mirror of
https://github.com/snapotter-hq/SnapOtter.git
synced 2026-08-03 07:46:42 +02:00
feat(passport-photo): SOTA passport photo maker with compliance validation (#64)
* feat(passport-photo): add passport specs database and tool constants * feat(passport-photo): add MediaPipe FaceMesh landmark detection script * feat(passport-photo): add TypeScript bridge for face landmark detection * feat(passport-photo): add API routes with analyze and generate endpoints * fix(passport-photo): accept landmarks from request body and fix pixel coordinate conversion - Generate endpoint now accepts landmarks + imageWidth/imageHeight in request body instead of re-running AI face detection (makes generate phase instant) - Fixed bug where normalized landmark coordinates (0-1) were used directly as pixel values in crop computation - now properly multiplied by imgW/imgH - Fixed same bug in pipeline process function * feat(passport-photo): add UI component with live preview and compliance overlay --------- Co-authored-by: stirling-image <stirling-image@users.noreply.github.com>
This commit is contained in:
stirling-image
co-authored by
stirling-image
stirling-image
parent
43821a955c
commit
2f11b9e101
@@ -27,6 +27,7 @@ import { registerImageToPdf } from "./image-to-pdf.js";
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import { registerInfo } from "./info.js";
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import { registerNoiseRemoval } from "./noise-removal.js";
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import { registerOcr } from "./ocr.js";
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import { registerPassportPhoto } from "./passport-photo.js";
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import { registerPdfToImage } from "./pdf-to-image.js";
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import { registerQrGenerate } from "./qr-generate.js";
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import { registerRedEyeRemoval } from "./red-eye-removal.js";
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@@ -138,6 +139,7 @@ export async function registerToolRoutes(app: FastifyInstance): Promise<void> {
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{ id: "colorize", register: registerColorize },
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{ id: "enhance-faces", register: registerEnhanceFaces },
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{ id: "noise-removal", register: registerNoiseRemoval },
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{ id: "passport-photo", register: registerPassportPhoto },
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{ id: "red-eye-removal", register: registerRedEyeRemoval },
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{ id: "restore-photo", register: registerRestorePhoto },
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];
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@@ -0,0 +1,538 @@
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import { randomUUID } from "node:crypto";
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import { readFile, writeFile } from "node:fs/promises";
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import { basename, join } from "node:path";
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import { detectFaceLandmarks, removeBackground } from "@stirling-image/ai";
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import { PASSPORT_SPECS, PRINT_LAYOUTS } from "@stirling-image/shared";
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import type { FastifyInstance, FastifyReply, FastifyRequest } from "fastify";
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import sharp from "sharp";
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import { z } from "zod";
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import { autoOrient } from "../../lib/auto-orient.js";
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import { validateImageBuffer } from "../../lib/file-validation.js";
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import { decodeHeic } from "../../lib/heic-converter.js";
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import { createWorkspace, getWorkspacePath } from "../../lib/workspace.js";
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import { updateSingleFileProgress } from "../progress.js";
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import { registerToolProcessFn } from "../tool-factory.js";
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const landmarkPointSchema = z.object({ x: z.number(), y: z.number() });
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const landmarksSchema = z.object({
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leftEye: landmarkPointSchema,
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rightEye: landmarkPointSchema,
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eyeCenter: landmarkPointSchema,
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chin: landmarkPointSchema,
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forehead: landmarkPointSchema,
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crown: landmarkPointSchema,
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nose: landmarkPointSchema,
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faceCenterX: z.number(),
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});
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const generateSettingsSchema = z.object({
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jobId: z.string(),
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filename: z.string(),
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countryCode: z.string(),
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documentType: z.string().default("passport"),
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bgColor: z.string().default("#FFFFFF"),
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printLayout: z.string().default("none"),
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adjustX: z.number().default(0),
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adjustY: z.number().default(0),
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landmarks: landmarksSchema,
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imageWidth: z.number(),
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imageHeight: z.number(),
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});
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/**
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* Generate a print sheet that tiles passport photos onto standard paper.
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* Returns JPEG buffer or null if layout is "none".
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*/
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async function generatePrintSheet(
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photoBuffer: Buffer,
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photoWidthMm: number,
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photoHeightMm: number,
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layoutId: string,
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): Promise<Buffer | null> {
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const layout = PRINT_LAYOUTS.find((l) => l.id === layoutId);
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if (!layout || layout.id === "none") return null;
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const DPI = 300;
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const MM_PER_INCH = 25.4;
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const GUTTER_MM = 2;
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const paperWidthPx = Math.round((layout.width / MM_PER_INCH) * DPI);
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const paperHeightPx = Math.round((layout.height / MM_PER_INCH) * DPI);
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const photoWidthPx = Math.round((photoWidthMm / MM_PER_INCH) * DPI);
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const photoHeightPx = Math.round((photoHeightMm / MM_PER_INCH) * DPI);
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const gutterPx = Math.round((GUTTER_MM / MM_PER_INCH) * DPI);
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const cols = Math.floor((paperWidthPx + gutterPx) / (photoWidthPx + gutterPx));
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const rows = Math.floor((paperHeightPx + gutterPx) / (photoHeightPx + gutterPx));
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if (cols < 1 || rows < 1) return null;
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// Center the grid on the paper
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const gridWidth = cols * photoWidthPx + (cols - 1) * gutterPx;
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const gridHeight = rows * photoHeightPx + (rows - 1) * gutterPx;
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const offsetX = Math.round((paperWidthPx - gridWidth) / 2);
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const offsetY = Math.round((paperHeightPx - gridHeight) / 2);
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// Resize photo to exact pixel dimensions
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const resizedPhoto = await sharp(photoBuffer)
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.resize(photoWidthPx, photoHeightPx, { fit: "fill" })
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.toBuffer();
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// Build composite inputs
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const composites: sharp.OverlayOptions[] = [];
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for (let row = 0; row < rows; row++) {
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for (let col = 0; col < cols; col++) {
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composites.push({
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input: resizedPhoto,
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left: offsetX + col * (photoWidthPx + gutterPx),
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top: offsetY + row * (photoHeightPx + gutterPx),
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});
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}
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}
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return sharp({
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create: {
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width: paperWidthPx,
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height: paperHeightPx,
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channels: 3,
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background: { r: 255, g: 255, b: 255 },
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},
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})
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.composite(composites)
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.jpeg({ quality: 95 })
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.toBuffer();
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}
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/**
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* Passport photo tool with two-phase flow:
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*
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* Phase 1 (POST /passport-photo/analyze): AI face detection + bg removal.
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* Returns landmarks, preview, and caches images for generate phase.
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*
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* Phase 2 (POST /passport-photo/generate): Sharp crop/resize/tile.
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* Uses cached images. No AI re-run. Fast response.
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*/
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export function registerPassportPhoto(app: FastifyInstance) {
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// ── Phase 1: Analyze (face landmarks + bg removal) ────────────────
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app.post(
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"/api/v1/tools/passport-photo/analyze",
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async (request: FastifyRequest, reply: FastifyReply) => {
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let fileBuffer: Buffer | null = null;
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let filename = "image";
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let clientJobId: string | null = null;
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try {
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const parts = request.parts();
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for await (const part of parts) {
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if (part.type === "file") {
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const chunks: Buffer[] = [];
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for await (const chunk of part.file) chunks.push(chunk);
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fileBuffer = Buffer.concat(chunks);
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filename = basename(part.filename ?? "image");
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} else if (part.fieldname === "clientJobId") {
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clientJobId = part.value as string;
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}
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}
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} catch (err) {
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return reply.status(400).send({
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error: "Failed to parse multipart request",
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details: err instanceof Error ? err.message : String(err),
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});
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}
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if (!fileBuffer || fileBuffer.length === 0) {
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return reply.status(400).send({ error: "No image file provided" });
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}
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const validation = await validateImageBuffer(fileBuffer);
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if (!validation.valid) {
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return reply.status(400).send({ error: `Invalid image: ${validation.reason}` });
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}
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try {
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// Decode HEIC/HEIF before processing
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if (validation.format === "heif") {
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fileBuffer = await decodeHeic(fileBuffer);
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const ext = filename.match(/\.[^.]+$/)?.[0];
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if (ext) filename = filename.slice(0, -ext.length) + ".png";
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}
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// Auto-orient to fix EXIF rotation
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fileBuffer = await autoOrient(fileBuffer);
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request.log.info(
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{ toolId: "passport-photo", imageSize: fileBuffer.length },
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"Starting passport photo analysis",
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);
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const jobId = randomUUID();
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const workspacePath = await createWorkspace(jobId);
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// Save original to workspace for generate phase
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const inputPath = join(workspacePath, "input", filename);
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await writeFile(inputPath, fileBuffer);
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// Progress callback
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const jobIdForProgress = clientJobId;
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const onProgress = jobIdForProgress
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? (percent: number, stage: string) => {
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updateSingleFileProgress({
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jobId: jobIdForProgress,
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phase: "processing",
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stage,
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percent: Math.min(percent, 95),
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});
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}
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: undefined;
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// Step 1: Detect face landmarks (0-30% of progress)
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const landmarkProgress = onProgress
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? (percent: number, stage: string) => {
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onProgress(Math.round(percent * 0.3), stage);
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}
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: undefined;
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const landmarksResult = await detectFaceLandmarks(fileBuffer, landmarkProgress);
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if (!landmarksResult.faceDetected || !landmarksResult.landmarks) {
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if (clientJobId) {
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updateSingleFileProgress({
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jobId: clientJobId,
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phase: "complete",
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percent: 100,
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});
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}
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return reply.status(422).send({
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error: "No face detected",
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details:
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"Could not detect a face in the uploaded image. Please upload a clear, front-facing photo with good lighting.",
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});
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}
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// Step 2: Remove background with birefnet-portrait (30-95%)
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const bgProgress = onProgress
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? (percent: number, stage: string) => {
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onProgress(30 + Math.round(percent * 0.65), stage);
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}
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: undefined;
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const bgRemovedBuffer = await removeBackground(
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fileBuffer,
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join(workspacePath, "output"),
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{ model: "birefnet-portrait" },
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bgProgress,
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);
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// Save bg-removed image to workspace
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const bgRemovedFilename = `${filename.replace(/\.[^.]+$/, "")}_nobg.png`;
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await writeFile(join(workspacePath, "output", bgRemovedFilename), bgRemovedBuffer);
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// Create a smaller preview for fast transfer (max 800px wide)
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const meta = await sharp(bgRemovedBuffer).metadata();
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const previewWidth = Math.min(meta.width ?? 800, 800);
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const previewBuffer = await sharp(bgRemovedBuffer)
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.resize({ width: previewWidth, withoutEnlargement: true })
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.png()
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.toBuffer({ resolveWithObject: true });
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const preview = previewBuffer.data.toString("base64");
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if (clientJobId) {
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updateSingleFileProgress({
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jobId: clientJobId,
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phase: "complete",
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percent: 100,
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});
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}
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return reply.send({
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jobId,
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filename,
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preview,
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previewWidth: previewBuffer.info.width,
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previewHeight: previewBuffer.info.height,
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landmarks: landmarksResult.landmarks,
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imageWidth: landmarksResult.imageWidth,
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imageHeight: landmarksResult.imageHeight,
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});
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} catch (err) {
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request.log.error({ err, toolId: "passport-photo" }, "Passport photo analysis failed");
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return reply.status(422).send({
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error: "Passport photo analysis failed",
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details: err instanceof Error ? err.message : "Unknown error",
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});
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}
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},
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);
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// ── Phase 2: Generate (crop + resize + tile) ─────────────────────
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app.post(
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"/api/v1/tools/passport-photo/generate",
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async (request: FastifyRequest, reply: FastifyReply) => {
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let parsed: z.infer<typeof generateSettingsSchema>;
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try {
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parsed = generateSettingsSchema.parse(request.body);
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} catch (err) {
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return reply.status(400).send({
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error: "Invalid settings",
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details: err instanceof Error ? err.message : String(err),
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});
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}
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const {
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jobId,
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filename,
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countryCode,
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documentType,
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bgColor,
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printLayout,
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adjustX,
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adjustY,
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landmarks: rawLandmarks,
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imageWidth: imgW,
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imageHeight: imgH,
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} = parsed;
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// Look up country spec
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const countrySpec = PASSPORT_SPECS.find((s) => s.code === countryCode);
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if (!countrySpec) {
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return reply.status(400).send({ error: `Unknown country code: ${countryCode}` });
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}
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const docSpec = countrySpec.documents.find((d) => d.type === documentType);
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if (!docSpec) {
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return reply.status(400).send({
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error: `No ${documentType} spec found for ${countryCode}`,
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});
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}
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try {
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const workspacePath = getWorkspacePath(jobId);
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const bgRemovedFilename = `${filename.replace(/\.[^.]+$/, "")}_nobg.png`;
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const [bgRemovedBuffer, originalBuffer] = await Promise.all([
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readFile(join(workspacePath, "output", bgRemovedFilename)),
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readFile(join(workspacePath, "input", filename)),
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]);
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// Convert normalized landmarks (0-1) to pixel coordinates
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const crownYPx = (rawLandmarks.crown.y + adjustY) * imgH;
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const chinYPx = (rawLandmarks.chin.y + adjustY) * imgH;
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const eyeYPx = (rawLandmarks.eyeCenter.y + adjustY) * imgH;
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const faceCenterXPx = (rawLandmarks.faceCenterX + adjustX) * imgW;
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// Compute crop region from landmarks
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const targetHeadRatio = (docSpec.headHeightMin + docSpec.headHeightMax) / 2;
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const headHeightPx = chinYPx - crownYPx;
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const photoHeightPx = headHeightPx / targetHeadRatio;
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const aspectRatio = docSpec.width / docSpec.height;
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const photoWidthPx = photoHeightPx * aspectRatio;
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// Position: eye line should be at eyeLineFromBottom from photo bottom
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const topY = eyeYPx - photoHeightPx * (1 - docSpec.eyeLineFromBottom);
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const leftX = faceCenterXPx - photoWidthPx / 2;
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// Clamp to image bounds
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const cropW = Math.min(Math.round(photoWidthPx), imgW);
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const cropH = Math.min(Math.round(photoHeightPx), imgH);
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let cropLeft = Math.max(0, Math.round(leftX));
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let cropTop = Math.max(0, Math.round(topY));
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if (cropLeft + cropW > imgW) cropLeft = imgW - cropW;
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if (cropTop + cropH > imgH) cropTop = imgH - cropH;
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cropLeft = Math.max(0, cropLeft);
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cropTop = Math.max(0, cropTop);
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// Parse background color
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const hex = bgColor.replace("#", "");
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const bgR = Number.parseInt(hex.slice(0, 2), 16);
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const bgG = Number.parseInt(hex.slice(2, 4), 16);
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const bgB = Number.parseInt(hex.slice(4, 6), 16);
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// Composite bg-removed onto colored background
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const bgRemovedMeta = await sharp(bgRemovedBuffer).metadata();
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const bgLayer = await sharp({
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create: {
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width: bgRemovedMeta.width ?? imgW,
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height: bgRemovedMeta.height ?? imgH,
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channels: 4,
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background: { r: bgR, g: bgG, b: bgB, alpha: 1 },
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},
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})
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.composite([{ input: bgRemovedBuffer, blend: "over" }])
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.png()
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.toBuffer();
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// Target pixel dimensions at 300 DPI
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const MM_PER_INCH = 25.4;
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const targetWidthPx = Math.round((docSpec.width / MM_PER_INCH) * docSpec.dpi);
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const targetHeightPx = Math.round((docSpec.height / MM_PER_INCH) * docSpec.dpi);
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// Extract crop region and resize to target dimensions
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const cropped = await sharp(bgLayer)
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.extract({
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left: cropLeft,
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top: cropTop,
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width: cropW,
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height: cropH,
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})
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.resize(targetWidthPx, targetHeightPx, { fit: "fill" })
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.jpeg({ quality: 95 })
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.toBuffer();
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// Save output
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const outputFilename = `${filename.replace(/\.[^.]+$/, "")}_passport.jpg`;
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const outputPath = join(workspacePath, "output", outputFilename);
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await writeFile(outputPath, cropped);
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const response: Record<string, unknown> = {
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jobId,
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downloadUrl: `/api/v1/download/${jobId}/${encodeURIComponent(outputFilename)}`,
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dimensions: {
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widthMm: docSpec.width,
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heightMm: docSpec.height,
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widthPx: targetWidthPx,
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heightPx: targetHeightPx,
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dpi: docSpec.dpi,
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},
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spec: {
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country: countrySpec.name,
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countryCode: countrySpec.code,
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documentType: docSpec.type,
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documentLabel: docSpec.label,
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},
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};
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// Generate print sheet if requested
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if (printLayout !== "none") {
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const printBuffer = await generatePrintSheet(
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cropped,
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docSpec.width,
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docSpec.height,
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printLayout,
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);
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if (printBuffer) {
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const printFilename = `${filename.replace(/\.[^.]+$/, "")}_passport_print_${printLayout}.jpg`;
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await writeFile(join(workspacePath, "output", printFilename), printBuffer);
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response.printDownloadUrl = `/api/v1/download/${jobId}/${encodeURIComponent(printFilename)}`;
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}
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}
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return reply.send(response);
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} catch (err) {
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request.log.error({ err, toolId: "passport-photo" }, "Passport photo generation failed");
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return reply.status(422).send({
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error: "Passport photo generation failed",
|
||||
details: err instanceof Error ? err.message : "Unknown error",
|
||||
});
|
||||
}
|
||||
},
|
||||
);
|
||||
|
||||
// ── Pipeline/batch registry ──────────────────────────────────────
|
||||
const pipelineSettingsSchema = z.object({
|
||||
countryCode: z.string(),
|
||||
documentType: z.string().default("passport"),
|
||||
bgColor: z.string().default("#FFFFFF"),
|
||||
printLayout: z.string().default("none"),
|
||||
adjustX: z.number().default(0),
|
||||
adjustY: z.number().default(0),
|
||||
});
|
||||
|
||||
registerToolProcessFn({
|
||||
toolId: "passport-photo",
|
||||
settingsSchema: pipelineSettingsSchema,
|
||||
process: async (inputBuffer, settings, filename) => {
|
||||
const s = settings as z.infer<typeof pipelineSettingsSchema>;
|
||||
const orientedBuffer = await autoOrient(inputBuffer);
|
||||
|
||||
// Step 1: Detect face landmarks
|
||||
const landmarksResult = await detectFaceLandmarks(orientedBuffer);
|
||||
if (!landmarksResult.faceDetected || !landmarksResult.landmarks) {
|
||||
throw new Error(
|
||||
"No face detected. Please upload a clear, front-facing photo with good lighting.",
|
||||
);
|
||||
}
|
||||
|
||||
const landmarks = landmarksResult.landmarks;
|
||||
const imgW = landmarksResult.imageWidth;
|
||||
const imgH = landmarksResult.imageHeight;
|
||||
|
||||
// Step 2: Remove background
|
||||
const jobId = randomUUID();
|
||||
const workspacePath = await createWorkspace(jobId);
|
||||
|
||||
const bgRemovedBuffer = await removeBackground(
|
||||
orientedBuffer,
|
||||
join(workspacePath, "output"),
|
||||
{
|
||||
model: "birefnet-portrait",
|
||||
},
|
||||
);
|
||||
|
||||
// Step 3: Look up spec and compute crop
|
||||
const countrySpec = PASSPORT_SPECS.find((sp) => sp.code === s.countryCode);
|
||||
if (!countrySpec) throw new Error(`Unknown country code: ${s.countryCode}`);
|
||||
|
||||
const docSpec = countrySpec.documents.find((d) => d.type === s.documentType);
|
||||
if (!docSpec) throw new Error(`No ${s.documentType} spec for ${s.countryCode}`);
|
||||
|
||||
// Convert normalized landmarks (0-1) to pixel coordinates
|
||||
const crownYPx = (landmarks.crown.y + s.adjustY) * imgH;
|
||||
const chinYPx = (landmarks.chin.y + s.adjustY) * imgH;
|
||||
const eyeYPx = (landmarks.eyeCenter.y + s.adjustY) * imgH;
|
||||
const faceCenterXPx = (landmarks.faceCenterX + s.adjustX) * imgW;
|
||||
|
||||
const targetHeadRatio = (docSpec.headHeightMin + docSpec.headHeightMax) / 2;
|
||||
const headHeightPx = chinYPx - crownYPx;
|
||||
const photoHeightPx = headHeightPx / targetHeadRatio;
|
||||
const aspectRatio = docSpec.width / docSpec.height;
|
||||
const photoWidthPx = photoHeightPx * aspectRatio;
|
||||
|
||||
const topY = eyeYPx - photoHeightPx * (1 - docSpec.eyeLineFromBottom);
|
||||
const leftX = faceCenterXPx - photoWidthPx / 2;
|
||||
|
||||
const cropW = Math.min(Math.round(photoWidthPx), imgW);
|
||||
const cropH = Math.min(Math.round(photoHeightPx), imgH);
|
||||
let cropLeft = Math.max(0, Math.round(leftX));
|
||||
let cropTop = Math.max(0, Math.round(topY));
|
||||
if (cropLeft + cropW > imgW) cropLeft = imgW - cropW;
|
||||
if (cropTop + cropH > imgH) cropTop = imgH - cropH;
|
||||
cropLeft = Math.max(0, cropLeft);
|
||||
cropTop = Math.max(0, cropTop);
|
||||
|
||||
// Composite onto background
|
||||
const hex = s.bgColor.replace("#", "");
|
||||
const bgR = Number.parseInt(hex.slice(0, 2), 16);
|
||||
const bgG = Number.parseInt(hex.slice(2, 4), 16);
|
||||
const bgB = Number.parseInt(hex.slice(4, 6), 16);
|
||||
|
||||
const bgRemovedMeta = await sharp(bgRemovedBuffer).metadata();
|
||||
const bgLayer = await sharp({
|
||||
create: {
|
||||
width: bgRemovedMeta.width ?? imgW,
|
||||
height: bgRemovedMeta.height ?? imgH,
|
||||
channels: 4,
|
||||
background: { r: bgR, g: bgG, b: bgB, alpha: 1 },
|
||||
},
|
||||
})
|
||||
.composite([{ input: bgRemovedBuffer, blend: "over" }])
|
||||
.png()
|
||||
.toBuffer();
|
||||
|
||||
const MM_PER_INCH = 25.4;
|
||||
const targetWidthPx = Math.round((docSpec.width / MM_PER_INCH) * docSpec.dpi);
|
||||
const targetHeightPx = Math.round((docSpec.height / MM_PER_INCH) * docSpec.dpi);
|
||||
|
||||
const result = await sharp(bgLayer)
|
||||
.extract({ left: cropLeft, top: cropTop, width: cropW, height: cropH })
|
||||
.resize(targetWidthPx, targetHeightPx, { fit: "fill" })
|
||||
.jpeg({ quality: 95 })
|
||||
.toBuffer();
|
||||
|
||||
const stem = filename.replace(/\.[^.]+$/, "");
|
||||
return { buffer: result, filename: `${stem}_passport.jpg`, contentType: "image/jpeg" };
|
||||
},
|
||||
});
|
||||
}
|
||||
Reference in New Issue
Block a user