"""
Overview:
Rough Prediction of illustration's completeness (``monochrome``, ``rough`` and ``polished``).
The following are sample images for testing.
.. image:: completeness.plot.py.svg
:align: center
This is an overall benchmark of all the completeness classification models:
.. image:: completeness_benchmark.plot.py.svg
:align: center
The models are hosted on
`huggingface - deepghs/anime_completeness <https://huggingface.co/deepghs/anime_completeness>`_.
"""
from typing import Tuple, Dict
from ..data import ImageTyping
from ..generic import classify_predict, classify_predict_score
__all__ = [
'anime_completeness_score',
'anime_completeness',
]
_DEFAULT_MODEL_NAME = 'mobilenetv3_v2.2_dist'
_REPO_ID = 'deepghs/anime_completeness'
[docs]def anime_completeness_score(image: ImageTyping, model_name: str = _DEFAULT_MODEL_NAME) -> Dict[str, float]:
"""
Rough Prediction of illustration's completeness, and return the score.
:param image: The input image.
:type image: ImageTyping
:param model_name: The model name. Default is 'mobilenetv3_v2.2_dist'.
:type model_name: str
:return: A dictionary with type scores.
:rtype: Dict[str, float]
Examples::
>>> from imgutils.validate import anime_completeness_score
>>>
>>> anime_completeness_score('completeness/monochrome/1.jpg')
{'polished': 5.910087929805741e-05, 'rough': 0.1241951510310173, 'monochrome': 0.8757457137107849}
>>> anime_completeness_score('completeness/monochrome/2.jpg')
{'polished': 0.00018937293498311192, 'rough': 0.030049214139580727, 'monochrome': 0.9697614312171936}
>>> anime_completeness_score('completeness/monochrome/3.jpg')
{'polished': 0.008933082222938538, 'rough': 0.05096456781029701, 'monochrome': 0.9401022791862488}
>>> anime_completeness_score('completeness/rough/4.jpg')
{'polished': 0.009832272306084633, 'rough': 0.9898634552955627, 'monochrome': 0.00030428936588577926}
>>> anime_completeness_score('completeness/rough/5.jpg')
{'polished': 0.013919820077717304, 'rough': 0.9819902181625366, 'monochrome': 0.004089969675987959}
>>> anime_completeness_score('completeness/rough/6.jpg')
{'polished': 0.04166747257113457, 'rough': 0.9582782983779907, 'monochrome': 5.4218515288084745e-05}
>>> anime_completeness_score('completeness/polished/7.jpg')
{'polished': 0.8849249482154846, 'rough': 0.1149177998304367, 'monochrome': 0.0001573085755808279}
>>> anime_completeness_score('completeness/polished/8.jpg')
{'polished': 0.9907882809638977, 'rough': 0.008958994410932064, 'monochrome': 0.0002527958422433585}
>>> anime_completeness_score('completeness/polished/9.jpg')
{'polished': 0.9831690788269043, 'rough': 0.01682441122829914, 'monochrome': 6.613856385229155e-06}
"""
return classify_predict_score(image, _REPO_ID, model_name)
[docs]def anime_completeness(image: ImageTyping, model_name: str = _DEFAULT_MODEL_NAME) -> Tuple[str, float]:
"""
Rough Prediction of illustration's completeness, return the predict result and its score.
:param image: The input image.
:type image: ImageTyping
:param model_name: The model name. Default is 'mobilenetv3_v2.2_dist'.
:type model_name: str
:return: A tuple with the primary type and its score.
:rtype: Tuple[str, float]
Examples::
>>> from imgutils.validate import anime_completeness
>>>
>>> anime_completeness('completeness/monochrome/1.jpg')
('monochrome', 0.8757458329200745)
>>> anime_completeness('completeness/monochrome/2.jpg')
('monochrome', 0.9697614312171936)
>>> anime_completeness('completeness/monochrome/3.jpg')
('monochrome', 0.9401022791862488)
>>> anime_completeness('completeness/rough/4.jpg')
('rough', 0.9898634552955627)
>>> anime_completeness('completeness/rough/5.jpg')
('rough', 0.9819902181625366)
>>> anime_completeness('completeness/rough/6.jpg')
('rough', 0.9582782983779907)
>>> anime_completeness('completeness/polished/7.jpg')
('polished', 0.8849248290061951)
>>> anime_completeness('completeness/polished/8.jpg')
('polished', 0.9907882809638977)
>>> anime_completeness('completeness/polished/9.jpg')
('polished', 0.9831690788269043)
"""
return classify_predict(image, _REPO_ID, model_name)