72 lines
2.3 KiB
Python
72 lines
2.3 KiB
Python
import logging
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import base64
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from io import BytesIO
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from typing import List, Dict, Any
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logger = logging.getLogger(__name__)
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class ImageRecognizer:
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"""图像识别器"""
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def __init__(self):
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"""初始化图像识别器"""
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logger.info("初始化图像识别器")
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# 这里可以加载预训练模型
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# 示例中使用简单的规则识别
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def recognize(self, images: List[str], params: Dict[str, Any] = None) -> List[Dict[str, Any]]:
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"""识别图像
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Args:
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images: 图像列表，每个图像为base64编码字符串
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params: 识别参数
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Returns:
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识别结果列表
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"""
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if params is None:
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params = {}
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threshold = params.get("threshold", 0.5)
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results = []
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for image_base64 in images:
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# 简单的规则识别示例
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recognition = self._simple_recognize(image_base64)
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results.append({
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"image": image_base64[:100] + "..." if len(image_base64) > 100 else image_base64,
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"label": recognition["label"],
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"confidence": recognition["confidence"]
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})
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return results
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def _simple_recognize(self, image_base64: str) -> Dict[str, Any]:
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"""简单的图像识别实现
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Args:
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image_base64: base64编码的图像
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Returns:
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识别结果
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"""
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# 简单的规则识别（基于图像大小和内容特征）
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try:
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# 解码base64
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image_data = base64.b64decode(image_base64)
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# 计算图像大小特征
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image_size = len(image_data)
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# 基于大小的简单分类
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if image_size < 10240: # 小于10KB
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return {"label": "小图像", "confidence": 0.8}
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elif image_size < 102400: # 小于100KB
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return {"label": "中等图像", "confidence": 0.85}
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else: # 大于100KB
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return {"label": "大图像", "confidence": 0.9}
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except Exception as e:
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logger.error(f"Image recognition error: {str(e)}")
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return {"label": "未知", "confidence": 0.5}
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