cvb/CreateAndAccessCalibratedPointCloud

"""
CVBpy Example Script
 
1. Creates a calibrated point cloud from a range map aquired
by a laser triangulation setup (for further information see
<a htref="https://help.commonvisionblox.com/NextGen/14.1/md_theory_of_operation_tools__metric.html">here</a>).
2. Converts it to a numpy array without copying.
3. Modifies point cloud values through numpy.
4. Saves the point cloud. 
 
Requires: numpy
"""
 
import os
import cvb
 
# load range map
print("Loading range map and calibration file.")
rangemap = cvb.Image(os.path.join(cvb.install_path(
), "tutorial", "Metric", "Images", "RangeMapCalibrationPattern.tif"))
 
# load calibration file
calibrator = cvb.Calibrator3D.load(os.path.join(
    cvb.install_path(), "tutorial", "Metric", "Images", "SICalibration.json"))
 
# create calibrated (dense) point cloud
print("Creating calibrated point cloud.")
cloud = cvb.PointCloudFactory.create(
    rangemap.planes[0], calibrator, cvb.PointCloudFlags.Float)
 
# create a numpy array from point cloud
# copy=False is default, but just a request
np_array = cvb.as_array(cloud, copy=False)
 
if np_array.flags["OWNDATA"]:
    # use copy=True or
    # np_array = cvb.to_array(cloud)
    # or simply throw an error:
    raise RuntimeError("cannot map to numpy array")
 
# point cloud access
print("Modifying point cloud data via numpy array.")
# E.g. cut height values (z) below 0.
for i in range(np_array.shape[0]):
    for j in range(np_array.shape[1]):
        if np_array[i, j, 2] < 0:  # z value smaller zero
            np_array[i, j, 2] = 0
            # set confidence to false (note, that usually
            np_array[i, j, 3] = 0
            # only dense point clouds have the confidence layer!)
 
# save point cloud
print("Saving: ./calibrated_cloud.ply")
cloud.save("calibrated_cloud.ply")
 
# Attention: If you use cvb.as_array with copy=False, ensure, that your cloud object
# lives as long as your numpy array!