feat: 实现CutThenThink P0阶段核心功能
项目初始化 - 创建完整项目结构（src/, data/, docs/, examples/, tests/） - 配置requirements.txt依赖 - 创建.gitignore P0基础框架 - 数据库模型：Record模型，6种分类类型 - 配置管理：YAML配置，支持AI/OCR/云存储/UI配置 - OCR模块：PaddleOCR本地识别，支持云端扩展 - AI模块：支持OpenAI/Claude/通义/Ollama，6种分类 - 存储模块：完整CRUD，搜索，统计，导入导出 - 主窗口框架：侧边栏导航，米白配色方案 - 图片处理：截图/剪贴板/文件选择/图片预览 - 处理流程整合：OCR→AI→存储串联，Markdown展示，剪贴板复制 - 分类浏览：卡片网格展示，分类筛选，搜索，详情查看 技术栈 - PyQt6 + SQLAlchemy + PaddleOCR + OpenAI/Claude SDK - 共47个Python文件，4000+行代码 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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congsh
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tests/test_ocr.py
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tests/test_ocr.py
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"""
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OCR 模块测试脚本
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用法:
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python test_ocr.py --image <图片路径> [--lang ch] [--gpu]
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"""
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import sys
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import argparse
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from pathlib import Path
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# 添加项目根目录到路径
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from src.core.ocr import (
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recognize_text,
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preprocess_image,
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PaddleOCREngine,
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CloudOCREngine,
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ImagePreprocessor,
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OCRLanguage
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)
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def test_ocr_basic(image_path: str, lang: str = "ch", use_gpu: bool = False):
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"""测试基本 OCR 识别"""
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print(f"\n{'='*60}")
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print(f"测试基本 OCR 识别")
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print(f"{'='*60}")
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print(f"图片路径: {image_path}")
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print(f"语言: {lang}")
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print(f"GPU: {use_gpu}")
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result = recognize_text(
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image=image_path,
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mode="local",
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lang=lang,
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use_gpu=use_gpu,
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preprocess=False
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)
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print(f"\n识别结果:")
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print(f" 成功: {result.success}")
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print(f" 识别行数: {len(result.results)}")
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print(f" 平均置信度: {result.total_confidence:.2f}")
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if result.success:
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print(f"\n完整文本:")
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print("-" * 60)
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print(result.full_text)
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print("-" * 60)
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# 显示前 5 行详细信息
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print(f"\n前 5 行详细信息:")
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for i, r in enumerate(result.results[:5]):
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print(f" [{i}] {r.text[:50]}... (置信度: {r.confidence:.2f})")
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else:
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print(f"\n错误: {result.error_message}")
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def test_ocr_with_preprocess(image_path: str, lang: str = "ch"):
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"""测试带预处理的 OCR 识别"""
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print(f"\n{'='*60}")
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print(f"测试带预处理的 OCR 识别")
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print(f"{'='*60}")
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result = recognize_text(
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image=image_path,
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mode="local",
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lang=lang,
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preprocess=True
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)
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print(f"\n识别结果:")
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print(f" 成功: {result.success}")
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print(f" 识别行数: {len(result.results)}")
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print(f" 平均置信度: {result.total_confidence:.2f}")
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if result.success:
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print(f"\n完整文本:")
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print("-" * 60)
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print(result.full_text)
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print("-" * 60)
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def test_preprocess(image_path: str, output_dir: str = None):
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"""测试图像预处理功能"""
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print(f"\n{'='*60}")
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print(f"测试图像预处理功能")
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print(f"{'='*60}")
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if output_dir is None:
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output_dir = Path(image_path).parent / "processed"
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else:
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output_dir = Path(output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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# 测试不同的预处理组合
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configs = {
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"原始图像": {},
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"调整大小": {"resize": True},
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"增强对比度": {"enhance_contrast": True},
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"增强锐度": {"enhance_sharpness": True},
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"去噪": {"denoise": True},
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"二值化": {"binarize": True},
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"综合增强": {
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"resize": True,
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"enhance_contrast": True,
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"enhance_sharpness": True
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}
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}
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for name, config in configs.items():
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print(f"\n处理: {name}")
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output_path = output_dir / f"{Path(image_path).stem}_{name.replace(' ', '_')}.jpg"
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try:
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processed = preprocess_image(
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image_path,
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output_path=str(output_path),
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**config
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)
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print(f" 保存到: {output_path}")
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print(f" 尺寸: {processed.size}")
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except Exception as e:
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print(f" 失败: {e}")
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def test_engine_directly():
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"""测试直接使用引擎"""
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print(f"\n{'='*60}")
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print(f"测试直接使用 OCR 引擎")
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print(f"{'='*60}")
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# 创建 PaddleOCR 引擎
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config = {
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'lang': 'ch',
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'use_gpu': False,
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'show_log': False
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}
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print(f"\n创建 PaddleOCR 引擎...")
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engine = PaddleOCREngine(config)
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print(f"引擎类型: {type(engine).__name__}")
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def main():
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parser = argparse.ArgumentParser(description="OCR 模块测试")
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parser.add_argument('--image', type=str, help='图片路径')
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parser.add_argument('--lang', type=str, default='ch', help='语言 (ch/en/chinese_chinese)')
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parser.add_argument('--gpu', action='store_true', help='使用 GPU')
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parser.add_argument('--preprocess-only', action='store_true', help='仅测试预处理')
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parser.add_argument('--engine-only', action='store_true', help='仅测试引擎创建')
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args = parser.parse_args()
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# 测试引擎创建
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test_engine_directly()
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# 如果指定了图片
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if args.image:
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if not Path(args.image).exists():
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print(f"\n错误: 图片不存在: {args.image}")
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return
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if args.preprocess_only:
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# 仅测试预处理
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test_preprocess(args.image)
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else:
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# 测试基本 OCR
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test_ocr_basic(args.image, args.lang, args.gpu)
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# 测试带预处理的 OCR
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test_ocr_with_preprocess(args.image, args.lang)
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# 测试预处理功能
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test_preprocess(args.image)
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else:
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print("\n提示: 使用 --image <图片路径> 来测试 OCR 识别功能")
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print("示例: python test_ocr.py --image /path/to/image.png")
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if __name__ == '__main__':
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main()
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