feat: Add KB knowledge base series skills
- kb-link-parser: Multi-platform URL parser (Bilibili, YouTube, Zhihu) - kb-bilibili-metadata: Bilibili video/article/up主 metadata extractor - kb-zhihu-metadata: Zhihu question/answer/user metadata extractor - kb-bilibili-content: Bilibili video download and content extraction - kb-ocr-processor: OCR text extraction from video frames - kb-deep-analyzer: AI-powered deep content analysis - kb-workflow: Workflow orchestration (inbox, archive, subscriptions, report) Also add: - ARCHITECTURE_FLOWCHART.md: System architecture diagrams - DETAILED_DESIGN.md: Complete design documentation
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OpenClaw Agent
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#!/usr/bin/env python3
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"""
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OCR Processor - 视频帧文字识别工具
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支持 EasyOCR 和 Tesseract 引擎
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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 glob
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from pathlib import Path
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from typing import List, Dict, Optional
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import easyocr
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import pytesseract
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from PIL import Image
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class OCRProcessor:
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"""OCR 文字识别处理器"""
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def __init__(self, engine: str = "easyocr", languages: List[str] = ["ch_sim", "en"]):
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"""
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初始化 OCR 处理器
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Args:
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engine: OCR 引擎 ("easyocr" 或 "tesseract")
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languages: 语言列表
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"""
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self.engine = engine
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self.languages = languages
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self.reader = None
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if engine == "easyocr":
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self.reader = easyocr.Reader(languages, gpu=False)
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def process_image(self, image_path: str) -> Dict:
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"""
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处理单张图片进行 OCR 识别
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Args:
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image_path: 图片路径
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Returns:
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包含 text 和 confidence 的字典
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"""
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if not os.path.exists(image_path):
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return {"text": "", "confidence": 0.0, "error": "File not found"}
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try:
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if self.engine == "easyocr":
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return self._process_with_easyocr(image_path)
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elif self.engine == "tesseract":
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return self._process_with_tesseract(image_path)
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else:
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return {"text": "", "confidence": 0.0, "error": f"Unknown engine: {self.engine}"}
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except Exception as e:
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return {"text": "", "confidence": 0.0, "error": str(e)}
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def _process_with_easyocr(self, image_path: str) -> Dict:
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"""使用 EasyOCR 处理图片"""
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results = self.reader.readtext(image_path)
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if not results:
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return {"text": "", "confidence": 0.0}
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texts = []
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confidences = []
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for bbox, text, conf in results:
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texts.append(text)
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confidences.append(conf)
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merged_text = "\n".join(texts)
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avg_confidence = sum(confidences) / len(confidences) if confidences else 0.0
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return {
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"text": merged_text,
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"confidence": round(avg_confidence, 4),
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"details": [
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{"text": text, "confidence": round(conf, 4)}
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for text, conf in zip(texts, confidences)
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]
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}
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def _process_with_tesseract(self, image_path: str) -> Dict:
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"""使用 Tesseract 处理图片"""
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image = Image.open(image_path)
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text = pytesseract.image_to_string(image, lang="+".join(self.languages))
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# Tesseract 不直接返回置信度，返回 0.0
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return {
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"text": text.strip(),
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"confidence": 0.0
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}
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def process_frames(self, input_dir: str, fps: float = 0.1) -> List[Dict]:
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"""
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处理目录下所有帧图片
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Args:
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input_dir: 帧图片目录
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fps: 采样帧率
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Returns:
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OCR 结果列表
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"""
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if not os.path.isdir(input_dir):
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print(f"Error: Directory not found: {input_dir}")
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return []
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# 支持的图片格式
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image_extensions = ["*.jpg", "*.jpeg", "*.png", "*.bmp", "*.webp"]
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image_files = []
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for ext in image_extensions:
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image_files.extend(glob.glob(os.path.join(input_dir, ext)))
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image_files.extend(glob.glob(os.path.join(input_dir, "**", ext), recursive=True))
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# 按文件名排序
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image_files = sorted(image_files)
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if not image_files:
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print(f"No images found in {input_dir}")
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return []
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print(f"Found {len(image_files)} images in {input_dir}")
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print(f"Processing with {self.engine} engine...")
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results = []
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for i, image_path in enumerate(image_files):
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print(f"Processing [{i+1}/{len(image_files)}]: {image_path}")
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ocr_result = self.process_image(image_path)
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ocr_result["source"] = os.path.basename(image_path)
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results.append(ocr_result)
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return results
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def merge_texts(self, results: List[Dict]) -> str:
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"""
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合并多个 OCR 结果的文本
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Args:
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results: OCR 结果列表
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Returns:
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合并后的文本
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"""
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texts = []
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for result in results:
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if result.get("text"):
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texts.append(result["text"])
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return "\n\n---\n\n".join(texts)
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def save_results(self, results: List[Dict], output_path: str, merge: bool = False):
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"""
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保存 OCR 结果到 JSON 文件
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Args:
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results: OCR 结果列表
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output_path: 输出文件路径
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merge: 是否同时保存合并后的文本文件
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"""
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output_data = {
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"engine": self.engine,
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"total_frames": len(results),
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"results": results
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}
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# 保存 JSON
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with open(output_path, "w", encoding="utf-8") as f:
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json.dump(output_data, f, ensure_ascii=False, indent=2)
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print(f"Results saved to {output_path}")
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# 如果需要，同时保存合并的文本
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if merge:
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text_path = output_path.replace(".json", ".txt")
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merged_text = self.merge_texts(results)
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with open(text_path, "w", encoding="utf-8") as f:
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f.write(merged_text)
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print(f"Merged text saved to {text_path}")
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def main():
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"""命令行入口"""
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parser = argparse.ArgumentParser(
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description="OCR 视频帧文字识别工具",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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示例:
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# 使用 EasyOCR 处理帧目录
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python ocr_processor.py --input ./frames/ --output ./ocr.json
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# 使用 Tesseract 处理
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python ocr_processor.py --input ./frames/ --output ./ocr.json --engine tesseract
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# 指定采样帧率
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python ocr_processor.py --input ./frames/ --output ./ocr.json --fps 0.5
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# 同时生成合并的文本文件
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python ocr_processor.py --input ./frames/ --output ./ocr.json --merge
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"""
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)
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parser.add_argument("--input", "-i", required=True, help="输入帧图片目录")
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parser.add_argument("--output", "-o", required=True, help="输出 JSON 文件路径")
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parser.add_argument("--fps", "-f", type=float, default=0.1, help="采样帧率 (默认: 0.1)")
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parser.add_argument("--engine", "-e", choices=["easyocr", "tesseract"],
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default="easyocr", help="OCR 引擎 (默认: easyocr)")
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parser.add_argument("--merge", action="store_true", help="同时保存合并的文本文件")
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parser.add_argument("--languages", "-l", nargs="+",
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default=["ch_sim", "en"], help="语言列表")
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args = parser.parse_args()
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# 初始化处理器
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processor = OCRProcessor(engine=args.engine, languages=args.languages)
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# 处理帧
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results = processor.process_frames(args.input, fps=args.fps)
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if results:
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# 保存结果
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processor.save_results(results, args.output, merge=args.merge)
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# 打印摘要
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total_texts = sum(1 for r in results if r.get("text"))
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avg_conf = sum(r.get("confidence", 0) for r in results) / len(results) if results else 0
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print(f"\n=== 摘要 ===")
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print(f"处理帧数: {len(results)}")
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print(f"识别到文字的帧: {total_texts}")
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print(f"平均置信度: {avg_conf:.2%}")
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else:
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print("未处理到任何结果")
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return 1
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return 0
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if __name__ == "__main__":
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exit(main())
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