tensorflow学习 tf.image.sample_distorted_bounding_box

____tz_zs此函数为图像生成单个随机变形的边界框。函数输出的是可用于裁剪原始图像的单个边框。返回值为3个张量：begin，size和 bboxes。前2个张量用于 tf.slice 剪裁图像。后者可以用于 tf.image.draw_bounding_boxes 函数来画出边界框。

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
)

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
)

image_size： 是包含 [height, width, channels] 三个值的一维数组。数值类型必须是 uint8，int8，int16，int32，int64 中的一种。bounding_boxes： 是一个 shape 为 [batch, N, 4] 的三维数组，数据类型为float32，第一个batch是因为函数是处理一组图片的，N表示描述与图像相关联的N个边界框的形状，而标注框由4个数字 [y_min, x_min, y_max, x_max] 表示出来。例如：tf.constant([[[0.05, 0.05, 0.9, 0.7], [0.35, 0.47, 0.5, 0.56]]]) 的 shape 为 [1,2,4] 表示一张图片中的两个标注框；tf.constant([[[0.  0.  1.  1.]]]) 的 shape 为 [1,1,4]表示一张图片中的一个标注框seed： （可选）数组类型为 int，默认为0。如果任一个seed或被seed2设置为非零，随机数生成器由给定的种子生成。否则，由随机种子生成。

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
)

seed2： （可选）数组类型为 int，默认为0。第二种子避免种子冲突。min_object_covered：（可选）数组类型为 float，默认为 0.1。图像的裁剪区域必须包含所提供的任意一个边界框的至少 min_object_covered 的内容。该参数的值应为非负数，当为0时，裁剪区域不必与提供的任何边界框有重叠部分。aspect_ratio_range： （可选）数组类型为 floats 的列表，默认为 [0.75, 1.33] 。图像的裁剪区域的宽高比（宽高比=宽/高） 必须在这个范围内。area_range： （可选）数组类型为 floats 的列表，默认为 [0.05, 1] 。图像的裁剪区域必须包含这个范围内的图像的一部分。max_attempts： （可选）数组类型为 int，默认为100。尝试生成图像指定约束的裁剪区域的次数。经过 max_attempts 次失败后，将返回整个图像。use_image_if_no_bounding_boxes：（可选）数组类型为 bool，默认为False。如果没有提供边框，则用它来控制行为。如果为True，则假设有一个覆盖整个输入的隐含边界框。如果为False，就报错。name： 操作的名称（可选）。

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

Return：一个Tensor对象的元组（begin，size，bboxes）。begin： 和 image_size 具有相同的类型。包含 [offset_height, offset_width, 0] 的一维数组。作为 tf.slice 的输入。size： 和 image_size 具有相同的类型。包含 [target_height, target_width, -1] 的一维数组。作为 tf.slice 的输入。bboxes：shape为 [1, 1, 4] 的三维矩阵，数据类型为float32，表示随机变形后的边界框。作为 tf.image.draw_bounding_boxes 的输入。官方文档：https://www.tensorflow.org/api_docs/python/tf/image/sample_distorted_bounding_boxtensorflow源码：tensorflow/python/ops/gen_image_ops.py

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):r"""Generate a single randomly distorted bounding box for an image.Bounding box annotations are often supplied in addition to ground-truth labelsin image recognition or object localization tasks. A common technique fortraining such a system is to randomly distort an image while preservingits content, i.e. *data augmentation*. This Op outputs a randomly distortedlocalization 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 theoriginal 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 theimage. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualizewhat the bounding box looks like.Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. Thebounding box coordinates are floats in `[0.0, 1.0]` relative to the width andheight 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 implicitbounding box covering the whole image. If `use_image_if_no_bounding_boxes` isfalse 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 boxesassociated with the image.seed: An optional `int`. Defaults to `0`.If either `seed` or `seed2` are set to non-zero, the random numbergenerator is seeded by the given `seed`.  Otherwise, it is seeded by a randomseed.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 thisfraction of any bounding box supplied. The value of this parameter should benon-negative. In the case of 0, the cropped area does not need to overlapany 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 thesupplied image within in this range.max_attempts: An optional `int`. Defaults to `100`.Number of attempts at generating a cropped region of the imageof the specified constraints. After `max_attempts` failures, return the entireimage.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`."""

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
)