def __call__(self, _image, nb_channels, random_state):
alpha_sample = self.alpha.draw_sample(random_state=random_state)
assert 0 <= alpha_sample <= 1.0, (
"Expected 'alpha' to be in the interval [0.0, 1.0], "
"got %.4f." % (alpha_sample, ))
direction_sample = self.direction.draw_sample(
random_state=random_state)
deg = int(direction_sample * 360) % 360
rad = np.deg2rad(deg)
x = np.cos(rad - 0.5 * np.pi)
y = np.sin(rad - 0.5 * np.pi)
direction_vector = np.array([x, y])
matrix_effect = np.array([[0, 0, 0], [0, 0, 0], [0, 0, 0]],
dtype=np.float32)
for x, y in itertools.product([-1, 0, 1], [-1, 0, 1]):
if (x, y) != (0, 0):
cell_vector = np.array([x, y])
distance_deg = np.rad2deg(
ia.angle_between_vectors(cell_vector, direction_vector))
distance = distance_deg / 180
similarity = (1 - distance)**4
matrix_effect[y + 1, x + 1] = similarity
matrix_effect = matrix_effect / np.sum(matrix_effect)
matrix_effect = matrix_effect * (-1)
matrix_effect[1, 1] = 1
matrix_nochange = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]],
dtype=np.float32)
matrix = ((1 - alpha_sample) * matrix_nochange +
alpha_sample * matrix_effect)
return [matrix] * nb_channels
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)
direction_sample = direction_param.draw_sample(
random_state=random_state_func)
deg = int(direction_sample * 360) % 360
rad = np.deg2rad(deg)
x = np.cos(rad - 0.5 * np.pi)
y = np.sin(rad - 0.5 * np.pi)
direction_vector = np.array([x, y])
matrix_effect = np.array([[0, 0, 0], [0, 0, 0], [0, 0, 0]],
dtype=np.float32)
for x in [-1, 0, 1]:
for y in [-1, 0, 1]:
if (x, y) != (0, 0):
cell_vector = np.array([x, y])
distance_deg = np.rad2deg(
ia.angle_between_vectors(cell_vector,
direction_vector))
distance = distance_deg / 180
similarity = (1 - distance)**4
matrix_effect[y + 1, x + 1] = similarity
matrix_effect = matrix_effect / np.sum(matrix_effect)
matrix_effect = matrix_effect * (-1)
matrix_effect[1, 1] = 1
matrix_nochange = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]],
dtype=np.float32)
matrix = (
1 - alpha_sample) * matrix_nochange + alpha_sample * matrix_effect
return [matrix] * nb_channels