mindspore.ops.SampleDistortedBoundingBoxV2

class mindspore.ops.SampleDistortedBoundingBoxV2(seed=0, seed2=0, \                  aspect_ratio_range=(0.75, 1.33), \                  area_range=(0.05, 1.0), \                  max_attempts=100, \                  use_image_if_no_bounding_boxes=False)[source]

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 is returned as 3 tensors: begin, size and bboxes. The first 2 tensors can be fed directly into mindspore.ops.Slice to crop the image. The latter is the generated distorted bounding box.

Parameters

  • seed (int, optional) – If either seed or seed2 is set to non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. Default: 0.

  • seed2 (int, optional) – A second seed to avoid seed collision. Default: 0.

  • aspect_ratio_range (Union[list(float), tuple(float)], optional) – Specifying the valild range of aspect ratio of cropped area. Aspect ratio of area = area_width / area_height. The value of this attribute should be positive. Default: (0.75, 1.33).

  • area_range (Union[list(float), tuple(float)], optional) – The cropped area of the image must contain a fraction of the supplied image within this range. The value of this attribute should be in range (0.0, 1.0]. Default: (0.05, 1.0).

  • max_attempts (int, optional) – Number of attempts at generating a cropped region of the image of the specified constraints. After max_attempts failures, return the entire image. The value of this attribute should be positive. Default: 100.

  • use_image_if_no_bounding_boxes (bool, optional) – Controls behavior if no bounding boxes supplied. If no bounding boxes supplied (bounding_boxes in shape [0, N, 4] or [batch, 0, 4]), and this attribute is set True, then assume an implicit bounding box covering the whole input, else if this attribute is set False, then raise an error. Default: False.

Inputs:

  • image_size (Tensor) - 1-D, containing [height, width, channels]. The value of this input tensor should be positive.

  • bounding_boxes (Tensor) - 3-D with shape [batch, N, 4] describing the N bounding boxes associated with the image. The value of this input tensor should be in range [0.0, 1.0]. The data type is float32.

  • min_object_covered (Tensor) - The cropped area of the image must contain at least this fraction of any bounding box supplied. The value of this parameter should be in range [0.0, 1.0]. In the case of 0, the cropped area does not need to overlap any of the bounding boxes supplied. The data type is float32.

Outputs:

  • begin (Tensor) - A 1-D Tensor, containing [offset_height, offset_width, 0]. The data type is same as image_size.

  • size (Tensor) - A 1-D Tensor, containing [target_height, target_width, -1]. The data type is same as image_size. When the data type of image_size is uint8, the last value of size, which is originally -1, will be forced to 255.

  • bboxes (Tensor) - A 3-D Tensor with shape [1, 1, 4], containing the distorted bounding box. The data type is float32.

Raises

  • TypeError – If image_size is not a Tensor.

  • TypeError – If bounding_boxes is not a Tensor.

  • TypeError – If min_object_covered is not a Tensor.

  • TypeError – If seed or seed2 is not an int.

  • TypeError – If aspect_ratio_range is not a list or a tuple with type float.

  • TypeError – If area_range is not a list or a tuple with type float.

  • TypeError – If use_image_if_no_bounding_boxes is not a bool.

  • ValueError – If the dimension of image_size is not 1.

  • ValueError – If the elements of image_size is not 3.

  • ValueError – If the dimension of bounding_boxes is not 3.

  • ValueError – If the elements of each bounding box in bounding_boxes is not 4.

  • ValueError – If the elements of min_object_covered is not 1.

  • ValueError – If the elements of aspect_ratio_range list or tuple is not 2.

  • ValueError – If the values of aspect_ratio_range is not positive.

  • ValueError – If the second value of aspect_ratio_range is less than or equal to the first one.

  • ValueError – If the elements of area_range list or tuple is not 2.

  • ValueError – If the values of area_range is out of range (0.0, 1.0].

  • ValueError – If the second value of area_range is less than or equal to the first one.

  • ValueError – If the value of max_attempts is not positive int.

  • ValueError – If use_image_if_no_bounding_boxes is False and no bounding boxes supplied.

  • RuntimeError – If the values of image_size is not positive.

  • RuntimeError – If the values of bounding_boxes is out of range [0.0, 1.0].

  • RuntimeError – If the bounding_boxes cannot make up bounding box.

  • RuntimeError – If the value of min_object_covered is out of range [0.0, 1.0].

Supported Platforms:

CPU

Examples

>>> image_size = Tensor([640, 480, 3], mindspore.int32)
>>> bounding_boxes = Tensor([[[0.38, 0.17, 0.95, 0.40]]], mindspore.float32)
>>> min_object_covered = Tensor([0.8], mindspore.float32)
>>> sample_distorted_bounding_box_v2 = \
...   ops.SampleDistortedBoundingBoxV2(seed=1, seed2=1, aspect_ratio_range=(0.9, 1.1),
...                                    area_range=(0.1,1.0), max_attempts=100,
...                                    use_image_if_no_bounding_boxes=False)
>>> output = sample_distorted_bounding_box_v2(image_size, bounding_boxes, min_object_covered)
>>> begin, size, bboxes = output[0], output[1], output[2]
>>> print(begin)
[133   1   0]
>>> print(size)
[502 457  -1]
>>> print(bboxes)
[[[0.2078125  0.00208333 0.9921875  0.95416665]]]