tf.compat.v2.image.sample_distorted_bounding_box

Generate a single randomly distorted bounding box for an image.

tf.compat.v2.image.sample_distorted_bounding_box(
    image_size, bounding_boxes, seed=0, 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 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,
        min_object_covered=0.1)

    # 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.compat.v1.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.

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`	An optional `int`. Defaults to `0`. If `seed` is set to non-zero, the random number generator is seeded by the given `seed`. Otherwise, it is seeded by a random seed.
`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.compat.v2.image.sample_distorted_bounding_box

Args

Returns