e61cab87273085258cd1c806208b983fda5607b6,imgaug/augmenters/convolutional.py,,Emboss,#Any#Any#Any#Any#Any#,249

Before Change

    

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

    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)
        strength_sample = strength_param.draw_sample(random_state=random_state_func)

After Change

    over the old image.

    
    alpha_param = iap.handle_continuous_param(alpha, "alpha", value_range=(0, 1.0), tuple_to_uniform=True, list_to_choice=True)
    strength_param = iap.handle_continuous_param(strength, "strength", 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)
        ia.do_assert(0 <= alpha_sample <= 1.0)

Instances