bonk is a very simple, yet powerful, command-line application that uses machine learning to detect whether or not an image contains nudity.

The tool analyzes image files or directories of images and provides predictions on the likelihood of each image containing nudity.

Features

• Detects nudity in images using a pre-trained machine learning model.
• Supports various image formats including JPEG, PNG, GIF, and WebP.
• Can process individual image files or entire directories of images.
• Allows customization of the probability threshold for nudity detection.
• Enables parallel processing of images for improved performance.
• Generates all output in JSON format for easy parsing and processing.

The name bonk comes from the "Go to horny jail" meme, because why not?

Why?

bonk exists for two reasons:

1. I needed a way to help automate at least part of the review process for customer websites. The company I rent servers from don't allow nudity, so I needed a way to detect it.
2. I wanted to learn Rust.

This is my first time writing Rust and I haven't even finished the Rust book yet. Keep that in mind when reviewing the code.

First, ensure the following dependencies are installed:

• Rust.
• Cargo.
• make.
• scdoc.

Then clone the repository, compile, and install:

make
sudo make install

$ bonk --help
Use machine learning to detect nudity in images.

Usage: bonk [OPTIONS] <PATH>

Arguments:
  <PATH>  path to an image file or directory of images to analyze

Options:
  -t, --threshold <THRESHOLD>  probability threshold above which an image is...
  -p, --parallel <PARALLEL>    number of images to process concurrently [default: 16]
  -h, --help                   Print help
  -V, --version                Print version

Refer to the bonk(1) manpage after installation for examples and more usage information.

The tool outputs the analysis results in JSON format. For each image, it provides the following information:

• has_nudity: A boolean indicating whether the image is considered to contain nudity based on the specified threshold.
• path: The path to the image file.
• filename: The name of the image file.
• predictions: An array of prediction objects, each containing a category and its corresponding probability.

Example output

{
  "has_nudity": true,
  "path": "/path/to/image",
  "filename": "example.jpg",
  "predictions": [
    {
      "category": "drawing",
      "probability": 0.00024631553
    },
    {
      "category": "hentai",
      "probability": 0.0049908394
    },
    {
      "category": "neutral",
      "probability": 0.0015131987
    },
    {
      "category": "porn",
      "probability": 0.9691646
    },
    {
      "category": "sexy",
      "probability": 0.024084995
    }
  ]
}

Anyone can help make bonk better. Send patches on the mailing list and report bugs on the issue tracker.

You must sign-off your work using git commit --signoff. Follow the Linux kernel developer's certificate of origin for more details.

All contributions are made under the GPL-2.0 license.

In no particular order, the following tasks need to be done:

• Add tests.
• Add documentation to the code.
• Add a --recursive flag to analyze entire directory trees.
• Change default output format to a human-readable table.
• Introduce a --json flag to output in JSON format once we change the default output format.

• The code in this project draws inspiration from by the Rust library Fyko/nsfw and the JavaScript library infinitered/nsfwjs.
• The machine learning model comes from GantMan/nsfw_model and was just converted to ONNX. Copyright belongs to the authors.

The following resources are available:

• Support and general discussions.
• Patches and development related questions.
• Instructions on how to prepare patches.
• Feature requests and bug reports.

Released under the GPL-2.0 license.