177 lines
6.2 KiB
Python
177 lines
6.2 KiB
Python
import cv2
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import time
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import argparse
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import numpy as np
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from face_utils import FaceDetector, FaceRecognizer, draw_face_box
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from face_db import FaceDatabase
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from voice_prompt import VoicePrompt
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def parse_args():
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"""解析命令行参数"""
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parser = argparse.ArgumentParser(description='人脸识别应用')
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parser.add_argument('--camera', type=int, default=0, help='摄像头索引')
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parser.add_argument('--confidence', type=float, default=0.5, help='人脸检测置信度阈值')
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parser.add_argument('--similarity', type=float, default=0.6, help='人脸相似度阈值')
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parser.add_argument('--display_width', type=int, default=640, help='显示窗口宽度')
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parser.add_argument('--display_height', type=int, default=480, help='显示窗口高度')
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return parser.parse_args()
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def main():
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"""人脸识别应用主函数"""
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args = parse_args()
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# 初始化组件
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print("正在初始化人脸识别系统...")
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voice = VoicePrompt()
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detector = FaceDetector(conf_thres=args.confidence)
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recognizer = FaceRecognizer(detector=detector, similarity_threshold=args.similarity)
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db = FaceDatabase()
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# 加载已知人脸特征
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known_faces = db.get_all_features()
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if not known_faces:
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print("警告: 数据库中没有注册的人脸，请先运行注册程序")
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voice.speak("数据库中没有注册的人脸，请先运行注册程序", block=True)
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else:
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print(f"已加载 {len(known_faces)} 个人脸特征")
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# 打开摄像头
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cap = cv2.VideoCapture(args.camera)
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if not cap.isOpened():
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print("无法打开摄像头")
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return
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# 设置摄像头分辨率
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cap.set(cv2.CAP_PROP_FRAME_WIDTH, args.display_width)
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cap.set(cv2.CAP_PROP_FRAME_HEIGHT, args.display_height)
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print("系统初始化完成，开始人脸识别...")
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voice.speak("人脸识别系统已启动", block=True)
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# 跟踪识别状态，避免重复语音提示
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recognized_persons = {} # {person_id: last_recognition_time}
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recognition_cooldown = 5.0 # 同一个人识别成功后的冷却时间（秒）
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# 绘制UI相关
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font = cv2.FONT_HERSHEY_SIMPLEX
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font_scale = 0.6
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line_thickness = 2
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fps_history = []
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try:
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while True:
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# 计时（用于计算FPS）
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start_time = time.time()
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# 读取摄像头帧
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ret, frame = cap.read()
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if not ret:
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print("无法获取摄像头画面")
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break
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# 镜像翻转以便更直观
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frame = cv2.flip(frame, 1)
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# 创建显示帧
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display_frame = frame.copy()
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# 检测人脸
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faces = detector.detect_faces(frame)
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# 处理每个检测到的人脸
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for face_box in faces:
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# 识别人脸
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result = recognizer.identify_face(frame, known_faces, face_box[:4])
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# 如果识别成功
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if result:
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person_id, name, similarity = result
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# 绘制人脸框和标签（绿色表示识别成功）
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draw_face_box(display_frame, face_box, name, similarity, color=(0, 255, 0))
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# 检查是否需要播放语音提示（避免频繁重复）
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current_time = time.time()
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if (person_id not in recognized_persons or
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current_time - recognized_persons[person_id] > recognition_cooldown):
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# 更新识别时间
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recognized_persons[person_id] = current_time
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# 播放语音提示
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voice.speak(f"您好，{name}", block=False)
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print(f"识别成功: {name} (相似度: {similarity:.2f})")
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else:
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# 未识别的人脸使用红色框
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draw_face_box(display_frame, face_box, "未知", color=(0, 0, 255))
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# 计算并显示FPS
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end_time = time.time()
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fps = 1.0 / (end_time - start_time)
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fps_history.append(fps)
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if len(fps_history) > 30:
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fps_history.pop(0)
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avg_fps = sum(fps_history) / len(fps_history)
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cv2.putText(
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display_frame,
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f"FPS: {avg_fps:.1f}",
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(10, 30),
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font,
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font_scale,
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(0, 255, 255),
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line_thickness
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)
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# 显示数据库信息
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cv2.putText(
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display_frame,
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f"数据库: {len(known_faces)} 个人脸",
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(10, 60),
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font,
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font_scale,
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(0, 255, 255),
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line_thickness
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)
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# 显示操作指南
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cv2.putText(
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display_frame,
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"按ESC退出, 按R刷新数据库",
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(10, display_frame.shape[0] - 10),
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font,
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font_scale,
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(255, 255, 255),
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line_thickness
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)
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# 显示图像
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cv2.imshow("人脸识别系统", display_frame)
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# 按键处理
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key = cv2.waitKey(1) & 0xFF
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# 按ESC键退出
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if key == 27:
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break
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# 按R键刷新数据库
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elif key == ord('r'):
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print("正在刷新人脸数据库...")
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known_faces = db.get_all_features()
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print(f"已刷新: 加载了 {len(known_faces)} 个人脸特征")
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voice.speak("已刷新人脸数据库", block=False)
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except Exception as e:
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print(f"发生错误: {e}")
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finally:
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# 释放资源
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cap.release()
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cv2.destroyAllWindows()
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db.close()
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print("人脸识别系统已关闭")
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if __name__ == "__main__":
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main()
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