imgutils.validate.completeness

Overview:

Rough Prediction of illustration’s completeness (monochrome, rough and polished).

The following are sample images for testing.

This is an overall benchmark of all the completeness classification models:

The models are hosted on huggingface - deepghs/anime_completeness.

anime_completeness_score

imgutils.validate.completeness.anime_completeness_score(image: str | PathLike | bytes | bytearray | BinaryIO | Image, model_name: str = 'mobilenetv3_v2.2_dist') → Dict[str, float]

Rough Prediction of illustration’s completeness, and return the score.

Parameters:

• image (ImageTyping) – The input image.

• model_name (str) – The model name. Default is ‘mobilenetv3_v2.2_dist’.

Returns:

A dictionary with type scores.

Return type:

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}

anime_completeness

imgutils.validate.completeness.anime_completeness(image: str | PathLike | bytes | bytearray | BinaryIO | Image, model_name: str = 'mobilenetv3_v2.2_dist') → Tuple[str, float]

Rough Prediction of illustration’s completeness, return the predict result and its score.

Parameters:

• image (ImageTyping) – The input image.

• model_name (str) – The model name. Default is ‘mobilenetv3_v2.2_dist’.

Returns:

A tuple with the primary type and its score.

Return type:

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)