mindspore.dataset.vision.ResizedCrop

class mindspore.dataset.vision.ResizedCrop(top, left, height, width, size, interpolation=Inter.BILINEAR)[source]

Crop the input image at a specific region and resize it to desired size.

Parameters

top (int) – Horizontal ordinate of the upper left corner of the crop region.
left (int) – Vertical ordinate of the upper left corner of the crop region.
height (int) – Height of the crop region.
width (int) – Width of the cropp region.
size (Union[int, Sequence[int, int]]) – The size of the output image. If int is provided, the smaller edge of the image will be resized to this value, keeping the image aspect ratio the same. If Sequence[int, int] is provided, it should be (height, width).
interpolation (Inter, optional) – Image interpolation method defined by Inter . Default: Inter.BILINEAR.

Raises

TypeError – If top is not of type int.
ValueError – If top is negative.
TypeError – If left is not of type int.
ValueError – If left is negative.
TypeError – If height is not of type int.
ValueError – If height is not positive.
TypeError – If width is not of type int.
ValueError – If width is not positive.
TypeError – If size is not of type int or Sequence[int, int].
ValueError – If size is not posotive.
TypeError – If interpolation is not of type Inter .
RuntimeError – If shape of the input image 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
>>> transforms_list = [vision.ResizedCrop(0, 0, 64, 64, (100, 75), Inter.BILINEAR)]
>>> data = np.random.randint(0, 255, size=(1, 100, 100, 3)).astype(np.uint8)
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["image"])
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms_list, 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
(100, 75, 3) uint8
>>>
>>> # Use the transform in eager mode
>>> data = np.random.randint(0, 255, size=(100, 100, 3)).astype(np.uint8)
>>> output = vision.ResizedCrop(0, 0, 1, 1, (5, 5), Inter.BILINEAR)(data)
>>> print(output.shape, output.dtype)
(5, 5, 3) uint8

Tutorial Examples:

Illustration of vision transforms

device(device_target='CPU')[source]

Set the device for the current operator execution.

When the device is Ascend, input/output shape should be limited from [4, 6] to [32768, 32768].

Parameters

device_target (str, optional) – The operator will be executed on this device. Currently supports CPU and Ascend . Default: CPU .

Raises

TypeError – If device_target is not of type str.
ValueError – If device_target is not within the valid set of [‘CPU’, ‘Ascend’].

Supported Platforms:: CPU Ascend

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=(1, 100, 100, 3)).astype(np.uint8)
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["image"])
>>> resize_crop_op = vision.ResizedCrop(0, 0, 64, 64, (100, 75)).device("Ascend")
>>> transforms_list = [resize_crop_op]
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=transforms_list, 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
(100, 75, 3) uint8
>>>
>>> # Use the transform in eager mode
>>> data = np.random.randint(0, 255, size=(100, 100, 3)).astype(np.uint8)
>>> output = vision.ResizedCrop(0, 0, 64, 64, (32, 16), Inter.BILINEAR).device("Ascend")(data)
>>> print(output.shape, output.dtype)
(32, 16, 3) uint8

Tutorial Examples:

Illustration of vision transforms