活体检测
作者:互联网
活体检测有很多方法
眨眼检测,张嘴检测,摇头检测
眨眼检测算法:利用眼睛纵横比(EAR, eyes aspect ratio) 通过计算这个EAR的数值,我们可以判断眼睛是张开还是闭合的,从而检测眨眼动作。
EAR = (||p2-p6|| +||p3-p5||) ➗( 2 * ||p1-p4||)
代码实现:
from scipy.spatial import distance import dlib import cv2 from imutils import face_utils def eye_aspect_ratio(eye): ''' 计算EAR值 :param eye: 眼部特征点数组 :return :EAR值 ''' A = distance.euclidean(eye[1], eye[5]) B = distance.euclidean(eye[2], eye[4]) C = distance.euclidean(eye[0], eye[3]) return (A + B) / 2.0 * C #通过dlib得到一个人脸检测器 detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor('libs/shape_predictor_68_face_landmarks.dat') #括号中将训练文件的路径传入 #设置眼睛纵横比的阈值 EAR_THRESH = 0.3 #我们假定连续三帧以上EAR的值都小于阈值才预设是产生了眨眼操作 EAR_CONSEC_FRAMES = 3 # 人脸特征点中对应眼睛那几个特征点的序号需要定义一下 RIGHT_EYE_START = 37 - 1 RIGHT_EYE_END = 42 - 1 LEFT_EYE_START = 43 - 1 LEFT_EYE_END = 48 - 1 frame_counter = 0 #连续帧的计数 blink_counter = 0 #眨眼的计数 #开始调用摄像头 cap = cv2.VideoCaootuer(0) while True: ret, frame = cap.read() gray = cv2.cvtColor(frame, cv2.COLOR_BGR2BRAY) #转化成灰度图像 rects = detector(gray, 1) #人脸检测 if len(rects) > 0: shape = predictor(gray, rects[0]) #检测特征点 points = face_utils.shape_to_np(shape) leftEye = points[LEFT_EYE_START:LEFT_EYE_END + 1] #取出左眼的特征点 rightEye = points[RIGHT_EYE_START:RIGHT_EYE_END + 1] leftEAR = eye_aspect_ratio(leftEye) rightEAR = eye_aspect_ratdio(rightEye) #计算左右眼的EAR值 #求左右眼EAR的平均值 ear = (leftEAR + rightEAR) / 2.0 #实际计算时,这两步非必需 #寻找左右眼轮廓 leftEyeHull =cv2.convexHull(leftEye) rightEyeHull = cv2.convexHull(rightEye) #绘制左右眼轮廓 cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1) cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1) #如果EAR小于阈值,开始计算连续帧 if ear < EAR_THRESH: frame_counter += 1 else: if frame_counter >= EAR_CONSEC_THRESH: print("眨眼检测成功,请进入。") blink_counter += 1 break frame_counter = 0 cv2.imshow('window', frame) if cv2.waitKey(1) & 0xFF == ord('q'): break cv2.realease() cv2.destoryAllWindows()
结果:
标签:活体,eye,检测,frame,counter,cv2,EYE,EAR 来源: https://www.cnblogs.com/isadoraytwwt/p/12906365.html