This content originally appeared on DEV Community and was authored by Ekemini Thompson
Introduction
In recent years, the field of machine learning has seen remarkable advancements, particularly in bringing powerful models to web applications. One such breakthrough is the ability to perform real-time object detection directly within a web browser, thanks to technologies like TensorFlow.js and models such as COCO-SSD. This article explores how developers can leverage these tools to create interactive applications that detect objects in live webcam streams, uploaded images, or videos, all without requiring server-side processing.
Understanding TensorFlow.js and COCO-SSD
TensorFlow.js is a JavaScript library developed by Google that allows developers to run machine learning models directly in the browser. It provides a way to deploy pre-trained models or train new ones using JavaScript APIs, making it accessible and easy to integrate with web applications. COCO-SSD (Common Objects in Context - Single Shot MultiBox Detector) is a popular pre-trained model for object detection. It is optimized to detect a wide variety of objects in real-time, making it suitable for interactive applications.
Setting Up the Environment
To begin, developers need to set up their development environment. This typically involves:
- Including TensorFlow.js and COCO-SSD in the HTML document using script tags:
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/coco-ssd@latest"></script>
- Creating HTML structure to handle user interface elements like video input, image upload, and control buttons.
Building the Application
1. Handling User Input
The application allows users to choose between different input types:
- Webcam: Directly captures live video feed from the user's webcam.
- Image: Allows users to upload an image file from their device.
- Video: Enables users to upload a video file for object detection.
<div id="inputSelection">
<label><input type="radio" name="inputType" value="webcam" checked> Webcam</label>
<label><input type="radio" name="inputType" value="image"> Image</label>
<label><input type="radio" name="inputType" value="video"> Video</label>
</div>
<input type="file" id="imageInput" accept="image/*" style="display:none;">
<input type="file" id="videoInput" accept="video/*" style="display:none;">
2. Displaying Input and Results
The application dynamically displays the selected input (video or image) and detection results using HTML5 elements like <video>, <img>, and <canvas>.
<div id="videoContainer">
<video id="videoElement" autoplay playsinline></video>
<div id="infoBox" class="infoBox">
<p><strong>Detected Object:</strong> <span id="objectLabel"></span></p>
<p><strong>Confidence:</strong> <span id="confidenceScore"></span></p>
</div>
</div>
<img id="imageDisplay">
<video id="videoDisplay" controls loop></video>
<canvas id="outputCanvas"></canvas>
3. Implementing Object Detection Logic
JavaScript (script.js) handles the object detection logic using TensorFlow.js and COCO-SSD. This involves:
- Initializing the model and loading it asynchronously:
async function loadModel() {
const model = await cocoSsd.load();
return model;
}
- Performing detection on selected input (video or image) and updating the UI with results:
async function detectObjects(input) {
const model = await loadModel();
const predictions = await model.detect(input);
// Update UI with predictions
}
- Handling different input types (webcam, image, video) and triggering detection based on user actions.
4. User Interaction and Controls
The application includes buttons for controlling object detection:
- Start Detection: Initiates object detection based on selected input.
- Stop Detection: Pauses or stops the detection process.
- Capture Screenshot: Allows users to capture a screenshot of the current detection result.
<div id="controls">
<button id="startButton">Start Detection</button>
<button id="stopButton" disabled>Stop Detection</button>
<button id="captureButton" disabled>Capture Screenshot</button>
</div>
5. Enhancing User Experience
To provide a seamless experience, the application includes a loading indicator (<div id="loadingIndicator">Loading...</div>) to notify users while TensorFlow.js and the COCO-SSD model are being loaded.
Conclusion
In conclusion, TensorFlow.js combined with COCO-SSD opens up exciting possibilities for real-time object detection directly within web browsers. This article has demonstrated the fundamental components and steps involved in building such an application, from setting up the environment to implementing object detection logic and enhancing user interaction. Developers can now leverage these technologies to create interactive and responsive web applications that push the boundaries of what's possible with machine learning on the web. As these technologies continue to evolve, the future looks promising for even more sophisticated and accessible AI-powered web experiences.
Here is the Github Repo
This content originally appeared on DEV Community and was authored by Ekemini Thompson
Ekemini Thompson | Sciencx (2024-07-25T02:56:03+00:00) Harnessing Real-Time Object Detection in the Browser with TensorFlow.js and COCO-SSD. Retrieved from https://www.scien.cx/2024/07/25/harnessing-real-time-object-detection-in-the-browser-with-tensorflow-js-and-coco-ssd/
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