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import os
from typing import List, Union
import datasets as ds
import evaluate
import numpy as np
import numpy.typing as npt
from PIL import Image
_DESCRIPTION = r"""\
Computes the average pixel value of areas covered by elements in S.
"""
_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}
}
"""
class LayoutOcculusion(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"))),
"saliency_maps_1": ds.Sequence(ds.Value("string")),
"saliency_maps_2": ds.Sequence(ds.Value("string")),
}
),
codebase_urls=[
"https://github.com/PKU-ICST-MIPL/PosterLayout-CVPR2023/blob/main/eval.py#L144-L171"
],
)
def load_saliency_map(
self,
filepath: Union[os.PathLike, List[os.PathLike]],
) -> npt.NDArray[np.float64]:
if isinstance(filepath, list):
assert len(filepath) == 1, filepath
filepath = filepath[0]
map_pil = Image.open(filepath) # type: ignore
map_pil = map_pil.convert("L")
if map_pil.size != (self.canvas_width, self.canvas_height):
map_pil = map_pil.resize((self.canvas_width, self.canvas_height))
map_arr = np.array(map_pil)
map_arr = map_arr / 255.0
return map_arr
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]],
saliency_maps_1: List[os.PathLike],
saliency_maps_2: List[os.PathLike],
) -> 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
)
score = 0.0
assert (
len(predictions)
== len(gold_labels)
== len(saliency_maps_1)
== len(saliency_maps_2)
)
num_predictions = len(predictions)
it = zip(predictions, gold_labels, saliency_maps_1, saliency_maps_2)
for prediction, gold_label, smap_1, smap_2 in it:
smap_arr_1 = self.load_saliency_map(smap_1)
smap_arr_2 = self.load_saliency_map(smap_2)
smap_arr = np.maximum(smap_arr_1, smap_arr_2)
cal_mask = np.zeros_like(smap_arr)
prediction = np.array(prediction, dtype=int)
gold_label = np.array(gold_label, dtype=int)
mask = (gold_label > 0).reshape(-1)
mask_prediction = prediction[mask]
for mp in mask_prediction:
xl, yl, xr, yr = mp
cal_mask[yl:yr, xl:xr] = 1
total_area = np.sum(cal_mask)
total_sal = np.sum(smap_arr[cal_mask == 1])
if total_sal and total_area:
score += total_sal / total_area
return score / num_predictions
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