CoPA-Map (Continuous Pedestrian Activity Map) is a method to model spatio-temporal human activity with Gaussian Processes. A focus lies on the creation based on observations by mobile robots.
- Python 3.8
GPU-based training/inference was developed and tested with tensorflow version 2.3.1 and CUDA 10.1. See also setup.cfg for dependencies
The module contains some Cython code (for FOV calculation) which must be compiled
Install numpy and cython
pip install -r requirements.txt
Install dependencies
python setup.py install
Install cython-setuptools and compile
cython-setuptools install
python setup.py build_ext --inplace
import copa_map.model.CoPAMapBase as copa_map
X = # numpy array of data with size (N, 3) (two spatial, one temporal location)
Y = # numpy array of rate with size (N, 1)
Z = # numpy array of initial inducing locations with size (M, 3)
X_test = # numpy array of grid-like test locations with size (N_t, 3)
# For an example of how to obtain these data vectors see TODO
model = copa_map.CoPAMapBase()
model.learn(X, Y, Z)
m_y, stdd_y = model.predict(X_test)The directory src/copa_map/examples includes example scripts to show the process of data preparation, training,
and predicting. The scripts are numbered and use the result from the preceding script as input. However, zip files
provide pre processed data, which is extracted if the resulting files are not found. Therefore every script can directly
be executed.
This script takes people detections and robot poses as input to create a spatio-temporal grid. Detections are counted in the grid and for every cell an observation duration of the robot is calculated based on a field of view and an occupancy map. By standard configuration, at the end of a script a window opens where the grid data can be inspected.
This script trains a CoPA-Map model based on the gridified data. The trained hyperparameters are then saved to a file.
This script uses the trained hyperparameters and makes predictions at test inputs.
TODO