aertb 0.4.1

Description:

aertb 0.4.1

AER-toolbox
This library intends to be a minimal tool for loading events from files with common event-camera file extensions into
Python.
See the project on PyPI or do pip3 install aertb
Usage
from aertb.core import FileLoader

datLoader = FileLoader('dat') # 'bin', or 'aedat'
datLoader.load_events('../example_data/dat/cars/obj_004414_td.dat')

Supported extensions:

.dat: N-Cars / Prophesee Cameras
.bin: N-MNIST, N-Caltech101
.aedat: PokerDVS
.mat: DVS-Barrel

It also make the process of loading and iterating HDF5 files easier.
from aertb.core import HDF5File

dataset_train = HDF5File('TRAIN.h5')
train_iterator = dataset_train.iterator(n_samples_group=10, rand=23)

for sample in tqdm(train_iterator):
# do something with sample.events, sample.label or sample.name

Example: making a GIF
from aertb.core import HDF5File, make_gif

file = HDF5File('../DVS_Barrel.hdf5')
sample = file.load_events(group='moving', name='11')
make_gif(sample, filename='sample_moving.gif', camera_size=(128, 128), n_frames=480, gtype='std')

The library also includes a command line interface for converting files from a given extension to hdf5, as well as gif
making capabilities for easy visualisation of the files.
Opening the CLI


If the install with pip worked perfectly, you can now type aertb in a terminal window and the CLI will open.


If you are installing it from Github: download you should download the project from github and follow the following
instructions:

a) git clone ...
b) Create a virual environment, if venv is not installed run pip install virtualenv,
then python3 -m venv aertb_env
c) Run source aertb_env/bin/activate
d) Run the following command: pip install -r requirements.txt
e) Open the cli with python3 . or with the __main__.py file



Using the CLI

Once the CLI is open you get a a similar output on your terminal:

type help to see supported commands and help <topic> to get more info of the command

Examples:
Creating an HDF5 out of a directory
tohdf5 -f 'example_data/dat' -e 'dat' -o 'mytest.h5'

The recommended directory shape is :
|--Parent (given as parameter)
|-- LabelClass1
|-- SampleName1
|-- SampleName2
|-- ....
|-- LabelClass2
|-- SampleName1
|-- SampleName2
|-- ....
|-- ...

And we suggest that train and test are kept as separate folders so they translate
to two different files
Creating an HDF5 out of a single file
tohdf5 -f 'example_data/bin/one/03263.bin' -o 'mytest2.h5'

Creating a gif out of a given file
makegif -f 'example_data/prophesee_dat/test_23l_td.dat' -o 'myGif.gif' -nfr 240 -g 'std'


Exiting the CLI:

type quit
Exit virtual environment: $ deactivate