Files
SnapOtter/packages/ai/src/face-landmarks.ts
T
SnapOtter 648c8c12f5 fix: convert input buffer to PNG before passing to face landmarks Python sidecar
AVIF (and other Sharp-native formats) were written as raw bytes to a
.png temp file, causing PIL to fail with "cannot identify image file".
Every other AI module wrapper already converts via sharp().png().toBuffer()
before writing; face-landmarks was the only one that skipped this step.
2026-05-13 14:18:49 +08:00

60 lines
1.6 KiB
TypeScript

import { unlink, writeFile } from "node:fs/promises";
import { tmpdir } from "node:os";
import { join } from "node:path";
import sharp from "sharp";
import { type ProgressCallback, parseStdoutJson, runPythonWithProgress } from "./bridge.js";
export interface FaceLandmarkPoint {
x: number;
y: number;
}
export interface FaceLandmarks {
leftEye: FaceLandmarkPoint;
rightEye: FaceLandmarkPoint;
eyeCenter: FaceLandmarkPoint;
chin: FaceLandmarkPoint;
forehead: FaceLandmarkPoint;
crown: FaceLandmarkPoint;
nose: FaceLandmarkPoint;
faceCenterX: number;
}
export interface FaceLandmarksResult {
faceDetected: boolean;
landmarks: FaceLandmarks | null;
imageWidth: number;
imageHeight: number;
}
export async function detectFaceLandmarks(
inputBuffer: Buffer,
onProgress?: ProgressCallback,
): Promise<FaceLandmarksResult> {
const inputPath = join(tmpdir(), `face_landmarks_${Date.now()}.png`);
try {
const pngBuffer = await sharp(inputBuffer).png().toBuffer();
await writeFile(inputPath, pngBuffer);
const { stdout } = await runPythonWithProgress(
"face_landmarks.py",
[inputPath, "unused", "{}"],
{ onProgress },
);
const result = parseStdoutJson(stdout);
if (!result.success) {
throw new Error(result.error || "Face landmark detection failed");
}
return {
faceDetected: result.faceDetected,
landmarks: result.landmarks ?? null,
imageWidth: result.imageWidth ?? 0,
imageHeight: result.imageHeight ?? 0,
};
} finally {
await unlink(inputPath).catch(() => {});
}
}