# CreateDetAugmenter¶

mxnet.image.CreateDetAugmenter(data_shape, resize=0, rand_crop=0, rand_pad=0, rand_gray=0, rand_mirror=False, mean=None, std=None, brightness=0, contrast=0, saturation=0, pca_noise=0, hue=0, inter_method=2, min_object_covered=0.1, aspect_ratio_range=(0.75, 1.33), area_range=(0.05, 3.0), min_eject_coverage=0.3, max_attempts=50, pad_val=(127, 127, 127))[source]

Create augmenters for detection.

Parameters: data_shape (tuple of int) – Shape for output data resize (int) – Resize shorter edge if larger than 0 at the begining rand_crop (float) – [0, 1], probability to apply random cropping rand_pad (float) – [0, 1], probability to apply random padding rand_gray (float) – [0, 1], probability to convert to grayscale for all channels 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). min_object_covered (float) – The cropped area of the image must contain at least this fraction of any bounding box supplied. The value of this parameter should be non-negative. In the case of 0, the cropped area does not need to overlap any of the bounding boxes supplied. min_eject_coverage (float) – The minimum coverage of cropped sample w.r.t its original size. With this constraint, objects that have marginal area after crop will be discarded. aspect_ratio_range (tuple of floats) – The cropped area of the image must have an aspect ratio = width / height within this range. area_range (tuple of floats) – The cropped area of the image must contain a fraction of the supplied image within in this range. max_attempts (int) – Number of attempts at generating a cropped/padded region of the image of the specified constraints. After max_attempts failures, return the original image. pad_val (float) – Pixel value to be filled when padding is enabled. pad_val will automatically be subtracted by mean and divided by std if applicable.

Examples

>>> # An example of creating multiple augmenters
>>> augs = mx.image.CreateDetAugmenter(data_shape=(3, 300, 300), rand_crop=0.5,
...    rand_pad=0.5, rand_mirror=True, mean=True, brightness=0.125, contrast=0.125,
...    saturation=0.125, pca_noise=0.05, inter_method=10, min_object_covered=[0.3, 0.5, 0.9],
...    area_range=(0.3, 3.0))
>>> # dump the details
>>> for aug in augs:
...    aug.dumps()