import math
from typing import List, Tuple
from PIL import Image # pip install pillow
from nudenet import NudeDetector # pip install nudenet
# NudeNet is a deep learning model for nudity detection and it is based on TensorFlow. It is trained on a large-scale \
# dataset and it is able to detect nudity in images with high accuracy. It is also able to detect different types of \
# nudity
# Original Repo for NudeNet: https://github.com/notAI-tech/NudeNet
# You can take a look at this for further information.
# The model checkpoint file will be downloaded at the first time of running.
detector = NudeDetector()
def _area_mosaic(image_: Image.Image, unit: int = 8) -> Image.Image:
"""
Just zooming it, and then squeezing it back.
:param image_: PIL Image object of the part to be censored.
:param unit: Granularity of mosaic squares, in pixels.
"""
width, height = image_.size
image_ = image_.resize((width // unit, height // unit))
image_ = image_.resize((width, height))
return image_
def mosaic(image_: Image.Image, fx, fy, tx, ty, unit: int = 8) -> Image.Image:
"""
:param image_: PIL Image object to censor.
:param fx: Left-upper horizontal position, in pixels.
:param fy: Left-upper vertical position, in pixels.
:param tx: Right-lower horizontal position, in pixels.
:param ty: Right-lower vertical position, in pixels.
:param unit: Granularity of mosaic squares, in pixels.
"""
c = image_.crop((fx, fy, tx, ty))
c = _area_mosaic(c, unit)
image_.paste(c, (fx, fy, tx, ty))
return image_
def get_censoring_boxes(filename: str, min_score: float = 0.6, scale_ratio: float = 0.85,
no_pussy: bool = True, no_penis: bool = True,
no_nipples: bool = False) -> List[Tuple[int, int, int, int]]:
"""
Get the areas to be censored with nudenet detection.
:param filename: Filename of the image file (Unfortunately, PIL.Image is not supported. If you want to do \
do, maybe you need to save the Image object to a temporary file and pass the file's path into here).
:param min_score: Min confidence of the detection. The higher the value, the less false positives will be, \
but it also means higher true negatives.
:param scale_ratio: The scaling factor of the censor area, 1 means using the original detected area. \
A lower value means less area will be censored, and vice versa.
:param no_pussy: Detect the female's pussy, default is `True`.
:param no_penis: Detect the male's penis, default is `True`.
:param no_nipples: Detect the female's nipples, default is `False`.
"""
result = detector.detect(filename)
boxes = []
for box in result:
if (no_penis and box['class'] == 'EXPOSED_GENITALIA_M') or \
(no_pussy and box['class'] == 'EXPOSED_GENITALIA_F') or \
(no_nipples and box['class'] == 'EXPOSED_BREAST_F'):
fx, fy, tx, ty = box['box']
width, height = tx - fx, ty - fy
cx, cy = int((fx + tx) / 2), int((fy + ty) / 2)
nfx, ntx = int(cx - width / 2 * scale_ratio), int(cx + width / 2 * scale_ratio)
nfy, nty = int(cy - height / 2 * scale_ratio), int(cy + height / 2 * scale_ratio)
boxes.append((nfx, nfy, ntx, nty))
return boxes
def censor_image(filename, min_score: float = 0.6, scale_ratio: float = 0.7, censor_unit: int = None,
no_pussy: bool = True, no_penis: bool = True, no_nipples: bool = False) -> Image.Image:
"""
Censor the given image.
:param filename: Filename of the image file (Unfortunately, PIL.Image is not supported. If you want to do \
do, maybe you need to save the Image object to a temporary file and pass the file's path into here).
:param min_score: Min confidence of the detection. The higher the value, the less false positives will be, \
but it also means higher true negatives.
:param scale_ratio: The scaling factor of the censor area, 1 means using the original detected area. \
A lower value means less area will be censored, and vice versa.
:param censor_unit: Granularity of mosaic squares, in pixels.
:param no_pussy: Detect the female's pussy, default is `True`.
:param no_penis: Detect the male's penis, default is `True`.
:param no_nipples: Detect the female's nipples, default is `False`.
"""
image_ = Image.open(filename)
width, height = image_.size
censor_unit = int(math.ceil(censor_unit or ((width + height) / 2 / 160)))
for fx, fy, tx, ty in get_censoring_boxes(filename, min_score, scale_ratio, no_pussy, no_penis,
no_nipples):
image_ = mosaic(image_, fx, fy, tx, ty, unit=censor_unit)
return image_
# Here is the usage
if __name__ == '__main__':
censored_image: Image.Image = censor_image(r'D:\\OneDrive - Huafan University\\Python_scripts\\123\\asdf\\20240414_083120.jpg')
censored_image.save(r'D:\\OneDrive - Huafan University\\Python_scripts\\123\\asdf\\asdfasdf.jpg')
