ValueError: Expected x_min for bbox (-0.02666666666666667, 0.3022222222222222, 0.10666666666666667, 0.48444444444444446, tensor(1)) to be in the range [0.0, 1.0], got -0.02666666666666667.

🐛 Bug

To Reproduce

Steps to reproduce the behavior:

Expected behavior

Environment

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

Additional context

I’m getting ValueError: Expected x_min for bbox (-0.02666666666666667, 0.3022222222222222, 0.10666666666666667, 0.48444444444444446, tensor(1)) to be in the range [0.0, 1.0], got -0.02666666666666667. message

my original box is

new_boxes->[[442. 79. 972. 564.]]

its x_min, y_min, x_max, y_max format then it only give this error message when

        if random.random() < 0.2:
            print('horizontalFlip')
            print(f'new_boxes->{new_boxes}')
            augment = albumentations.HorizontalFlip(p=0.5)
            aug = albumentations.Compose([augment],bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})
            aug_result = aug(image=np.array(new_image), bboxes=new_boxes, labels=labels)
            new_image = aug_result['image']
            new_boxes = aug_result['bboxes']
            new_image = FT.to_pil_image(new_image)

[marcocaccin]

This unfortunately is regularly happening for me as well. Here’s my env along with a reproducible script.

It seems to me that all of these errors seem to deal with numerical errors when doing geometric operations on totally legit bounding boxes right at the image boundary. One way to deal with the issue would be to allow an “unsafe” transformation by letting the user control the check_validity flag from BBoxParams and deal with truncations manually afterwards: that’s still a better option than having a training job fail because of uncontrollable exceptions.

CC: @BeckerFelix

Environment

• Albumentations version: 0.4.6, 0.3.3
• Python version: 3.6.12
• OS (e.g., Linux): ubuntu 20.04
• How you installed albumentations (conda, pip, source): pip

import numpy as np
import albumentations as A

np.random.seed(123)
HEIGHT, WIDTH = 720, 1280

def random_bbox():
    x1 = np.random.randint(low=0, high=WIDTH)
    y1 = np.random.randint(low=0, high=HEIGHT)
    x2 = np.random.randint(low=x1 + 1, high=WIDTH + 1)
    y2 = np.random.randint(low=y1 + 1, high=HEIGHT + 1)
    bbox_albu = A.convert_bbox_to_albumentations([x1, y1, x2, y2], source_format='pascal_voc', rows=HEIGHT, cols=WIDTH)
    bbox_yolo = A.convert_bbox_from_albumentations(bbox_albu, target_format='yolo', rows=HEIGHT, cols=WIDTH, check_validity=True)
    # NOTE: at this point the bounding box has been checked to be valid.

    return bbox_yolo


transform = A.Compose(
    [A.HorizontalFlip(), A.RandomBrightnessContrast()],
    bbox_params=A.BboxParams(format='yolo', label_fields=["class_labels"])
)
img = np.zeros((HEIGHT, WIDTH, 3), dtype=np.uint8)

for i in range(1000):
    bboxes = [random_bbox()]
    try:
        transform(image=img, bboxes=bboxes, class_labels=[1])
    except:
        print(f"[{i}] Invalid transformation of box: {str(bboxes[0])}")

>>> [327] Invalid transformation of box: (0.755859375, 0.5944444444444444, 0.48671875, 0.3611111111111111)
>>> [363] Invalid transformation of box: (0.373046875, 0.9409722222222222, 0.68828125, 0.11527777777777778)
>>> [683] Invalid transformation of box: (0.465625, 0.9881944444444445, 0.5765625, 0.020833333333333332)

[Dipet]

Should be fixed by #924