def EdgeDetect(alpha=<NUM_LIT:0>, name=None, deterministic=False, random_state=None):
alpha_param = iap.handle_continuous_param(alpha, "<STR_LIT>", value_range=(<NUM_LIT:0>, <NUM_LIT:1.0>), tuple_to_uniform=True,<EOL>list_to_choice=True)<EOL>def create_matrices(_image, nb_channels, random_state_func):<EOL><INDENT>alpha_sample = alpha_param.draw_sample(random_state=random_state_func)<EOL>ia.do_assert(<NUM_LIT:0> <= alpha_sample <= <NUM_LIT:1.0>)<EOL>matrix_nochange = np.array([<EOL>[<NUM_LIT:0>, <NUM_LIT:0>, <NUM_LIT:0>],<EOL>[<NUM_LIT:0>, <NUM_LIT:1>, <NUM_LIT:0>],<EOL>[<NUM_LIT:0>, <NUM_LIT:0>, <NUM_LIT:0>]<EOL>], dtype=np.float32)<EOL>matrix_effect = np.array([<EOL>[<NUM_LIT:0>, <NUM_LIT:1>, <NUM_LIT:0>],<EOL>[<NUM_LIT:1>, -<NUM_LIT:4>, <NUM_LIT:1>],<EOL>[<NUM_LIT:0>, <NUM_LIT:1>, <NUM_LIT:0>]<EOL>], dtype=np.float32)<EOL>matrix = (<NUM_LIT:1>-alpha_sample) * matrix_nochange + alpha_sample * matrix_effect<EOL>return [matrix] * nb_channels<EOL><DEDENT>if name is None:<EOL><INDENT>name = "<STR_LIT>" % (ia.caller_name(),)<EOL><DEDENT>return Convolve(create_matrices, name=name, deterministic=deterministic, random_state=random_state)<EOL>
Augmenter that detects all edges in images, marks them in
a black and white image and then overlays the result with the original
image.

dtype support::

    See ``imgaug.augmenters.convolutional.Convolve``.

Parameters
----------
alpha : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
    Visibility of the sharpened image. At 0, only the original image is
    visible, at 1.0 only its sharpened version is visible.

        * If an int or float, exactly that value will be used.
        * If a tuple ``(a, b)``, a random value from the range ``a <= x <= b`` will
          be sampled per image.
        * If a list, then a random value will be sampled from that list
          per image.
        * If a StochasticParameter, a value will be sampled from the
          parameter per image.

name : None or str, optional
    See :func:`imgaug.augmenters.meta.Augmenter.__init__`.

deterministic : bool, optional
    See :func:`imgaug.augmenters.meta.Augmenter.__init__`.

random_state : None or int or numpy.random.RandomState, optional
    See :func:`imgaug.augmenters.meta.Augmenter.__init__`.

Examples
--------
>>> aug = EdgeDetect(alpha=(0.0, 1.0))

detects edges in an image  and overlays the result with a variable alpha
in the range ``0.0 <= a <= 1.0`` over the old image.