dev(narugo): replace aicheck (295af1f4) · Commits · git-mirror / Imgutils

docs/source/api_doc/validate/aicheck_benchmark.plot.py

+6 −5

Original line number	Diff line number	Diff line
		import random

		from benchmark import BaseBenchmark, create_plot_cli
		from imgutils.generic.classify import _open_models_for_repo_id
		from imgutils.validate import get_ai_created_score
		from imgutils.validate.aicheck import _MODEL_NAMES
		from imgutils.validate.aicheck import _REPO_ID

		_MODEL_NAMES = _open_models_for_repo_id(_REPO_ID).model_names


		class AnimeAICheckBenchmark(BaseBenchmark):
		@@ -11,12 +14,10 @@ class AnimeAICheckBenchmark(BaseBenchmark):
		self.model = model

		def load(self):
		from imgutils.validate.aicheck import _open_anime_aicheck_model
		_ = _open_anime_aicheck_model(self.model)
		_open_models_for_repo_id(_REPO_ID)._open_model(self.model)

		def unload(self):
		from imgutils.validate.aicheck import _open_anime_aicheck_model
		_open_anime_aicheck_model.cache_clear()
		_open_models_for_repo_id(_REPO_ID).clear()

		def run(self):
		image_file = random.choice(self.all_images)

docs/source/api_doc/validate/aicheck_benchmark.plot.py.svg

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imgutils/validate/aicheck.py

+48 −85

Original line number	Diff line number	Diff line
		@@ -15,62 +15,72 @@ Overview:
		The models are hosted on
		`huggingface - deepghs/anime_ai_check <https://huggingface.co/deepghs/anime_ai_check>`_.
		"""
		from functools import lru_cache
		from typing import Tuple, Optional

		import numpy as np
		from PIL import Image
		from huggingface_hub import hf_hub_download

		from imgutils.data import rgb_encode, ImageTyping, load_image
		from imgutils.utils import open_onnx_model
		from ..data import ImageTyping
		from ..generic import classify_predict, classify_predict_score

		__all__ = [
		'get_ai_created_score',
		'is_ai_created',
		]

		_LABELS = ['ai', 'human']
		_MODEL_NAMES = [
		'caformer_s36_plus_sce',
		'mobilenetv3_sce',
		'mobilenetv3_sce_dist',
		]
		_DEFAULT_MODEL_NAME = 'mobilenetv3_sce_dist'
		_REPO_ID = 'deepghs/anime_ai_check'


		@lru_cache()
		def _open_anime_aicheck_model(model_name):
		return open_onnx_model(hf_hub_download(
		f'deepghs/anime_ai_check',
		f'{model_name}/model.onnx',
		))


		def _img_encode(image: Image.Image, size: Tuple[int, int] = (384, 384),
		normalize: Optional[Tuple[float, float]] = (0.5, 0.5)):
		image = image.resize(size, Image.BILINEAR)
		data = rgb_encode(image, order_='CHW')
		def get_ai_created_score(image: ImageTyping, model_name: str = _DEFAULT_MODEL_NAME) -> float:
		"""
		Overview:
		Predict if the given image is created by AI (mainly by stable diffusion), given a score.

		if normalize is not None:
		mean_, std_ = normalize
		mean = np.asarray([mean_]).reshape((-1, 1, 1))
		std = np.asarray([std_]).reshape((-1, 1, 1))
		data = (data - mean) / std
		:param image: Image to be predicted.
		:param model_name: Name of the model. Default is ``mobilenetv3_sce_dist``.
		If you need better accuracy, use ``caformer_s36_plus_sce``.
		All the available values are listed on the benchmark graph.
		:return: A float number which represent the score of AI-check.

		return data.astype(np.float32)
		Examples::
		>>> from imgutils.validate import get_ai_created_score
		>>>
		>>> get_ai_created_score('aicheck/ai/1.jpg')
		0.9996960163116455
		>>> get_ai_created_score('aicheck/ai/2.jpg')
		0.9999125003814697
		>>> get_ai_created_score('aicheck/ai/3.jpg')
		0.997803270816803
		>>> get_ai_created_score('aicheck/ai/4.jpg')
		0.9960069060325623
		>>> get_ai_created_score('aicheck/ai/5.jpg')
		0.9887709021568298
		>>> get_ai_created_score('aicheck/ai/6.jpg')
		0.9998629093170166
		>>> get_ai_created_score('aicheck/human/7.jpg')
		0.0013722758740186691
		>>> get_ai_created_score('aicheck/human/8.jpg')
		0.00020673229300882667
		>>> get_ai_created_score('aicheck/human/9.jpg')
		0.0001895089662866667
		>>> get_ai_created_score('aicheck/human/10.jpg')
		0.0008857478387653828
		>>> get_ai_created_score('aicheck/human/11.jpg')
		4.552320024231449e-05
		>>> get_ai_created_score('aicheck/human/12.jpg')
		0.001168627175502479
		"""
		return classify_predict_score(image, _REPO_ID, model_name)['ai']


		def get_ai_created_score(image: ImageTyping, model_name: str = _DEFAULT_MODEL_NAME) -> float:
		def is_ai_created(image: ImageTyping, model_name: str = _DEFAULT_MODEL_NAME, threshold: float = 0.5) -> bool:
		"""
		Overview:
		Predict if the given image is created by AI (mainly by stable diffusion), given a score.
		Predict if the given image is created by AI (mainly by stable diffusion).

		:param image: Image to be predicted.
		:param model_name: Name of the model. Default is ``mobilenetv3_sce_dist``.
		If you need better accuracy, use ``caformer_s36_plus_sce``.
		All the available values are listed on the benchmark graph.
		:return: A float number which represent the score of AI-check.
		:param threshold: Threshold of the score. When the score is no less than ``threshold``, this image
		will be predicted as ``AI-created``. Default is ``0.5``.
		:return: This image is ``AI-created`` or not.

		Examples::
		>>> from imgutils.validate import is_ai_created
		@@ -100,52 +110,5 @@ def get_ai_created_score(image: ImageTyping, model_name: str = _DEFAULT_MODEL_NA
		>>> is_ai_created('aicheck/human/12.jpg')
		False
		"""
		image = load_image(image, force_background='white', mode='RGB')
		input_ = _img_encode(image)[None, ...]
		output, = _open_anime_aicheck_model(model_name).run(['output'], {'input': input_})

		return output[0][0].item()


		def is_ai_created(image: ImageTyping, model_name: str = _DEFAULT_MODEL_NAME, threshold: float = 0.5) -> bool:
		"""
		Overview:
		Predict if the given image is created by AI (mainly by stable diffusion).

		:param image: Image to be predicted.
		:param model_name: Name of the model. Default is ``mobilenetv3_sce_dist``.
		If you need better accuracy, use ``caformer_s36_plus_sce``.
		All the available values are listed on the benchmark graph.
		:param threshold: Threshold of the score. When the score is no less than ``threshold``, this image
		will be predicted as ``AI-created``. Default is ``0.5``.
		:return: This image is ``AI-created`` or not.

		Examples::
		>>> from imgutils.validate import get_ai_created_score
		>>>
		>>> get_ai_created_score('aicheck/ai/1.jpg')
		0.9996960163116455
		>>> get_ai_created_score('aicheck/ai/2.jpg')
		0.9999125003814697
		>>> get_ai_created_score('aicheck/ai/3.jpg')
		0.997803270816803
		>>> get_ai_created_score('aicheck/ai/4.jpg')
		0.9960069060325623
		>>> get_ai_created_score('aicheck/ai/5.jpg')
		0.9887709021568298
		>>> get_ai_created_score('aicheck/ai/6.jpg')
		0.9998629093170166
		>>> get_ai_created_score('aicheck/human/7.jpg')
		0.0013722758740186691
		>>> get_ai_created_score('aicheck/human/8.jpg')
		0.00020673229300882667
		>>> get_ai_created_score('aicheck/human/9.jpg')
		0.0001895089662866667
		>>> get_ai_created_score('aicheck/human/10.jpg')
		0.0008857478387653828
		>>> get_ai_created_score('aicheck/human/11.jpg')
		4.552320024231449e-05
		>>> get_ai_created_score('aicheck/human/12.jpg')
		0.001168627175502479
		"""
		return get_ai_created_score(image, model_name) >= threshold
		type_, _ = classify_predict(image, _REPO_ID, model_name)
		return type_ == 'ai'

test/validate/test_aicheck.py

+3 −2

Original line number	Diff line number	Diff line
		@@ -3,7 +3,8 @@ import os.path

		import pytest

		from imgutils.validate.aicheck import _open_anime_aicheck_model, is_ai_created, get_ai_created_score
		from imgutils.generic.classify import _open_models_for_repo_id
		from imgutils.validate.aicheck import is_ai_created, get_ai_created_score, _REPO_ID
		from test.testings import get_testfile

		_ROOT_DIR = get_testfile('anime_aicheck')
		@@ -18,7 +19,7 @@ def _release_model_after_run():
		try:
		yield
		finally:
		_open_anime_aicheck_model.cache_clear()
		_open_models_for_repo_id(_REPO_ID).clear()


		@pytest.mark.unittest