]. This is the input stride, specifying how far two consecutive patch samples are in the input. Equivalent to extracting patches with patch_sizes_eff = patch_sizes + (patch_sizes - 1) * (rates - 1), followed by subsampling them spatially by a factor of rates. This is equivalent to rate in dilated (a.k.a. Atrous) convolutions.
  • padding: A string from: "SAME", "VALID". The type of padding algorithm to use.

    We specify the size-related attributes as:

      ksizes = [1, ksize_rows, ksize_cols, 1]
      strides = [1, strides_rows, strides_cols, 1]
      rates = [1, rates_rows, rates_cols, 1]
  • name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as images.