.

.dockerignore

@@ -0,0 +1,8 @@
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+*.log
+.git
+.gitignore
+.venv/

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.index filter=lfs diff=lfs merge=lfs -text
+/*.index filter=lfs diff=lfs merge=lfs -text
+/*.pkl filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,6 @@
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+*.log
+.venv/

Dockerfile

@@ -0,0 +1,29 @@
+# Dockerfile
+FROM python:3.10-slim
+
+# 필요한 패키지, 개발 도구 및 git 설치 (root 권한)
+RUN apt-get update && \
+    apt-get install -y tzdata build-essential libsm6 libxext6 libgl1 libglib2.0-0 git curl wget && \
+    ln -sf /usr/share/zoneinfo/Asia/Seoul /etc/localtime && \
+    echo "Asia/Seoul" > /etc/timezone && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# ✅ 중요: AdaFace 저장소 클론 및 가중치 미리 다운로드 (서버 시작 속도 최적화)
+RUN git clone https://github.com/mk-minchul/AdaFace.git /app/AdaFace
+RUN python -c "from huggingface_hub import hf_hub_download; import shutil; cache_path = hf_hub_download(repo_id='VishalMishraTss/AdaFace', filename='adaface_ir101_webface12m.ckpt'); shutil.copy(cache_path, '/app/adaface_ir101_webface12m.ckpt')"
+
+# 최종 앱 코드 복사
+COPY --chown=user . /app 
+
+# 서버 실행 명령 (Hugging Face Spaces 기본 포트 7860)
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -0,0 +1,26 @@
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import JSONResponse
+from insightface.app import FaceAnalysis
+import numpy as np
+import cv2
+import faiss
+import pickle
+import os
+import uvicorn
+import tempfile
+
+from routers import embed_v2, predict
+
+# ✅ FastAPI 앱 생성
+app = FastAPI()
+
+app.include_router(embed_v2.router, prefix="/embed")
+app.include_router(predict.router, prefix="/predict")
+
+@app.get("/")
+def hello():
+    return {"msg": "Hello FastAPI!"}
+
+# ✅ 로컬에서 실행할 경우
+if __name__ == "__main__":
+    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)

binaryfile_force_push.txt

@@ -0,0 +1,4 @@
+git lfs install
+git lfs migrate import --everything --include "*.index"
+git lfs migrate import --everything --include "*.pkl"
+git push origin main --force

embed.py

@@ -0,0 +1,114 @@
+import os
+import cv2
+import faiss
+import pickle
+import numpy as np
+import pandas as pd
+from pathlib import Path
+import insightface
+import albumentations as A
+
+# 🔧 증강 설정
+augment = A.Compose([
+    A.HorizontalFlip(p=0.5),
+    A.RandomBrightnessContrast(p=0.3),
+    A.Rotate(limit=15, p=0.3),
+])
+
+# 🚀 모델 초기화 함수
+def load_face_model(device: str = "cpu"):
+    providers = ["CPUExecutionProvider"] if device == "cpu" else ["CUDAExecutionProvider"]
+    model = insightface.app.FaceAnalysis(name='buffalo_l', providers=providers)
+    model.prepare(ctx_id=0 if device != "cpu" else -1)
+    return model
+
+# 🚀 임베딩 추출 함수
+def get_face_embedding(image_path: str, model, n_augment: int = 5):
+    img = cv2.imread(str(image_path))
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    embeddings = []
+
+    # 원본
+    faces = model.get(img)
+    if faces:
+        embeddings.append(faces[0].embedding)
+    else:
+        print(f"❌ 얼굴 인식 실패 (원본): {image_path}")
+
+    # 증강
+    for i in range(n_augment):
+        augmented = augment(image=img)
+        img_aug = augmented['image']
+        faces = model.get(img_aug)
+        if faces:
+            embeddings.append(faces[0].embedding)
+        else:
+            print(f"❌ 얼굴 인식 실패 (증강 {i+1}): {image_path}")
+
+    if embeddings:
+        return np.mean(embeddings, axis=0)
+    else:
+        print(f"❌ 모든 시도 실패: {image_path}")
+        return None
+
+# 🚀 폴더 스캔 및 임베딩 추출
+def process_folder(data_folder: str, model) -> pd.DataFrame:
+    data = []
+    data_path = Path(data_folder)
+    for person_dir in data_path.iterdir():
+        if not person_dir.is_dir():
+            continue
+        label = person_dir.name
+        print(f"▶ 폴더: {label}")
+        count = 0
+        for image_path in person_dir.glob("*"):
+            if image_path.suffix.lower() not in [".jpg", ".jpeg", ".png"]:
+                continue
+            emb = get_face_embedding(image_path, model)
+            if emb is not None:
+                data.append({
+                    "label": label,
+                    "image_path": str(image_path),
+                    "embedding": emb
+                })
+                count += 1
+        print(f"✅ 얼굴 인식 성공 수: {count}")
+    return pd.DataFrame(data)
+
+# 🚀 FAISS 인덱스 생성 및 저장
+def build_and_save_faiss(train_df: pd.DataFrame, save_path: str):
+    embeddings = np.stack(train_df['embedding'].values).astype('float32')
+    embeddings /= np.linalg.norm(embeddings, axis=1, keepdims=True)
+
+    index = faiss.IndexFlatIP(embeddings.shape[1])
+    index.add(embeddings)
+    faiss.write_index(index, os.path.join(save_path, "faiss_index.index"))
+
+    labels = train_df['label'].tolist()
+    with open(os.path.join(save_path, "faiss_labels.pkl"), "wb") as f:
+        pickle.dump(labels, f)
+
+    # 전체 데이터프레임 저장 (선택)
+    train_df.to_pickle(os.path.join(save_path, "train_df.pkl"))
+
+    print("✅ FAISS 인덱스 & 라벨 저장 완료")
+    return index, labels, train_df
+
+# 🚀 전체 실행 함수
+def run_pipeline(data_folder: str, save_path: str, device: str = "cpu"):
+    os.makedirs(save_path, exist_ok=True)
+    print("🚀 얼굴 모델 불러오는 중...")
+    model = load_face_model(device)
+
+    print("🚀 임베딩 추출 시작...")
+    train_df = process_folder(data_folder, model)
+
+    print("🚀 FAISS 인덱스 생성 및 저장 중...")
+    index, labels, df = build_and_save_faiss(train_df, save_path)
+
+    return index, labels, df
+
+
+data_folder = "./person"
+save_path = "./embedding/person"
+index, labels, df = run_pipeline(data_folder, save_path, device="cpu")

embedding/person/face_faiss_index_v2.index

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:60e32bc0841fc0502971525daa6ca6ead0475d9f13155e6383e5c1d8648c0e94
+size 8818733

embedding/person/face_faiss_labels_v2.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f890a916860025325ff32ca0cf98c325e549d0c3a1da1b35d0ec6aa87eb5a0b3
+size 8700

requirements.txt

@@ -0,0 +1,16 @@
+insightface
+faiss-cpu
+opencv-python
+numpy
+pandas
+albumentations
+torch
+torchvision
+scikit-learn
+fastapi
+uvicorn[standard]
+onnxruntime
+python-multipart
+sqlalchemy
+psycopg2-binary
+huggingface_hub

routers/embed_v2.py

@@ -0,0 +1,138 @@
+from fastapi import FastAPI
+from fastapi import APIRouter
+from fastapi.responses import JSONResponse
+import os
+import cv2
+import faiss
+import pickle
+import numpy as np
+import pandas as pd
+from pathlib import Path
+import insightface
+import albumentations as A
+
+# 🔧 경로 설정 (하드코딩)
+data_folder = os.path.abspath("person") 
+save_path = os.path.abspath("embedding/person") 
+faiss_index_name="face_faiss_index_v2.index"
+faiss_label_name="face_faiss_labels_v2.pkl"
+traindf_name="train_df.pkl"
+
+
+# ✅ FastAPI 인스턴스 생성
+router = APIRouter()
+
+# 🔧 증강 설정
+augment = A.Compose([
+    A.HorizontalFlip(p=0.5),
+    A.RandomBrightnessContrast(p=0.3),
+    A.Rotate(limit=15, p=0.3),
+])
+
+# 🚀 모델 초기화 함수
+def load_face_model(device: str = "cpu"):
+    providers = ["CPUExecutionProvider"] if device == "cpu" else ["CUDAExecutionProvider"]
+    model = insightface.app.FaceAnalysis(name='buffalo_l', providers=providers)
+    model.prepare(ctx_id=0 if device != "cpu" else -1)
+    return model
+
+# 🚀 임베딩 추출 함수
+def get_face_embedding(image_path: str, model, n_augment: int = 5):
+    img = cv2.imread(str(image_path))
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    embeddings = []
+
+    # 원본
+    faces = model.get(img)
+    if faces:
+        embeddings.append(faces[0].embedding)
+    else:
+        print(f"❌ 얼굴 인식 실패 (원본): {image_path}")
+
+    # 증강
+    for i in range(n_augment):
+        augmented = augment(image=img)
+        img_aug = augmented['image']
+        faces = model.get(img_aug)
+        if faces:
+            embeddings.append(faces[0].embedding)
+        else:
+            print(f"❌ 얼굴 인식 실패 (증강 {i+1}): {image_path}")
+
+    if embeddings:
+        return np.mean(embeddings, axis=0)
+    else:
+        print(f"❌ 모든 시도 실패: {image_path}")
+        return None
+
+# 🚀 폴더 스캔 및 임베딩 추출
+def process_folder(data_folder: str, model) -> pd.DataFrame:
+    data = []
+    data_path = Path(data_folder)
+    for person_dir in data_path.iterdir():
+        if not person_dir.is_dir():
+            continue
+        label = person_dir.name
+        print(f"▶ 폴더: {label}")
+        count = 0
+        for image_path in person_dir.glob("*"):
+            if image_path.suffix.lower() not in [".jpg", ".jpeg", ".png"]:
+                continue
+            emb = get_face_embedding(image_path, model)
+            if emb is not None:
+                data.append({
+                    "label": label,
+                    "image_path": str(image_path),
+                    "embedding": emb
+                })
+                count += 1
+        print(f"✅ 얼굴 인식 성공 수: {count}")
+    return pd.DataFrame(data)
+
+# 🚀 FAISS 인덱스 생성 및 저장
+def build_and_save_faiss(train_df: pd.DataFrame, save_path: str):
+    embeddings = np.stack(train_df['embedding'].values).astype('float32')
+    embeddings /= np.linalg.norm(embeddings, axis=1, keepdims=True)
+
+    index = faiss.IndexFlatIP(embeddings.shape[1])
+    index.add(embeddings)
+    faiss.write_index(index, os.path.join(save_path, faiss_index_name))
+
+    labels = train_df['label'].tolist()
+    with open(os.path.join(save_path, faiss_label_name), "wb") as f:
+        pickle.dump(labels, f)
+
+    train_df.to_pickle(os.path.join(save_path, traindf_name))
+
+    print("✅ FAISS 인덱스 & 라벨 저장 완료")
+    return index, labels, train_df
+
+# 🚀 전체 실행 함수
+def run_pipeline(data_folder: str, save_path: str, device: str = "cpu"):
+    os.makedirs(save_path, exist_ok=True)
+    print("🚀 얼굴 모델 불러오는 중...")
+    model = load_face_model(device)
+
+    print("🚀 임베딩 추출 시작...")
+    train_df = process_folder(data_folder, model)
+
+    print("🚀 FAISS 인덱스 생성 및 저장 중...")
+    index, labels, df = build_and_save_faiss(train_df, save_path)
+
+    return len(df)
+
+# ✅ API 엔드포인트
+@router.post("/train")
+def train_faces():
+    try:
+        count = run_pipeline(data_folder, save_path)
+        return {"status": "success", "count": count}
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"status": "error", "message": str(e)})
+
+"""
+# ✅ 로컬 실행
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run("embed_v2:app", host="0.0.0.0", port=8000, reload=True)
+"""

routers/predict.py

@@ -0,0 +1,94 @@
+from fastapi import APIRouter, File, UploadFile
+import numpy as np
+import cv2
+import faiss
+import pickle
+import os
+import torch
+import insightface
+from insightface.utils import face_align
+import sys
+
+# ✅ Dockerfile에서 클론한 AdaFace 경로 추가
+sys.path.append('/app/AdaFace')
+import net
+
+router = APIRouter()
+
+# --- 설정 및 경로 ---
+faiss_index_name = "face_faiss_index_v2.index"
+faiss_label_name = "face_faiss_labels_v2.pkl"
+load_path = os.path.abspath("embedding/person")  # 실제 FAISS 파일 위치로 변경 필요
+threshold = 45.0  # Unknown 판별 임계값
+
+# ✅ Hugging Face 무료 CPU 강제 설정
+device = torch.device('cpu')
+
+# --- 1. InsightFace (탐지기) 로드 ---
+detector = insightface.app.FaceAnalysis(name='buffalo_l', providers=['CPUExecutionProvider'], allowed_modules=['detection'])
+detector.prepare(ctx_id=0, det_size=(640, 640))
+
+# --- 2. AdaFace (인식기) 로드 ---
+model_path = "/app/adaface_ir101_webface12m.ckpt"
+adaface_model = net.build_model('ir_101')
+statedict = torch.load(model_path, map_location=device)["state_dict"]
+model_statedict = {key[6:]: val for key, val in statedict.items() if key.startswith("model.")}
+adaface_model.load_state_dict(model_statedict)
+adaface_model.to(device)
+adaface_model.eval()
+
+# --- 3. FAISS 로드 ---
+index = faiss.read_index(os.path.join(load_path, faiss_index_name))
+with open(os.path.join(load_path, faiss_label_name), "rb") as f:
+    labels = pickle.load(f)
+
+# ✅ AdaFace 임베딩 추출 함수
+def extract_adaface_embedding(img_bgr, face_kps):
+    aligned_face = face_align.norm_crop(img_bgr, landmark=face_kps, image_size=112)
+    img_norm = (aligned_face / 255.0 - 0.5) / 0.5
+    img_tensor = torch.tensor(img_norm.transpose(2, 0, 1)).float().unsqueeze(0).to(device)
+    with torch.no_grad():
+        embedding, _ = adaface_model(img_tensor)
+    return embedding.cpu().numpy()[0]
+
+@router.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    contents = await file.read()
+    nparr = np.frombuffer(contents, np.uint8)
+    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+
+    if img is None:
+        return {"success": False, "message": "❌ 이미지를 읽을 수 없습니다."}
+
+    faces = detector.get(img)
+    if not faces:
+        return {"success": False, "message": "❌ 얼굴을 찾을 수 없습니다."}
+
+    results = []
+    for face in faces:
+        # AdaFace 임베딩 추출 및 정규화
+        emb = extract_adaface_embedding(img, face.kps)
+        emb = np.array([emb], dtype='float32')
+        emb /= np.linalg.norm(emb, axis=1, keepdims=True)
+
+        # FAISS 검색
+        distances, indices = index.search(emb, k=1)
+        best_match_idx = indices[0][0]
+        similarity_score = distances[0][0]
+        
+        # 일치율 계산 및 Threshold 적용
+        score_percent = max(0, similarity_score) * 100
+        if score_percent >= threshold:
+            predicted_name = labels[best_match_idx]
+        else:
+            predicted_name = "Unknown"
+
+        box = face.bbox.astype(int).tolist()
+
+        results.append({
+            "label": predicted_name,
+            "score": float(score_percent),
+            "bbox": box # 프론트엔드에서 박스를 그릴 수 있도록 좌표 반환
+        })
+
+    return {"success": True, "results": results, "message": f"✅ 총 {len(faces)}명의 얼굴을 처리했습니다."}