End to end software to capture new objects using RGBD camera and augment them to get a annotated dataset to train deep nets.
End to end software to capture new objects using RGBD camera and augment them to get a annotated dataset to train deep nets
- Pipeline to artificial generate annotated data for training deep learning models.
- Pipeline includes starting from capturing images using provided camera (Realsense), generate semantic labels of the captured image and then generate the artificial images.
- GUI required for the end user from capturing to labelling and generating new data.
- Ubuntu 16.04 (Testing for Ubuntu 18.04)
- Intel Realsense Camera
- Processer intel i5 or higher
- python 3.5
- Number of classes captured should be more than or equal to 2.
pip3 install easy-augmentor
- First release for crowd testing
- Now user can save RGB, pointcloud, depth, boundingbox, semantic label
- Continous mode added
- Santosh Muthireddy – https://github.com/santoshreddy254
- Naresh Kumar Gurulingan - https://github.com/NareshGuru77
- Deepan Chakravarthi Padmanabhan - https://github.com/DeepanChakravarthiPadmanabhan
- M.Sc Deebul Nair - https://github.com/deebuls
Distributed under the MLP license. See
LICENSE for more information.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size easy_augment-1.1.0-py3-none-any.whl (35.5 kB)||File type Wheel||Python version py3||Upload date||Hashes View|
Hashes for easy_augment-1.1.0-py3-none-any.whl