def try_open3d_viz(args):
"""Try to visualize sparse 3d point cloud using open3d"""
try:
from open3d import visualization as viz
from open3d import io
ply_file = os.path.join(args.outdir, "sparse_inliers.ply")
pc = io.read_point_cloud(ply_file)
viz.draw_geometries([pc])
except ImportError as err:
print("Failed to import `open3d` package, can not visualize"
" point-cloud, try installing open3d or use meshlab to visualize"
" ply file.")
def runSubsample(self, sourcePath, targetPath, skipScaling, parameters):
from open3d import io
print(":: Loading point clouds and downsampling")
source = io.read_point_cloud(sourcePath)
sourceSize = np.linalg.norm(
np.asarray(source.get_max_bound()) -
np.asarray(source.get_min_bound()))
target = io.read_point_cloud(targetPath)
targetSize = np.linalg.norm(
np.asarray(target.get_max_bound()) -
np.asarray(target.get_min_bound()))
voxel_size = targetSize / (55 * parameters["pointDensity"])
scaling = (targetSize) / sourceSize
if skipScaling != 0:
scaling = 1
source.scale(scaling, center=False)
source_down, source_fpfh = self.preprocess_point_cloud(
source, voxel_size, parameters["normalSearchRadius"],
parameters["FPFHSearchRadius"])
target_down, target_fpfh = self.preprocess_point_cloud(
target, voxel_size, parameters["normalSearchRadius"],
parameters["FPFHSearchRadius"])
return source, target, source_down, target_down, source_fpfh, target_fpfh, voxel_size, scaling
def stp2pcd(stpName, modelDir):
baseName = stpName.split('.')[0]
stlName = baseName + '.stl'
plyName = baseName + '.ply'
pcdName = baseName + '.pcd'
stp2stl(filename=stpName, fileIODir=modelDir, linDeflection=0.1, solidOnly=True)
stl2ply(filename=stlName, fileIODir=modelDir)
print("Load a ply point cloud, print it, and render it")
pcd = o3d.read_point_cloud(os.path.join(modelDir, plyName))
# print(pcd)
# print(np.asarray(pcd.points))
# draw_geometries([pcd])
# os.remove(os.path.join(modelDir, stlName))
os.remove(os.path.join(modelDir, plyName))
o3d.write_point_cloud(os.path.join(modelDir, pcdName), pcd)
def label_loop(dir_name):
global STATUS
for name in listdir(dir_name):
if ".zip" in name:
continue
try:
with open(f"{dir_name}/{name}.meta", 'x') as file:
pcd = io.read_point_cloud(f"{dir_name}/{name}")
STATUS = 0
draw_geometries_with_key_callbacks([pcd], {
ord('0'): _pass,
ord('1'): _alert,
},
left=0,
top=0)
file.write(f"{STATUS}\n")
except FileExistsError:
pass
def db_read_point_cloud(file):
path = fsdb._file_path(file, file.filename)
return read_point_cloud(path)
def main():
model_orig_pcd = io.read_point_cloud("model.pcd")
sample_pcd = io.read_point_cloud(
"sample.pcd") # Any sample point cloud can be given here
Pose = pose3D(model_orig_pcd, sample_pcd)
Pose.estimate_3Dpose()
def show(cpd_path):
pcd = io.read_point_cloud(cpd_path)
draw_geometries_with_key_callbacks([pcd], {32: _pass})
def _get_annotation(self, pair):
pcd = io.read_point_cloud(pair[0]).voxel_down_sample(voxel_size=0.05)
image = np.asarray(pcd.points, dtype=np.float32)
image = cv2.resize(image, (10000, 3))
image = (image - image.mean()) / (image.std() + 1e-6)
return image, np.array(pair[1], dtype=np.int32)
