Image augmentation with bounding boxes.
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- Dominant language
- Python
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Description
🚀 Feature
Image augmentation with bounding boxes.
Motivation
Torchvision already has data augmentation for images, but I think it would be convinient to support images with bounding boxes as well.
Here is a jupyter notebook with an example: github.com/maximlopin/boxaug/blob/master/example.ipynb
Implementation
One way to implement this is to add an optional argument bboxes to __call__ methods, and some transforms will just ignore it (e.g. ColorJitter).
Alternatives
Wihout having this built-in, you have to use and learn other libraries. Torchvision already has a simple interface for image augmentation, and adapting bounding boxes support to the same interface would be very convinient.
cc @vfdev-5
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the linked boxaug example.ipynb and the existing torchvision image-transform interface. Determine which transforms must update bounding boxes and which can ignore them, then define the required API and coverage for the feature. Done means bounding-box-aware augmentation works through the same interface while preserving the expected image and box relationship.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- computer-vision, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100