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 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 plot_hilbert_curve(ga: GaussianAccumulatorKDPy, plot=False):
normals = np.asarray(ga.get_bucket_normals())
normalized_counts = np.asarray(ga.get_normalized_bucket_counts())
colors = get_colors(normalized_counts)[:, :3]
bucket_normals_hv = np.asarray(ga.get_bucket_sfc_values())
num_buckets = ga.num_buckets
idx_sort = np.argsort(bucket_normals_hv)
bucket_normals_hv_sorted = bucket_normals_hv[idx_sort]
colors = colors[idx_sort, :]
accumulator_normalized_sorted = normalized_counts[idx_sort]
gaussian_normals_sorted = normals[idx_sort, :]
# Find Peaks using 1D signal detector
peaks, clusters, avg_peaks, avg_weights = find_peaks_from_accumulator(
gaussian_normals_sorted, accumulator_normalized_sorted)
# 2D Plots
if plot:
class_name_str = type(ga).__name__
if class_name_str == 'GaussianAccumulatorS2':
fig, ax = plt.subplots(1, 1, figsize=(5, 5))
else:
proj_ = np.asarray(ga.get_bucket_projection())
proj = proj_[idx_sort, :]
fig, axs = plt.subplots(2, 1, figsize=(8, 10))
ax = axs[0]
scatter1 = ax.scatter(proj[:, 0],
proj[:, 1],
c=colors,
label='Projected Buckets')
scatter2 = ax.scatter(proj[peaks, :][:, 0],
proj[peaks, :][:, 1],
marker='x',
c=clusters,
label='Clusters',
cmap='tab20')
ax.set_title("Hilbert Curve with Azimuth Equidistant Projection")
ax.set_xlabel("x*")
ax.set_ylabel("y*")
line1 = ax.plot(proj[:, 0],
proj[:, 1],
c='k',
label='Hilbert Curve Connections')[0]
ax.axis('equal')
leg = ax.legend(loc='upper left', fancybox=True, shadow=True)
# we will set up a dict mapping legend line to orig line, and enable
# picking on the legend line
lines = [line1, scatter1, scatter2]
lined = dict()
for legline, origline in zip(leg.legendHandles, lines):
legline.set_picker(5) # 5 pts tolerance
lined[legline] = origline
def onpick(event):
# on the pick event, find the orig line corresponding to the
# legend proxy line, and toggle the visibility
legline = event.artist
origline = lined[legline]
vis = not origline.get_visible()
origline.set_visible(vis)
# Change the alpha on the line in the legend so we can see what lines
# have been toggled
if vis:
legline.set_alpha(1.0)
else:
legline.set_alpha(0.2)
fig.canvas.draw()
ax = axs[1]
fig.canvas.mpl_connect('pick_event', onpick)
ax.bar(np.arange(num_buckets), accumulator_normalized_sorted)
ax.scatter(peaks,
accumulator_normalized_sorted[peaks],
marker='x',
c=clusters,
cmap='tab20')
ax.set_title("Histogram of Normal Counts sorted by Hilbert Values")
ax.set_xlabel("Hilbert Value (Ascending)")
ax.set_ylabel("Normal Counts")
fig.tight_layout()
plt.show()
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]