def test_random_walk_vert(self):
tensor = np.zeros((8, 128, 265, 6), dtype=np.float32)
width = 5
num_steps=300
tensor, pos, cars = random_walk(tensor, width, num_steps, horizontal=False)
img_from_above = np.zeros((tensor.shape[1], tensor.shape[2], 3), dtype=np.uint8)
img = view_2D(tensor, img_from_above, 0)
fig = plt.figure
plt.imshow(img)
plt.show()
def test_random_walk_diagonal_neg(self):
tensor = np.zeros((8, 128, 265, 6), dtype=np.float32)
width = 5
num_steps = 300
tensor, pos, cars= random_walk(tensor, width, num_steps, diagonal=True, dir=-1)
img_from_above = np.zeros((tensor.shape[1], tensor.shape[2], 3), dtype=np.uint8)
img = view_2D(tensor, img_from_above, 0)
fig = plt.figure
plt.imshow(img)
plt.show()
def test_one_path(self):
tensor=np.zeros((8,128, 265,6), dtype=np.float32)
width=5
num_crossings=2
# tensor, point_1, thetas, steps, cars
tensor,point ,thetas, steps, cars=random_path(tensor, width, num_crossings)
img_from_above=np.zeros((tensor.shape[1],tensor.shape[2],3), dtype=np.uint8)
img=view_2D(tensor, img_from_above, 0)
fig = plt.figure
plt.imshow(img)
plt.show()
people_rec,
pos_x,
pos_y,
tensor,
True,
people_dict=ped_dict,
car_dict=cars_dict,
init_frames=init_frames,
init_frames_cars=init_frames_cars,
seq_len_pfnn=seq_len_pfnn)
img_from_above = np.ones(
(ep.reconstruction.shape[1],
ep.reconstruction.shape[2], 3),
dtype=np.uint8) * 255
img = view_2D(ep.reconstruction, img_from_above, 0)
print("View PEDESTRIANS")
img = view_pedestrians(
0,
ep.people,
img,
0,
trans=.15,
) # tensor=ep.reconstruction)
img = view_cars(img,
ep.cars,
img.shape[0],
img.shape[1],
0,
frame=0) # ,tensor=ep.reconstruction)