A No-code solution to create the images transformation pipeline.
- Installed as a pip package.
- Runs in a browser.
- Uses Albumentations library to apply transformations.
- Benchmarks the pipeline.
- Generates a YAML config and a tiny chunk of python code to integrate with PyTorch code.
Powered by Albumentations and Streamlit.
Work still in progress.
We'll appreciate any feedback from the community: bug-report, feature-request, pull-request.
You can leave anonimous feedback here.
You need python 3.6+ and pip to install the app.
pip install augbuilder
Run augbuilder
from the terminal.
After a few seconds the browser will open the page localhost:8501.
To stop the application press the ctrl+c
combination in the terminal.
Watch this demo video of usage.
- Drop an image to the upload area.
- Use dropdown on left side to select transformations.
- Configure transformations below the list of dropdowns.
- Random results are shown in the main area.
- To regenerate results click "Refresh images" button.
- Click "Download config" to get the yaml config and a python integration script.
Select oneof in list if you want to add this into you transformation list. Then you can add different transformations in it. To close oneof select StopOneOf. Please, don't select THE SAME transformation, it can caused some errors which will be removed later.
RandomResizedCrop:
height: 299
width: 299
scale: (0.24, 1.0)
ratio: (0.75, 1.3333333333333333)
interpolation: 0
Flip:
Transpose:
OneOf:
MotionBlur: {'blur_limit': (3, 53)}
Blur: {'blur_limit': (3, 22)}
ShiftScaleRotate:
shift_limit: (-0.06, 0.06)
scale_limit: (-0.1, 0.1)
rotate_limit: (-90, 90)
interpolation: 0
border_mode: 3
value: [0, 0, 0]
HueSaturationValue:
hue_shift_limit: (-20, 20)
sat_shift_limit: (-30, 30)
val_shift_limit: (-20, 20)