62 lines
1.8 KiB
TypeScript
62 lines
1.8 KiB
TypeScript
import type { FaceValidationPort, FaceValidationResult } from '@gridpilot/media';
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/**
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* Demo implementation of FaceValidationPort.
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*
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* In production, this would use a real face detection API like:
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* - AWS Rekognition
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* - Google Cloud Vision
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* - Azure Face API
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* - OpenCV / face-api.js
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*
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* For demo purposes, this always returns a valid face if the image data is provided.
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*/
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export class DemoFaceValidationAdapter implements FaceValidationPort {
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async validateFacePhoto(imageData: string | Buffer): Promise<FaceValidationResult> {
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// Simulate some processing time
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await this.delay(500);
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// Check if we have any image data
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const dataString = typeof imageData === 'string' ? imageData : imageData.toString();
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if (!dataString || dataString.length < 100) {
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return {
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isValid: false,
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hasFace: false,
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faceCount: 0,
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confidence: 0,
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errorMessage: 'Invalid or empty image data',
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};
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}
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// Check for valid base64 image data or data URL
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const isValidImage =
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dataString.startsWith('data:image/') ||
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dataString.startsWith('/9j/') || // JPEG magic bytes in base64
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dataString.startsWith('iVBOR') || // PNG magic bytes in base64
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dataString.length > 1000; // Assume long strings are valid image data
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if (!isValidImage) {
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return {
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isValid: false,
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hasFace: false,
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faceCount: 0,
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confidence: 0,
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errorMessage: 'Please upload a valid image file (JPEG or PNG)',
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};
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}
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// For demo: always return success with high confidence
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// In production, this would actually analyze the image
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return {
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isValid: true,
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hasFace: true,
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faceCount: 1,
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confidence: 0.95,
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};
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}
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private delay(ms: number): Promise<void> {
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return new Promise(resolve => setTimeout(resolve, ms));
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}
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} |