This project is deploying web demo of IMGAUG [option: Random augment] for who want to augment images easily.
Random Augmentation helps you with augmenting images for your machine learning projects.
It converts a set of input images into a new, much larger set of slightly altered images.
- Mix many augmentation techniques.
- affine transformations, gausian noise, dropout, blurring, contrast changes, cropping/padding, ...
- Optimized for high performance.
- Easy to use.
$ git clone https://github.com/Wook-2/RandomAugmentation.git
$ cd RandomAugmentation
$ docker build -t {your_image_name} .
$ docker run -it --rm -p 8000:8000 {your_image_name}
then visit : localhost:8000
$ curl -X POST "https://master-random-augmentation-wook-2.endpoint.ainize.ai/augment" \
-H "accept: application/octet-stream" -H "Content-Type: multipart/form-data" \
-F "file=@{your_image_path};type=image/jpeg" \
-F "number={numbers of augmented images}" -o {output_file_name}.zip
Fill in {your_image_path, numbers of augmented images, output_file_name} to suit your situation.
-
IMGAUG
: https://github.com/aleju/imgaug -
Ainize
: https://ainize.ai/