vfdhbvjfn

controllers/main.py

@@ -4149,20 +4149,26 @@ async def register(
                 raise Exception("Số CCCD này đã được đăng ký trong hệ thống!")
 
         user_descriptor = None
+        avatar_b64 = ""
+        sample_errors = []
         for i, img_file in enumerate(images):
             img_bytes  = await img_file.read()
             detections = face_ai_service.extract_faces(img_bytes)
 
             if len(detections) == 0:
+                sample_errors.append(f"Anh mau {i + 1}: khong tim thay khuon mat.")
+                continue
                 raise Exception(f"Không tìm thấy khuôn mặt trong ảnh mẫu thứ {i + 1}.")
             if len(detections) > 1:
+                sample_errors.append(f"Anh mau {i + 1}: co nhieu hon 1 khuon mat.")
+                continue
                 raise Exception(f"Ảnh mẫu thứ {i + 1} có nhiều hơn 1 khuôn mặt.")
 
             descriptor = detections[0]["descriptor"]
             emb_id     = str(uuid.uuid4())
             img_b64    = face_ai_service.bytes_to_base64(img_bytes)
 
-            if i == 0:
+            if user_descriptor is None:
                 user_descriptor = descriptor
                 avatar_b64      = img_b64
                 cursor.execute(
@@ -4178,6 +4184,10 @@ async def register(
             )
             new_encodings.append((person_id, name, role, avatar_b64, expiry_val, descriptor))
 
+        if user_descriptor is None:
+            detail = " ".join(sample_errors[:3])
+            raise Exception(f"Khong tim thay khuon mat hop le trong cac anh mau. {detail}".strip())
+
         front_b64 = back_b64 = ""
 
         if cccd_front:

database/database.py

@@ -117,6 +117,14 @@ def _ensure_recognition_log_columns(cursor):
         if not _column_exists(cursor, "recognition_logs", column_name):
             cursor.execute(statement)
 
+    indexes = [
+        ("idx_recognition_person_status_created", "CREATE INDEX idx_recognition_person_status_created ON recognition_logs (person_id, status, created_at)"),
+        ("idx_recognition_created", "CREATE INDEX idx_recognition_created ON recognition_logs (created_at)"),
+    ]
+    for index_name, statement in indexes:
+        if not _index_exists(cursor, "recognition_logs", index_name):
+            cursor.execute(statement)
+
 def _ensure_attendance_notification_columns(cursor):
     additions = [
         ("recognition_log_id", "ALTER TABLE attendance_notifications ADD COLUMN recognition_log_id VARCHAR(36) NULL AFTER person_id"),
@@ -144,11 +152,25 @@ def _ensure_attendance_notification_columns(cursor):
     indexes = [
         ("idx_notification_person_status", "CREATE INDEX idx_notification_person_status ON attendance_notifications (person_id, status)"),
         ("idx_notification_attendance_time", "CREATE INDEX idx_notification_attendance_time ON attendance_notifications (attendance_time)"),
+        ("idx_notification_person_time", "CREATE INDEX idx_notification_person_time ON attendance_notifications (person_id, attendance_time)"),
+        ("idx_notification_log", "CREATE INDEX idx_notification_log ON attendance_notifications (recognition_log_id)"),
+        ("idx_notification_person_status_time", "CREATE INDEX idx_notification_person_status_time ON attendance_notifications (person_id, status, attendance_time)"),
     ]
     for index_name, statement in indexes:
         if not _index_exists(cursor, "attendance_notifications", index_name):
             cursor.execute(statement)
 
+def _ensure_performance_indexes(cursor):
+    indexes = [
+        ("persons", "idx_person_status_name", "CREATE INDEX idx_person_status_name ON persons (status, name)"),
+        ("persons", "idx_person_status_registered", "CREATE INDEX idx_person_status_registered ON persons (status, registered_at)"),
+        ("face_embeddings", "idx_face_person_created", "CREATE INDEX idx_face_person_created ON face_embeddings (person_id, created_at)"),
+        ("citizen_ids", "idx_citizen_person_id_number", "CREATE INDEX idx_citizen_person_id_number ON citizen_ids (person_id, id_number)"),
+    ]
+    for table_name, index_name, statement in indexes:
+        if not _index_exists(cursor, table_name, index_name):
+            cursor.execute(statement)
+
 def init_database():
     """Tu dong tao Schema theo chuan MySQL ban cung cap"""
     
@@ -318,6 +340,7 @@ def init_database():
         _ensure_employee_account_columns(cursor)
         _ensure_recognition_log_columns(cursor)
         _ensure_attendance_notification_columns(cursor)
+        _ensure_performance_indexes(cursor)
         _ensure_default_admin(cursor)
         conn.commit()
         print("[OK] Da kiem tra va khoi tao cau truc CSDL thanh cong tren Railway!")

database/migrate_db_v3_employee_login.py

@@ -77,6 +77,14 @@ def ensure_recognition_log_columns(cursor) -> None:
         if not column_exists(cursor, "recognition_logs", column_name):
             cursor.execute(statement)
 
+    indexes = [
+        ("idx_recognition_person_status_created", "CREATE INDEX idx_recognition_person_status_created ON recognition_logs (person_id, status, created_at)"),
+        ("idx_recognition_created", "CREATE INDEX idx_recognition_created ON recognition_logs (created_at)"),
+    ]
+    for index_name, statement in indexes:
+        if not index_exists(cursor, "recognition_logs", index_name):
+            cursor.execute(statement)
+
 
 def ensure_attendance_notification_columns(cursor) -> None:
     additions = [
@@ -105,12 +113,27 @@ def ensure_attendance_notification_columns(cursor) -> None:
     indexes = [
         ("idx_notification_person_status", "CREATE INDEX idx_notification_person_status ON attendance_notifications (person_id, status)"),
         ("idx_notification_attendance_time", "CREATE INDEX idx_notification_attendance_time ON attendance_notifications (attendance_time)"),
+        ("idx_notification_person_time", "CREATE INDEX idx_notification_person_time ON attendance_notifications (person_id, attendance_time)"),
+        ("idx_notification_log", "CREATE INDEX idx_notification_log ON attendance_notifications (recognition_log_id)"),
+        ("idx_notification_person_status_time", "CREATE INDEX idx_notification_person_status_time ON attendance_notifications (person_id, status, attendance_time)"),
     ]
     for index_name, statement in indexes:
         if not index_exists(cursor, "attendance_notifications", index_name):
             cursor.execute(statement)
 
 
+def ensure_performance_indexes(cursor) -> None:
+    indexes = [
+        ("persons", "idx_person_status_name", "CREATE INDEX idx_person_status_name ON persons (status, name)"),
+        ("persons", "idx_person_status_registered", "CREATE INDEX idx_person_status_registered ON persons (status, registered_at)"),
+        ("face_embeddings", "idx_face_person_created", "CREATE INDEX idx_face_person_created ON face_embeddings (person_id, created_at)"),
+        ("citizen_ids", "idx_citizen_person_id_number", "CREATE INDEX idx_citizen_person_id_number ON citizen_ids (person_id, id_number)"),
+    ]
+    for table_name, index_name, statement in indexes:
+        if not index_exists(cursor, table_name, index_name):
+            cursor.execute(statement)
+
+
 def migrate():
     conn = get_db_connection()
     cursor = conn.cursor(dictionary=True)
@@ -201,6 +224,7 @@ def migrate():
     ensure_employee_columns(cursor)
     ensure_recognition_log_columns(cursor)
     ensure_attendance_notification_columns(cursor)
+    ensure_performance_indexes(cursor)
 
     admin_username = DEFAULT_ADMIN_USERNAME
     admin_password = DEFAULT_ADMIN_PASSWORD

service/face_service.py

@@ -264,7 +264,7 @@
 import cv2, numpy as np, io, os, threading, logging, urllib.request
 from dataclasses import dataclass, field
 from typing import Optional
-from PIL import Image
+from PIL import Image, ImageOps
 
 logger = logging.getLogger(__name__)
 
@@ -379,35 +379,80 @@ class FaceAiService:
         _download_model(YUNET_URL, YUNET_PATH, "YuNet")
         _download_model(SFACE_URL, SFACE_PATH, "SFace")
         logger.info("[AI] Khởi tạo YuNet + SFace...")
-        self._detector   = cv2.FaceDetectorYN.create(YUNET_PATH, "", (320,240), score_threshold=0.6, nms_threshold=0.3, top_k=5)
+        self._detector   = cv2.FaceDetectorYN.create(YUNET_PATH, "", (320,240), score_threshold=0.45, nms_threshold=0.3, top_k=5)
         self._recognizer = cv2.FaceRecognizerSF.create(SFACE_PATH, "")
         logger.info("[AI]  Sẵn sàng")
 
     @staticmethod
     def _decode(file_bytes: bytes):
+        try:
+            pil = Image.open(io.BytesIO(file_bytes))
+            pil = ImageOps.exif_transpose(pil).convert("RGB")
+            return cv2.cvtColor(np.array(pil), cv2.COLOR_RGB2BGR)
+        except Exception: pass
         try:
             arr = np.frombuffer(file_bytes, np.uint8)
             img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
             if img is not None: return img
-        except Exception: pass
-        try:
-            pil = Image.open(io.BytesIO(file_bytes)).convert("RGB")
-            return cv2.cvtColor(np.array(pil), cv2.COLOR_RGB2BGR)
         except Exception as e:
             logger.error(f"[AI] Không đọc ảnh: {e}"); return None
 
-    def extract_faces(self, file_bytes: bytes) -> list[dict]:
-        img = self._decode(file_bytes)
-        if img is None: return []
+    @staticmethod
+    def _image_variants(img):
+        variants = [("original", img)]
+        h, w = img.shape[:2]
+        max_side = max(h, w)
+        min_side = min(h, w)
+
+        if max_side > 1600:
+            scale = 1600 / max_side
+            variants.append(("downscale", cv2.resize(img, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_AREA)))
+        if min_side < 480:
+            scale = 480 / min_side
+            variants.append(("upscale", cv2.resize(img, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_CUBIC)))
+
+        try:
+            lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
+            l, a, b = cv2.split(lab)
+            l = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)).apply(l)
+            variants.append(("contrast", cv2.cvtColor(cv2.merge((l, a, b)), cv2.COLOR_LAB2BGR)))
+        except Exception:
+            pass
+
+        variants.extend([
+            ("rotate90", cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)),
+            ("rotate270", cv2.rotate(img, cv2.ROTATE_90_COUNTERCLOCKWISE)),
+            ("rotate180", cv2.rotate(img, cv2.ROTATE_180)),
+        ])
+        return variants
+
+    def _detect_raw(self, img):
         h, w = img.shape[:2]
         self._detector.setInputSize((w, h))
         _, faces_raw = self._detector.detect(img)
-        if faces_raw is None or len(faces_raw) == 0: return []
+        if faces_raw is None or len(faces_raw) == 0:
+            return None
+        return sorted(faces_raw, key=lambda fd: float(fd[-1]), reverse=True)
+
+    def extract_faces(self, file_bytes: bytes) -> list[dict]:
+        img = self._decode(file_bytes)
+        if img is None: return []
+        selected_img = img
+        faces_raw = None
+        used_variant = "original"
+        for variant_name, candidate in self._image_variants(img):
+            faces_raw = self._detect_raw(candidate)
+            if faces_raw is not None:
+                selected_img = candidate
+                used_variant = variant_name
+                break
+        if faces_raw is None: return []
+        h, w = selected_img.shape[:2]
         results = []
         for fd in faces_raw:
             x,y,fw,fh = [int(v) for v in fd[:4]]
             x=max(0,x); y=max(0,y); fw=min(fw,w-x); fh=min(fh,h-y)
-            aligned  = self._recognizer.alignCrop(img, fd)
+            aligned  = self._recognizer.alignCrop(selected_img, fd)
             feature  = self._recognizer.feature(aligned)
             results.append({
                 "box": {"x":x,"y":y,"width":fw,"height":fh},
@@ -438,4 +483,4 @@ class FaceAiService:
 
 
 face_ai_service   = FaceAiService()
-face_memory_store = FaceMemoryStore()
+face_memory_store = FaceMemoryStore()