def main():
EXAMPLE_INDEX = 2
kwargs_base = dict(level=4, max_phi=180)
kwargs_s2 = dict(**kwargs_base)
kwargs_opt_integrate = dict(num_nbr=12)
query_max_phi = kwargs_base['max_phi'] - 5
# Get an Example Mesh
ga_cpp_s2 = GaussianAccumulatorS2(**kwargs_s2)
example_mesh = o3d.io.read_triangle_mesh(str(ALL_MESHES[EXAMPLE_INDEX]))
r = ALL_MESHES_ROTATIONS[EXAMPLE_INDEX]
example_mesh_filtered = example_mesh
if r is not None:
example_mesh_filtered = example_mesh_filtered.rotate(r.as_matrix())
example_mesh_filtered = example_mesh_filtered.filter_smooth_laplacian(
5)
example_mesh_filtered.compute_triangle_normals()
np.save('fixtures/normals/basement.npy',
np.asarray(example_mesh_filtered.triangle_normals))
colored_icosahedron_s2, normals, neighbors_s2 = visualize_gaussian_integration(
ga_cpp_s2,
example_mesh_filtered,
max_phi=query_max_phi,
integrate_kwargs=kwargs_opt_integrate)
o3d.visualization.draw_geometries([example_mesh_filtered])
o3d.visualization.draw_geometries([colored_icosahedron_s2])
# Visualize unwrapping
ico_chart_ = IcoCharts(4)
t2 = time.perf_counter()
normalized_bucket_counts_by_vertex = ga_cpp_s2.get_normalized_bucket_counts_by_vertex(
True)
ico_chart_.fill_image(normalized_bucket_counts_by_vertex)
find_peaks_kwargs = dict(threshold_abs=50,
min_distance=1,
exclude_border=False,
indices=False)
print(np.asarray(ico_chart_.image).shape)
cluster_kwargs = dict(t=0.1, criterion='distance')
_, _, avg_peaks, _ = find_peaks_from_ico_charts(
ico_chart_,
np.asarray(normalized_bucket_counts_by_vertex),
find_peaks_kwargs=find_peaks_kwargs,
cluster_kwargs=cluster_kwargs)
t3 = time.perf_counter()
print(t3 - t2)
print(avg_peaks)
full_image = np.asarray(ico_chart_.image)
plt.imshow(full_image)
plt.axis('off')
# plt.xticks(np.arange(0, full_image.shape[1], step=1))
# plt.yticks(np.arange(0, full_image.shape[0], step=1))
plt.show()
def get_image_peaks(ga_cpp_s2, level=2, **kwargs):
ico_chart = IcoCharts(level)
normalized_bucket_counts_by_vertex = ga_cpp_s2.get_normalized_bucket_counts_by_vertex(
True)
ico_chart.fill_image(normalized_bucket_counts_by_vertex)
find_peaks_kwargs = dict(threshold_abs=25,
min_distance=1,
exclude_border=False,
indices=False)
cluster_kwargs = dict(t=0.10, criterion='distance')
average_filter = dict(min_total_weight=0.10)
peaks, clusters, avg_peaks, avg_weights = find_peaks_from_ico_charts(
ico_chart, np.asarray(normalized_bucket_counts_by_vertex),
find_peaks_kwargs, cluster_kwargs, average_filter)
gaussian_normals_sorted = np.asarray(ico_chart.sphere_mesh.vertices)
pcd_all_peaks = get_pc_all_peaks(peaks, clusters, gaussian_normals_sorted)
arrow_avg_peaks = get_arrow_normals(avg_peaks, avg_weights)
return [pcd_all_peaks, *arrow_avg_peaks]
def example_normals(normals: np.ndarray):
LEVEL = 2
kwargs_base = dict(level=LEVEL, max_phi=180)
kwargs_s2 = dict(**kwargs_base)
# Create Gaussian Accumulator
ga_cpp_s2 = GaussianAccumulatorS2(**kwargs_s2)
# Integrate the normals and get open3d visualization
colored_icosahedron = integrate_normals_and_visualize(normals, ga_cpp_s2)
o3d.visualization.draw_geometries([colored_icosahedron])
# Create the IcoChart for unwrapping
ico_chart_ = IcoCharts(LEVEL)
normalized_bucket_counts_by_vertex = ga_cpp_s2.get_normalized_bucket_counts_by_vertex(
True)
ico_chart_.fill_image(normalized_bucket_counts_by_vertex)
triangles_vertex_14 = [2, 15, 7, 260, 267, 256]
# 2D Peak Detection
find_peaks_kwargs = dict(threshold_abs=20,
min_distance=1,
exclude_border=False,
indices=False)
cluster_kwargs = dict(t=0.2, criterion='distance')
average_filter = dict(min_total_weight=0.05)
_, _, avg_peaks, _ = find_peaks_from_ico_charts(
ico_chart_,
np.asarray(normalized_bucket_counts_by_vertex),
find_peaks_kwargs=find_peaks_kwargs,
cluster_kwargs=cluster_kwargs)
print("Detected Peaks: {}".format(avg_peaks))
full_image = np.asarray(ico_chart_.image)
plt.imshow(full_image)
plt.xticks(np.arange(0, full_image.shape[1], step=1))
plt.yticks(np.arange(0, full_image.shape[0], step=1))
plt.show()
# Don't forget to reset the GA
ga_cpp_s2.clear_count()
def analyze_mesh(mesh):
"""Demonstrates unwrapping and peak detection of a S2 Histogram"""
LEVEL = 4
kwargs_base = dict(level=LEVEL, max_phi=180)
kwargs_s2 = dict(**kwargs_base)
kwargs_opt_integrate = dict(num_nbr=12)
# Create Gaussian Accumulator
ga_cpp_s2 = GaussianAccumulatorS2(**kwargs_s2)
# This function will integrate the normals and return an open3d mesh for visualization.
colored_icosahedron_s2, _, _ = visualize_gaussian_integration(
ga_cpp_s2,
mesh,
max_phi=kwargs_base['max_phi'],
integrate_kwargs=kwargs_opt_integrate)
num_triangles = ga_cpp_s2.num_buckets
# for verification
ico_s2_organized_mesh = ga_cpp_s2.copy_ico_mesh(True)
_, _, ico_o3d_s2_om = decompose(ico_s2_organized_mesh)
colors_s2 = get_colors(range(num_triangles), colormap=plt.cm.tab20)[:, :3]
colored_ico_s2_organized_mesh = assign_vertex_colors(
ico_o3d_s2_om, colors_s2)
# Demonstrate the five charts for visualization
bucket_counts = np.asarray(ga_cpp_s2.get_normalized_bucket_counts(True))
bucket_colors = get_colors(bucket_counts)[:, :3]
charts_triangles = []
for chart_idx in range(5):
chart_size = int(num_triangles / 5)
chart_start_idx = chart_idx * chart_size
chart_end_idx = chart_start_idx + chart_size
icochart_square = refine_icochart(level=LEVEL, square=True)
_, _, icochart_square_o3d = decompose(icochart_square)
colored_icochart_square = assign_vertex_colors(
icochart_square_o3d,
bucket_colors[chart_start_idx:chart_end_idx, :])
charts_triangles.append(colored_icochart_square)
# Plot the unwrapped icosahedron
new_charts = translate_meshes(charts_triangles,
current_translation=-4.0,
axis=1)
all_charts = functools.reduce(lambda a, b: a + b, new_charts)
plot_meshes(colored_ico_s2_organized_mesh, colored_icosahedron_s2,
all_charts, mesh)
ico_chart_ = IcoCharts(LEVEL)
normalized_bucket_counts_by_vertex = ga_cpp_s2.get_normalized_bucket_counts_by_vertex(
True)
ico_chart_.fill_image(normalized_bucket_counts_by_vertex)
find_peaks_kwargs = dict(threshold_abs=25,
min_distance=1,
exclude_border=False,
indices=False)
average_filter = dict(min_total_weight=0.05)
_, _, avg_peaks, _ = find_peaks_from_ico_charts(
ico_chart_,
np.asarray(normalized_bucket_counts_by_vertex),
find_peaks_kwargs=find_peaks_kwargs,
average_filter=average_filter)
print(avg_peaks)
full_image = np.asarray(ico_chart_.image)
plt.imshow(full_image)
plt.xticks(np.arange(0, full_image.shape[1], step=1))
plt.yticks(np.arange(0, full_image.shape[0], step=1))
plt.show()
