mirage-shield-image-detection
v1.0.3
Published
A module for image detection with MirageShield
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MirageShield Image Detection Module Documentation
Welcome to the documentation for MirageShield Image Detection module. This module provides functionalities to detect various attributes of uploaded images using machine learning models.
Overview
MirageShield Image Detection module allows developers to integrate image analysis capabilities into their applications. It provides functions to detect image blur and analyze images for NSFW (Not Safe for Work) content using pre-trained machine learning models.
Installation
To use MirageShield Image Detection module in your project, install it via npm:
npm install mirage-shield-image-detection
Usage
Functions
detectBlur(image: HTMLImageElement): Promise
- Detects whether an image appears to be blurred or out-of-focus.
analyzeImage(imageFile: File): Promise
- Analyzes an uploaded image to detect NSFW content using a machine learning model.
Integration
Import the module and use its functions in your JavaScript or TypeScript code:
import { detectBlur, analyzeImage } from 'mirage-shield-image-detection';
// Example usage
async function processImage(file) {
const isBlurred = await detectBlur(file);
if (isBlurred) {
console.log('The uploaded image appears to be blurred.');
} else {
const detectionResult = await analyzeImage(file);
console.log('Detection Result:', detectionResult);
}
}
Contributing
Contributions to MirageShield Image Detection module are welcome! Fork the repository, make changes, and submit a pull request.
Issues
For bug reports or feature requests, please visit our GitHub Issues page.
Contact
For inquiries or support, contact Muhammed Adnan at [email protected], +91 9656778508.
This documentation outline provides a clear overview of MirageShield Image Detection module, its functionalities, usage instructions, contribution guidelines, and contact information, helping developers effectively integrate and utilize your module in their projects. Adjust and expand the content based on specific features and details of your module.