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