def main():
input1 = "Data/MantecaFiltered/MantecaRailSlice.las"
with File(input1, mode='r') as f:
input_header = f.header
to_plot = [] # list of VTK Point Clouds to plot
points = subsample_frac_from_las_data(input1, .01)
pc = create_vtkpc_from_array(points)
to_plot.append(pc)
create_point_cloud_plot_qt(to_plot,
input_header=input_header,
axes_on=True)
input2 = "../MantecaDock/palletsRadial.las"
pointCloud = VtkPointCloud()
filteredPointCloud = VtkPointCloud()
pc2 = VtkPointCloud()
with File(input_file, mode='r') as f:
input_header = f.header
print("reading %s" % input_file)
points = read_raw_las_data(input_file)
for point in tqdm(points, total=len(points), desc="Adding"):
pointCloud.addPoint(point)
points2 = read_raw_las_data(input2)
for point in tqdm(points, total=len(points2), desc="Adding"):
pc2.addPoint(point)
filtered_points = ann_guided_filter(points2, 40, .1)
# filtered_points = radius_outlier_filter(points, r=1, search_eps=0, sd_cutoff=1)
for filtered_point in tqdm(filtered_points,
total=len(filtered_points),
desc="Adding"):
filteredPointCloud.addPoint(filtered_point)
print("Original has %d points" % len(points))
print("Filtered has %d points" % len(filtered_points))
to_plot = [pointCloud, pc2, filteredPointCloud]
create_point_cloud_plot_qt(to_plot)
return max(H)
with File(input1, mode='r') as f:
input_header = f.header
to_plot = []
# points = subsample_frac_from_las_data(input1, .0625)
# pc = create_vtkpc_from_array(points)
# to_plot.append(pc)
points2 = subsample_frac_from_las_data(input2, .0625)
pc2 = create_vtkpc_from_array(points2)
to_plot.append(pc2)
theta = cp.Variable(1)
objective = cp.Maximize(max_bin(points2, theta))
constraints = [0 <= theta, theta <= np.pi / 2.]
prob = cp.Problem(objective, constraints)
result = prob.solve()
# print(max_bin(points2, theta))
print(theta.value)
pc3 = create_vtkpc_from_array(points2)
to_plot.append(pc3)
# pdb.set_trace()
create_point_cloud_plot_qt(to_plot,
input_header=input_header,
axes_on=True)
input_header = f.header
# print("reading %s" % input_file)
points = read_raw_las_data(input1)
for point in tqdm(points, total=len(points), desc="Adding"):
pc.addPoint(point)
points_z = [point[2] for point in points]
points2d = [point[:2] for point in points]
points_f = ann_guided_filter(points2d, neighbors=1000, filter_eps=.1, dim=2)
# pdb.set_trace()
for i in range(len(points)):
# points_f[i].append(points_z[i])
points_f[i] = np.append(points_f[i], points_z[i])
pdb.set_trace()
for point in tqdm(points_f, total=len(points_f), desc="Adding"):
pc2.addPoint(point)
points3 = read_raw_las_data(input2)
for point in tqdm(points3, total=len(points3), desc="Adding"):
pc3.addPoint(point)
to_plot = [pc, pc3, pc2]
create_point_cloud_plot_qt(to_plot, axes_on=False)
pdb.set_trace()
# close after creating, else save won't work
# f.close()