mindspore.dataset.vision.CenterCrop
- class mindspore.dataset.vision.CenterCrop(size)[source]
Crop the input image at the center to the given size. If input image size is smaller than output size, input image will be padded with 0 before cropping.
- 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). The size value(s) must be larger than 0.
- Raises
TypeError – If size is not of type integer or sequence.
ValueError – If size is less than or equal to 0.
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 >>> >>> # 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"]) >>> >>> # crop image to a square >>> transforms_list1 = [vision.CenterCrop(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 >>> >>> # crop image to portrait style >>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["image"]) >>> transforms_list2 = [vision.CenterCrop((60, 40))] >>> 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 (60, 40, 3) uint8 >>> >>> # Use the transform in eager mode >>> data = np.array([[0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5]], dtype=np.uint8).reshape((2, 2, 3)) >>> output = vision.CenterCrop(1)(data) >>> print(output.shape, output.dtype) (1, 1, 3) uint8
- Tutorial Examples: