Function transform

tensorflow/contrib/image/python/ops/image_ops.py:222–308

Applies the given transform(s) to the image(s). Args: images: A tensor of shape (num_images, num_rows, num_columns, num_channels) (NHWC), (num_rows, num_columns, num_channels) (HWC), or (num_rows, num_columns) (HW). The rank must be statically known (the shape is not `Ten

(images,
              transforms,
              interpolation="NEAREST",
              output_shape=None,
              name=None)

def transform(images,
              transforms,
              interpolation="NEAREST",
              output_shape=None,
              name=None):
  """Applies the given transform(s) to the image(s).

  Args:
    images: A tensor of shape (num_images, num_rows, num_columns, num_channels)
       (NHWC), (num_rows, num_columns, num_channels) (HWC), or
       (num_rows, num_columns) (HW). The rank must be statically known (the
       shape is not `TensorShape(None)`.
    transforms: Projective transform matrix/matrices. A vector of length 8 or
       tensor of size N x 8. If one row of transforms is
       [a0, a1, a2, b0, b1, b2, c0, c1], then it maps the *output* point
       `(x, y)` to a transformed *input* point
       `(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`,
       where `k = c0 x + c1 y + 1`. The transforms are *inverted* compared to
       the transform mapping input points to output points. Note that gradients
       are not backpropagated into transformation parameters.
    interpolation: Interpolation mode. Supported values: "NEAREST", "BILINEAR".
    output_shape: Output dimesion after the transform, [height, width].
       If None, output is the same size as input image.

    name: The name of the op.

  Returns:
    Image(s) with the same type and shape as `images`, with the given
    transform(s) applied. Transformed coordinates outside of the input image
    will be filled with zeros.

  Raises:
    TypeError: If `image` is an invalid type.
    ValueError: If output shape is not 1-D int32 Tensor.
  """
  with ops.name_scope(name, "transform"):
    image_or_images = ops.convert_to_tensor(images, name="images")
    transform_or_transforms = ops.convert_to_tensor(
        transforms, name="transforms", dtype=dtypes.float32)
    if image_or_images.dtype.base_dtype not in _IMAGE_DTYPES:
      raise TypeError("Invalid dtype %s." % image_or_images.dtype)
    elif image_or_images.get_shape().ndims is None:
      raise TypeError("image_or_images rank must be statically known")
    elif len(image_or_images.get_shape()) == 2:
      images = image_or_images[None, :, :, None]
    elif len(image_or_images.get_shape()) == 3:
      images = image_or_images[None, :, :, :]
    elif len(image_or_images.get_shape()) == 4:
      images = image_or_images
    else:
      raise TypeError("Images should have rank between 2 and 4.")

    if output_shape is None:
      output_shape = array_ops.shape(images)[1:3]
      if not context.executing_eagerly():
        output_shape_value = tensor_util.constant_value(output_shape)
        if output_shape_value is not None:
          output_shape = output_shape_value