113 lines
3.0 KiB
Python
113 lines
3.0 KiB
Python
import cv2
|
|
import face_recognition
|
|
import os
|
|
import sqlite3
|
|
import numpy as np
|
|
|
|
# 初始化摄像头
|
|
cap = cv2.VideoCapture(0)
|
|
photo_count = 0
|
|
max_photos = 10
|
|
captured_images = []
|
|
|
|
# 创建目录以保存照片
|
|
save_path = "captured_faces"
|
|
os.makedirs(save_path, exist_ok=True)
|
|
|
|
while photo_count < max_photos:
|
|
ret, frame = cap.read()
|
|
if not ret:
|
|
break
|
|
|
|
# 将图像转换为RGB颜色
|
|
rgb_frame = frame[:, :, ::-1]
|
|
|
|
# 检测人脸
|
|
face_locations = face_recognition.face_locations(rgb_frame)
|
|
|
|
for face_location in face_locations:
|
|
top, right, bottom, left = face_location
|
|
face_image = frame[top:bottom, left:right]
|
|
|
|
# 保存抓拍的照片
|
|
image_path = os.path.join(save_path, f"face_{photo_count + 1}.jpg")
|
|
cv2.imwrite(image_path, face_image)
|
|
captured_images.append(image_path)
|
|
|
|
photo_count += 1
|
|
if photo_count >= max_photos:
|
|
break
|
|
|
|
# 显示结果
|
|
cv2.imshow("Capturing Faces", frame)
|
|
if cv2.waitKey(1) & 0xFF == ord('q'):
|
|
break
|
|
|
|
cap.release()
|
|
cv2.destroyAllWindows()
|
|
|
|
print(f"Captured {photo_count} images.")
|
|
|
|
|
|
def create_face_database(db_name="face_database.db"):
|
|
conn = sqlite3.connect(db_name)
|
|
c = conn.cursor()
|
|
c.execute('''CREATE TABLE IF NOT EXISTS faces
|
|
(id INTEGER PRIMARY KEY AUTOINCREMENT,
|
|
name TEXT NOT NULL,
|
|
encoding BLOB NOT NULL)''')
|
|
conn.commit()
|
|
conn.close()
|
|
|
|
|
|
def add_face_to_database(name, image_path, db_name="face_database.db"):
|
|
conn = sqlite3.connect(db_name)
|
|
c = conn.cursor()
|
|
|
|
# 加载图片并生成编码
|
|
image = face_recognition.load_image_file(image_path)
|
|
face_encodings = face_recognition.face_encodings(image)
|
|
|
|
if face_encodings:
|
|
face_encoding = face_encodings[0]
|
|
# 将编码转换为可以存储的格式
|
|
encoding_blob = np.array(face_encoding).tobytes()
|
|
c.execute("INSERT INTO faces (name, encoding) VALUES (?, ?)",
|
|
(name, encoding_blob))
|
|
conn.commit()
|
|
conn.close()
|
|
|
|
|
|
def match_faces(image_path, db_name="face_database.db"):
|
|
conn = sqlite3.connect(db_name)
|
|
c = conn.cursor()
|
|
|
|
# 加载待匹配图片并生成编码
|
|
unknown_image = face_recognition.load_image_file(image_path)
|
|
unknown_encoding = face_recognition.face_encodings(unknown_image)[0]
|
|
|
|
c.execute("SELECT name, encoding FROM faces")
|
|
matches = []
|
|
for row in c.fetchall():
|
|
name, encoding_blob = row
|
|
known_encoding = np.frombuffer(encoding_blob, dtype=np.float64)
|
|
match = face_recognition.compare_faces([known_encoding], unknown_encoding)
|
|
matches.append((name, match))
|
|
|
|
conn.close()
|
|
return matches
|
|
|
|
|
|
# 创建人脸数据库
|
|
create_face_database()
|
|
|
|
# 向数据库中添加人脸
|
|
add_face_to_database("Alice", "captured_faces/face_1.jpg")
|
|
|
|
# 匹配人脸
|
|
for captured_image in captured_images:
|
|
matches = match_faces(captured_image)
|
|
for name, match in matches:
|
|
if match[0]:
|
|
print(f"Match found: {name} in image {captured_image}")
|