* feat(transforms): add 8 robotics-relevant image augmentations
Add GaussianNoise, MotionBlur, JPEGCompression, GaussianPatchBrightness,
RandomShadow, CoarseDropout, GammaCorrection, and PlanckianJitter.
Each transform addresses a real-world failure mode not covered by the
existing 6 defaults (sensor noise, motion blur, compression artifacts,
uneven lighting, cast shadows, partial occlusion, exposure variation,
color temperature shift).
All transforms are pure PyTorch, follow the make_params/transform
pattern, and integrate with ImageTransformConfig via a registry.
* add augmentation showcase image for PR
* update showcase with better sample frame
* tune showcase to balanced augmentation intensity
* tune showcase: softer shadow, dropout, jitter intensity
* refactor(transforms): several updates
* update image
* chore(media): remove example
* chore: add link to example
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Co-authored-by: Yuxian LI <liyuxian1358@gmail.com>
Steven Palma