改用API驗證

app/utils/api_auth.py

@@ -0,0 +1,277 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+API 認證服務
+用於與 PANJIT Auth API 整合認證
+
+Author: PANJIT IT Team
+Created: 2025-10-01
+"""
+
+import requests
+import json
+from datetime import datetime, timedelta
+from typing import Optional, Dict, Any, Tuple
+from flask import current_app
+from .logger import get_logger
+from .exceptions import AuthenticationError
+
+logger = get_logger(__name__)
+
+
+class APIAuthService:
+    """API 認證服務"""
+
+    def __init__(self):
+        self.config = current_app.config
+        self.api_base_url = "https://pj-auth-api.vercel.app"
+        self.login_endpoint = "/api/auth/login"
+        self.logout_endpoint = "/api/auth/logout"
+        self.timeout = 30  # 30 秒超時
+
+    def authenticate_user(self, username: str, password: str) -> Dict[str, Any]:
+        """
+        透過 API 驗證使用者憑證
+
+        Args:
+            username: 使用者帳號
+            password: 密碼
+
+        Returns:
+            Dict: 包含使用者資訊和 Token 的字典
+
+        Raises:
+            AuthenticationError: 認證失敗時拋出
+        """
+        try:
+            login_url = f"{self.api_base_url}{self.login_endpoint}"
+
+            payload = {
+                "username": username,
+                "password": password
+            }
+
+            headers = {
+                "Content-Type": "application/json"
+            }
+
+            logger.info(f"正在透過 API 驗證使用者: {username}")
+
+            # 發送認證請求
+            response = requests.post(
+                login_url,
+                json=payload,
+                headers=headers,
+                timeout=self.timeout
+            )
+
+            # 解析回應
+            if response.status_code == 200:
+                data = response.json()
+
+                if data.get('success'):
+                    logger.info(f"API 認證成功: {username}")
+                    return self._parse_auth_response(data)
+                else:
+                    error_msg = data.get('error', '認證失敗')
+                    logger.warning(f"API 認證失敗: {username} - {error_msg}")
+                    raise AuthenticationError(f"認證失敗: {error_msg}")
+
+            elif response.status_code == 401:
+                data = response.json()
+                error_msg = data.get('error', '帳號或密碼錯誤')
+                logger.warning(f"API 認證失敗 (401): {username} - {error_msg}")
+                raise AuthenticationError("帳號或密碼錯誤")
+
+            else:
+                logger.error(f"API 認證請求失敗: HTTP {response.status_code}")
+                raise AuthenticationError(f"認證服務錯誤 (HTTP {response.status_code})")
+
+        except requests.exceptions.Timeout:
+            logger.error(f"API 認證請求超時: {username}")
+            raise AuthenticationError("認證服務回應超時，請稍後再試")
+
+        except requests.exceptions.ConnectionError:
+            logger.error(f"API 認證連線錯誤: {username}")
+            raise AuthenticationError("無法連接認證服務，請檢查網路連線")
+
+        except requests.exceptions.RequestException as e:
+            logger.error(f"API 認證請求錯誤: {username} - {str(e)}")
+            raise AuthenticationError(f"認證服務錯誤: {str(e)}")
+
+        except json.JSONDecodeError:
+            logger.error(f"API 認證回應格式錯誤: {username}")
+            raise AuthenticationError("認證服務回應格式錯誤")
+
+        except Exception as e:
+            logger.error(f"API 認證未知錯誤: {username} - {str(e)}")
+            raise AuthenticationError(f"認證過程發生錯誤: {str(e)}")
+
+    def _parse_auth_response(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        解析 API 認證回應
+
+        Args:
+            data: API 回應資料
+
+        Returns:
+            Dict: 標準化的使用者資訊
+        """
+        try:
+            auth_data = data.get('data', {})
+            user_info = auth_data.get('userInfo', {})
+
+            # 解析 Token 過期時間
+            expires_at = None
+            issued_at = None
+
+            if 'expiresAt' in auth_data:
+                try:
+                    expires_at = datetime.fromisoformat(auth_data['expiresAt'].replace('Z', '+00:00'))
+                except (ValueError, AttributeError):
+                    logger.warning("無法解析 API Token 過期時間")
+
+            if 'issuedAt' in auth_data:
+                try:
+                    issued_at = datetime.fromisoformat(auth_data['issuedAt'].replace('Z', '+00:00'))
+                except (ValueError, AttributeError):
+                    logger.warning("無法解析 API Token 發行時間")
+
+            # 標準化使用者資訊 (方案 A: API name 是姓名+email 格式)
+            api_name = user_info.get('name', '')  # 例: "劉怡明 ymirliu@panjit.com.tw"
+            api_email = user_info.get('email', '')  # 例: "ymirliu@panjit.com.tw"
+
+            result = {
+                # 基本使用者資訊 (方案 A: username 和 display_name 都用 API name)
+                'username': api_name,                    # 姓名+email 格式
+                'display_name': api_name,                # 姓名+email 格式
+                'email': api_email,                      # 純 email
+                'department': user_info.get('jobTitle'), # 使用 jobTitle 作為部門
+                'user_principal_name': api_email,
+
+                # API 特有資訊
+                'api_user_id': user_info.get('id', ''),  # Azure Object ID
+                'job_title': user_info.get('jobTitle'),
+                'office_location': user_info.get('officeLocation'),
+                'business_phones': user_info.get('businessPhones', []),
+
+                # Token 資訊
+                'api_access_token': auth_data.get('access_token', ''),
+                'api_id_token': auth_data.get('id_token', ''),
+                'api_token_type': auth_data.get('token_type', 'Bearer'),
+                'api_expires_in': auth_data.get('expires_in', 0),
+                'api_issued_at': issued_at,
+                'api_expires_at': expires_at,
+
+                # 完整的 API 回應 (用於記錄)
+                'full_api_response': data,
+                'api_user_info': user_info
+            }
+
+            return result
+
+        except Exception as e:
+            logger.error(f"解析 API 回應時發生錯誤: {str(e)}")
+            raise AuthenticationError(f"解析認證回應時發生錯誤: {str(e)}")
+
+    def logout_user(self, access_token: str) -> bool:
+        """
+        透過 API 登出使用者
+
+        Args:
+            access_token: 使用者的 access token
+
+        Returns:
+            bool: 登出是否成功
+        """
+        try:
+            logout_url = f"{self.api_base_url}{self.logout_endpoint}"
+
+            headers = {
+                "Authorization": f"Bearer {access_token}",
+                "Content-Type": "application/json"
+            }
+
+            response = requests.post(
+                logout_url,
+                headers=headers,
+                timeout=self.timeout
+            )
+
+            if response.status_code == 200:
+                data = response.json()
+                if data.get('success'):
+                    logger.info("API 登出成功")
+                    return True
+
+            logger.warning(f"API 登出失敗: HTTP {response.status_code}")
+            return False
+
+        except Exception as e:
+            logger.error(f"API 登出時發生錯誤: {str(e)}")
+            return False
+
+    def validate_token(self, access_token: str) -> bool:
+        """
+        驗證 Token 是否有效
+
+        Args:
+            access_token: 要驗證的 token
+
+        Returns:
+            bool: Token 是否有效
+        """
+        try:
+            # 這裡可以實作 Token 驗證邏輯
+            # 目前 API 沒有提供專門的驗證端點，可以考慮解析 JWT 或調用其他端點
+
+            # 簡單的檢查：Token 不能為空且格式看起來像 JWT
+            if not access_token or len(access_token.split('.')) != 3:
+                return False
+
+            # TODO: 實作更完整的 JWT 驗證邏輯
+            # 可以解析 JWT payload 檢查過期時間等
+
+            return True
+
+        except Exception as e:
+            logger.error(f"驗證 Token 時發生錯誤: {str(e)}")
+            return False
+
+    def test_connection(self) -> bool:
+        """
+        測試 API 連線
+
+        Returns:
+            bool: 連線是否正常
+        """
+        try:
+            # 嘗試連接 API 基礎端點
+            response = requests.get(
+                self.api_base_url,
+                timeout=10
+            )
+
+            return response.status_code in [200, 404]  # 404 也算正常，表示能連接到伺服器
+
+        except Exception as e:
+            logger.error(f"API 連線測試失敗: {str(e)}")
+            return False
+
+    def calculate_internal_expiry(self, api_expires_at: Optional[datetime], extend_days: int = 3) -> datetime:
+        """
+        計算內部 Token 過期時間
+
+        Args:
+            api_expires_at: API Token 過期時間
+            extend_days: 延長天數
+
+        Returns:
+            datetime: 內部 Token 過期時間
+        """
+        if api_expires_at:
+            # 基於 API Token 過期時間延長
+            return api_expires_at + timedelta(days=extend_days)
+        else:
+            # 如果沒有 API 過期時間，從現在開始計算
+            return datetime.utcnow() + timedelta(days=extend_days)

app/utils/image_preprocessor.py

@@ -0,0 +1,248 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+圖像預處理工具 - 用於提升 OCR 識別準確度
+
+Author: PANJIT IT Team
+Created: 2025-10-01
+Modified: 2025-10-01
+"""
+
+import io
+import numpy as np
+from PIL import Image, ImageEnhance, ImageFilter
+from typing import Optional, Tuple
+from app.utils.logger import get_logger
+
+logger = get_logger(__name__)
+
+# 檢查 OpenCV 是否可用
+try:
+    import cv2
+    _HAS_OPENCV = True
+    logger.info("OpenCV is available for advanced image preprocessing")
+except ImportError:
+    _HAS_OPENCV = False
+    logger.warning("OpenCV not available, using PIL-only preprocessing")
+
+
+class ImagePreprocessor:
+    """圖像預處理器 - 提升掃描文件 OCR 品質"""
+
+    def __init__(self, use_opencv: bool = True):
+        """
+        初始化圖像預處理器
+
+        Args:
+            use_opencv: 是否使用 OpenCV 進行進階處理（若可用）
+        """
+        self.use_opencv = use_opencv and _HAS_OPENCV
+        logger.info(f"ImagePreprocessor initialized (OpenCV: {self.use_opencv})")
+
+    def preprocess_for_ocr(self, image_bytes: bytes,
+                          enhance_level: str = 'medium') -> bytes:
+        """
+        對圖像進行 OCR 前處理
+
+        Args:
+            image_bytes: 原始圖像字節數據
+            enhance_level: 增強級別 ('low', 'medium', 'high')
+
+        Returns:
+            處理後的圖像字節數據 (PNG格式)
+        """
+        try:
+            # 1. 載入圖像
+            image = Image.open(io.BytesIO(image_bytes))
+            original_mode = image.mode
+            logger.debug(f"Original image: {image.size}, mode={original_mode}")
+
+            # 2. 轉換為 RGB (如果需要)
+            if image.mode not in ('RGB', 'L'):
+                image = image.convert('RGB')
+                logger.debug(f"Converted to RGB mode")
+
+            # 3. 根據增強級別選擇處理流程
+            if self.use_opencv:
+                processed_image = self._preprocess_with_opencv(image, enhance_level)
+            else:
+                processed_image = self._preprocess_with_pil(image, enhance_level)
+
+            # 4. 轉換為 PNG 字節
+            output_buffer = io.BytesIO()
+            processed_image.save(output_buffer, format='PNG', optimize=True)
+            processed_bytes = output_buffer.getvalue()
+
+            logger.info(f"Image preprocessed: {len(image_bytes)} -> {len(processed_bytes)} bytes (level={enhance_level})")
+            return processed_bytes
+
+        except Exception as e:
+            logger.error(f"Image preprocessing failed: {e}, returning original image")
+            return image_bytes  # 失敗時返回原圖
+
+    def _preprocess_with_opencv(self, image: Image.Image, level: str) -> Image.Image:
+        """使用 OpenCV 進行進階圖像處理"""
+        # PIL Image -> NumPy array
+        img_array = np.array(image)
+
+        # 轉換為 BGR (OpenCV 格式)
+        if len(img_array.shape) == 3 and img_array.shape[2] == 3:
+            img_bgr = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
+        else:
+            img_bgr = img_array
+
+        # 1. 灰階化
+        gray = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)
+        logger.debug("Applied grayscale conversion (OpenCV)")
+
+        # 2. 去噪 - 根據級別調整
+        if level == 'high':
+            # 高級別：較強去噪
+            denoised = cv2.fastNlMeansDenoising(gray, None, h=10, templateWindowSize=7, searchWindowSize=21)
+            logger.debug("Applied strong denoising (h=10)")
+        elif level == 'medium':
+            # 中級別：中等去噪
+            denoised = cv2.fastNlMeansDenoising(gray, None, h=7, templateWindowSize=7, searchWindowSize=21)
+            logger.debug("Applied medium denoising (h=7)")
+        else:
+            # 低級別：輕度去噪
+            denoised = cv2.bilateralFilter(gray, 5, 50, 50)
+            logger.debug("Applied light denoising (bilateral)")
+
+        # 3. 對比度增強 - CLAHE
+        clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+        enhanced = clahe.apply(denoised)
+        logger.debug("Applied CLAHE contrast enhancement")
+
+        # 4. 銳化 (高級別才使用)
+        if level == 'high':
+            kernel = np.array([[-1,-1,-1],
+                             [-1, 9,-1],
+                             [-1,-1,-1]])
+            sharpened = cv2.filter2D(enhanced, -1, kernel)
+            logger.debug("Applied sharpening filter")
+        else:
+            sharpened = enhanced
+
+        # 5. 自適應二值化 (根據級別決定是否使用)
+        if level in ('medium', 'high'):
+            # 使用自適應閾值
+            binary = cv2.adaptiveThreshold(
+                sharpened, 255,
+                cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+                cv2.THRESH_BINARY,
+                blockSize=11,
+                C=2
+            )
+            logger.debug("Applied adaptive thresholding")
+            final_image = binary
+        else:
+            final_image = sharpened
+
+        # NumPy array -> PIL Image
+        return Image.fromarray(final_image)
+
+    def _preprocess_with_pil(self, image: Image.Image, level: str) -> Image.Image:
+        """使用 PIL 進行基礎圖像處理（當 OpenCV 不可用時）"""
+
+        # 1. 灰階化
+        gray = image.convert('L')
+        logger.debug("Applied grayscale conversion (PIL)")
+
+        # 2. 對比度增強
+        enhancer = ImageEnhance.Contrast(gray)
+        if level == 'high':
+            contrast_factor = 2.0
+        elif level == 'medium':
+            contrast_factor = 1.5
+        else:
+            contrast_factor = 1.2
+
+        enhanced = enhancer.enhance(contrast_factor)
+        logger.debug(f"Applied contrast enhancement (factor={contrast_factor})")
+
+        # 3. 銳化
+        if level in ('medium', 'high'):
+            sharpness = ImageEnhance.Sharpness(enhanced)
+            sharp_factor = 2.0 if level == 'high' else 1.5
+            sharpened = sharpness.enhance(sharp_factor)
+            logger.debug(f"Applied sharpening (factor={sharp_factor})")
+        else:
+            sharpened = enhanced
+
+        # 4. 去噪 (使用中值濾波)
+        if level == 'high':
+            denoised = sharpened.filter(ImageFilter.MedianFilter(size=3))
+            logger.debug("Applied median filter (size=3)")
+        else:
+            denoised = sharpened
+
+        return denoised
+
+    def auto_detect_enhance_level(self, image_bytes: bytes) -> str:
+        """
+        自動偵測最佳增強級別
+
+        Args:
+            image_bytes: 圖像字節數據
+
+        Returns:
+            建議的增強級別 ('low', 'medium', 'high')
+        """
+        try:
+            image = Image.open(io.BytesIO(image_bytes))
+
+            if self.use_opencv:
+                # 使用 OpenCV 計算圖像品質指標
+                img_array = np.array(image.convert('L'))
+
+                # 計算拉普拉斯方差 (評估清晰度)
+                laplacian_var = cv2.Laplacian(img_array, cv2.CV_64F).var()
+
+                # 計算對比度 (標準差)
+                contrast = np.std(img_array)
+
+                logger.debug(f"Image quality metrics: laplacian_var={laplacian_var:.2f}, contrast={contrast:.2f}")
+
+                # 根據指標決定增強級別
+                if laplacian_var < 50 or contrast < 40:
+                    # 模糊或低對比度 -> 高級別增強
+                    return 'high'
+                elif laplacian_var < 100 or contrast < 60:
+                    # 中等品質 -> 中級別增強
+                    return 'medium'
+                else:
+                    # 高品質 -> 低級別增強
+                    return 'low'
+            else:
+                # PIL 簡易判斷
+                gray = image.convert('L')
+                img_array = np.array(gray)
+
+                # 簡單對比度評估
+                contrast = np.std(img_array)
+
+                if contrast < 40:
+                    return 'high'
+                elif contrast < 60:
+                    return 'medium'
+                else:
+                    return 'low'
+
+        except Exception as e:
+            logger.error(f"Auto enhance level detection failed: {e}")
+            return 'medium'  # 預設使用中級別
+
+    def preprocess_smart(self, image_bytes: bytes) -> bytes:
+        """
+        智能預處理 - 自動偵測並應用最佳處理級別
+
+        Args:
+            image_bytes: 原始圖像字節數據
+
+        Returns:
+            處理後的圖像字節數據
+        """
+        enhance_level = self.auto_detect_enhance_level(image_bytes)
+        logger.info(f"Auto-detected enhancement level: {enhance_level}")
+        return self.preprocess_for_ocr(image_bytes, enhance_level)