mindspore.dataset.vision.ResizeWithBBox
- class mindspore.dataset.vision.ResizeWithBBox(size, interpolation=Inter.LINEAR)[source]
Resize the input image to the given size and adjust bounding boxes accordingly.
- Parameters
size (Union[int, Sequence[int]]) – The output size of the resized image. If size is an integer, smaller edge of the image will be resized to this value with the same image aspect ratio. If size is a sequence of length 2, it should be (height, width).
interpolation (Inter, optional) – Image interpolation method defined by
Inter
. Default:Inter.LINEAR
.
- Raises
TypeError – If size is not of type int or Sequence[int].
ValueError – If size is not positive.
RuntimeError – If given tensor shape is not <H, W> or <H, W, C>.
- Supported Platforms:
CPU
Examples
>>> import numpy as np >>> import mindspore.dataset as ds >>> import mindspore.dataset.vision as vision >>> from mindspore.dataset.vision import Inter >>> >>> # Use the transform in dataset pipeline mode >>> data = np.random.randint(0, 255, size=(100, 100, 3)).astype(np.float32) >>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["image"]) >>> func = lambda img: (data, np.array([[0, 0, data.shape[1], data.shape[0]]]).astype(np.float32)) >>> numpy_slices_dataset = numpy_slices_dataset.map(operations=[func], ... input_columns=["image"], ... output_columns=["image", "bbox"]) >>> bbox_op = vision.ResizeWithBBox(50, Inter.NEAREST) >>> transforms_list = [bbox_op] >>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms_list, input_columns=["image", "bbox"]) >>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True): ... print(item["image"].shape, item["image"].dtype) ... print(item["bbox"].shape, item["bbox"].dtype) ... break (50, 50, 3) float32 (1, 4) float32 >>> >>> # Use the transform in eager mode >>> data = np.random.randint(0, 255, size=(100, 100, 3)).astype(np.float32) >>> func = lambda img: (data, np.array([[0, 0, data.shape[1], data.shape[0]]]).astype(data.dtype)) >>> func_data, func_bboxes = func(data) >>> output = vision.ResizeWithBBox(100)(func_data, func_bboxes) >>> print(output[0].shape, output[0].dtype) (100, 100, 3) float32 >>> print(output[1].shape, output[1].dtype) (1, 4) float32
- Tutorial Examples: