Generate a single randomly distorted bounding box for an image.
tf.raw_ops.SampleDistortedBoundingBoxV2(
image_size, bounding_boxes, min_object_covered, seed=0, seed2=0,
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.
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.
|
min_object_covered
|
A Tensor of type float32 .
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.
|
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.
|
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 .
|
size
|
A Tensor . Has the same type as image_size .
|
bboxes
|
A Tensor of type float32 .
|