Table Of Contents
Table Of Contents


mxnet.image.CreateAugmenter(data_shape, resize=0, rand_crop=False, rand_resize=False, rand_mirror=False, mean=None, std=None, brightness=0, contrast=0, saturation=0, hue=0, pca_noise=0, rand_gray=0, inter_method=2)[source]

Creates an augmenter list.

  • data_shape (tuple of int) – Shape for output data

  • resize (int) – Resize shorter edge if larger than 0 at the begining

  • rand_crop (bool) – Whether to enable random cropping other than center crop

  • rand_resize (bool) – Whether to enable random sized cropping, require rand_crop to be enabled

  • rand_gray (float) – [0, 1], probability to convert to grayscale for all channels, the number of channels will not be reduced to 1

  • rand_mirror (bool) – Whether to apply horizontal flip to image with probability 0.5

  • mean (np.ndarray or None) – Mean pixel values for [r, g, b]

  • std (np.ndarray or None) – Standard deviations for [r, g, b]

  • brightness (float) – Brightness jittering range (percent)

  • contrast (float) – Contrast jittering range (percent)

  • saturation (float) – Saturation jittering range (percent)

  • hue (float) – Hue jittering range (percent)

  • pca_noise (float) – Pca noise level (percent)

  • inter_method (int, default=2(Area-based)) –

    Interpolation method for all resizing operations

    Possible values: 0: Nearest Neighbors Interpolation. 1: Bilinear interpolation. 2: Area-based (resampling using pixel area relation). It may be a preferred method for image decimation, as it gives moire-free results. But when the image is zoomed, it is similar to the Nearest Neighbors method. (used by default). 3: Bicubic interpolation over 4x4 pixel neighborhood. 4: Lanczos interpolation over 8x8 pixel neighborhood. 9: Cubic for enlarge, area for shrink, bilinear for others 10: Random select from interpolation method metioned above. Note: When shrinking an image, it will generally look best with AREA-based interpolation, whereas, when enlarging an image, it will generally look best with Bicubic (slow) or Bilinear (faster but still looks OK).


>>> # An example of creating multiple augmenters
>>> augs = mx.image.CreateAugmenter(data_shape=(3, 300, 300), rand_mirror=True,
...    mean=True, brightness=0.125, contrast=0.125, rand_gray=0.05,
...    saturation=0.125, pca_noise=0.05, inter_method=10)
>>> # dump the details
>>> for aug in augs:
...    aug.dumps()