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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examples/ocr_example.py
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examples/ocr_example.py
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"""
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OCR 模块使用示例
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演示如何使用 OCR 模块进行文字识别
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"""
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# 导入 OCR 模块
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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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OCRFactory,
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ImagePreprocessor,
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OCRResult,
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OCRLanguage
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)
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def example_1_quick_recognize():
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"""示例 1: 快速识别文本（最简单）"""
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print("示例 1: 快速识别文本")
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print("-" * 50)
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result = recognize_text(
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image="path/to/your/image.png",
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mode="local", # 本地识别
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lang="ch", # 中文
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use_gpu=False, # 不使用 GPU
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preprocess=False # 不预处理
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)
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if result.success:
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print(f"识别成功！")
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print(f"平均置信度: {result.total_confidence:.2f}")
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print(f"识别行数: {len(result.results)}")
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print(f"完整文本:\n{result.full_text}")
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else:
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print(f"识别失败: {result.error_message}")
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def example_2_with_preprocess():
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"""示例 2: 带预处理的识别（适合低质量图片）"""
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print("\n示例 2: 带预处理的识别")
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print("-" * 50)
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result = recognize_text(
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image="path/to/your/image.png",
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mode="local",
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lang="ch",
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preprocess=True # 启用预处理（增强对比度、锐度等）
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)
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if result.success:
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print(f"识别成功！")
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print(f"完整文本:\n{result.full_text}")
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def example_3_engine_directly():
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"""示例 3: 直接使用 OCR 引擎"""
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print("\n示例 3: 直接使用 OCR 引擎")
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print("-" * 50)
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# 创建引擎
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config = {
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'lang': 'ch', # 语言
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'use_gpu': False, # 是否使用 GPU
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'show_log': False # 是否显示日志
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}
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engine = PaddleOCREngine(config)
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# 识别图片
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result = engine.recognize(
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image="path/to/your/image.png",
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preprocess=False
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)
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if result.success:
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print(f"识别成功！")
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print(f"完整文本:\n{result.full_text}")
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# 遍历每一行
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for line_result in result.results:
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print(f"行 {line_result.line_index}: {line_result.text} (置信度: {line_result.confidence:.2f})")
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def example_4_batch_images():
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"""示例 4: 批量处理多张图片"""
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print("\n示例 4: 批量处理多张图片")
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print("-" * 50)
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image_list = [
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"path/to/image1.png",
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"path/to/image2.png",
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"path/to/image3.png"
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]
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engine = PaddleOCREngine({'lang': 'ch'})
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for i, image_path in enumerate(image_list, 1):
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print(f"\n处理图片 {i}: {image_path}")
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result = engine.recognize(image_path)
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if result.success:
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print(f" 识别成功，置信度: {result.total_confidence:.2f}")
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print(f" 文本预览: {result.full_text[:100]}...")
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else:
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print(f" 识别失败: {result.error_message}")
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def example_5_image_preprocess():
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"""示例 5: 图像预处理（增强识别效果）"""
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print("\n示例 5: 图像预处理")
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print("-" * 50)
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# 预处理并保存
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processed = preprocess_image(
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image_path="path/to/input.png",
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output_path="path/to/output_processed.png",
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resize=True, # 调整大小
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enhance_contrast=True, # 增强对比度
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enhance_sharpness=True, # 增强锐度
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denoise=False, # 不去噪
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binarize=False # 不二值化
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)
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print(f"预处理完成，图像尺寸: {processed.size}")
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# 然后对预处理后的图片进行 OCR
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result = recognize_text(
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image=processed, # 可以传入 PIL Image
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mode="local",
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lang="ch"
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)
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if result.success:
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print(f"识别文本: {result.full_text}")
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def example_6_multilanguage():
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"""示例 6: 多语言识别"""
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print("\n示例 6: 多语言识别")
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print("-" * 50)
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# 中文
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result_ch = recognize_text(
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image="path/to/chinese_image.png",
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lang="ch" # 中文
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)
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print(f"中文识别置信度: {result_ch.total_confidence:.2f}")
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# 英文
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result_en = recognize_text(
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image="path/to/english_image.png",
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lang="en" # 英文
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)
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print(f"英文识别置信度: {result_en.total_confidence:.2f}")
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# 中英混合
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result_mix = recognize_text(
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image="path/to/mixed_image.png",
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lang="chinese_chinese" # 中英混合
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)
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print(f"混合识别置信度: {result_mix.total_confidence:.2f}")
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def example_7_cloud_ocr():
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"""示例 7: 云端 OCR（需要配置）"""
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print("\n示例 7: 云端 OCR")
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print("-" * 50)
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# 配置云端 OCR
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config = {
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'api_endpoint': 'https://api.example.com/ocr',
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'api_key': 'your_api_key_here',
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'provider': 'custom',
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'timeout': 30
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}
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engine = CloudOCREngine(config)
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# 注意：云端 OCR 需要根据具体 API 实现 _send_request 方法
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result = engine.recognize("path/to/image.png")
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if result.success:
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print(f"识别成功: {result.full_text}")
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else:
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print(f"云端 OCR 尚未实现: {result.error_message}")
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def example_8_factory_pattern():
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"""示例 8: 使用工厂模式创建引擎"""
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print("\n示例 8: 使用工厂模式创建引擎")
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print("-" * 50)
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# 创建本地引擎
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local_engine = OCRFactory.create_engine(
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mode="local",
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config={'lang': 'ch'}
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)
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print(f"本地引擎类型: {type(local_engine).__name__}")
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# 创建云端引擎
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cloud_engine = OCRFactory.create_engine(
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mode="cloud",
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config={'api_endpoint': 'https://api.example.com/ocr'}
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)
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print(f"云端引擎类型: {type(cloud_engine).__name__}")
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def example_9_detailed_result():
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"""示例 9: 处理详细识别结果"""
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print("\n示例 9: 处理详细识别结果")
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print("-" * 50)
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result = recognize_text(
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image="path/to/image.png",
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mode="local",
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lang="ch"
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)
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if result.success:
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# 遍历每一行结果
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for line_result in result.results:
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print(f"\n行 {line_result.line_index}:")
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print(f" 文本: {line_result.text}")
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print(f" 置信度: {line_result.confidence:.2f}")
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# 如果有坐标信息
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if line_result.bbox:
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print(f" 坐标: {line_result.bbox}")
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# 统计信息
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total_chars = sum(len(r.text) for r in result.results)
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avg_confidence = sum(r.confidence for r in result.results) / len(result.results)
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print(f"\n统计:")
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print(f" 总行数: {len(result.results)}")
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print(f" 总字符数: {total_chars}")
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print(f" 平均置信度: {avg_confidence:.2f}")
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def example_10_pil_image_input():
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"""示例 10: 使用 PIL Image 作为输入"""
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print("\n示例 10: 使用 PIL Image 作为输入")
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print("-" * 50)
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from PIL import Image
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# 加载图像
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pil_image = Image.open("path/to/image.png")
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# 裁剪感兴趣区域
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cropped = pil_image.crop((100, 100, 500, 300))
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# 直接识别 PIL Image
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result = recognize_text(
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image=cropped, # 直接传入 PIL Image 对象
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mode="local",
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lang="ch"
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)
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if result.success:
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print(f"识别结果: {result.full_text}")
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if __name__ == '__main__':
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print("OCR 模块使用示例")
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print("=" * 50)
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print("\n注意：运行这些示例前，请确保:")
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print("1. 安装依赖: pip install paddleocr paddlepaddle")
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print("2. 将示例中的 'path/to/image.png' 替换为实际图片路径")
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print("=" * 50)
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# 取消注释想要运行的示例
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# example_1_quick_recognize()
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# example_2_with_preprocess()
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# example_3_engine_directly()
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# example_4_batch_images()
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# example_5_image_preprocess()
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# example_6_multilanguage()
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# example_7_cloud_ocr()
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# example_8_factory_pattern()
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# example_9_detailed_result()
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# example_10_pil_image_input()
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