### `tf.image.sample_distorted_bounding_box(image_size, bounding_boxes, seed=None, seed2=None, min_object_covered=None, aspect_ratio_range=None, area_range=None, max_attempts=None, use_image_if_no_bounding_boxes=None, name=None)` {#sample_distorted_bounding_box}

Generate a single randomly distorted bounding box for an image.

Bounding box annotations are often supplied in addition to ground-truth labels
in image recognition or object localization tasks. A common technique for
training such a system is to randomly distort an image while preserving
its content, i.e. *data augmentation*. This Op outputs a randomly distorted
localization of an object, i.e. bounding box, given an `image_size`,
`bounding_boxes` and a series of constraints.

The output of this Op is a single bounding box that may be used to crop the
original image. The output is returned as 3 tensors: `begin`, `size` and
`bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the
image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize
what the bounding box looks like.

Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The
bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
height of the underlying image.

For example,

```python
    # Generate a single distorted bounding box.
    begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
        tf.shape(image),
        bounding_boxes=bounding_boxes)

    # Draw the bounding box in an image summary.
    image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
                                                  bbox_for_draw)
    tf.image_summary('images_with_box', image_with_box)

    # Employ the bounding box to distort the image.
    distorted_image = tf.slice(image, begin, size)
```

Note that if no bounding box information is available, setting
`use_image_if_no_bounding_boxes = true` will assume there is a single implicit
bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is
false and no bounding boxes are supplied, an error is raised.

##### Args:


*  <b>`image_size`</b>: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`.
    1-D, containing `[height, width, channels]`.
*  <b>`bounding_boxes`</b>: A `Tensor` of type `float32`.
    3-D with shape `[batch, N, 4]` describing the N bounding boxes
    associated with the image.
*  <b>`seed`</b>: An optional `int`. Defaults to `0`.
    If either `seed` or `seed2` are set to non-zero, the random number
    generator is seeded by the given `seed`.  Otherwise, it is seeded by a random
    seed.
*  <b>`seed2`</b>: An optional `int`. Defaults to `0`.
    A second seed to avoid seed collision.
*  <b>`min_object_covered`</b>: An optional `float`. Defaults to `0.1`.
    The cropped area of the image must contain at least this
    fraction of any bounding box supplied. The value of this parameter should be
    non-negative. In the case of 0, the cropped area does not need to overlap
    any of the bounding boxes supplied.
*  <b>`aspect_ratio_range`</b>: An optional list of `floats`. Defaults to `[0.75, 1.33]`.
    The cropped area of the image must have an aspect ratio =
    width / height within this range.
*  <b>`area_range`</b>: An optional list of `floats`. Defaults to `[0.05, 1]`.
    The cropped area of the image must contain a fraction of the
    supplied image within in this range.
*  <b>`max_attempts`</b>: An optional `int`. Defaults to `100`.
    Number of attempts at generating a cropped region of the image
    of the specified constraints. After `max_attempts` failures, return the entire
    image.
*  <b>`use_image_if_no_bounding_boxes`</b>: An optional `bool`. Defaults to `False`.
    Controls behavior if no bounding boxes supplied.
    If true, assume an implicit bounding box covering the whole input. If false,
    raise an error.
*  <b>`name`</b>: A name for the operation (optional).

##### Returns:

  A tuple of `Tensor` objects (begin, size, bboxes).

*  <b>`begin`</b>: A `Tensor`. Has the same type as `image_size`. 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to
    `tf.slice`.
*  <b>`size`</b>: A `Tensor`. Has the same type as `image_size`. 1-D, containing `[target_height, target_width, -1]`. Provide as input to
    `tf.slice`.
*  <b>`bboxes`</b>: A `Tensor` of type `float32`. 3-D with shape `[1, 1, 4]` containing the distorted bounding box.
    Provide as input to `tf.image.draw_bounding_boxes`.

