mindspore.dataset.vision.NormalizePad
- class mindspore.dataset.vision.NormalizePad(mean, std, dtype='float32', is_hwc=True)[source]
Normalize the input image with respect to mean and standard deviation then pad an extra channel with value zero.
- Parameters
mean (sequence) – List or tuple of mean values for each channel, with respect to channel order. The mean values must be in range (0.0, 255.0].
std (sequence) – List or tuple of standard deviations for each channel, with respect to channel order. The standard deviation values must be in range (0.0, 255.0].
dtype (str, optional) – Set the output data type of normalized image. Default:
"float32"
.is_hwc (bool, optional) – Specify the format of input image.
True
- HW(C) format,False
- CHW format. Default:True
.
- Raises
TypeError – If mean is not of type sequence.
TypeError – If std is not of type sequence.
TypeError – If dtype is not of type string.
TypeError – If is_hwc is not of type bool.
ValueError – If mean is not in range [0.0, 255.0].
ValueError – If mean is not in range (0.0, 255.0].
RuntimeError – If given tensor shape is not <H, W>, <H, W, C> or <C, H, W>.
- 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"]) >>> normalize_pad_op = vision.NormalizePad(mean=[121.0, 115.0, 100.0], ... std=[70.0, 68.0, 71.0], ... dtype="float32") >>> transforms_list = [normalize_pad_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, 100, 4) float32 >>> >>> # Use the transform in eager mode >>> data = np.random.randint(0, 255, size=(100, 100, 3)).astype(np.uint8) >>> output = vision.NormalizePad(mean=[121.0, 115.0, 100.0], std=[70.0, 68.0, 71.0], dtype="float32")(data) >>> print(output.shape, output.dtype) (100, 100, 4) float32