Getting started with Object Detection Using TensorFlow.js

In deep learning, one of the most widely-used technologies is TensorFlow, an end-to-end open-source platform for building models. It has a vast, flexible ecosystem of tools, libraries, and community resources. With the power of TensorFlow, researchers and developers can develop and deploy ML-powered applications.

In this tutorial, we’re going to work with TensorFlow.js, TensorFlow’s JavaScript library. We’ll use this library to learn to perform object detection—and specifically, detect instances of people—using our device’s webcam. The idea is fairly simple. We launch the camera in observation mode. Then, when it detects a human, it starts to record the movement of the person until the person is no longer in the view of the camera.

For our object detection model, we are going to use the COCO-SSD, one of TensorFlow’s pre-built models. More on that next.

What is COCO-SSD?

COCO-SSD is an object detection model powered by the TensorFlow object detection API. SSD is an acronym from Single-Shot MultiBox Detection. This model has the ability to detect 90 Class in the COCO Dataset. It makes use of large scale object detection, segmentation, and a captioning dataset in order to detect the target objects.

Now that we have some context for our project, let’s get started!

Imports and Configurations

First, we need to import the libraries required for this tutorial—React and TensorFlow:

import React, { useRef, useEffect, useState } from "react";
import ReactDOM from "react-dom";

import * as cocoSsd from "@tensorflow-models/coco-ssd";
import "@tensorflow/tfjs";

Next, we need to create a functional component with a new variable, as shown in the code snippet below:

const App = () => {
  const [rec, setRec] = useState([]);

   const video = useRef(null);
  const startBtn = useRef(null);
  const stopBtn = useRef(null);

  const willRec = useRef(false);
  const model = useRef(null);
  const recnow = useRef(false);
  const recorderRef = useRef(null);

Here, the component name is App, which is the main component itself, and the state variable is named records and is initialized using the useState function. There are several other constant variables defined using the useRef function required for manual video configuration.

Now, we need to initialize the plug video source to the COCO-SSD on initial load, as shown in the code snippet below:

async function prepare() {
    startBtn.current.setAttribute("disabled", true);
    stopBtn.current.setAttribute("disabled", true);
    if (navigator.mediaDevices && navigator.mediaDevices.getUserMedia) {
      try {
        const stream = await navigator.mediaDevices.getUserMedia({
          audio: true,
          video: true
        });
        window.stream = stream;
        video.current.srcObject = stream;
        const model = await cocoSsd.load();

        model.current = model;
        startButtonElement.current.removeAttribute("disabled");
      } catch (error) {
        console.error(error);
      }
    }
  }

Here, we have a prepare function that performs the following operations:

1. First, disables start and stop buttons.
2. Second, starts access to webcam.
3. Third, loads COCO-SSD and assigns it to Model.

#heartbeat #object-detection #guides-and-tutorials #tensorflow #tensorflowjs

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Updating FreeBSD repository catalogue...
FreeBSD repository is up to date.
All repositories are up to date.
Checking integrity... done (0 conflicting)
The following 8 package(s) will be affected (of 0 checked):

New packages to be INSTALLED:
	libffi: 3.2.1_3
	py37-dateutil: 2.8.1
	py37-magic: 5.38
	py37-s3cmd: 2.1.0
	py37-setuptools: 44.0.0
	py37-six: 1.14.0
	python37: 3.7.8
	readline: 8.0.4

Number of packages to be installed: 8

The process will require 118 MiB more space.

Proceed with this action? [y/N]: y
[rsnapshot] [1/8] Installing readline-8.0.4...
[rsnapshot] [1/8] Extracting readline-8.0.4: 100%
[rsnapshot] [2/8] Installing libffi-3.2.1_3...
....
..
[rsnapshot] [8/8] Extracting py37-s3cmd-2.1.0: 100%
=====
Message from python37-3.7.8:

--
Note that some standard Python modules are provided as separate ports
as they require additional dependencies. They are available as:

py37-gdbm       databases/py-gdbm@py37
py37-sqlite3    databases/py-sqlite3@py37
py37-tkinter    x11-toolkits/py-tkinter@py37

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