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()
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()
circles.append(Circle(origin=[.1, .25], radius=0.05))
circles.append(Circle(origin=[.2, .25], radius=0.05))
circles.append(Circle(origin=[.0, .25], radius=0.05))
circles.append(Circle(origin=[-.2, 0], radius=0.1))
workspace = Workspace()
workspace.obstacles = circles
X, Y = workspace.box.meshgrid(N)
occ = occupancy_map(N, workspace)
f = sdf(occ).T
U = -1 * np.gradient(f.T, axis=0).T
V = -1 * np.gradient(f.T, axis=1).T
phi = hd.distance_from_gradient(U, V, 1. / N, f)
phi -= phi.min() # set min to 0 for comparison
f -= f.min()
# d = np.linalg.norm(phi - f)
# print(d)
renderer = WorkspaceDrawer(workspace, rows=1, cols=2)
renderer.set_drawing_axis(0)
renderer.draw_ws_obstacles()
renderer.draw_ws_img(sdf(occupancy_map(100, workspace)),
interpolate="none",
color_style=plt.cm.hsv)
renderer._ax.quiver(X, Y, U, V, units='width')
renderer._ax.set_title("original")
renderer.set_drawing_axis(1)
renderer.draw_ws_obstacles()
renderer.draw_ws_img(phi.T, interpolate="none", color_style=plt.cm.hsv)
renderer._ax.quiver(X, Y, U, V, units='width')
renderer._ax.set_title("recovered from vector field")
renderer.show()