Function crop_to_bounding_box

tensorflow/python/ops/image_ops_impl.py:813–889

Crops an image to a specified bounding box. This op cuts a rectangular part out of `image`. The top-left corner of the returned image is at `offset_height, offset_width` in `image`, and its lower-right corner is at `offset_height + target_height, offset_width + target_width`. Args: i

(image, offset_height, offset_width, target_height,
                         target_width)

@tf_export('image.crop_to_bounding_box')
def crop_to_bounding_box(image, offset_height, offset_width, target_height,
                         target_width):
  """Crops an image to a specified bounding box.

  This op cuts a rectangular part out of `image`. The top-left corner of the
  returned image is at `offset_height, offset_width` in `image`, and its
  lower-right corner is at
  `offset_height + target_height, offset_width + target_width`.

  Args:
    image: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor
      of shape `[height, width, channels]`.
    offset_height: Vertical coordinate of the top-left corner of the result in
      the input.
    offset_width: Horizontal coordinate of the top-left corner of the result in
      the input.
    target_height: Height of the result.
    target_width: Width of the result.

  Returns:
    If `image` was 4-D, a 4-D float Tensor of shape
    `[batch, target_height, target_width, channels]`
    If `image` was 3-D, a 3-D float Tensor of shape
    `[target_height, target_width, channels]`

  Raises:
    ValueError: If the shape of `image` is incompatible with the `offset_*` or
      `target_*` arguments, or either `offset_height` or `offset_width` is
      negative, or either `target_height` or `target_width` is not positive.
  """
  with ops.name_scope(None, 'crop_to_bounding_box', [image]):
    image = ops.convert_to_tensor(image, name='image')

    is_batch = True
    image_shape = image.get_shape()
    if image_shape.ndims == 3:
      is_batch = False
      image = array_ops.expand_dims(image, 0)
    elif image_shape.ndims is None:
      is_batch = False
      image = array_ops.expand_dims(image, 0)
      image.set_shape([None] * 4)
    elif image_shape.ndims != 4:
      raise ValueError('\'image\' must have either 3 or 4 dimensions.')

    assert_ops = _CheckAtLeast3DImage(image, require_static=False)

    batch, height, width, depth = _ImageDimensions(image, rank=4)

    assert_ops += _assert(offset_width >= 0, ValueError,
                          'offset_width must be >= 0.')
    assert_ops += _assert(offset_height >= 0, ValueError,
                          'offset_height must be >= 0.')
    assert_ops += _assert(target_width > 0, ValueError,
                          'target_width must be > 0.')
    assert_ops += _assert(target_height > 0, ValueError,
                          'target_height must be > 0.')
    assert_ops += _assert(width >= (target_width + offset_width), ValueError,