Generate a single randomly distorted bounding box for an image.

    image_size, bounding_boxes, seed=0, seed2=0, min_object_covered=0.1,
    aspect_ratio_range=[0.75, 1.33], area_range=[0.05, 1], max_attempts=100,
    use_image_if_no_bounding_boxes=False, name=None

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,

    # Generate a single distorted bounding box.
    begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(

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


  • image_size: A Tensor. Must be one of the following types: uint8, int8, int16, int32, int64. 1-D, containing [height, width, channels].
  • bounding_boxes: A Tensor of type float32. 3-D with shape [batch, N, 4] describing the N bounding boxes associated with the image.
  • seed: 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.
  • seed2: An optional int. Defaults to 0. A second seed to avoid seed collision.
  • min_object_covered: 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.
  • aspect_ratio_range: 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.
  • area_range: 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 this range.
  • max_attempts: 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.
  • use_image_if_no_bounding_boxes: 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.
  • name: A name for the operation (optional).


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

  • begin: A Tensor. Has the same type as image_size.
  • size: A Tensor. Has the same type as image_size.
  • bboxes: A Tensor of type float32.