def test_same_output(num_clusters, normals_per_cluster):
fixture = setup_fastga_simple(level=4)
clusters, normals = cluster_normals(
num_clusters=num_clusters,
normals_per_cluster=normals_per_cluster,
patch_deg=5)
clusters = np.concatenate(clusters)
ga = fixture['ga']
ico = fixture['ico']
ga.integrate(MatX3d(clusters))
# New API
new_api_peaks = np.array(
ga.find_peaks(threshold_abs=20,
cluster_distance=0.1,
min_cluster_weight=0.1))
# Old API
normalized_bucket_counts_by_vertex = ga.get_normalized_bucket_counts_by_vertex(
True)
average_vertex_normals = np.asarray(ga.get_average_normals_by_vertex(True))
ico.fill_image(normalized_bucket_counts_by_vertex)
_, _, old_api_peaks, _ = find_peaks_from_ico_charts(
ico,
np.asarray(normalized_bucket_counts_by_vertex),
vertices=average_vertex_normals)
# Sort the peaks
new_api_peaks = sort_by_distance_from_point(new_api_peaks)
old_api_peaks = sort_by_distance_from_point(old_api_peaks)
print(new_api_peaks)
print(old_api_peaks)
np.testing.assert_allclose(new_api_peaks, old_api_peaks)
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=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)
peaks, clusters, avg_peaks, avg_weights = find_peaks_from_ico_charts(
ico_chart,
np.asarray(normalized_bucket_counts_by_vertex),
find_peaks_kwargs=find_peaks_kwargs,
cluster_kwargs=cluster_kwargs,
average_filter=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)
print(avg_peaks)
return [pcd_all_peaks, *arrow_avg_peaks]
def example_normals(normals: np.ndarray):
LEVEL = 4
kwargs_base = dict(level=LEVEL)
kwargs_s2 = dict(**kwargs_base)
axis_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(
0.5).translate([-2.0, 0, 0])
# Create Gaussian Accumulator
ga_cpp_s2 = GaussianAccumulatorS2Beta(**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, axis_frame])
# 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)
average_vertex_normals = np.asarray(
ga_cpp_s2.get_average_normals_by_vertex(True))
# 2D Peak Detection
find_peaks_kwargs = dict(threshold_abs=20,
min_distance=1,
exclude_border=False,
indices=False)
cluster_kwargs = dict(t=0.05, criterion='distance')
average_filter = dict(min_total_weight=0.2)
# New simplified API for finding peaks
res = np.array(
ga_cpp_s2.find_peaks(
threshold_abs=find_peaks_kwargs['threshold_abs'],
cluster_distance=cluster_kwargs['t'],
min_cluster_weight=average_filter['min_total_weight']))
print("New Detected Peaks:")
res = sort_by_distance_from_point(res)
print(res)
# Old Way of finding peaks
_, _, avg_peaks, _ = find_peaks_from_ico_charts(
ico_chart_,
np.asarray(normalized_bucket_counts_by_vertex),
vertices=average_vertex_normals,
find_peaks_kwargs=find_peaks_kwargs,
cluster_kwargs=cluster_kwargs)
avg_peaks = sort_by_distance_from_point(avg_peaks)
print("Detected Peaks:")
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()
# Don't forget to reset the GA
ga_cpp_s2.clear_count()
def find_peaks_from_ico_charts_updated(ga, ico, find_peaks_kwargs,
cluster_kwargs, average_filter):
normalized_bucket_counts_by_vertex = np.asarray(
ga.get_normalized_bucket_counts_by_vertex(True))
average_vertex_normals = np.asarray(ga.get_average_normals_by_vertex(True))
res = find_peaks_from_ico_charts(ico,
normalized_bucket_counts_by_vertex,
vertices=average_vertex_normals,
find_peaks_kwargs=find_peaks_kwargs,
cluster_kwargs=cluster_kwargs,
average_filter=average_filter)
return res
def get_image_peaks(ico_chart,
ga,
level=2,
with_o3d=False,
find_peaks_kwargs=dict(threshold_abs=2,
min_distance=1,
exclude_border=False,
indices=False),
cluster_kwargs=dict(t=0.10, criterion='distance'),
average_filter=dict(min_total_weight=0.01),
**kwargs):
normalized_bucket_counts_by_vertex = ga.get_normalized_bucket_counts_by_vertex(
True)
t1 = time.perf_counter()
ico_chart.fill_image(
normalized_bucket_counts_by_vertex) # this takes microseconds
# plt.imshow(np.asarray(ico_chart.image))
# plt.show()
peaks, clusters, avg_peaks, avg_weights = find_peaks_from_ico_charts(
ico_chart,
np.asarray(normalized_bucket_counts_by_vertex),
find_peaks_kwargs=find_peaks_kwargs,
cluster_kwargs=cluster_kwargs,
average_filter=average_filter)
t2 = time.perf_counter()
gaussian_normals_sorted = np.asarray(ico_chart.sphere_mesh.vertices)
# Create Open3D structures for visualization
if with_o3d:
pcd_all_peaks = get_pc_all_peaks(peaks, clusters,
gaussian_normals_sorted)
arrow_avg_peaks = get_arrow_normals(avg_peaks, avg_weights)
else:
pcd_all_peaks = None
arrow_avg_peaks = None
elapsed_time = (t2 - t1) * 1000
timings = dict(t_fastga_peak=elapsed_time)
logging.debug("Peak Detection - Took (ms): %.2f", (t2 - t1) * 1000)
return avg_peaks, pcd_all_peaks, arrow_avg_peaks, timings
def main():
EXAMPLE_INDEX = 1
kwargs_base = dict(level=2, 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(kwargs_base['level'])
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, avg_weights = 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)
arrow_avg_peaks = get_arrow_normals(avg_peaks, avg_weights)
o3d.visualization.draw_geometries(
[colored_icosahedron_s2, *arrow_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()