Mask Detection

Mask detection model that aims to localize, identify and distinguish workers wearing security masks from those not wearing security masks in a single image.


This TensorFlow.js model does not require you to know about machine learning.
It can take input as any browser-based image elements (, ,
elements, for example) and returns an array of bounding boxes with class name and confidence level.

Usage

There are one main way to get this model in your JavaScript project : by installing it from NPM and using a build tool like Parcel, WebPack, or Rollup.

$3

``js
// Note: you do not need to import @tensorflow/tfjs here.

import * as mask from 'maskdetection';

const img = document.getElementById('img');

// Load the model.
const model = await mask.load(PATH_TO_JSON_MODEL);

// Classify the image.
const predictions = await model.detect(img);

console.log('Predictions: ');
console.log(predictions);
`

API

#### Loading the model
maskdetection is the module name. When using ES6 imports, mask is the module.

`ts

mask.load(PATH_TO_JSON_MODEL);
`

Args:
PATH_TO_JSON_MODEL string that specifies json file as input of the model. This file can be an url or a locally stored file.

Returns a model object.

#### Detecting workers

You can detect workers wearing masks and those who are not with the model without needing to create a Tensor.
model.detect takes an input image element and returns an array of bounding boxes with class name and confidence level.

This method exists on the model that is loaded from mask.load.

`ts
model.detect(
img: tf.Tensor3D | ImageData | HTMLImageElement |
HTMLCanvasElement | HTMLVideoElement
)
`

Args:

img: A Tensor or an image element to make a detection on.

Returns an array of classes and probabilities that looks like:

`js
[{
bbox: [x, y, width, height],
class: "person",
score: 0.8380282521247864
}, {
bbox: [x, y, width, height],
class: "person with mask",
score: 0.74644153267145157
}]
``