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Project description

NudeNet: Neural Nets for Nudity Classification, Detection and selective censoring

Classifier classes:

class name Description
safe Image is not sexually explicit
unsafe Image is sexually explicit

Detector classes:

class name Description
EXPOSED_ANUS Exposed Anus; Any gender
EXPOSED_ARMPITS Exposed Armpits; Any gender
COVERED_BELLY Provocative, but covered Belly; Any gender
EXPOSED_BELLY Exposed Belly; Any gender
COVERED_BUTTOCKS Provocative, but covered Buttocks; Any gender
EXPOSED_BUTTOCKS Exposed Buttocks; Any gender
FACE_F Female Face
FACE_M Male Face
COVERED_FEET Covered Feet; Any gender
EXPOSED_FEET Exposed Feet; Any gender
COVERED_BREAST_F Provocative, but covered Breast; Female
EXPOSED_BREAST_F Exposed Breast; Female
COVERED_GENITALIA_F Provocative, but covered Genitalia; Female
EXPOSED_GENITALIA_F Exposed Genitalia; Female
EXPOSED_BREAST_M Exposed Breast; Male
EXPOSED_GENITALIA_M Exposed Genitalia; Male

Usage

As Python module

Installation:

pip install --upgrade nudenetupdated

Classifier Usage:

# Import module
from nudenet import NudeClassifier

# initialize classifier (downloads the checkpoint file automatically the first time)
classifier = NudeClassifier()

# Classify single image
classifier.classify('path_to_image_1')
# Returns {'path_to_image_1': {'safe': PROBABILITY, 'unsafe': PROBABILITY}}
# Classify multiple images (batch prediction)
# batch_size is optional; defaults to 4
classifier.classify(['path_to_image_1', 'path_to_image_2'], batch_size=BATCH_SIZE)
# Returns {'path_to_image_1': {'safe': PROBABILITY, 'unsafe': PROBABILITY},
#          'path_to_image_2': {'safe': PROBABILITY, 'unsafe': PROBABILITY}}

Detector Usage:

# Import module
from nudenet import NudeDetector

# initialize detector (downloads the checkpoint file automatically the first time)
detector = NudeDetector() # detector = NudeDetector('base') for the "base" version of detector.

# Detect single image
detector.detect('path_to_image')
# fast mode is ~3x faster compared to default mode with slightly lower accuracy.
detector.detect('path_to_image', mode='fast')
# Returns [{'box': LIST_OF_COORDINATES, 'score': PROBABILITY, 'label': LABEL}, ...]

Developpers

To get started, simply clone the repository and install the dependencies:

poetry install

Once the dependencies are installed, you can start developing your project.

Command Description
make test Run your unit tests
make lint Lint your code
make format Format your code
make mypy Run static type checking

Notes

Fork notes

  • The original project made by notAI-tech is here
  • The forked version made by platelminto taken for this project is here

Contributing

If you have any suggestions for improvements, please feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License.

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