Graphically view input from mat.
Built in Python3 and relies on the SciPy stack. On Windows, the easiest way to use the SciPy stack is through Anaconda.
- Install the SciPy stack using your preferred method.
- Install virtualenv (it may already be installed) with
python -m pip install virtualenv
- Install virtualenv to the
env/
folder usingpython -m virtualenv env
- Activate the env using
env\Scripts\activate
(Windows) orenv/bin/activate
otherwise. - With the env activated, install requirements with
pip install -r requirements.txt
- Modify the configuration in
serial_com/
for the size of the matt. - Use the Arduino IDE to install the sketch to the device.
- Modify the configuration in
heatmap/__init__.py
for rows and columns and any other config. - Activate the env using
env\Scripts\activate
(Windows) orenv/bin/activate
otherwise. - Run configurations:
- To run with random sample data, run
python run.py fake
- To get data from serial, run
python run.py serial
- To save the data after you quit, add a filename to the command, i.e.
python run.py serial push_up
to save the session todata/push_up_TIMESTAMP.json'
- While running, press Enter to save a new section in the data.
- To replay saved data, run
python run.py replay FILENAME.json
- you can also request a single frame by adding arguments, i.e.
python run.py replay FILANAME 3 22
will grab index 22 from section 3 of FILENAME.
- you can also request a single frame by adding arguments, i.e.
- To run with random sample data, run
- Install TensorFlow: https://www.tensorflow.org/install/
- To convert saved data into digestible formats, run
python model.py create
- results can be previewed as images by running
python model.py preview
- results can be previewed as images by running
- To train the model, run
python model.py train
- To train on top of a prior model, run
python model.py train r
- To train on top of a prior model, run