tf.image.stateless_sample_distorted_bounding_box

Generate a randomly distorted bounding box for an image deterministically.

tf.image.stateless_sample_distorted_bounding_box(
    image_size, bounding_boxes, seed, min_object_covered=0.1,
    aspect_ratio_range=None, area_range=None, max_attempts=None,
    use_image_if_no_bounding_boxes=None, 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, given the same seed, deterministically 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 the height of the underlying image.

The output of this Op is guaranteed to be the same given the same seed and is independent of how many times the function is called, and independent of global seed settings (e.g. tf.random.set_seed).

Example usage:

image = np.array([[[1], [2], [3]], [[4], [5], [6]], [[7], [8], [9]]])
bbox = tf.constant(
  [0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])
seed = (1, 2)
# Generate a single distorted bounding box.
bbox_begin, bbox_size, bbox_draw = (
  tf.image.stateless_sample_distorted_bounding_box(
    tf.shape(image), bounding_boxes=bbox, seed=seed))
# Employ the bounding box to distort the image.
tf.slice(image, bbox_begin, bbox_size)
<tf.Tensor: shape=(2, 2, 1), dtype=int64, numpy=
array([[[1],
        [2]],
       [[4],
        [5]]])>
# Draw the bounding box in an image summary.
colors = np.array([[1.0, 0.0, 0.0], [0.0, 0.0, 1.0]])
tf.image.draw_bounding_boxes(
  tf.expand_dims(tf.cast(image, tf.float32),0), bbox_draw, colors)
<tf.Tensor: shape=(1, 3, 3, 1), dtype=float32, numpy=
array([[[[1.],
         [1.],
         [3.]],
        [[1.],
         [1.],
         [6.]],
        [[7.],
         [8.],
         [9.]]]], dtype=float32)>

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
`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`	A shape [2] Tensor, the seed to the random number generator. Must have dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.)
`min_object_covered`	A Tensor of type `float32`. 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).

Returns
A tuple of `Tensor` objects (begin, size, bboxes).
`begin`	A `Tensor`. Has the same type as `image_size`. 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to `tf.slice`.
`size`	A `Tensor`. Has the same type as `image_size`. 1-D, containing `[target_height, target_width, -1]`. Provide as input to `tf.slice`.
`bboxes`	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`.

tf.image.stateless_sample_distorted_bounding_box

Example usage:

Args

Returns