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.
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
Objective
The objective of bject 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,
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Feature
-
- ✅ Toggle switch to turn AI on or off
-
- ✅ Range slider to control frame rate
Multiple browser support
| Browser | supported |
|---|---|
| Firefox | ✅ |
| Chrome | ✅ |
| Edge | ✅ |
| Internet Explorer | ❌ |
| Mobile Browser | supported |
|---|---|
| Firefox | ✅ |
| Chrome | ✅ |
Software And Tools Required
- : Vs Code
- : Python
- : JavaScript
Credits (Use these libraries)
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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
index.html
<!DOCTYPE html>
<html lang="en">
<head>
<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>
</head>
<body>
<h2 id="loadingText">Loading...</h2>
<!-- video with size of 0px because of chrome -->
<video playsinline autoplay muted controls="true" id="video"></video>
<br><br>
<canvas id="c1"></canvas>
<br><br>
<table>
<tr>
<td>AI:</td>
<td>
<div class="switch">
<label>
Off
<input type="checkbox" id="ai" disabled>
<span class="lever"></span>
On
</label>
</div>
</td>
</tr>
<tr>
<td>FPS:</td>
<td>
<p class="range-field">
<input type="range" id="fps" min="1" max="60" value="50">
</p>
</td>
</tr>
</table>
<script>
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>
<script src="video.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/1.0.0/js/materialize.min.js"></script>
</body>
</html>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 */
camera();
function camera() {
if (!cameraAvailable) {
console.log("camera")
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 () {
timerCallback();
}
function timerCallback() {
if (isReady()) {
setResolution();
ctx1.drawImage(video, 0, 0, c1.width, c1.height);
if (aiEnabled) {
ai();
}
}
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.beginPath();
ctx1.strokeStyle = "red";
ctx1.rect(element.x, element.y, element.width, element.height);
ctx1.stroke();
console.log(element.label);
}
});
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