342f4ebec593fb7e0c76b022a1e4b9d35fb79053,imgaug/augmenters/convolutional.py,,Sharpen,#Any#Any#Any#Any#Any#,167

Before Change

    0.75 <= x <= 2.0 and with a variable alpha.

    
    if ia.is_single_number(alpha):
        alpha_param = Deterministic(alpha)
    elif ia.is_iterable(alpha):
        ia.do_assert(len(alpha) == 2, "Expected tuple/list with 2 entries, got %d entries." % (len(alpha),))
        alpha_param = Uniform(alpha[0], alpha[1])
    elif isinstance(alpha, StochasticParameter):
        alpha_param = alpha
    else:
        raise Exception("Expected float, int, tuple/list with 2 entries or StochasticParameter. Got %s." % (type(alpha),))

    if ia.is_single_number(lightness):
        lightness_param = Deterministic(lightness)
    elif ia.is_iterable(lightness):
        ia.do_assert(len(lightness) == 2, "Expected tuple/list with 2 entries, got %d entries." % (len(lightness),))
        lightness_param = Uniform(lightness[0], lightness[1])
    elif isinstance(lightness, StochasticParameter):
        lightness_param = lightness
    else:
        raise Exception("Expected float, int, tuple/list with 2 entries or StochasticParameter. Got %s." % (type(lightness),))

    def create_matrices(image, nb_channels, random_state_func):
        alpha_sample = alpha_param.draw_sample(random_state=random_state_func)
        ia.do_assert(0 <= alpha_sample <= 1.0)
        lightness_sample = lightness_param.draw_sample(random_state=random_state_func)

After Change

    0.75 <= x <= 2.0 and with a variable alpha.

    
    alpha_param = iap.handle_continuous_param(alpha, "alpha", value_range=(0, 1.0), tuple_to_uniform=True, list_to_choice=True)
    lightness_param = iap.handle_continuous_param(lightness, "lightness", value_range=(0, None), tuple_to_uniform=True, list_to_choice=True)

    def create_matrices(image, nb_channels, random_state_func):
        alpha_sample = alpha_param.draw_sample(random_state=random_state_func)

Instances