# 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](https://pypi.org/project/aertb/) or do `pip3 install aertb`
### Usage
```py
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.
```py
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
```py
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
1. If the install with pip worked perfectly, you can now type `aertb` in a terminal window and the CLI will open.
2. 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
1. Once the CLI is open you get a a similar output on your terminal:
2. 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:
1. type `quit`
2. Exit virtual environment: `$ deactivate`
