113 lines
3.0 KiB
Python
113 lines
3.0 KiB
Python
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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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# 初始化摄像头
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cap = cv2.VideoCapture(0)
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photo_count = 0
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max_photos = 10
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captured_images = []
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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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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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# 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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for face_location in face_locations:
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top, right, bottom, left = face_location
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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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cap.release()
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cv2.destroyAllWindows()
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print(f"Captured {photo_count} images.")
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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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encoding BLOB NOT NULL)''')
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conn.commit()
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conn.close()
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def add_face_to_database(name, 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, encoding) VALUES (?, ?)",
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(name, encoding_blob))
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conn.commit()
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conn.close()
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def match_faces(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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unknown_encoding = face_recognition.face_encodings(unknown_image)[0]
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c.execute("SELECT name, encoding FROM faces")
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matches = []
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for row in c.fetchall():
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name, encoding_blob = row
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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)
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matches.append((name, match))
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conn.close()
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return matches
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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("Alice", "captured_faces/face_1.jpg")
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# 匹配人脸
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for captured_image in captured_images:
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matches = match_faces(captured_image)
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for name, match in matches:
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if match[0]:
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print(f"Match found: {name} in image {captured_image}")
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