def noise_vis():
print 'best actions', np.max(action.metadata['vertex_probs'], axis=0)
render_3d_scene(env)
j = 113
j = np.argmax(action.metadata['vertex_probs'][:, 1])
a = j * policy.toppling_model.n_trials
b = (j + 1) * policy.toppling_model.n_trials
for i in range(a, b):
start = policy.toppling_model.vertices[i]
end = start - .01 * policy.toppling_model.push_directions[i]
vis3d.plot([start, end], color=[0, 1, 0], radius=.0002)
vis3d.show(starting_camera_pose=camera_pose())
#figure_0()
# figure_3()
# figure_0()
#failure_modes()
action = policy.action(env.state)
#noise_vis()
if False:
j = np.argmax(action.metadata['vertex_probs'][:, 2])
R = policy.toppling_model.tipping_point_rotations()[1]
mesh.apply_transform(R.matrix)
vis3d.mesh(mesh, color=env.state.color)
new_point = (R * Point(action.metadata['vertices'][j], 'world')).data
new_normal = (R * Point(action.metadata['normals'][j], 'world')).data
vis3d.points(new_point, radius=.001, color=[0, 1, 0])
vis3d.plot([new_point, new_point + .01 * new_normal], radius=.0001)
vis3d.show(starting_camera_pose=CAMERA_POSE)
# state.T_obj_world.translation[:2] = np.array([0,0])
# Visualize
if args.topple_probs:
vis3d.figure()
env.render_3d_scene()
for vertex, prob in zip(action.metadata['vertices'],
action.metadata['topple_probs']):
color = [min(1, 2 * (1 - prob)), min(2 * prob, 1), 0]
vis3d.points(Point(vertex, 'world'), scale=.001, color=color)
# for bottom_point in policy.toppling_model.bottom_points:
# vis3d.points(Point(bottom_point, 'world'), scale=.001, color=[0,0,0])
display_or_save('{}_topple_probs.gif'.format(obj_name))
