def ply2pcd(file_path = None):
if file_path is None:
file_path, file_format = get_file()
cloud = pcl.load(file_path, cloud_format)
file_path = file_path[:-3]
print(file_path)
pcl.save_XYZRGBA(cloud, (file_path + "pcd"), format="pcd")
[0, 1],
[0, 2],
[0, 3],
]
#each vector is red, green blue are PCA1, PCA2 and PCA3 respectivley.
colors = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
#create open3d linesets.
line_set = o3d.LineSet()
line_set.points = o3d.Vector3dVector(points)
line_set.lines = o3d.Vector2iVector(lines)
line_set.colors = o3d.Vector3dVector(colors)
return line_set
if __name__ == "__main__":
cloud_path, cloud_format = load.get_file()
# cloud = pcl.load(cloud_path,cloud_format)
cloud = o3d.io.read_point_cloud(cloud_path, format="ply")
#Perform PCA on entire object
vectors, center, result = get_pca(cloud)
#create vectors in o3d linesets
line_set = o3d_pca_geometry(vectors, center)
#display in o3d
o3d.draw_geometries([cloud, line_set])
for point in range(len(picked_points) - 1):
p1 = np_cloud[picked_points[point]]
p2 = np_cloud[picked_points[point + 1]]
#triangulate the distance between the two points
angle = get_angle(p1, p2)
distance = trangulate_dist(p1[2], p2[2], angle)
print("Section " + str(point + 1) + ": " + str(distance) + " meters")
#distance = euclid_dist(p1, p2)
distance_segments.append(distance)
total_distance += distance
print(str(total_distance * 100) + " centimeters")
print(str(total_distance) + " meters")
return total_distance, distance_segments
if __name__ == "__main__":
print("Starting...")
# Grab new intrinsics (may be changed by decimation)
#angle = get_angle((10,1,1), (1,1,0) )
#length = get_length(3,2,angle)
cloud_path, cloud_format = get_file()
cloud = o3d.io.read_point_cloud(cloud_path, format=cloud_format)
#intrinsics = read_intrinsics((cloud_path[:-4] + "_intrinsics.txt"))
length = manual_measure(cloud)
print("Stopping...")
import pcl
import open3d
from load import get_file
if __name__ == "__main__":
target_path, target_format = get_file()
target_cloud = pcl.load(target_path)
open3d.registration.registration_ransac_based_on_feature_matching()
input_path, input_format = get_file()
input_cloud = pcl.load(input_path)
ndt = pcl.NormalDistributionsTransform()
ndt.set_Resolution(1.0)
ndt.set_StepSize(0.1)
ndt.set_InputTarget(target_cloud)
step = ndt.get_StepSize()
final = ndt.get_FinalNumIteration()
prob = ndt.get_TransformationProbability()
ndt.set_InputTarget()
print(step)
print(final)
print(prob)
import pcl
import load
def filter(pcl_cloud, leaf_size=0.01):
sor = pcl_cloud.make_voxel_grid_filter()
sor.set_leaf_size(leaf_size, leaf_size, leaf_size)
cloud_filtered = sor.filter()
return cloud_filtered
if __name__ == "__main__":
cp, cf = load.get_file()
cloud = pcl.load(cp, cf)
cloud_filtered = filter(cloud, 0.001)
pcl.save(cloud_filtered, "./data/tests/_test_voxel_grid2.ply")