class Test_Shapes(unittest.TestCase):
"""docstring for Test_Roadgen"""
#Setup the test instance of the class
def setUp(self):
self.test0 = Shapes()
def tearDown(self):
pass
def test_unit_vector(self):
npt.assert_almost_equal(self.test0.unit_vector([345.345, 0]),
np.array([1., 0.]),
decimal=7)
npt.assert_almost_equal(self.test0.unit_vector([0, 345.345]),
np.array([0., 1.]),
decimal=7)
npt.assert_almost_equal(self.test0.unit_vector([-345.345, 0]),
np.array([-1., 0.]),
decimal=7)
npt.assert_almost_equal(self.test0.unit_vector([0, -345.345]),
np.array([0., -1.]),
decimal=7)
npt.assert_almost_equal(self.test0.unit_vector([-3, -4]),
np.array([-.6, -.8]),
decimal=7)
def middle_points(self, y_train, y_noise, orientation=1):
#Assign the central control point:
y_train[1, 0,
1] = np.random.randint(0, self.view_res[0]) + self.cropsize[0]
y_train[1, 1,
1] = np.random.randint(0, self.view_res[1]) + self.cropsize[1]
'''Calculate the control point axis:
control point axis is defined by a unit vector parallel to a line
originating at the midpoint of the line's start and end and
terminating at the curves second control point'''
u_delta = Shapes.unit_vector(y_train[1, :, 1] - (
y_train[1, :, 0] + (y_train[1, :, 2] - y_train[1, :, 0]) / 2))
#print(u_delta)
#scales the sideways road outputs to account for pitch
vertical_excursion = np.absolute(u_delta[1]) * self.max_road_width
#rotational correction:
u_rot = Shapes.rot_by_vector(y_train[1, :, 2] - y_train[1, :, 0],
u_delta)
#Checks to see which side of the line between start and end the midpoint falls on
if u_rot[1] > 0:
#If u_delta has a negative y component then it falls south of the line and need to be inverted
u_delta *= -1
#Set the left side no.1 control point
y_train[0, :, 1] = (y_train[1, :, 1] + u_delta * np.multiply(
(self.max_road_width - y_noise[0, 1]),
(1 - self.lane_convergence + self.lane_convergence * np.maximum(
.2,
(y_train[1, 1, 1] + vertical_excursion)) / self.gen_res[1])))
#Set the right side no.1 control point
y_train[2, :, 1] = (y_train[1, :, 1] - u_delta * np.multiply(
(self.max_road_width - y_noise[1, 1]),
(1 - self.lane_convergence + self.lane_convergence * np.maximum(
.2,
(y_train[1, 1, 1] - vertical_excursion)) / self.gen_res[1])))
return y_train
