mindspore.dataset.vision.NormalizePad

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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) – Whether the input image is HWC. True - HWC 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 mindspore.dataset as ds
>>> import mindspore.dataset.vision as vision
>>>
>>> image_folder_dataset = ds.ImageFolderDataset("/path/to/image_folder_dataset_directory")
>>> decode_op = vision.Decode()
>>> normalize_pad_op = vision.NormalizePad(mean=[121.0, 115.0, 100.0],
...                                        std=[70.0, 68.0, 71.0],
...                                        dtype="float32")
>>> transforms_list = [decode_op, normalize_pad_op]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
...                                                 input_columns=["image"])