174 lines
5.5 KiB
Python
174 lines
5.5 KiB
Python
import cv2
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import face_recognition
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import os
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import sqlite3
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import numpy as np
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from datetime import datetime
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import time
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# 初始化摄像头
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cap = cv2.VideoCapture(0)
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max_photos = 10
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# 创建目录以保存照片
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save_path = "./captured_faces"
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os.makedirs(save_path, exist_ok=True)
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def create_face_database(db_name="face_database.db"):
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conn = sqlite3.connect(db_name)
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c = conn.cursor()
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c.execute('''CREATE TABLE IF NOT EXISTS faces
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(id INTEGER PRIMARY KEY AUTOINCREMENT,
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name TEXT NOT NULL,
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identity TEXT NOT NULL,
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encoding BLOB NOT NULL)''')
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# 创建匹配日志表
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c.execute('''CREATE TABLE IF NOT EXISTS match_logs
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(id INTEGER PRIMARY KEY AUTOINCREMENT,
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name TEXT NOT NULL,
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identity TEXT NOT NULL,
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image_path TEXT NOT NULL,
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match_time TEXT NOT NULL)''')
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conn.commit()
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conn.close()
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def add_face_to_database(name, identity, image_path, db_name="face_database.db"):
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conn = sqlite3.connect(db_name)
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c = conn.cursor()
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# 加载图片并生成编码
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image = face_recognition.load_image_file(image_path)
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face_encodings = face_recognition.face_encodings(image)
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if face_encodings:
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face_encoding = face_encodings[0]
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# 将编码转换为可以存储的格式
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encoding_blob = np.array(face_encoding).tobytes()
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c.execute("INSERT INTO faces (name, identity, encoding) VALUES (?, ?, ?)",
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(name, identity, encoding_blob))
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conn.commit()
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conn.close()
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def log_match(name, identity, image_path, db_name="face_database.db", log_file="match_log.txt"):
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conn = sqlite3.connect(db_name)
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c = conn.cursor()
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# 获取当前时间
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match_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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# 将匹配信息插入到匹配日志表中
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c.execute("INSERT INTO match_logs (name, identity, image_path, match_time) VALUES (?, ?, ?, ?)",
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(name, identity, image_path, match_time))
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conn.commit()
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conn.close()
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# 将匹配信息写入文本文件
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with open(log_file, "a") as f:
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f.write(f"匹配成功: {name} ({identity}) 在 {image_path} 时间: {match_time}\n")
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def match_faces(captured_images, db_name="face_database.db", tolerance=0.4, log_file="match_log.txt"):
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conn = sqlite3.connect(db_name)
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c = conn.cursor()
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# 获取数据库中所有存储的人脸编码
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c.execute("SELECT name, identity, encoding FROM faces")
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known_faces = c.fetchall()
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for image_path in captured_images:
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# 加载待匹配图片并生成编码
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unknown_image = face_recognition.load_image_file(image_path)
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face_encodings = face_recognition.face_encodings(unknown_image)
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if len(face_encodings) == 0:
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print(f"没有人脸 {image_path}")
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continue # 如果没有检测到人脸，跳过该图片
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unknown_encoding = face_encodings[0]
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for name, identity, encoding_blob in known_faces:
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known_encoding = np.frombuffer(encoding_blob, dtype=np.float64)
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match = face_recognition.compare_faces([known_encoding], unknown_encoding, tolerance=tolerance)
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if match[0]: # 如果匹配成功
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print(f"发现匹配: {name} ({identity}) 在 {image_path}")
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log_match(name, identity, image_path, db_name, log_file) # 记录匹配信息和时间到数据库和TXT文件
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conn.close()
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return True # 一旦找到匹配，返回成功
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print(f"没发现匹配: 在 {image_path}")
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conn.close()
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return False # 如果所有比较都没有匹配，返回失败
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# 创建人脸数据库
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create_face_database()
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# 向数据库中添加人脸
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add_face_to_database("屈礼", "居民", "./db_image/test.jpg")
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# 主程序循环
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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# 将图像转换为RGB颜色
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rgb_frame = frame[:, :, ::-1]
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# 检测人脸
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face_locations = face_recognition.face_locations(rgb_frame)
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if face_locations:
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print("检测到人脸，开始抓拍...")
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captured_images = []
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photo_count = 0
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while photo_count < max_photos:
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ret, frame = cap.read()
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if not ret:
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break
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rgb_frame = frame[:, :, ::-1]
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face_locations = face_recognition.face_locations(rgb_frame)
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for face_location in face_locations:
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top, right, bottom, left = face_location
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# 在图像上绘制绿框
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cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
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# 提取人脸区域
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face_image = frame[top:bottom, left:right]
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# 保存抓拍的照片
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image_path = os.path.join(save_path, f"face_{photo_count + 1}.jpg")
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cv2.imwrite(image_path, face_image)
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captured_images.append(image_path)
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photo_count += 1
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if photo_count >= max_photos:
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break
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# 显示结果
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cv2.imshow("Capturing Faces", frame)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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# 关闭窗口
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cv2.destroyAllWindows()
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if match_faces(captured_images):
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print("至少一张匹配")
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else:
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print("没有匹配")
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# 等待60秒后继续循环检测
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print("等待30秒后继续...")
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time.sleep(30)
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cap.release()
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