helmetdetection v1.2.2

Helmet Detection

Helmet detection model that aims to localize, identify and distinguish workers wearing security helmets from those not wearing security helmets 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 helmet from 'helmet-detection';

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

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

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

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

API

Loading the model

helmet-detection is the module name. When using ES6 imports, helmet is the module.

helmet.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 helmets 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 helmet.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 helmet",
  score: 0.74644153267145157
}]