Uses iterative closest point (ICP) to match sample point clouds to templates. Includes utilities to convert existing .stl, .obj, .xaml, .pkl etc. objects into point cloud, numpy arrays. To visualize the results in an interactive viewer, see viewer/.
The project is written in Python 3 and is not guaranteed to successfully backport to Python 2.
(Optional) We recommend setting up a virtual environment.
virtualenv pcm --python=python3
source activate pcm/bin/activate
Say $PCM_ROOT is the root of your repository. Navigate to your root repository.
cd $PCM_ROOT
We need to setup our Python dependencies.
pip install -r requirements.txt
By default, the script looks for sample point clouds in ./data/raw and
template point clouds in ./data/templates. All point cloud files are .npy
files containing nx3 matrices of x,y,z respectively.
python label.py
Here are full usage instructions:
Usage:
label.py [options]
Options:
--template=<path> Path to templates [default: ./data/templates/*.npy]
--raw=<path> Path to unclassified data [default: ./data/raw/*.npy]
--out=<out> Path for final results [default: ./out/labels.npy]
