diff options
Diffstat (limited to 'tensorflow/python/ops/image_ops_impl.py')
| -rw-r--r-- | tensorflow/python/ops/image_ops_impl.py | 102 |
1 files changed, 102 insertions, 0 deletions
diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index 65a1399c5b..2be92fac63 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.py @@ -47,6 +47,7 @@ ops.NotDifferentiable('RGBToHSV') ops.NotDifferentiable('HSVToRGB') ops.NotDifferentiable('DrawBoundingBoxes') ops.NotDifferentiable('SampleDistortedBoundingBox') +ops.NotDifferentiable('SampleDistortedBoundingBoxV2') # TODO(bsteiner): Implement the gradient function for extract_glimpse # TODO(b/31222613): This op may be differentiable, and there may be # latent bugs here. @@ -1472,3 +1473,104 @@ def total_variation(images, name=None): math_ops.reduce_sum(math_ops.abs(pixel_dif2), axis=sum_axis)) return tot_var + + +def sample_distorted_bounding_box(image_size, bounding_boxes, seed=None, + seed2=None, min_object_covered=None, + aspect_ratio_range=None, area_range=None, + max_attempts=None, + use_image_if_no_bounding_boxes=None, + name=None): + """Generate a single randomly distorted bounding box for an image. + + 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. + + 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 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. + min_object_covered: An optional `float`. 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 in 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`. + """ + with ops.name_scope(name, 'sample_distorted_bounding_box'): + # TODO (yongtang): Need to switch to v2 after 3 weeks. + return gen_image_ops._sample_distorted_bounding_box(image_size, + bounding_boxes, seed=seed, + seed2=seed2, min_object_covered=min_object_covered, + aspect_ratio_range=aspect_ratio_range, area_range=area_range, + max_attempts=max_attempts, + use_image_if_no_bounding_boxes=use_image_if_no_bounding_boxes, + name=name) |