python活体检测代码
时间: 2023-09-09 10:07:08 浏览: 64
以下是一个简单的Python活体检测代码示例,使用OpenCV和dlib库进行面部检测和68个面部特征点标记,通过计算眼睛和嘴巴的纵横比来判断是否为真人脸。
```python
import cv2
import dlib
# 初始化dlib检测器和面部特征点预测器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
# 定义纵横比阈值
EYE_AR_THRESH = 0.3
MOUTH_AR_THRESH = 0.5
# 定义计数器和帧数
COUNTER = 0
TOTAL = 0
# 读取视频流或摄像头
cap = cv2.VideoCapture(0)
while True:
# 读取一帧图像
ret, frame = cap.read()
if not ret:
break
# 将图像转换为灰度图像
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 检测图像中的面部
faces = detector(gray, 0)
# 遍历检测到的面部
for face in faces:
# 获取面部特征点
shape = predictor(gray, face)
shape = dlib.full_object_detection(shape)
# 计算眼睛的纵横比
left_eye = shape.part(36).x, shape.part(36).y, shape.part(37).x, shape.part(37).y, shape.part(38).x, shape.part(38).y, shape.part(39).x, shape.part(39).y, shape.part(40).x, shape.part(40).y, shape.part(41).x, shape.part(41).y
right_eye = shape.part(42).x, shape.part(42).y, shape.part(43).x, shape.part(43).y, shape.part(44).x, shape.part(44).y, shape.part(45).x, shape.part(45).y, shape.part(46).x, shape.part(46).y, shape.part(47).x, shape.part(47).y
eye_ar = (cv2.norm((left_eye[1], left_eye[2]), (left_eye[5], left_eye[6])) + cv2.norm((left_eye[3], left_eye[4]), (left_eye[11], left_eye[10]))) / (2.0 * cv2.norm((left_eye[7], left_eye[8]), (left_eye[9], left_eye[0])))
eye_ar = round(eye_ar, 2)
# 计算嘴巴的纵横比
mouth = shape.part(48).y, shape.part(49).y, shape.part(50).y, shape.part(51).y, shape.part(52).y, shape.part(53).y, shape.part(54).y, shape.part(55).y, shape.part(56).y, shape.part(57).y, shape.part(58).y, shape.part(59).y, shape.part(60).y, shape.part(61).y, shape.part(62).y, shape.part(63).y, shape.part(64).y, shape.part(65).y, shape.part(66).y, shape.part(67).y
mouth_ar = (cv2.norm((mouth[3], mouth[4]), (mouth[11], mouth[12])) + cv2.norm((mouth[5], mouth[6]), (mouth[9], mouth[10])) + cv2.norm((mouth[2], mouth[3]), (mouth[16], mouth[17]))) / (3.0 * cv2.norm((mouth[0], mouth[1]), (mouth[13], mouth[14])))
mouth_ar = round(mouth_ar, 2)
# 如果眼睛或嘴巴的纵横比低于阈值,则将计数器加1
if eye_ar < EYE_AR_THRESH or mouth_ar < MOUTH_AR_THRESH:
COUNTER += 1
else:
TOTAL += 1
# 在图像上绘制纵横比和总计数器
cv2.putText(frame, "Eye AR: {:.2f}".format(eye_ar), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.putText(frame, "Mouth AR: {:.2f}".format(mouth_ar), (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.putText(frame, "COUNTER: {}".format(COUNTER), (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.putText(frame, "TOTAL: {}".format(TOTAL), (10, 120), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
# 显示图像
cv2.imshow("Frame", frame)
# 按q退出
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
# 释放资源
cap.release()
cv2.destroyAllWindows()
```
注意:这只是一个简单的示例,实际应用中可能需要更复杂的算法和处理步骤来提高准确性和鲁棒性。
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