73 lines
2.8 KiB
Python
73 lines
2.8 KiB
Python
import cv2
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from core.ocr import load_model as ocrLoadModel, detect as ocrDetect
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from core.face import load_model as faceLoadModel, detect as faceDetect
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from core.yolo import load_model as yoloLoadModel, detect as yoloDetect
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# 导入保存路径函数（根据实际文件位置调整导入路径）
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from core.establish import get_image_save_path
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# 模型加载状态标记（避免重复加载）
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_model_loaded = False
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def load_model():
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"""加载所有检测模型（仅首次调用时执行）"""
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global _model_loaded
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if _model_loaded:
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print("模型已加载，无需重复执行")
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return
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# 依次加载OCR、人脸、YOLO模型
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ocrLoadModel()
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faceLoadModel()
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yoloLoadModel()
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_model_loaded = True
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print("所有检测模型加载完成")
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def detect(frame):
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"""
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执行模型检测，检测到违规时按指定格式保存图片
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参数：
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frame: 待检测的图像帧（OpenCV格式，numpy.ndarray类型）
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返回：
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(检测结果布尔值, 检测详情, 检测模型类型)
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"""
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# 1. YOLO检测（优先级1）
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yolo_flag, yolo_result = yoloDetect(frame)
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print(f"YOLO检测结果：{yolo_result}")
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if yolo_flag:
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# 元组解构：获取「完整保存路径」和「显示用短路径」
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full_save_path, display_path = get_image_save_path(model_type="yolo")
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if full_save_path: # 只判断完整路径是否有效（用于保存）
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cv2.imwrite(full_save_path, frame)
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# 打印时使用「显示用短路径」，符合需求格式
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print(f"✅ YOLO违规图片已保存：{display_path}")
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return (True, yolo_result, "yolo")
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# 2. 人脸检测（优先级2）
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face_flag, face_result = faceDetect(frame)
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print(f"人脸检测结果：{face_result}")
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if face_flag:
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# 同样解构元组，分离保存路径和显示路径
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full_save_path, display_path = get_image_save_path(model_type="face")
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if full_save_path:
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cv2.imwrite(full_save_path, frame)
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print(f"✅ 人脸违规图片已保存：{display_path}")
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return (True, face_result, "face")
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# 3. OCR检测（优先级3）
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ocr_flag, ocr_result = ocrDetect(frame)
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print(f"OCR检测结果：{ocr_result}")
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if ocr_flag:
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# 解构元组，保存用完整路径，打印用短路径
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full_save_path, display_path = get_image_save_path(model_type="ocr")
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if full_save_path:
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cv2.imwrite(full_save_path, frame)
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print(f"✅ OCR违规图片已保存：{display_path}")
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return (True, ocr_result, "ocr")
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# 4. 无违规内容（不保存图片）
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print(f"❌ 未检测到任何违规内容，不保存图片")
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return (False, "未检测到任何内容", "none") |