imgutils.validate.monochrome

Overview:

A model for screening monochrome images, with the definition of monochrome images referring to Danbooru.

The following are testing images. The top two rows are monochrome images, and the bottom two rows are color images. Please note that monochrome images are not only those with all pixels in grayscale.

../../_images/monochrome.plot.py.svg

This is an overall benchmark of all the monochrome validation models:

../../_images/monochrome_benchmark.plot.py.svg

The models are hosted on huggingface - deepghs/monochrome_detect.

get_monochrome_score

imgutils.validate.monochrome.get_monochrome_score(image: str | PathLike | bytes | bytearray | BinaryIO | Image, model_name: str = 'mobilenetv3_large_100_dist_safe2') → float[source]
Overview:

Get monochrome score of the given image.

Parameters:

  • image – Image to predict, can be a PIL.Image object or the path of the image file.

  • model_name – The model used for inference. The default value is mobilenetv3_dist, which offers high runtime performance. If you need better accuracy, just use caformer_s36.

Examples::
>>> from imgutils.validate import get_monochrome_score
>>>
>>> get_monochrome_score('mono/1.jpg')  # monochrome images
0.9614395499229431
>>> get_monochrome_score('mono/2.jpg')
0.9458909034729004
>>> get_monochrome_score('mono/3.jpg')
0.9559807777404785
>>> get_monochrome_score('mono/4.jpg')
0.9651952981948853
>>> get_monochrome_score('mono/5.jpg')
0.9379720687866211
>>> get_monochrome_score('mono/6.jpg')
0.8814834356307983
>>>
>>> get_monochrome_score('colored/7.jpg')  # colored images
0.03941023349761963
>>> get_monochrome_score('colored/8.jpg')
0.07492382079362869
>>> get_monochrome_score('colored/9.jpg')
0.09546589106321335
>>> get_monochrome_score('colored/10.jpg')
0.016521310433745384
>>> get_monochrome_score('colored/11.jpg')
0.005693843588232994
>>> get_monochrome_score('colored/12.jpg')
0.0315730981528759

is_monochrome

imgutils.validate.monochrome.is_monochrome(image: str | PathLike | bytes | bytearray | BinaryIO | Image, threshold: float = 0.5, model_name: str = 'mobilenetv3_large_100_dist_safe2') → bool[source]
Overview:

Predict if the image is monochrome.

Parameters:

  • image – Image to predict, can be a PIL.Image object or the path of the image file.

  • threshold – Threshold value during prediction. If the score is higher than the threshold, the image will be classified as monochrome.

  • model_name – The model used for inference. The default value is mobilenetv3_dist, which offers high runtime performance. If you need better accuracy, just use caformer_s36.

Examples:
>>> import os
>>> from imgutils.validate import is_monochrome
>>>
>>> is_monochrome('mono/1.jpg')  # monochrome images
True
>>> is_monochrome('mono/2.jpg')
True
>>> is_monochrome('mono/3.jpg')
True
>>> is_monochrome('mono/4.jpg')
True
>>> is_monochrome('mono/5.jpg')
True
>>> is_monochrome('mono/6.jpg')
True
>>> is_monochrome('colored/7.jpg')  # colored images
False
>>> is_monochrome('colored/8.jpg')
False
>>> is_monochrome('colored/9.jpg')
False
>>> is_monochrome('colored/10.jpg')
False
>>> is_monochrome('colored/11.jpg')
False
>>> is_monochrome('colored/12.jpg')
False