maskdetection v0.2.2
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 (<img>
, <video>
, <canvas>
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.
via NPM
// 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.
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
.
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:
[{
bbox: [x, y, width, height],
class: "person",
score: 0.8380282521247864
}, {
bbox: [x, y, width, height],
class: "person with mask",
score: 0.74644153267145157
}]
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