技能备份 - 2026-04-15 (40个技能)
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#!/usr/bin/env python3
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"""
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Tesseract OCR wrapper with optimized configuration for mixed Chinese/English content
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"""
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import logging
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import subprocess
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import sys
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import time
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from pathlib import Path
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from typing import Dict, List, Optional
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try:
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import cv2
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import numpy as np
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from PIL import Image
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except ImportError:
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print("[ERROR] Install dependencies: pip install opencv-python numpy pillow")
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sys.exit(1)
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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class TesseractOCR:
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"""Tesseract OCR processor with preprocessing pipeline"""
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def __init__(
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self,
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verbose: bool = False,
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lang: str = 'chi_sim+eng',
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psm: int = 6,
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oem: int = 3
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):
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"""
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Initialize Tesseract processor.
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Args:
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verbose: Enable detailed logging
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lang: Tesseract language code (e.g., 'eng', 'chi_sim', 'chi_sim+eng')
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psm: Page segmentation mode (default: 6, uniform block)
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oem: OCR engine mode (default: 3, LSTM only)
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"""
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self.verbose = verbose
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self.lang = lang
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self.psm = psm
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self.oem = oem
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# Check tesseract availability
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self._check_tesseract()
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def _check_tesseract(self) -> bool:
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"""Check if tesseract is installed"""
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try:
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result = subprocess.run(
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['tesseract', '--version'],
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capture_output=True,
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text=True,
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timeout=5
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)
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if self.verbose:
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logger.info(f"Tesseract version: {result.stdout.split()[2]}")
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return True
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except (subprocess.TimeoutExpired, FileNotFoundError, IndexError):
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logger.error("Tesseract not found. Install with:")
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logger.error(" macOS: brew install tesseract")
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logger.error(" Ubuntu: sudo apt install tesseract-ocr")
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logger.error(" Windows: Download from https://github.com/UB-Mannheim/tesseract/wiki")
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return False
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def _preprocess_image(self, image_path: str) -> str:
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"""
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Apply preprocessing pipeline.
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Returns:
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Path to processed image
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"""
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if self.verbose:
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logger.info("Preprocessing image...")
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start_time = time.time()
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# Read image
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img = cv2.imread(image_path)
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if img is None:
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raise ValueError(f"Could not load image: {image_path}")
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# Convert to grayscale
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# Apply bilateral filter (noise reduction + edge preservation)
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bilateral = cv2.bilateralFilter(gray, 9, 75, 75)
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# Adaptive thresholding for low contrast images
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min_val, max_val, _, _ = cv2.minMaxLoc(bilateral)
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contrast = max_val - min_val
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if contrast < 100: # Low contrast
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processed = cv2.adaptiveThreshold(
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bilateral, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
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cv2.THRESH_BINARY, 11, 2
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)
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else:
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processed = bilateral
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# Save processed image (temp file)
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input_path = Path(image_path)
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processed_path = input_path.parent / f"{input_path.stem}_tess_processed.png"
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cv2.imwrite(str(processed_path), processed)
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if self.verbose:
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logger.info(f"Preprocessing completed in {time.time() - start_time:.2f}s")
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return str(processed_path)
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def _run_tesseract(
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self,
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image_path: str,
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lang: Optional[str] = None,
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psm: Optional[int] = None,
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oem: Optional[int] = None
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) -> Dict:
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"""
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Run Tesseract OCR.
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Returns:
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Dict with text, confidence, timing
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"""
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if lang is None:
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lang = self.lang
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if psm is None:
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psm = self.psm
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if oem is None:
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oem = self.oem
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# Run tesseract
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cmd = [
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'tesseract', image_path, 'stdout',
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'-l', lang,
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'--psm', str(psm),
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'--oem', str(oem),
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'_stdout'
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]
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if self.verbose:
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logger.info(f"Running: {' '.join(cmd)}")
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start_time = time.time()
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try:
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result = subprocess.run(
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cmd,
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capture_output=True,
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text=True,
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timeout=60
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)
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processing_time = time.time() - start_time
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if result.returncode != 0:
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logger.error(f"Tesseract error: {result.stderr}")
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return {
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'text': '',
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'confidence': 0.0,
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'error': result.stderr,
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'processing_time_ms': processing_time * 1000
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}
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# Extract confidence if available
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text = result.stdout.strip()
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confidence = self._estimate_confidence(text)
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return {
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'text': text,
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'confidence': confidence,
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'processing_time_ms': processing_time * 1000
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}
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except subprocess.TimeoutExpired:
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return {
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'text': '',
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'confidence': 0.0,
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'error': 'Tesseract timeout',
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'processing_time_ms': 60000
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}
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def _estimate_confidence(self, text: str) -> float:
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"""
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Estimate OCR confidence from output quality.
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Simple heuristic: longer text with fewer garbage characters = higher confidence
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"""
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if not text:
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return 0.0
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# Count printable characters
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printable = sum(1 for c in text if c.isprintable() or c in '\n\r\t')
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# Length factor (more text = more reliable)
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length_factor = min(1.0, len(text) / 100)
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# Quality factor
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quality = printable / max(len(text), 1)
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# Combined score
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confidence = (0.6 * quality) + (0.4 * length_factor)
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return round(confidence, 2)
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def extract(self, image_path: str) -> Dict:
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"""
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Extract text from image using Tesseract.
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Args:
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image_path: Path to input image
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Returns:
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Dict with text, confidence, processing time
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"""
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if self.verbose:
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logger.info(f"Processing: {image_path}")
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start_time = time.time()
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# Preprocess
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processed_path = self._preprocess_image(image_path)
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try:
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# Run OCR
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result = self._run_tesseract(
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processed_path,
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lang=self.lang,
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psm=self.psm,
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oem=self.oem
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)
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# Clean up temp file
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Path(processed_path).unlink(missing_ok=True)
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result['processing_time_ms'] = round(time.time() - start_time, 2)
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return result
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except Exception as e:
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Path(processed_path).unlink(missing_ok=True)
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return {
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'text': '',
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'confidence': 0.0,
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'error': str(e),
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'processing_time_ms': (time.time() - start_time) * 1000
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}
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if __name__ == '__main__':
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import argparse
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parser = argparse.ArgumentParser(description='Tesseract OCR wrapper')
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parser.add_argument('image', help='Image file to process')
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parser.add_argument('--lang', default='chi_sim+eng', help='Language code')
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parser.add_argument('--verbose', '-v', action='store_true')
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args = parser.parse_args()
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processor = TesseractOCR(verbose=args.verbose, lang=args.lang)
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result = processor.extract(args.image)
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print(f"\nText:\n{result['text']}")
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print(f"\nConfidence: {result['confidence']:.2%}")
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print(f"Time: {result.get('processing_time_ms', 0):.2f}ms")
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