Client-side indecent content checking

`, ``) and returns an array of most likely predictions and their confidence levels. ```js // Return top 3 guesses (instead of all 5) const predictions = await model.classify(img, 3) ``` **Parameters** - Tensor, Image data, Image element, video element, or canvas element to check - Number of results to return (default all 5) **Returns** - Array of objects that contain `className` and `probability`. Array size is determined by the second parameter in the `classify` function. #### `classifyGif`  This function can take a browser-based image element (``) that is a GIF, and returns an array of prediction arrays. It breaks a GIF into its frames and runs `classify` on each with a given configuration. This can take a while, as GIFs are frequently hundreds of frames. ```js // Returns all predictions of each GIF frame const framePredictions = await model.classifyGif(img) ``` If you're looking to update the user on status (_e.g. progress bar_) or change the number of top results per frame, then you can utilize the configuration parameter. Example of passing a configuration: ```js // returns top 1 prediction of each GIF frame, and logs the status to console const myConfig = { topk: 1, fps: 1, onFrame: ({ index, totalFrames, predictions, image }) => { console.log({ index, totalFrames, predictions }) // document.body.appendChild(image) // require('fs').writeFileSync(`./file.jpeg`, require('jpeg-js').encode(image).data) } } const framePredictions = await classifyGif(img, myConfig) ``` **Parameters** - Image element to check - Configuration object with the following possible key/values: - `topk` - Number of results to return per frame (default all 5) - `fps` - Frames per seconds, frames picks proportionally from the middle (default all frames) - `onFrame` - Function callback on each frame - Param is an object with the following key/values: - `index` - the current GIF frame that was classified (starting at 0) - `totalFrames` - the complete number of frames for this GIF (for progress calculations) - `predictions` - an array of length `topk`, returning top results from classify - `image` - an image of specific frame **Returns** - Array of the same order as number of frames in GIF. Each index corresponding to that frame, an returns array of objects that contain `className` and `probability`; sorted by probability and limited by topk config parameter. ## Production Tensorflow.js offers two flags, `enableProdMode` and `enableDebugMode`. If you're going to use NSFWJS in production, be sure to enable prod mode before loading the NSFWJS model. ```js import * as tf from '@tensorflow/tfjs' import * as nsfwjs from 'nsfwjs' tf.enableProdMode() //... let model = await nsfwjs.load(`${urlToNSFWJSModel}`) ``` ## Install NSFWJS is powered by TensorFlow.js as a peer dependency. If your project does not already have TFJS you'll need to add it. ```bash # peer dependency $ yarn add @tensorflow/tfjs # install NSFWJS $ yarn add nsfwjs ``` For script tags add . Then simply access the nsfwjs global variable. This requires that you've already imported TensorFlow.js as well. #### Host your own model The magic that powers NSFWJS is the [NSFW detection model](https://github.com/gantman/nsfw_model). By default, this node module is pulling from my S3, but I make no guarantees that I'll keep that download link available forever. It's best for the longevity of your project that you download and host your own version of [the model files](https://github.com/infinitered/nsfwjs/tree/master/example/nsfw_demo/public/model). You can then pass the relative URL to your hosted files in the `load` function. If you can come up with a way to bundle the model into the NPM package, I'd love to see a PR to this repo! ## Run the Examples ### Tensorflow.js in the browser The demo that powers https://nsfwjs.com/ is available in the `nsfw_demo` example folder. To run the demo, run `yarn prep` which will copy the latest code into the demo. After that's done, you can `cd` into the demo folder and run with `yarn start`. ### Browserify A browserified version using nothing but promises and script tags is available in the `minimal_demo` folder. Please do not use the script tags hosted in this demo as a CDN. This can and should be hosted in your project along side the model files. ### React Native The [NSFWJS React Native app](https://github.com/infinitered/nsfwjs-mobile)  Loads a local copy of the model to reduce network load and utilizes TFJS-React-Native. [Blog Post](https://shift.infinite.red/nsfw-js-for-react-native-a37c9ba45fe9) ### Node JS App Using NPM, you can also use the model on the server side. ```bash $ npm install nsfwjs $ npm install @tensorflow/tfjs-node ``` ```javascript const axios = require('axios') //you can use any http client const tf = require('@tensorflow/tfjs-node') const nsfw = require('nsfwjs') async function fn() { const pic = await axios.get(`link-to-picture`, { responseType: 'arraybuffer', }) const model = await nsfw.load() // To load a local model, nsfw.load('file://./path/to/model/') // Image must be in tf.tensor3d format // you can convert image to tf.tensor3d with tf.node.decodeImage(Uint8Array,channels) const image = await tf.node.decodeImage(pic.data,3) const predictions = await model.classify(image) image.dispose() // Tensor memory must be managed explicitly (it is not sufficient to let a tf.Tensor go out of scope for its memory to be released). console.log(predictions) } fn() ``` Here is another full example of a [multipart/form-data POST using Express](example/node_demo), supposing you are using JPG format. ```javascript const express = require('express') const multer = require('multer') const jpeg = require('jpeg-js') const tf = require('@tensorflow/tfjs-node') const nsfw = require('nsfwjs') const app = express() const upload = multer() let _model const convert = async (img) => { // Decoded image in UInt8 Byte array const image = await jpeg.decode(img, true) const numChannels = 3 const numPixels = image.width * image.height const values = new Int32Array(numPixels * numChannels) for (let i = 0; i < numPixels; i++) for (let c = 0; c < numChannels; ++c) values[i * numChannels + c] = image.data[i * 4 + c] return tf.tensor3d(values, [image.height, image.width, numChannels], 'int32') } app.post('/nsfw', upload.single('image'), async (req, res) => { if (!req.file) res.status(400).send('Missing image multipart/form-data') else { const image = await convert(req.file.buffer) const predictions = await _model.classify(image) image.dispose() res.json(predictions) } }) const load_model = async () => { _model = await nsfw.load() } // Keep the model in memory, make sure it's loaded only once load_model().then(() => app.listen(8080)) // curl --request POST localhost:8080/nsfw --header 'Content-Type: multipart/form-data --data-binary 'image=@/full/path/to/picture.jpg' ``` You can also use [`lovell/sharp`](https://github.com/lovell/sharp) for preprocessing tasks and more file formats. ### NSFW Filter [**NSFW Filter**](https://github.com/navendu-pottekkat/nsfw-filter) is a web extension that uses NSFWJS for filtering out NSFW images from your browser. It is currently available for Chrome and Firefox and is completely open-source. Check out the project [here](https://github.com/navendu-pottekkat/nsfw-filter). ## More! An [FAQ](https://github.com/infinitered/nsfwjs/wiki/FAQ:-NSFW-JS) page is available. More about NSFWJS and TensorFlow.js - https://youtu.be/uzQwmZwy3yw The [model was trained in Keras over several days](https://medium.freecodecamp.org/how-to-set-up-nsfw-content-detection-with-machine-learning-229a9725829c) and 60+ Gigs of data. Be sure to [check out the model code](https://github.com/GantMan/nsfw_model) which was trained on data provided by [Alexander Kim's](https://github.com/alexkimxyz) [nsfw_data_scraper](https://github.com/alexkimxyz/nsfw_data_scraper). #### Open Source NSFWJS, as open source, is free to use and always will be :heart:. It's MIT licensed, and we'll always do our best to help and quickly answer issues. If you'd like to get a hold of us, join our [community slack](http://community.infinite.red). #### Premium [Infinite Red](https://infinite.red/) offers premium training and support. Email us at [hello@infinite.red](mailto:hello@infinite.red) to get in touch. ## Contributors Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):

Gant Laborde 💬 📝 💻 💡 🤔 🚇 👀 ⚠️	Jamon Holmgren 📖 🤔 💻 🖋	Jeff Studenski 🎨	Frank von Hoven III 📖 🤔	Sandesh Soni 💻	Sean Nam 📖	Gilbert Emerson 💻
Oleksandr Kozlov 🚇 ⚠️ 💻	Morgan 💻 🤔	Michel Hua 💻 📖	Kevin VanGelder 💻 📖	Jesse Nicholson 🔣 🤔	camhart 📖	Cameron Burkholder 🎨
qwertyforce 📖

This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!