Support for imgaug

[5starkarma]

🚀 Feature

imgaug is very popular and is easy to work with. I am wondering if I put in PR's which allowed use of imgaug, such as BoundingBox instances, to be used in parts of the program if it would be approved.

e.g. The plot_one_box function could accept then convert BoundingBox instances to the proper format (tensor) for easy plotting of bboxes.

Example:

def plot_one_box(x, img, color=None, label=None, line_thickness=None):
    # If bbox is instance of BoundingBox
    if isinstance(x, BoundingBox):
        # Convert to tensor
        x = torch.tensor([x.x1, x.y1, x.x2, x.y2])

Motivation

I use imgaug, albumentations uses imgaug. It is very easy to use.

Pitch

Add functionality which allows use of imgaug.

[glenn-jocher]

@5starkarma yes we welcome PRs! I'm not familiar with imgaug, though we have had requests for Albumentations ops, which might go in datasets.py.

Note that you should profile any modifications to verify that performance speed is unaffected by the proposed changes as part of the PR as well.

[github-actions]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.

[lleye]

@5starkarma can you give an example of how you integrate imgaug in yolov5, trying to augment as well

[glenn-jocher]

the best place to insert Albumentation augmentations into the YOLOv5 dataloader is here:

yolov5/utils/datasets.py

Lines 536 to 552 in 6306091

	if self.augment:
	# Augment imagespace
	if not mosaic:
	img, labels = random_perspective(img, labels,
	degrees=hyp['degrees'],
	translate=hyp['translate'],
	scale=hyp['scale'],
	shear=hyp['shear'],
	perspective=hyp['perspective'])
	# Augment colorspace
	augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
	# Apply cutouts
	# if random.random() < 0.9:
	# labels = cutout(img, labels)

where img is the image and labels are the bounding box labels. Note that any Albumentation augmentations you add will be in addition to the existing automatic YOLOv5 augmentations defined in your hyperparameter file:

yolov5/data/hyp.scratch.yaml

Lines 21 to 33 in 6306091

	fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
	hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
	hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
	hsv_v: 0.4 # image HSV-Value augmentation (fraction)
	degrees: 0.0 # image rotation (+/- deg)
	translate: 0.1 # image translation (+/- fraction)
	scale: 0.5 # image scale (+/- gain)
	shear: 0.0 # image shear (+/- deg)
	perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
	flipud: 0.0 # image flip up-down (probability)
	fliplr: 0.5 # image flip left-right (probability)
	mosaic: 1.0 # image mosaic (probability)
	mixup: 0.0 # image mixup (probability)

[lleye]

@glenn-jocher quick question on your comment here "Note that any Albumentation augmentations you add will be in addition to the existing automatic YOLOv5 augmentations defined in your hyperparameter file"

Do you mean if i set mixup to 0.1, it will augment the data with an additional 10% of mixed up images making it 110%? Or is it going to be 90% normal and 10% mixed augmented?

Also i cant seem to integrate imgaug, any pointers in case you saw someone integrate it before? Thanks

[glenn-jocher]

@lleye 0.10 mixup means 10% of your images will use mixup.