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from typing import List, Union
import datasets as ds
import evaluate
import numpy as np
import numpy.typing as npt
from evaluate.utils.file_utils import add_start_docstrings
_DESCRIPTION = r"""\
Computes the ratio of valid elements to all elements in the layout, where the area within the canvas of a valid element must be greater than 0.1% of the canvas.
"""
_KWARGS_DESCRIPTION = """\
Args:
predictions (`list` of `list` of `float`): A list of lists of floats representing normalized `ltrb`-format bounding boxes.
gold_labels (`list` of `list` of `int`): A list of lists of integers representing class labels.
canvas_width (`int`, *optional*): Width of the canvas in pixels. Can be provided at initialization or during computation.
canvas_height (`int`, *optional*): Height of the canvas in pixels. Can be provided at initialization or during computation.
Returns:
float: The ratio of valid elements to all elements (0.0 to 1.0). An element is considered valid if its area within the canvas is greater than 0.1% of the canvas area.
Examples:
>>> import evaluate
>>> import numpy as np
>>> metric = evaluate.load("creative-graphic-design/layout-validity")
>>> # Normalized bounding boxes (left, top, right, bottom)
>>> predictions = [[[0.1, 0.1, 0.5, 0.5], [0.6, 0.6, 0.9, 0.9]]]
>>> gold_labels = [[1, 2]] # Non-zero labels indicate valid elements
>>> result = metric.compute(predictions=predictions, gold_labels=gold_labels, canvas_width=512, canvas_height=512)
>>> print(result)
1.0
"""
_CITATION = """\
@inproceedings{hsu2023posterlayout,
title={Posterlayout: A new benchmark and approach for content-aware visual-textual presentation layout},
author={Hsu, Hsiao Yuan and He, Xiangteng and Peng, Yuxin and Kong, Hao and Zhang, Qing},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={6018--6026},
year={2023}
}
"""
@add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class LayoutValidity(evaluate.Metric):
def __init__(
self,
canvas_width: int | None = None,
canvas_height: int | None = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.canvas_width = canvas_width
self.canvas_height = canvas_height
def _info(self) -> evaluate.EvaluationModuleInfo:
return evaluate.MetricInfo(
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=ds.Features(
{
"predictions": ds.Sequence(ds.Sequence(ds.Value("float64"))),
"gold_labels": ds.Sequence(ds.Sequence(ds.Value("int64"))),
}
),
codebase_urls=[
"https://github.com/PKU-ICST-MIPL/PosterLayout-CVPR2023/blob/main/eval.py#L105-L127"
],
)
def _compute(
self,
*,
predictions: Union[npt.NDArray[np.float64], List[List[float]]],
gold_labels: Union[npt.NDArray[np.int64], List[int]],
canvas_width: int | None = None,
canvas_height: int | None = None,
) -> float:
# パラメータの優先順位処理
canvas_width = canvas_width if canvas_width is not None else self.canvas_width
canvas_height = (
canvas_height if canvas_height is not None else self.canvas_height
)
if canvas_width is None or canvas_height is None:
raise ValueError(
"canvas_width and canvas_height must be provided either "
"at initialization or during computation"
)
predictions = np.array(predictions)
gold_labels = np.array(gold_labels)
predictions[:, :, ::2] *= canvas_width
predictions[:, :, 1::2] *= canvas_height
total_elements, empty_elements = 0, 0
w = canvas_width / 100
h = canvas_height / 100
assert len(predictions) == len(gold_labels)
for gold_label, prediction in zip(gold_labels, predictions):
mask = (gold_label > 0).reshape(-1)
mask_prediction = prediction[mask]
total_elements += len(mask_prediction)
for mp in mask_prediction:
xl, yl, xr, yr = mp
xl = max(0, xl)
yl = max(0, yl)
xr = min(canvas_width, xr)
yr = min(canvas_height, yr)
if abs((xr - xl) * (yr - yl)) < w * h * 10:
empty_elements += 1
return 1 - empty_elements / total_elements
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