mindspore.dataset.vision.RandomCropWithBBox

class mindspore.dataset.vision.RandomCropWithBBox(size, padding=None, pad_if_needed=False, fill_value=0, padding_mode=Border.CONSTANT)[source]

Crop the input image at a random location and adjust bounding boxes accordingly.

Parameters

size (Union[int, Sequence[int]]) – The output size of the cropped image. The size value(s) must be positive. If size is an integer, a square crop of size (size, size) is returned. If size is a sequence of length 2, an image of size (height, width) will be cropped.
padding (Union[int, Sequence[int]], optional) – The number of pixels to pad the image The padding value(s) must be non-negative. Default: None. If padding is not None, first pad image with padding values. If a single number is provided, pad all borders with this value. If a tuple or lists of 2 values are provided, pad the (left and right) with the first value and (top and bottom) with the second value. If 4 values are provided as a list or tuple, pad the left, top, right and bottom respectively.
pad_if_needed (bool, optional) – Pad the image if either side is smaller than the given output size. Default: False.
fill_value (Union[int, tuple[int]], optional) – The pixel intensity of the borders, only valid for padding_mode Border.CONSTANT. If it is a 3-tuple, it is used to fill R, G, B channels respectively. If it is an integer, it is used for all RGB channels. The fill_value values must be in range [0, 255]. Default: 0.
padding_mode (Border, optional) –
The method of padding. Default: Border.CONSTANT. It can be any of [Border.CONSTANT, Border.EDGE, Border.REFLECT, Border.SYMMETRIC].
- Border.CONSTANT, means it fills the border with constant values.
- Border.EDGE, means it pads with the last value on the edge.
- Border.REFLECT, means it reflects the values on the edge omitting the last value of edge.
- Border.SYMMETRIC, means it reflects the values on the edge repeating the last
  
  value of edge.

Note

The behavior when padding is a sequence of length 2 will change from padding left/top with the first value and right/bottom with the second, to padding left/right with the first one and top/bottom with the second in the future. Or you can pass in a 4-element sequence to specify left, top, right and bottom respectively.

Raises

TypeError – If size is not of type int or Sequence[int].
TypeError – If padding is not of type int or Sequence[int].
TypeError – If pad_if_needed is not of type boolean.
TypeError – If fill_value is not of type int or tuple[int].
TypeError – If padding_mode is not of type mindspore.dataset.vision.Border .
ValueError – If size is not positive.
ValueError – If padding is negative.
ValueError – If fill_value is not in range [0, 255].
RuntimeError – If given tensor shape is not <H, W> or <H, W, C>.

Supported Platforms:: CPU

Examples

>>> decode_op = vision.Decode()
>>> random_crop_with_bbox_op = vision.RandomCropWithBBox([512, 512], [200, 200, 200, 200])
>>> transforms_list = [decode_op, random_crop_with_bbox_op]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
...                                                 input_columns=["image"])