.gitignore

@@ -0,0 +1,10 @@
+__pycache__/
+*.pyc
+*.pyo
+logs/
+*.log
+nohup.out
+nohup_gpu*.out
+py3/
+*.egg-info/
+*.pid

README.md

@@ -1,35 +0,0 @@
-# Face Service — 人脸识别服务
-
-## 概述
-
-基于 InsightFace (buffalo_l) 的人脸检测与识别服务，部署于 GPU 服务器，提供人脸检测、特征提取、1:1 比对、1:N 识别 API。
-
-## 模型
-
-- InsightFace buffalo_l (检测 + 识别)
-- 检测尺寸: 640×640
-- 特征维度: 512
-
-## API
-
-| 端点 | 方法 | 说明 |
-|------|------|------|
-| /api/status | GET | 服务状态和模型信息 |
-| /api/detect | POST | 人脸检测 (返回 bbox + 关键点) |
-| /api/recognize | POST | 人脸识别 (返回特征向量 + 匹配身份) |
-| /api/compare | POST | 1:1 比对 (两张人脸 → 相似度) |
-| /api/face-detect | POST | 别名: 同 /api/detect |
-| /api/face-recognize | POST | 别名: 同 /api/recognize |
-| /api/face-compare | POST | 别名: 同 /api/compare |
-
-## 部署
-
-```bash
-cd /data/ymq/face-service
-bash build.sh
-sudo systemctl restart face-service
-```
-
-## 端口
-
-9091

ah.py

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+# -*- coding:utf-8 -*-
+from ahserver.webapp import webapp
+from init import load_face_service
+
+if __name__ == '__main__':
+    webapp(load_face_service)

build.sh

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+#!/usr/bin/env bash
+# Face Service (InsightFace)
+set -e
+cd "$(dirname "$0")"
+
+SERVICE_NAME="face-service"
+PORT=9091
+GPU=5
+PY=/data/ymq/wan22-service/py3/bin/python
+action="${1:-status}"
+
+case "$action" in
+    deploy|update)
+        echo "=== $SERVICE_NAME Deploy (GPU $GPU, port $PORT) ==="
+        if [ -f ah.pid ] && kill -0 $(cat ah.pid) 2>/dev/null; then
+            kill $(cat ah.pid) 2>/dev/null || true; sleep 2
+        fi
+        if [ -d .git ] && [ -f .git/HEAD ]; then
+            git pull origin master 2>/dev/null || true
+        fi
+        mkdir -p logs files wwwroot
+        export PYTHONPATH="$(pwd)"
+        export CUDA_VISIBLE_DEVICES=$GPU
+        nohup $PY ah.py > nohup.out 2>&1 &
+        echo $! > ah.pid
+        echo "Started PID $(cat ah.pid) on port $PORT (GPU $GPU)"
+        sleep 5
+        if curl -s http://localhost:$PORT/api/status > /dev/null 2>&1; then
+            echo "Service healthy"
+        else
+            echo "WARNING: not responding, check nohup.out"
+            tail -20 nohup.out
+        fi
+        ;;
+    stop)
+        if [ -f ah.pid ]; then
+            kill $(cat ah.pid) 2>/dev/null || true; rm -f ah.pid; echo "Stopped"
+        else echo "Not running"; fi
+        ;;
+    start)
+        mkdir -p logs files wwwroot
+        export PYTHONPATH="$(pwd)"; export CUDA_VISIBLE_DEVICES=$GPU
+        nohup $PY ah.py > nohup.out 2>&1 &
+        echo $! > ah.pid; echo "Started PID $(cat ah.pid)"
+        ;;
+    status)
+        echo "=== $SERVICE_NAME Status ==="
+        if [ -f ah.pid ] && kill -0 $(cat ah.pid) 2>/dev/null; then
+            echo "Process: running (PID $(cat ah.pid))"
+        else echo "Process: not running"; fi
+        echo "Port: $PORT, GPU: $GPU"
+        if curl -s --max-time 3 http://localhost:$PORT/api/status > /dev/null 2>&1; then
+            echo "HTTP: OK"
+        else echo "HTTP: not responding"; fi
+        ;;
+    *) echo "Usage: $0 {deploy|update|stop|start|status}"; exit 1 ;;
+esac

conf/config.json

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+{
+    "password_key": "FaceService2026Key",
+    "filesroot": "$[workdir]$/files",
+    "logger": {
+        "name": "face-service",
+        "levelname": "info",
+        "logfile": "$[workdir]$/logs/face-service.log"
+    },
+    "website": {
+        "paths": [["$[workdir]$/wwwroot", ""]],
+        "client_max_size": 52428800,
+        "host": "0.0.0.0",
+        "port": 9091,
+        "coding": "utf-8",
+        "indexes": ["index.html"],
+        "startswiths": [
+            {"leading": "/api/status", "registerfunction": "status"},
+            {"leading": "/api/detect", "registerfunction": "detect"},
+            {"leading": "/api/recognize", "registerfunction": "recognize"},
+            {"leading": "/api/compare", "registerfunction": "compare"}
+        ],
+        "processors": [
+            [".tmpl", "tmpl"], [".app", "app"], [".ui", "bui"],
+            [".dspy", "dspy"], [".md", "md"]
+        ]
+    }
+}

init.py

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+# -*- coding:utf-8 -*-
+from traceback import format_exc
+from ahserver.serverenv import ServerEnv
+from appPublic.registerfunction import RegisterFunction
+from appPublic.log import exception
+import json
+
+
+async def status_handler(request, params_kw, *args, **kwargs):
+    import sys, os
+    sys.path.insert(0, os.getcwd())
+    from workers.face_model import health_check
+    health = health_check()
+    return json.dumps({
+        "service": "face-service",
+        "model": health["model"],
+        "model_loaded": health["loaded"],
+        "det_size": health["det_size"],
+        "endpoints": ["/api/status", "/api/detect", "/api/recognize", "/api/compare"]
+    }, indent=2, ensure_ascii=False)
+
+
+async def detect_handler(request, params_kw, *args, **kwargs):
+    import sys, os, time
+    sys.path.insert(0, os.getcwd())
+    from workers.face_model import detect
+    try:
+        images = params_kw.get("images", [])
+        if not images:
+            return json.dumps({"error": "images list required"})
+        
+        start = time.time()
+        all_results = []
+        for img in images:
+            result = detect(img)
+            all_results.append(result)
+        elapsed = round(time.time() - start, 4)
+        
+        return json.dumps({
+            "status": "SUCCEEDED",
+            "results": all_results,
+            "elapsed": elapsed
+        }, ensure_ascii=False)
+    except Exception as e:
+        exception(f"{e}, {format_exc()}")
+        return json.dumps({"error": str(e)})
+
+
+async def recognize_handler(request, params_kw, *args, **kwargs):
+    import sys, os, time
+    sys.path.insert(0, os.getcwd())
+    from workers.face_model import recognize
+    try:
+        images = params_kw.get("images", [])
+        if not images:
+            return json.dumps({"error": "images list required"})
+        
+        start = time.time()
+        all_results = []
+        for img in images:
+            result = recognize(img)
+            all_results.append(result)
+        elapsed = round(time.time() - start, 4)
+        
+        return json.dumps({
+            "status": "SUCCEEDED",
+            "results": all_results,
+            "elapsed": elapsed
+        }, ensure_ascii=False)
+    except Exception as e:
+        exception(f"{e}, {format_exc()}")
+        return json.dumps({"error": str(e)})
+
+
+async def compare_handler(request, params_kw, *args, **kwargs):
+    import sys, os
+    sys.path.insert(0, os.getcwd())
+    from workers.face_model import compare
+    try:
+        embedding1 = params_kw.get("embedding1")
+        embedding2 = params_kw.get("embedding2")
+        
+        if not embedding1 or not embedding2:
+            return json.dumps({"error": "embedding1 and embedding2 required"})
+        
+        result = compare(embedding1, embedding2)
+        
+        return json.dumps({
+            "status": "SUCCEEDED",
+            **result
+        }, ensure_ascii=False)
+    except Exception as e:
+        exception(f"{e}, {format_exc()}")
+        return json.dumps({"error": str(e)})
+
+
+def load_face_service():
+    """Register API handlers"""
+    env = ServerEnv()
+    rf = RegisterFunction()
+    rf.register("status", status_handler)
+    rf.register("detect", detect_handler)
+    rf.register("recognize", recognize_handler)
+    rf.register("compare", compare_handler)

workers/face_model.py

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+# -*- coding:utf-8 -*-
+"""InsightFace buffalo_l model wrapper."""
+import os
+import numpy as np
+from PIL import Image
+
+_model = None
+_lock = False
+
+MODEL_NAME = "buffalo_l"
+
+def get_model():
+    global _model, _lock
+    if _model is None and not _lock:
+        _lock = True
+        try:
+            import insightface
+            from insightface.app import FaceAnalysis
+            _model = FaceAnalysis(name=MODEL_NAME, providers=['CUDAExecutionProvider'])
+            _model.prepare(ctx_id=0, det_size=(640, 640))
+        except Exception as e:
+            _lock = False
+            raise RuntimeError(f"Failed to load InsightFace: {e}")
+    return _model
+
+
+def load_image(image_input):
+    """Load image from path, URL, or base64."""
+    if isinstance(image_input, str):
+        if image_input.startswith("/") and os.path.exists(image_input):
+            return Image.open(image_input).convert("RGB")
+        elif image_input.startswith("http"):
+            import urllib.request
+            import io
+            with urllib.request.urlopen(image_input, timeout=10) as resp:
+                return Image.open(io.BytesIO(resp.read())).convert("RGB")
+    raise ValueError(f"Cannot load image: {image_input[:50] if isinstance(image_input, str) else 'unknown'}")
+
+
+def detect(image_input):
+    """Detect faces in an image. Returns list of face info."""
+    model = get_model()
+    img = load_image(image_input)
+    img_np = np.array(img)
+    
+    faces = model.get(img_np)
+    
+    results = []
+    for i, face in enumerate(faces):
+        results.append({
+            "face_id": f"face_{i}",
+            "bbox": face.bbox.tolist(),
+            "det_score": round(float(face.det_score), 4),
+            "age": int(face.age) if face.age > 0 else None,
+            "gender": "M" if face.gender == 1 else "F",
+            "embedding_dim": len(face.embedding)
+        })
+    
+    return {"faces": results, "count": len(results)}
+
+
+def recognize(image_input):
+    """Get face embeddings from an image."""
+    model = get_model()
+    img = load_image(image_input)
+    img_np = np.array(img)
+    
+    faces = model.get(img_np)
+    
+    results = []
+    for i, face in enumerate(faces):
+        # Normalize embedding to unit vector
+        embedding = face.embedding / np.linalg.norm(face.embedding)
+        results.append({
+            "face_id": f"face_{i}",
+            "bbox": face.bbox.tolist(),
+            "det_score": round(float(face.det_score), 4),
+            "embedding": embedding.tolist()
+        })
+    
+    return {"faces": results, "count": len(results)}
+
+
+def compare(embed1, embed2):
+    """Compare two face embeddings. Returns cosine similarity."""
+    e1 = np.array(embed1)
+    e2 = np.array(embed2)
+    
+    # Normalize
+    e1 = e1 / np.linalg.norm(e1)
+    e2 = e2 / np.linalg.norm(e2)
+    
+    similarity = float(np.dot(e1, e2))
+    
+    return {
+        "similarity": round(similarity, 6),
+        "is_same": similarity > 0.4,
+        "confidence": "high" if similarity > 0.6 else ("medium" if similarity > 0.4 else "low")
+    }
+
+
+def health_check():
+    """Check model status."""
+    model = get_model()
+    return {
+        "model": MODEL_NAME,
+        "loaded": model is not None,
+        "det_size": [640, 640]
+    }