mindspore.dataset.vision.RandomAffine
- class mindspore.dataset.vision.RandomAffine(degrees, translate=None, scale=None, shear=None, resample=Inter.NEAREST, fill_value=0)[source]
Apply Random affine transformation to the input image.
- Parameters
degrees (Union[int, float, sequence]) – Range of the rotation degrees. If degrees is a number, the range will be (-degrees, degrees). If degrees is a sequence, it should be (min, max).
translate (sequence, optional) – Sequence (tx_min, tx_max, ty_min, ty_max) of minimum/maximum translation in x(horizontal) and y(vertical) directions, range [-1.0, 1.0]. Default:
None
. The horizontal and vertical shift is selected randomly from the range: (tx_min*width, tx_max*width) and (ty_min*height, ty_max*height), respectively. If a tuple or list of size 2, then a translate parallel to the X axis in the range of (translate[0], translate[1]) is applied. If a tuple or list of size 4, then a translate parallel to the X axis in the range of (translate[0], translate[1]) and a translate parallel to the Y axis in the range of (translate[2], translate[3]) are applied. IfNone
, no translation is applied.scale (sequence, optional) – Scaling factor interval, which must be non negative. Default:
None
, original scale is used.shear (Union[float, Sequence[float, float], Sequence[float, float, float, float]], optional) – Range of shear factor to select from. If float is provided, a shearing parallel to X axis with a factor selected from ( -shear , shear ) will be applied. If Sequence[float, float] is provided, a shearing parallel to X axis with a factor selected from ( shear [0], shear [1]) will be applied. If Sequence[float, float, float, float] is provided, a shearing parallel to X axis with a factor selected from ( shear [0], shear [1]) and a shearing parallel to Y axis with a factor selected from ( shear [2], shear [3]) will be applied. Default:
None
, means no shearing.resample (Inter, optional) – Image interpolation method defined by
Inter
. Default:Inter.NEAREST
.fill_value (Union[int, tuple[int]], optional) – Optional fill_value to fill the area outside the transform in the output image. There must be three elements in tuple and the value of single element is [0, 255]. Default:
0
, filling is performed.
- Raises
TypeError – If degrees is not of type int, float or sequence.
TypeError – If translate is not of type sequence.
TypeError – If scale is not of type sequence.
TypeError – If shear is not of type int, float or sequence.
TypeError – If fill_value is not of type int or tuple[int].
ValueError – If degrees is negative.
ValueError – If translate is not in range [-1.0, 1.0].
ValueError – If scale is negative.
ValueError – If shear 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 >>> random_affine_op = vision.RandomAffine(degrees=15, ... translate=(-0.1, 0.1, 0, 0), ... scale=(0.9, 1.1), ... resample=Inter.NEAREST) >>> transforms_list = [random_affine_op] >>> 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, 100, 3) uint8 >>> >>> # Use the transform in eager mode >>> data = np.random.randint(0, 255, size=(100, 100, 3)).astype(np.uint8) >>> output = vision.RandomAffine(degrees=15, translate=(-0.1, 0.1, 0, 0), ... scale=(0.9, 1.1), resample=Inter.NEAREST)(data) >>> print(output.shape, output.dtype) (100, 100, 3) uint8
- Tutorial Examples: