tf.extract_image_patches(images, ksizes, strides, rates, padding, name=None)

tf.extract_image_patches(images, ksizes, strides, rates, padding, name=None)

See the guide: Tensor Transformations > Slicing and Joining

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

Args:

• images: A Tensor. Must be one of the following types: float32, float64, int32, int64, uint8, int16, int8, uint16, half. 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.

• padding: A string from: "SAME", "VALID". The type of padding algorithm to use.

  We specify the size-related attributes as:

  python 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. 4-D Tensor with shape [batch, out_rows, out_cols, ksize_rows * ksize_cols * depth] containing image patches with size ksize_rows x ksize_cols x depth vectorized in the "depth" dimension.

Defined in tensorflow/python/ops/gen_array_ops.py.