mindspore.dataset.vision.RandomResize
- class mindspore.dataset.vision.RandomResize(size)[源代码]
对输入图像使用随机选择的
mindspore.dataset.vision.Inter
插值方式去调整它的尺寸大小。- 参数:
size (Union[int, Sequence[int]]) - 调整后图像的输出尺寸大小。值必须为正。若输入整型,则放缩至(size, size)大小;若输入2元素序列,则以2个元素分别为高和宽放缩至(高度, 宽度)大小。
- 异常:
TypeError - 如果 size 不是int或Sequence[int]类型。
ValueError - 如果 size 不是正数。
RuntimeError - 如果输入图像的shape不是 <H, W> 或 <H, W, C>。
- 支持平台:
CPU
样例:
>>> import numpy as np >>> import mindspore.dataset as ds >>> import mindspore.dataset.vision as vision >>> >>> # Use the transform in dataset pipeline mode >>> data = np.random.randint(0, 255, size=(1, 100, 100, 3)).astype(np.uint8) >>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["image"]) >>> # 1) randomly resize image, keeping aspect ratio >>> transforms_list1 = [vision.RandomResize(50)] >>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms_list1, input_columns=["image"]) >>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True): ... print(item["image"].shape, item["image"].dtype) ... break (50, 50, 3) uint8 >>> # 2) randomly resize image to landscape style >>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["image"]) >>> transforms_list2 = [vision.RandomResize((40, 60))] >>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms_list2, input_columns=["image"]) >>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True): ... print(item["image"].shape, item["image"].dtype) ... break (40, 60, 3) uint8 >>> >>> # Use the transform in eager mode >>> data = np.random.randint(0, 255, size=(100, 100, 3)).astype(np.uint8) >>> output = vision.RandomResize(10)(data) >>> print(output.shape, output.dtype) (10, 10, 3) uint8
- 教程样例: