Machine Learning Projects: Object Detection Project in python Free Source code

Project: Object Detection Project in python

Object Detection Project in python is a subset of the wider topic of Computer Vision. This technology is capable of recognizing and tracking objects in photos and movies. Face recognition, vehicle recognition, pedestrian counting, self-driving vehicles, security systems, and many more applications use object recognition, also known as object detection.

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The two main goals of object recognition are as follows:
Identification of all items in a photograph
Filtration of the thing vying for attention

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Object Detection Project in python

Java Project – Updategadh


The objective of Object Detection Project in python is to develop a robust and efficient object detection system using Python, leveraging state-of-the-art computer vision techniques and deep learning frameworks. The system should be capable of accurately identifying and localizing objects within images or video streams. Key goals include implementing a pre-trained deep learning model for object detection, fine-tuning the model on a specific dataset relevant to the application domain,

How To Run Object Detection Project in python ? 🤖

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  • ✅ Toggle switch to turn AI on or off
  • ✅ Range slider to control frame rate

Multiple browser support

Internet Explorer
Mobile Browsersupported

Software And Tools Required

  • : Vs Code
  • : Python
  • : JavaScript

Credits (Use these libraries)


Home Page:

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Free Object Detection Project in python :-Click below

Download Source Code Project:

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Copy below Source code :-

Object Detection Project in python
Object Detection Project in python


<!DOCTYPE html>
<html lang="en">

  <title>AI object detection</title>
  <meta charset="utf-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/1.0.0/css/materialize.min.css">
  <link rel="stylesheet" href="style.css">
  <script src="https://unpkg.com/ml5@latest/dist/ml5.min.js"></script>

  <h2 id="loadingText">Loading...</h2>
  <!-- video with size of 0px because of chrome -->
  <video playsinline autoplay muted controls="true" id="video"></video>
  <canvas id="c1"></canvas>
        <div class="switch">
            <input type="checkbox" id="ai" disabled>
            <span class="lever"></span>
        <p class="range-field">
          <input type="range" id="fps" min="1" max="60" value="50">

    var modelIsLoaded = false;

    // Create a ObjectDetector method
    const objectDetector = ml5.objectDetector('cocossd', {}, modelLoaded);

    // When the model is loaded
    function modelLoaded() {
      console.log("Model Loaded!");
      modelIsLoaded = true;
  <script src="video.js"></script>
  <script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/1.0.0/js/materialize.min.js"></script>

body {
    text-align: center;

video {
    width: 0px;
    height: 0px;

table {
    width: auto;
    margin: auto;

tr, td {
    border: 0px;
    text-align: center; 

document.getElementById("ai").addEventListener("change", toggleAi)
document.getElementById("fps").addEventListener("input", changeFps)

const video = document.getElementById("video");
const c1 = document.getElementById('c1');
const ctx1 = c1.getContext('2d');
var cameraAvailable = false;
var aiEnabled = false;
var fps = 16;

/* Setting up the constraint */
var facingMode = "environment"; // Can be 'user' or 'environment' to access back or front camera (NEAT!)
var constraints = {
    audio: false,
    video: {
        facingMode: facingMode

/* Stream it to video element */
function camera() {
    if (!cameraAvailable) {
        navigator.mediaDevices.getUserMedia(constraints).then(function (stream) {
            cameraAvailable = true;
            video.srcObject = stream;
        }).catch(function (err) {
            cameraAvailable = false;
            if (modelIsLoaded) {
                if (err.name === "NotAllowedError") {
                    document.getElementById("loadingText").innerText = "Waiting for camera permission";
            setTimeout(camera, 1000);

window.onload = function () {

function timerCallback() {
    if (isReady()) {
        ctx1.drawImage(video, 0, 0, c1.width, c1.height);
        if (aiEnabled) {
    setTimeout(timerCallback, fps);

function isReady() {
    if (modelIsLoaded && cameraAvailable) {
        document.getElementById("loadingText").style.display = "none";
        document.getElementById("ai").disabled = false;
        return true;
    } else {
        return false;

function setResolution() {
    if (window.screen.width < video.videoWidth) {
        c1.width = window.screen.width * 0.9;
        let factor = c1.width / video.videoWidth;
        c1.height = video.videoHeight * factor;
    } else if (window.screen.height < video.videoHeight) {
        c1.height = window.screen.height * 0.50;
        let factor = c1.height / video.videoHeight;
        c1.width = video.videoWidth * factor;
    else {
        c1.width = video.videoWidth;
        c1.height = video.videoHeight;

function toggleAi() {
    aiEnabled = document.getElementById("ai").checked;

function changeFps() {
    fps = 1000 / document.getElementById("fps").value;

function ai() {
    // Detect objects in the image element
    objectDetector.detect(c1, (err, results) => {
        console.log(results); // Will output bounding boxes of detected objects
        for (let index = 0; index < results.length; index++) {
            const element = results[index];
            ctx1.font = "15px Arial";
            ctx1.fillStyle = "red";
            ctx1.fillText(element.label + " - " + (element.confidence * 100).toFixed(2) + "%", element.x + 10, element.y + 15);
            ctx1.strokeStyle = "red";
            ctx1.rect(element.x, element.y, element.width, element.height);

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