tf.image.sample_distorted_bounding_box

TensorFlow 1 version

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Generate a single randomly distorted bounding box for an image.

tf.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 the 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.

For producing deterministic results given a seed value, use tf.image.stateless_sample_distorted_bounding_box. Unlike using the seed param with tf.image.random_* ops, tf.image.stateless_random_* ops guarantee the same results given the same seed independent of how many times the function is called, and independent of global seed settings (e.g. tf.random.set_seed).