Yolo boxes format overflowing image and thowing an exception

🐛 Bug

I have some bounding boxes in yolo format that are off the image.

To Reproduce

1. Have boxes outside image in yolo format and try to augment

\lib\site-packages\albumentations\augmentations\bbox_utils.py", line 330, in check_bbox “to be in the range [0.0, 1.0], got {value}.”.format(bbox=bbox, name=name, value=value) ValueError: Expected x_max for bbox (0.9967206790123457, 0.5379050925925926, 1.0001929012345678, 0.5529513888888888, 0) to be in the range [0.0, 1.0], got 1.0001929012345678.

Expected behavior

I would prefer it to clip the boxes, or at least give me the option to do so.

Environment

• Albumentations version (e.g., 0.1.8): 0.5.2
• Python version (e.g., 3.7): 3.6
• OS (e.g., Linux): Windows
• How you installed albumentations (conda, pip, source): pip
• Any other relevant information:

[Trud09]

The image might be proprietary, but here is one box causing the issue:

Yolo Format: [0.99662423, 0.7520255 , 0.00675154, 0.01446759]

My albumentations conversion: [0.99324846, 0.7447917, 1.0, 0.7592593]

Exception for this box: Exception has occurred: ValueError Expected x_max for bbox (0.9934413580246914, 0.7450810185185185, 1.0001929012345678, 0.7592592592592593, 0) to be in the range [0.0, 1.0], got 1.0001929012345678.

My conversion code:

def yolo_to_norm_voc(bboxes):
    x_mean, y_mean, w, h = bboxes[:, 0], bboxes[:, 1], bboxes[:, 2], bboxes[:, 3]

    x_min = x_mean - (w/2)
    y_min = y_mean - (h/2)

    x_min, x_max, y_min, y_max = x_min, x_min + w, y_min, y_min + h
    return np.stack([x_min, y_min, x_max, y_max], 1)

[Dipet]

Should be fixed by #924