CV library for image data augmentation
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Zokyo is a no-nonsense low-code computer vision augmentation library specifically built for automotive deep learning development which is easy to integrate with your MLOps pipelines.
With your venv activated:
$ make installFrom your activated venv run:
$ make testAfter done adding a new module with necessary docstrings, make sure to run the following command to generate sphinx documentation before submitting the PR.
$ make docsA Computer Vision or ML engineer can try out different operations to finalize a configuration file for augmenting their images. A sample Configuration json file for Zokyo looks like this,
Note: Refer Documentation for more details.
{
"input_dir" : "images", # input directory
"output_dir" : "output", # output directory
"annotation_dir" : "annotations", # ground truth annotations directory (Pascal VOC format)
"annotation_format" : "pascal_voc", # annotation format
"mask_dir" : "mask" # segmentation masks directory
"sample" : 5000, # number of output samples required
"multi_threaded" : true, # TODO
"batch_ingestion": true, # set to true to turn on batch ingestion to have internal batch size
"internal_batch": 20, # internal batch size
"save_annotation_mask" : false, # set to true to save output anotation masks
"operations":[
{
"operation": "DarkenScene", # operation name
"operation_module" : "zokyo.augmentation", # module of that operation, refer examples on how to write custom ops
"args": { # arguments required by that operation
"probability": 0.7, # probability of applying that operation
"darkness" : 0.5, # argument specific to the augmentation operation
"is_mask" : true, # apply augmentation operation to specific mask class label
"mask_label" : 2, # that specific mask label
"is_annotation" : true, # apply augmentation operation to specific annotation class label
"annotation_label : 1 # that specific annotation label
}
}
...
]
}
The above created config can then be used by a DevOps engineer to load and augment the data with the following 3 lines of code.
from zokyo.augmentation import Builder
pipeline = Builder(config_json="config.json")
pipeline.process_and_save()
Zokyo is an open-source project and adheres to the Contributor Convent Code of Conduct. By participating you are expected to uphold our Code of Conduct. We request our contributors and users to take a few minutes to review our Code of Conduct.
If you are interested in contributing to Zokyo through code, documentation, feature ideas, bug fixing, etc., use the Issues section as the place to start the discussion.
Zokyo is licensed under Apache License 2.0.
For more usage tutorials, take a look at the notebooks in the examples folder.
Note: Zokyo currently supports only Pascal VOC format. To convert other annotation formats to Pascal VOC see this.