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op {
graph_op_name: "SampleDistortedBoundingBoxV2"
in_arg {
name: "image_size"
description: <<END
1-D, containing `[height, width, channels]`.
END
}
in_arg {
name: "bounding_boxes"
description: <<END
3-D with shape `[batch, N, 4]` describing the N bounding boxes
associated with the image.
END
}
in_arg {
name: "min_object_covered"
description: <<END
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.
END
}
out_arg {
name: "begin"
description: <<END
1-D, containing `[offset_height, offset_width, 0]`. Provide as input to
`tf.slice`.
END
}
out_arg {
name: "size"
description: <<END
1-D, containing `[target_height, target_width, -1]`. Provide as input to
`tf.slice`.
END
}
out_arg {
name: "bboxes"
description: <<END
3-D with shape `[1, 1, 4]` containing the distorted bounding box.
Provide as input to `tf.image.draw_bounding_boxes`.
END
}
attr {
name: "seed"
description: <<END
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.
END
}
attr {
name: "seed2"
description: <<END
A second seed to avoid seed collision.
END
}
attr {
name: "aspect_ratio_range"
description: <<END
The cropped area of the image must have an aspect ratio =
width / height within this range.
END
}
attr {
name: "area_range"
description: <<END
The cropped area of the image must contain a fraction of the
supplied image within in this range.
END
}
attr {
name: "max_attempts"
description: <<END
Number of attempts at generating a cropped region of the image
of the specified constraints. After `max_attempts` failures, return the entire
image.
END
}
attr {
name: "use_image_if_no_bounding_boxes"
description: <<END
Controls behavior if no bounding boxes supplied.
If true, assume an implicit bounding box covering the whole input. If false,
raise an error.
END
}
summary: "Generate a single randomly distorted bounding box for an image."
description: <<END
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,
```python
# 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.image_summary('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.
END
}
|
