albumentations.augmentations.other.annotation_artifacts

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Add synthetic text, arrows, boxes, guide lines, and callouts that mimic scientific markup. Use to harden models against annotation artifacts.

Members

AnnotationArtifactsclass

AnnotationArtifacts(
    element_types: tuple[['text', 'rectangle', 'arrow', 'line', 'callout'], ...] = ('text', 'rectangle', 'arrow', 'line', 'callout'),
    element_probabilities: tuple[float, ...] = (0.35, 0.2, 0.2, 0.15, 0.1),
    count_range: tuple[int, int] = (1, 3),
    text_length_range: tuple[int, int] = (1, 5),
    font_scale_range: tuple[float, float] = (0.3, 1.2),
    thickness_range: tuple[int, int] = (1, 3),
    size_ratio_range: tuple[float, float] = (0.1, 0.35),
    line_length_ratio_range: tuple[float, float] = (0.1, 0.8),
    tip_length_range: tuple[float, float] = (0.2, 0.4),
    corner_prob: float = 0.6,
    black_white_prob: float = 0.85,
    p: float = 0.5,
    line_geometry: 'axis_aligned' | 'random_endpoints' | 'random_angle' = axis_aligned,
    line_styles: tuple[['solid', 'dashed', 'dotted'], ...] = ('solid', 'dashed', 'dotted'),
    line_style_probabilities: tuple[float, ...] = (0.55, 0.35, 0.1),
    random_color_prob: float = 0.0,
    color_palette: tuple[tuple[int, ...], ...] | None,
    color_palette_probabilities: tuple[float, ...] | None,
    line_length_range: tuple[int, int] | None
)

Add synthetic text, arrows, boxes, guide lines, and callouts that mimic scientific markup. Use to harden models against annotation artifacts. This transform simulates sparse human annotation artifacts commonly found in scientific figures, medical images, microscopy screenshots, and competition data. It draws short text tokens, rectangles, arrows, horizontal or vertical guide lines, and zoom-callout boxes directly on the image.

Parameters

NameTypeDefaultDescription
element_typestuple[['text', 'rectangle', 'arrow', 'line', 'callout'], ...]('text', 'rectangle', 'arrow', 'line', 'callout')Artifact types to sample. Default: ("text", "rectangle", "arrow", "line", "callout").
element_probabilitiestuple[float, ...](0.35, 0.2, 0.2, 0.15, 0.1)Sampling weights matching `element_types`. Values must be non-negative and at least one value must be positive. Default: (0.35, 0.2, 0.2, 0.15, 0.1).
count_rangetuple[int, int](1, 3)Range for the number of artifacts drawn per image. Default: (1, 3).
text_length_rangetuple[int, int](1, 5)Range for generated text token length. Text uses uppercase ASCII letters and digits. Default: (1, 5).
font_scale_rangetuple[float, float](0.3, 1.2)Range for OpenCV Hershey font scale. Default: (0.3, 1.2).
thickness_rangetuple[int, int](1, 3)Range for line, rectangle, arrow, and text thickness. Default: (1, 3).
size_ratio_rangetuple[float, float](0.1, 0.35)Range for rectangle and callout size as a fraction of image width and height. Default: (0.1, 0.35).
line_length_ratio_rangetuple[float, float](0.1, 0.8)Range for line and arrow length as a fraction of the smaller image dimension. Default: (0.1, 0.8).
tip_length_rangetuple[float, float](0.2, 0.4)Range for arrowhead length as a fraction of arrow length. Default: (0.2, 0.4).
corner_probfloat0.6Probability of placing artifacts near image corners or edges instead of uniformly inside the image. Default: 0.6.
black_white_probfloat0.85Probability of choosing black or white instead of red for an artifact. Default: 0.85.
pfloat0.5Probability of applying the transform. Default: 0.5.
line_geometry
One of:
  • 'axis_aligned'
  • 'random_endpoints'
  • 'random_angle'
axis_alignedGeometry used for line artifacts. `"axis_aligned"` preserves horizontal and vertical lines. `"random_endpoints"` samples both endpoints independently. `"random_angle"` samples a start point, angle, and length. Default: `"axis_aligned"`.
line_stylestuple[['solid', 'dashed', 'dotted'], ...]('solid', 'dashed', 'dotted')Line styles sampled for lines, arrows, and callouts. Default: ("solid", "dashed", "dotted").
line_style_probabilitiestuple[float, ...](0.55, 0.35, 0.1)Sampling weights matching `line_styles`. Values must be non-negative and at least one value must be positive. Default: (0.55, 0.35, 0.1).
random_color_probfloat0.0Probability of sampling every image channel independently and uniformly from `[0, 255]`. The remaining probability uses `color_palette` when provided, otherwise the legacy black/white/red policy. Default: 0.0.
color_palette
One of:
  • tuple[tuple[int, ...], ...]
  • None
-Optional artifact colors in `[0, 255]`. Colors are truncated or extended using their last value to match the image channels. Default: None.
color_palette_probabilities
One of:
  • tuple[float, ...]
  • None
-Optional sampling weights matching `color_palette`. When omitted, palette colors are sampled uniformly. Default: None.
line_length_range
One of:
  • tuple[int, int]
  • None
-Optional length range in pixels for `"random_angle"` lines. When omitted, `line_length_ratio_range` controls their length. Default: None.

Examples

>>> import numpy as np
>>> import albumentations as A
>>>
>>> image = np.random.randint(0, 256, (320, 320, 3), dtype=np.uint8)
>>> transform = A.Compose([
...     A.AnnotationArtifacts(
...         element_types=("text", "rectangle", "arrow", "line", "callout"),
...         element_probabilities=(0.35, 0.2, 0.2, 0.15, 0.1),
...         count_range=(1, 3),
...         corner_prob=0.6,
...         p=1.0,
...     )
... ])
>>> result = transform(image=image)
>>> augmented_image = result["image"]

Notes

- This is an image-only transform: masks, bounding boxes, and keypoints are not modified. - Colors are adapted to the number of channels; black and white affect all channels, while red maps to the first channel and pads remaining channels with zero. - Palette and uniformly sampled colors use uint8-scale values. Float32 images are rendered through the transform's dtype adapter, which maps those values to `[0, 1]`. - Random values are sampled before drawing, so replay and deterministic pipelines preserve the exact generated artifacts.

References

  • [{'description': 'Uladzislau Leketush', 'source': 'https://www.linkedin.com/in/leketush/'}, {'description': 'Original augmentation gist', 'source': 'https://gist.github.com/vlad3996/00724aafce45374214e16eb9eb07e893'}, {'description': 'Kaggle 1st place solution', 'source': 'https://github.com/vlad3996/forgeryscope/'}, {'description': 'Competition', 'source': 'https://www.kaggle.com/competitions/recodai-luc-scientific-image-forgery-detection'}]