def main():
points = load_csv('robust_1.csv')
polylidar_kwargs = dict(alpha=0.0, lmax=1000.0, min_triangles=1)
points_mat = MatrixDouble(points)
polylidar = Polylidar3D(**polylidar_kwargs)
# Extracts planes and polygons, time
t1 = time.time()
# pick large lmax to get the convex hull, no triangles filtered
mesh, planes, polygons = polylidar.extract_planes_and_polygons(points_mat)
t2 = time.time()
print("Took {:.2f} milliseconds".format((t2 - t1) * 1000))
print(
"If Robust Geometric Predicates is NOT activated, you should see a malformed convex hull"
)
print(
"See README.md to activate if desired. ~20% performance penalty when active."
)
print("")
# Convert to numpy format, no copy with np.asarray()
triangles = np.asarray(mesh.triangles)
fig, ax = plt.subplots(figsize=(10, 10), nrows=1, ncols=1)
# plot points
ax.scatter(points[:, 0], points[:, 1], c='k')
# plot all triangles
plot_triangles(get_triangles_from_list(triangles, points), ax)
# plot seperated planar triangular segments
triangle_meshes = get_colored_planar_segments(planes, triangles, points)
plot_triangle_meshes(triangle_meshes, ax)
# plot polygons
plot_polygons(polygons, points, ax)
plt.axis('equal')
plt.show()
def main():
letter_map = read_alphabet()
# letters = ['P', 'O', 'L', 'Y', 'L', 'I', 'D', 'A', 'R']
points = stitch_letters(letter_map)
# plt.scatter(points[:, 0], points[:, 1], s=0.2)
# plt.show()
print(int(points.shape[0] / 2))
idx = np.random.randint(0, points.shape[0], size=int(points.shape[0] / 2))
points = np.ascontiguousarray(points[idx, :])
lmax = 10.0
polylidar_kwargs = dict(alpha=0.0, lmax=lmax, min_triangles=5)
# print(polylidar_kwargs)
# Convert Points and make Polylidar
points_mat = MatrixDouble(points)
polylidar = Polylidar3D(**polylidar_kwargs)
# Extracts planes and polygons, time
t1 = time.perf_counter()
mesh, planes, polygons = polylidar.extract_planes_and_polygons(points_mat)
t2 = time.perf_counter()
triangles = np.asarray(mesh.triangles)
# print(triangles, triangles.shape)
triangles = triangles.flatten()
# print(triangles, triangles.shape)
planes_np = planes
# print(planes_np)
print("Took {:.2f} milliseconds".format((t2 - t1) * 1000))
# Plot Data
if points.shape[0] < 100000:
fig, ax = plt.subplots(figsize=(10, 10), nrows=1, ncols=1)
ax.set_xticks([])
ax.set_yticks([])
plt.axis('equal')
plt.axis('off')
# plot points
plot_points(points, ax)
fig.savefig('assets/scratch/pl_logo_points.png',
bbox_inches='tight',
transparent=True)
# plot all triangles
plot_triangles(get_triangles_from_list(triangles, points), ax)
# plot mesh triangles
triangle_meshes = get_colored_planar_segments(planes_np, triangles,
points)
plot_triangle_meshes(triangle_meshes, ax)
# plot polygons
plot_polygons(polygons, points, ax, linewidth=4.0)
plt.subplots_adjust(wspace=0.185,
hspace=0.185,
left=0.110,
top=0.535,
right=0.750,
bottom=0.110)
fig.savefig('assets/scratch/pl_logo.png',
bbox_inches='tight',
transparent=True)
plt.show()
def main():
kwargs = dict(num_groups=2, group_size=1000, dist=100.0, seed=1)
# generate random normally distributed clusters of points, 200 X 2 numpy array.
points = generate_test_points(**kwargs)
lmax = get_estimated_lmax(**kwargs)
polylidar_kwargs = dict(alpha=0.0, lmax=lmax, min_triangles=5)
# print(polylidar_kwargs)
# Convert Points and make Polylidar
points_mat = MatrixDouble(points)
polylidar = Polylidar3D(**polylidar_kwargs)
# Extracts planes and polygons, time
t1 = time.perf_counter()
mesh, planes, polygons = polylidar.extract_planes_and_polygons(points_mat)
t2 = time.perf_counter()
triangles = np.asarray(mesh.triangles)
# print(triangles, triangles.shape)
triangles = triangles.flatten()
# print(triangles, triangles.shape)
planes_np = np.asarray(planes)
# print(planes_np)
print("Took {:.2f} milliseconds".format((t2 - t1) * 1000))
# Plot Data
if points.shape[0] < 100000:
fig, ax = plt.subplots(figsize=(10, 10), nrows=1, ncols=1)
# plot points
print("Plot Points")
ax.scatter(points[:, 0], points[:, 1], c='k', s=20.0)
fig.savefig("assets/scratch/Basic2DAlgorithm_pointcloud.png",
bbox_inches='tight',
pad_inches=-0.6)
plt.show()
print("Plot Mesh")
fig, ax = plt.subplots(figsize=(10, 10), nrows=1, ncols=1)
# plot points
ax.scatter(points[:, 0], points[:, 1], c='k', s=0.1)
# plot all triangles
plot_triangles(get_triangles_from_list(triangles, points), ax)
fig.savefig("assets/scratch/Basic2DAlgorithm_mesh.png",
bbox_inches='tight',
pad_inches=-0.6)
plt.show()
print("Planes and Polygons")
fig, ax = plt.subplots(figsize=(10, 10), nrows=1, ncols=1)
# plot points
ax.scatter(points[:, 0], points[:, 1], c='k', s=0.1)
# plot all triangles
plot_triangles(get_triangles_from_list(triangles, points), ax)
# plot mesh triangles
triangle_meshes = get_colored_planar_segments(planes_np, triangles,
points)
plot_triangle_meshes(triangle_meshes, ax)
# plot polygons
plot_polygons(polygons, points, ax)
plt.axis('equal')
plt.show()