Method __init__

tensorpack/dataflow/imgaug/imgproc.py:168–185

Args: size_range (tuple[int]): Gaussian window size would be 2 * size + 1, where size is randomly sampled from this [low, high) range. sigma_range (tuple[float]): min,max of the sigma value. 0 means opencv's default. symmet

(self, size_range=(0, 3), sigma_range=(0, 0), symmetric=True, max_size=None)

    """ Gaussian blur the image with random window size"""

    def __init__(self, size_range=(0, 3), sigma_range=(0, 0), symmetric=True, max_size=None):
        """
        Args:
            size_range (tuple[int]): Gaussian window size would be 2 * size +
                1, where size is randomly sampled from this [low, high) range.
            sigma_range (tuple[float]): min,max of the sigma value. 0 means
                opencv's default.
            symmetric (bool): whether to use the same size & sigma for x and y.
            max_size (int): deprecated
        """
        super(GaussianBlur, self).__init__()
        if not isinstance(size_range, (list, tuple)):
            size_range = (0, size_range)
        assert isinstance(sigma_range, (list, tuple)), sigma_range
        if max_size is not None:
            log_deprecated("GaussianBlur(max_size=)", "Use size_range= instead!", "2020-09-01")
            size_range = (0, max_size)
        self._init(locals())

    def _get_augment_params(self, _):
        size_xy = self.rng.randint(self.size_range[0], self.size_range[1], size=(2,)) * 2 + 1