mirror of
https://github.com/snapotter-hq/SnapOtter.git
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- Replace OpenCV Haar Cascades with MediaPipe for face detection, using short-range model first with full-range fallback for better accuracy - Add auto-orient to remove-background route for EXIF-rotated photos - Change default background removal model from u2net to birefnet-general-lite - Fix flaky test by setting SQLite busy_timeout before journal_mode pragma Co-authored-by: Siddharth Kumar Sah <siddharth123sk@gmail.com>
150 lines
5.1 KiB
TypeScript
150 lines
5.1 KiB
TypeScript
import { randomUUID } from "node:crypto";
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import { writeFile } from "node:fs/promises";
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import { basename, join } from "node:path";
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import { blurFaces } from "@stirling-image/ai";
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import type { FastifyInstance, FastifyReply, FastifyRequest } from "fastify";
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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 { createWorkspace } 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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/** Face detection and blurring route. */
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export function registerBlurFaces(app: FastifyInstance) {
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app.post("/api/v1/tools/blur-faces", 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 settingsRaw: string | null = null;
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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) {
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chunks.push(chunk);
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}
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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 === "settings") {
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settingsRaw = part.value as string;
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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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const settings = settingsRaw ? JSON.parse(settingsRaw) : {};
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request.log.info(
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{
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toolId: "blur-faces",
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imageSize: fileBuffer.length,
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blurRadius: settings.blurRadius,
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sensitivity: settings.sensitivity,
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},
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"Starting face blur",
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);
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// Auto-orient to fix EXIF rotation before face detection
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fileBuffer = await autoOrient(fileBuffer);
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const jobId = randomUUID();
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const workspacePath = await createWorkspace(jobId);
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// Save input
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const inputPath = join(workspacePath, "input", filename);
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await writeFile(inputPath, fileBuffer);
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// Process
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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,
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});
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}
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: undefined;
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const result = await blurFaces(
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fileBuffer,
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join(workspacePath, "output"),
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{
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blurRadius: settings.blurRadius ?? 30,
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sensitivity: settings.sensitivity ?? 0.5,
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},
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onProgress,
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);
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// Save output
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const outputFilename = `${filename.replace(/\.[^.]+$/, "")}_blurred.png`;
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const outputPath = join(workspacePath, "output", outputFilename);
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await writeFile(outputPath, result.buffer);
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if (clientJobId) {
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updateSingleFileProgress({
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jobId: clientJobId,
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phase: "complete",
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percent: 100,
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});
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}
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return reply.send({
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jobId,
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downloadUrl: `/api/v1/download/${jobId}/${encodeURIComponent(outputFilename)}`,
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originalSize: fileBuffer.length,
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processedSize: result.buffer.length,
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facesDetected: result.facesDetected,
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faces: result.faces,
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});
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} catch (err) {
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request.log.error({ err, toolId: "blur-faces" }, "Face blur failed");
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return reply.status(422).send({
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error: "Face blur 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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// Register in the pipeline/batch registry so this tool can be used
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// as a step in automation pipelines (without progress callbacks).
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registerToolProcessFn({
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toolId: "blur-faces",
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settingsSchema: z.object({
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blurRadius: z.number().min(1).max(100).default(30),
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sensitivity: z.number().min(0).max(1).default(0.5),
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}),
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process: async (inputBuffer, settings, filename) => {
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const s = settings as { blurRadius?: number; sensitivity?: number };
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const orientedBuffer = await autoOrient(inputBuffer);
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const jobId = randomUUID();
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const workspacePath = await createWorkspace(jobId);
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const result = await blurFaces(orientedBuffer, join(workspacePath, "output"), {
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blurRadius: s.blurRadius ?? 30,
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sensitivity: s.sensitivity ?? 0.5,
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});
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const outputFilename = `${filename.replace(/\.[^.]+$/, "")}_blurred.png`;
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return { buffer: result.buffer, filename: outputFilename, contentType: "image/png" };
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},
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});
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}
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