tf.raw_ops.SampleDistortedBoundingBox

Generate a single randomly distorted bounding box for an image.

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.raw_ops.SampleDistortedBoundingBox

 
tf.raw_ops.SampleDistortedBoundingBox(
    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(
        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.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.