def plot_costmaps():
workspace = sample_circle_workspaces(nb_circles=4)
grid_sparse = workspace.box.stacked_meshgrid(24)
grid_dense = workspace.box.stacked_meshgrid(100)
extent = workspace.box.extent_data()
sdf = SignedDistanceWorkspaceMap(workspace)
viewer = WorkspaceDrawer(workspace,
wait_for_keyboard=True,
rows=1,
cols=2,
scale=1.)
viewer.set_drawing_axis(0)
viewer.set_workspace(workspace)
viewer.draw_ws_img(sdf(grid_dense).T)
viewer.draw_ws_obstacles()
viewer.set_drawing_axis(1)
viewer.set_workspace(workspace)
viewer.draw_ws_img(sdf(grid_sparse).T)
viewer.draw_ws_obstacles()
viewer.show_once()
diagonal = workspace.box.diag()
max_h = diagonal * boxes_height
min_h = diagonal * boxes_height * .5
max_w = diagonal * boxes_width
min_w = diagonal * boxes_width * .5
workspace.obstacles = []
while len(workspace.obstacles) < 5:
origin = workspace.box.sample_uniform()
h = (max_h - min_h) * np.random.rand() + min_h
w = (max_w - min_w) * np.random.rand() + min_w
if workspace.min_dist(origin)[0] < np.linalg.norm([h, w]):
continue
dimensions = np.array([w, h])
theta = np.pi * np.random.rand() - np.pi
orientation = rotation_matrix_2d_radian(theta)
workspace.obstacles.append(OrientedBox(origin, dimensions, orientation))
# Compute Occupancy map
matrix = occupancy_map(150, workspace)
# Compute Signed distance field
# meshgrid = workspace.box.stacked_meshgrid(150)
# matrix = SignedDistanceWorkspaceMap(workspace)(meshgrid).T
# Setup viewer
viewer = WorkspaceDrawer(workspace, wait_for_keyboard=True)
viewer.draw_ws_img(matrix)
viewer.draw_ws_obstacles()
viewer.show_once()
vx = RegressedPixelGridSpline(U.T, grid_sparse.resolution, grid_sparse.extent)
vy = RegressedPixelGridSpline(V.T, grid_sparse.resolution, grid_sparse.extent)
for i, j in itertools.product(range(X.shape[0]), range(X.shape[1])):
p = np.array([X[i, j], Y[i, j]])
vxx = vx.gradient(p)[0]
vyy = vy.gradient(p)[1]
div[i, j] = vxx + vyy
for i in range(iterations):
if ROWS * COLS == 1 and i < iterations - 1:
continue
print("plot..")
p_source = grid_sparse.world_to_grid(x_source)
p = grid_sparse.grid_to_world(p_source)
phi = phi.T
phi = hd.distance(U, V, div, 1. / N).T
renderer.set_drawing_axis(i)
renderer.draw_ws_obstacles()
renderer.draw_ws_point(p, color='r', shape='o')
renderer.background_matrix_eval = False
renderer.draw_ws_img(phi, interpolate="bicubic", color_style=plt.cm.hsv)
f = RegressedPixelGridSpline(phi, grid_sparse.resolution,
grid_sparse.extent)
for i, j in itertools.product(range(X.shape[0]), range(X.shape[1])):
g = f.gradient(np.array([X[i, j], Y[i, j]]))
g /= np.linalg.norm(g)
U[i, j] = g[0]
V[i, j] = g[1]
renderer._ax.quiver(X, Y, U, V, units='width')
renderer.show()