The dataset folder contains the reference data acquired using Terrestrial Laser Scanner (TLS). In particular the txt file contains the data with the following header:
X,Y,Z,nx,ny,nz,dip, dip direction
where X,Y,Z is the coordinate linked to a given point, nx,ny,nz are the normals. In that folder there are also the centroids for dip and dip direction angles (in degrees) derived from in situ observations.
The classification algorithm is able to cluster data acquired with the TLS; classification takes into account the dip and dip direction values. User could change:
- the number of classes;
- cluster initializations (random or manual);
- the number of iterations.
User could change the path of fileName. To run the classification:
python classification.py
The main outputs are:
- centroid
- clustered data (data + labels)
- ply of point cloud
- figures of classified data
To run the spacing calculation you need to provide the output of classification (clustered data); user could change params or file path / name. To run the code:
python distances.py
The main output is a file with the statics for distances of each class.
It is possible also to create a stereonet starting from the classified data. To generate the stereonet run the code in the following way:
python stereo_plot.py
Python 3.8+ matplotlib numpy mplstereonet