init face embedding service

README.md

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+# SMAN 1 Garut – Face Embedding Service
+
+Layanan Python kecil untuk menghasilkan **embedding wajah** + metrik kualitas yang dipakai backend CI4 (`FaceService`).
+
+## Fitur
+
+- Framework: **FastAPI** + **InsightFace**.
+- Endpoint utama: `POST /embed` (single image → embedding + quality).
+- Deteksi wajah, pilih wajah terbesar, hitung:
+  - `embedding` (vector float, default dimensi 512),
+  - `faces_count`,
+  - `face_size` (px),
+  - `blur` (variance of Laplacian),
+  - `brightness` (0..1),
+  - `quality_score` (kombinasi sederhana dari metrik di atas).
+
+## Setup Cepat
+
+```bash
+cd face-service
+python -m venv .venv
+source .venv/bin/activate   # Windows: .venv\Scripts\activate
+pip install -r requirements.txt
+
+uvicorn main:app --host 0.0.0.0 --port 5000
+```
+
+Lalu di `.env` backend CI4 (`backend/.env`), set:
+
+```ini
+FACE_SERVICE_URL = 'http://localhost:5000'
+FACE_EMBEDDING_DIM = 512
+FACE_SIM_THRESHOLD = 0.85
+FACE_MIN_SIZE = 80
+FACE_MIN_BLUR = 30
+FACE_MIN_BRIGHTNESS = 0.2
+```
+
+## Kontrak API: `POST /embed`
+
+**Request** (multipart/form-data):
+
+- Field `image`: file gambar (.jpg/.png).
+
+**Response** (`200 OK`, JSON):
+
+```json
+{
+  "embedding": [0.01, -0.23, ...],
+  "quality_score": 0.93,
+  "faces_count": 1,
+  "face_size": 120.5,
+  "blur": 45.2,
+  "brightness": 0.55
+}
+```
+
+Backend CI4 (`FaceService::extractEmbeddingWithQuality`) akan:
+
+- Menolak gambar dengan:
+  - `faces_count != 1`,
+  - `face_size < FACE_MIN_SIZE`,
+  - `blur < FACE_MIN_BLUR`,
+  - `brightness < FACE_MIN_BRIGHTNESS`.
+
+## Health Check
+
+`GET /health` → `{"status": "ok", "model": "buffalo_l"}`
+
+Dipakai untuk cek cepat apakah service sudah siap dipakai backend CI4.
+

main.py

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+import io
+import math
+import os
+from typing import Any, Dict
+
+import cv2
+import numpy as np
+from fastapi import FastAPI, File, HTTPException, UploadFile
+from fastapi.responses import JSONResponse
+from insightface.app import FaceAnalysis
+
+app = FastAPI(title="SMAN1 Face Embedding Service", version="1.0.0")
+
+MODEL_NAME = os.getenv("FACE_MODEL_NAME", "buffalo_l")
+DETECT_SIZE = int(os.getenv("FACE_DET_SIZE", "640"))
+
+app_insight: FaceAnalysis | None = None
+
+
+def get_app() -> FaceAnalysis:
+    global app_insight
+    if app_insight is None:
+        app_insight = FaceAnalysis(name=MODEL_NAME)
+        # ctx_id = 0 -> GPU, -1 -> CPU
+        ctx_id = int(os.getenv("FACE_CTX_ID", "-1"))
+        app_insight.prepare(ctx_id=ctx_id, det_size=(DETECT_SIZE, DETECT_SIZE))
+    return app_insight
+
+
+def compute_blur_score(gray: np.ndarray) -> float:
+    # Variance of Laplacian
+    return float(cv2.Laplacian(gray, cv2.CV_64F).var())
+
+
+def compute_brightness(img: np.ndarray) -> float:
+    # Normalize brightness to 0..1 (approx)
+    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    v = hsv[:, :, 2]
+    return float(v.mean() / 255.0)
+
+
+@app.post("/embed")
+async def embed(image: UploadFile = File(...)) -> JSONResponse:
+    """
+    Terima satu file gambar, kembalikan embedding + metrik kualitas.
+
+    Response JSON:
+    {
+      "embedding": [...],
+      "quality_score": float,
+      "faces_count": int,
+      "face_size": float,
+      "blur": float,
+      "brightness": float
+    }
+    """
+    if not image.filename:
+        raise HTTPException(status_code=400, detail="File gambar wajib diisi")
+
+    content = await image.read()
+    img_array = np.frombuffer(content, dtype=np.uint8)
+    img = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
+    if img is None:
+        raise HTTPException(status_code=400, detail="Gagal membaca gambar")
+
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    app_face = get_app()
+    faces = app_face.get(img)
+
+    faces_count = len(faces)
+    if faces_count == 0:
+        raise HTTPException(status_code=422, detail="Wajah tidak ditemukan")
+
+    # Ambil face terbesar
+    main_face = max(faces, key=lambda f: (f.bbox[2] - f.bbox[0]) * (f.bbox[3] - f.bbox[1]))
+    x1, y1, x2, y2 = map(int, main_face.bbox)
+    w = max(0, x2 - x1)
+    h = max(0, y2 - y1)
+    face_size = float(max(w, h))
+
+    blur_score = compute_blur_score(gray[y1:y2, x1:x2]) if w > 0 and h > 0 else 0.0
+    brightness = compute_brightness(img)
+
+    # InsightFace sudah memberi embedding ter-normalisasi
+    embedding_vec = main_face.embedding.astype(float).tolist()
+
+    # Quality score sederhana: kombinasi face_size, blur, brightness (bisa disesuaikan)
+    quality_score = float(
+        math.tanh((face_size / 100.0))
+        * math.tanh(blur_score / 50.0)
+        * math.tanh(brightness * 2.0)
+    )
+
+    return JSONResponse(
+        {
+            "embedding": embedding_vec,
+            "quality_score": quality_score,
+            "faces_count": faces_count,
+            "face_size": face_size,
+            "blur": blur_score,
+            "brightness": brightness,
+        }
+    )
+
+
+@app.get("/health")
+async def health() -> Dict[str, Any]:
+    return {"status": "ok", "model": MODEL_NAME}
+

requirements.txt

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+fastapi==0.115.0
+uvicorn[standard]==0.30.0
+numpy==1.26.4
+opencv-python==4.10.0.84
+insightface==0.7.3
+onnxruntime==1.18.0