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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Super OCR - Main entry point with multi-engine parallel support
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Usage:
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python main.py --image path/to/image.png [--engine auto|tesseract|paddle|macvision|all]
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python main.py --images ./images/*.png [--output ./results]
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Examples:
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# Auto mode (recommended) - runs all available engines on macOS
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python main.py --image screenshot.png
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# Force Tesseract
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python main.py --image document.jpg --engine tesseract
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# Force PaddleOCR (high accuracy Chinese)
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python main.py --image chinese_menu.png --engine paddle
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# Force MacVision (macOS only)
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python main.py --image document.png --engine macvision
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# Run all available engines (macOS: tesseract + paddle + macvision)
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python main.py --image complex_doc.png --engine all
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# Batch mode with verbose output
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python main.py --images ./invoices/*.png --output ./results --verbose
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"""
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import argparse
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import json
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import os
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import sys
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import time
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from pathlib import Path
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from typing import Dict, List, Optional
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# Add parent to path for imports when running from skill directory
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sys.path.insert(0, str(Path(__file__).parent))
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try:
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from engine.selector import select_engine, get_available_engines, select_best_result
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from engine.tesseract import TesseractOCR
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from engine.paddle import PaddleOCR
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from output_formatter import format_output
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from dependencies import check_all_dependencies
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except ImportError as e:
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print(f"[ERROR] Import failed: {e}")
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print("\n[INSTALL INSTRUCTIONS]")
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print("="*60)
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print("Missing dependencies detected. Please install:")
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print()
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print(" pip install paddleocr paddlepaddle pytesseract pillow opencv-python numpy")
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print()
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print("Or for macOS with Tesseract:")
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print()
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print(" brew install tesseract")
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print(" pip install paddleocr paddlepaddle pytesseract pillow opencv-python numpy")
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print()
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print("For other platforms, see: https://github.com/openclaw/super-ocr")
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print("="*60)
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sys.exit(1)
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# Try to import MacVision if on macOS
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macvision_available = False
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if sys.platform == 'darwin':
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try:
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from engine.macvision import MacVisionOCR
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macvision_available = True
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print("[INFO] MacVision OCR available")
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except ImportError:
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print("[WARN] MacVision OCR not available (pip install pyobjc)")
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class OCRProcessor:
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"""Main OCR processor with multi-engine parallel support"""
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def __init__(self, engine: str = 'auto', verbose: bool = False):
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"""
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Initialize OCR processor.
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Args:
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engine: 'auto', 'tesseract', 'paddle', 'macvision', or 'all'
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verbose: Enable detailed logging
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"""
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self.engine = engine.lower()
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self.verbose = verbose
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self.engines_to_use = []
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self.processors = {}
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def _select_and_init(self, image_path: str) -> None:
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"""Select engines based on content and initialize all"""
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self.engines_to_use = select_engine(image_path, self.engine)
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if self.verbose:
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print(f"[INFO] Using engines: {', '.join(self.engines_to_use)}")
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# Initialize selected engines
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for eng in self.engines_to_use:
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try:
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if eng == 'tesseract':
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self.processors['tesseract'] = TesseractOCR(verbose=self.verbose)
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elif eng == 'paddle':
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self.processors['paddle'] = PaddleOCR(verbose=self.verbose)
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elif eng == 'macvision':
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if macvision_available:
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self.processors['macvision'] = MacVisionOCR(verbose=self.verbose)
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else:
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if self.verbose:
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print(f"[WARN] MacVision not available on this platform")
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else:
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if self.verbose:
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print(f"[WARN] Unknown engine: {eng}")
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except Exception as e:
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if self.verbose:
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print(f"[ERROR] Failed to initialize {eng}: {e}")
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def extract_parallel(self, image_path: str) -> Dict:
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"""
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Extract text from image using multiple engines in parallel.
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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, selected_engine, average confidence, etc.
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"""
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if not self.processors:
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self._select_and_init(image_path)
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if not self.processors:
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return {
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'text': '',
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'confidence': 0.0,
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'error': 'No engines available',
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'processing_time_ms': 0
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}
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start_time = time.time()
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results = []
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def run_engine(engine_name: str, processor):
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try:
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result = processor.extract(image_path)
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result['engine'] = engine_name
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return result
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except Exception as e:
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return {
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'engine': engine_name,
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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': 0
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}
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# Run engines in parallel
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with ThreadPoolExecutor(max_workers=len(self.processors)) as executor:
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futures = {
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executor.submit(run_engine, name, proc): name
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for name, proc in self.processors.items()
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}
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for future in as_completed(futures):
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result = future.result()
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results.append(result)
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if self.verbose:
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engine_name = result.get('engine', 'unknown')
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if result.get('error'):
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print(f"[{engine_name}] Error: {result['error']}")
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else:
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print(f"[{engine_name}] Confidence: {result.get('confidence', 0):.2%}, Time: {result.get('processing_time_ms', 0):.2f}ms")
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# Select best result using agent择优 logic
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best = select_best_result(
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results,
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preferred_engine='paddle' # PaddleOCR preferred for Chinese
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)
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# Add summary info
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best['processing_time_ms'] = round((time.time() - start_time) * 1000, 2)
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best['total_engines'] = len(results)
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best['engines_used'] = self.engines_to_use
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return best
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def extract(self, image_path: str) -> Dict:
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"""
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Extract text from image (legacy single-engine mode).
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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, engine, timing info
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"""
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return self.extract_parallel(image_path)
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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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def main():
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parser = argparse.ArgumentParser(
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description='Super OCR - Multi-engine parallel text extraction with intelligent selection'
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)
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# Input options
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input_group = parser.add_mutually_exclusive_group(required=True)
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input_group.add_argument('--image', help='Single image file to process')
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input_group.add_argument('--images', nargs='+', help='Multiple image files')
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# Engine selection
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parser.add_argument(
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'--engine',
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choices=['auto', 'tesseract', 'paddle', 'macvision', 'all'],
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default='auto',
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help='OCR engine(s) to use (default: auto)'
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)
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# Output options
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parser.add_argument(
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'--output', '-o',
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help='Output directory for results (default: stdout)'
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)
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parser.add_argument(
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'--format',
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choices=['text', 'json', 'structured'],
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default='json',
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help='Output format (default: json)'
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)
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parser.add_argument(
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'--verbose', '-v',
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action='store_true',
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help='Enable verbose output'
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)
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args = parser.parse_args()
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# Check dependencies
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if args.engine in ['auto', 'paddle', 'all']:
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check_all_dependencies(interactive=False)
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# Warn about macOS
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if sys.platform != 'darwin' and args.engine in ['macvision', 'all']:
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print("[WARN] MacVision only available on macOS")
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# Create processor
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processor = OCRProcessor(
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engine=args.engine,
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verbose=args.verbose
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)
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# Process images
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if args.image:
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image_paths = [args.image]
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else:
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# Expand glob patterns
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image_paths = []
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for pattern in args.images:
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image_paths.extend(sorted(Path().glob(pattern)))
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image_paths = [str(p) for p in image_paths]
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if args.verbose:
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print(f"\n[INFO] Processing {len(image_paths)} image(s)")
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# Extract text
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results = processor.batch_extract(image_paths)
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# Format and output
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output_func = format_output(args.format)
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if args.output:
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# Save to files
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output_path = Path(args.output)
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output_path.mkdir(parents=True, exist_ok=True)
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for image_path, result in zip(image_paths, results):
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stem = Path(image_path).stem
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output_file = output_path / f"{stem}_ocr.json"
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with open(output_file, 'w', encoding='utf-8') as f:
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json.dump(result, f, ensure_ascii=False, indent=2)
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if args.verbose:
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print(f"[OK] Saved: {output_file}")
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else:
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# Print to stdout
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for image_path, result in zip(image_paths, results):
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print(f"\n{'='*60}")
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print(f"File: {image_path}")
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print(f"Selected Engine: {result.get('selected_engine', 'unknown')}")
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print(f"Confidence: {result.get('confidence', 0):.2%}")
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print(f"Total Engines: {result.get('total_engines', 1)}")
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print(f"Processes: {', '.join(result.get('engines_used', []))}")
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print(f"Time: {result.get('processing_time_ms', 0):.2f}ms")
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print(f"{'='*60}")
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if result.get('error'):
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print(f"[ERROR] {result['error']}")
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else:
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print(output_func(result))
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# Summary
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total_time = sum(r.get('processing_time_ms', 0) for r in results)
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if args.verbose and len(results) > 1:
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print(f"\n[INFO] Processed {len(results)} images in {total_time:.2f}ms")
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if __name__ == '__main__':
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main()
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