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import copy
import math
from typing import List, Union
import datasets as ds
import evaluate
import numpy as np
import numpy.typing as npt
_DESCRIPTION = r"""\
Computes the extent of spatial non-alignment between elements.
"""
_KWARGS_DESCRIPTION = """\
FIXME
"""
_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}
}
@article{li2020attribute,
title={Attribute-conditioned layout gan for automatic graphic design},
author={Li, Jianan and Yang, Jimei and Zhang, Jianming and Liu, Chang and Wang, Christina and Xu, Tingfa},
journal={IEEE Transactions on Visualization and Computer Graphics},
volume={27},
number={10},
pages={4039--4048},
year={2020},
publisher={IEEE}
}
"""
class LayoutNonAlignment(evaluate.Metric):
def __init__(
self,
canvas_width: int,
canvas_height: int,
**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#L306-L339"
],
)
def ali_delta(self, xs: npt.NDArray[np.float64]) -> float:
n = len(xs)
min_delta = np.inf
for i in range(n):
for j in range(i + 1, n):
delta = abs(xs[i] - xs[j])
min_delta = min(min_delta, delta)
return min_delta
def ali_g(self, x: float) -> float:
return -math.log(1 - x, 10)
def get_rid_of_invalid(
self, predictions: npt.NDArray[np.float64], gold_labels: npt.NDArray[np.int64]
) -> npt.NDArray[np.int64]:
assert len(predictions) == len(gold_labels)
w = self.canvas_width / 100
h = self.canvas_height / 100
for i, prediction in enumerate(predictions):
for j, b in enumerate(prediction):
xl, yl, xr, yr = b
xl = max(0, xl)
yl = max(0, yl)
xr = min(self.canvas_width, xr)
yr = min(self.canvas_height, yr)
if abs((xr - xl) * (yr - yl)) < w * h * 10:
if gold_labels[i, j]:
gold_labels[i, j] = 0
return gold_labels
def _compute(
self,
*,
predictions: Union[npt.NDArray[np.float64], List[List[float]]],
gold_labels: Union[npt.NDArray[np.int64], List[int]],
) -> float:
predictions = np.array(predictions)
gold_labels = np.array(gold_labels)
predictions[:, :, ::2] *= self.canvas_width
predictions[:, :, 1::2] *= self.canvas_height
gold_labels = self.get_rid_of_invalid(
predictions=predictions, gold_labels=gold_labels
)
metrics: float = 0.0
for gold_label, prediction in zip(gold_labels, predictions):
ali = 0.0
mask = (gold_label > 0).reshape(-1)
mask_box = prediction[mask]
theda = []
for mb in mask_box:
pos = copy.deepcopy(mb)
pos[0] /= self.canvas_width
pos[2] /= self.canvas_width
pos[1] /= self.canvas_height
pos[3] /= self.canvas_height
theda.append(
[
pos[0],
pos[1],
(pos[0] + pos[2]) / 2,
(pos[1] + pos[3]) / 2,
pos[2],
pos[3],
]
)
theda_arr = np.array(theda)
if theda_arr.shape[0] <= 1:
continue
n = len(mask_box)
for _ in range(n):
g_val = []
for j in range(6):
xys = theda_arr[:, j]
delta = self.ali_delta(xys)
g_val.append(self.ali_g(delta))
ali += min(g_val)
metrics += ali
return metrics / len(gold_labels)
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