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 list 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 if the padding_mode is Border.CONSTANT (default=0). If it is a 3-tuple, it is used to fill R, G, B channels respectively.
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
>>> import mindspore.dataset.vision.c_transforms as c_vision >>> >>> decode_op = c_vision.Decode() >>> random_crop_with_bbox_op = c_vision.RandomCrop([512, 512], [200, 200, 200, 200]) >>> transforms_list = [decode_op, random_crop_with_bbox_op] >>> data3 = data3.map(operations=transforms_list, input_columns=["image"])