mindspore.dataset.vision.c_transforms.RandomCropWithBBox

class mindspore.dataset.vision.c_transforms.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]) – The output size of the cropped image. If size is an integer, a square crop of size (size, size) is returned. If size is a sequence of length 2, it should be (height, width).

  • padding (Union[int, sequence], optional) – The number of pixels to pad the image (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 top) with the first value and (right 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], 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 mode, 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.

Examples

>>> decode_op = c_vision.Decode()
>>> random_crop_with_bbox_op = c_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"])