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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PaddleOCR wrapper - High accuracy Chinese OCR engine
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Compatible with PaddleOCR 3.4.0+ API (predict() method returns list)
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Based on Emma's testing: result[0][0][1][0] for text, result[0][0][1][1] for confidence
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"""
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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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class PaddleOCR:
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"""PaddleOCR processor for high-accuracy text extraction"""
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def __init__(
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self,
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verbose: bool = False,
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lang: str = 'ch',
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use_angle_cls: bool = True
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):
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"""Initialize PaddleOCR - lang and use_angle_cls parameters"""
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self.verbose = verbose
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self.lang = lang
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self.use_angle_cls = use_angle_cls
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self._init_ocr()
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def _init_ocr(self) -> None:
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"""Initialize PaddleOCR model"""
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from paddleocr import PaddleOCR
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self.ocr = PaddleOCR(lang=self.lang, use_angle_cls=self.use_angle_cls)
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def extract(self, image_path: str) -> Dict:
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"""
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Extract text from image using PaddleOCR.
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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, results, processing time
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"""
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if self.verbose:
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print(f"[PaddleOCR] Processing: {image_path}")
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start_time = time.time()
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try:
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# Run OCR - using predict() for PaddleOCR 3.4.0+
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result = self.ocr.predict(image_path)
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processing_time = time.time() - start_time
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# Parse results - legacy format: [[box], [text, confidence]]
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parsed = self._parse_results(result)
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parsed['processing_time_ms'] = round(processing_time * 1000, 2)
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return parsed
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except Exception as e:
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processing_time = time.time() - start_time
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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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'results': [],
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'processing_time_ms': round(processing_time * 1000, 2)
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}
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def _parse_results(self, results) -> Dict:
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"""
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Parse PaddleOCR 3.4.0+ output format.
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PaddleOCR 3.4.0+ predict() returns dict with:
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- rec_texts: list of detected text strings
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- rec_scores: list of confidence scores
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- dt_polys: list of text polygon coordinates
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Returns:
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Dict with text, confidence, results, line_count
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"""
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if not results or not results[0]:
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return {
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'text': '',
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'confidence': 0.0,
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'results': [],
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'error': 'No text detected'
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}
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# New format: result[0] is a dict with 'rec_texts' and 'rec_scores'
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result_dict = results[0]
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if isinstance(result_dict, dict):
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texts = result_dict.get('rec_texts', [])
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scores = result_dict.get('rec_scores', [])
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if not texts:
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return {
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'text': '',
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'confidence': 0.0,
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'results': [],
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'error': 'No text detected'
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}
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# Combine all text (PaddleOCR already splits by lines)
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full_text = '\n'.join(texts)
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# Calculate average confidence
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avg_confidence = sum(scores) / len(scores) if scores else 0.0
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# Build detailed results
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detailed_results = []
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for i, (text, score) in enumerate(zip(texts, scores)):
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# Get bbox if available (dt_polys is list of arrays)
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dt_polys = result_dict.get('dt_polys', [])
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bbox = None
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if i < len(dt_polys):
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try:
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poly = dt_polys[i]
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if hasattr(poly, 'tolist'):
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bbox = poly.tolist()
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else:
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bbox = poly
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except:
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bbox = None
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detailed_results.append({
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'text': text,
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'confidence': round(score, 4),
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'bbox': bbox
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})
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return {
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'text': full_text.strip(),
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'confidence': round(avg_confidence, 4),
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'results': detailed_results,
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'line_count': len(texts)
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}
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else:
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# Fallback: handle legacy format if still used
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return self._parse_results_legacy(results)
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def _parse_results_legacy(self, results) -> Dict:
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"""
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Legacy parser for backward compatibility.
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Old format: [[bbox], [text, confidence]]
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"""
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if not results or not results[0]:
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return {
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'text': '',
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'confidence': 0.0,
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'results': [],
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'error': 'No text detected'
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}
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text_lines = []
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all_confidences = []
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for line in results:
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if len(line) < 2:
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continue
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box = line[0]
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if isinstance(line[1], (list, tuple)) and len(line[1]) >= 2:
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text, confidence = line[1][0], line[1][1]
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else:
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text, confidence = str(line[1]), 0.0
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text_lines.append(text)
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all_confidences.append(confidence)
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full_text = '\n'.join(text_lines)
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avg_confidence = sum(all_confidences) / len(all_confidences) if all_confidences else 0.0
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detailed_results = []
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for line in results:
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if len(line) >= 2:
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box = line[0]
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if isinstance(line[1], (list, tuple)) and len(line[1]) >= 2:
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text, confidence = line[1][0], line[1][1]
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else:
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text, confidence = str(line[1]), 0.0
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detailed_results.append({
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'text': text,
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'confidence': confidence,
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'bbox': box
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})
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return {
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'text': full_text.strip(),
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'confidence': round(avg_confidence, 4),
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'results': detailed_results,
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'line_count': len(detailed_results)
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}
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def batch_extract(self, image_paths: List[str]) -> List[Dict]:
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"""Process multiple images"""
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return [self.extract(path) for path in image_paths]
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if __name__ == '__main__':
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import argparse
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parser = argparse.ArgumentParser(description='PaddleOCR wrapper')
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parser.add_argument('image', help='Image file to process')
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parser.add_argument('--lang', default='ch', help='Language code (ch, en, etc.)')
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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 = PaddleOCR(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']:.4f}")
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print(f"Lines detected: {result.get('line_count', 0)}")
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print(f"Time: {result.get('processing_time_ms', 0):.2f}ms")
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