OpenCV+dlib实现疲劳检测
本文基于OpenCV并利用dlib工具包，通过实时计算EAR值来统计眨眼次数实现了疲劳检测。
步骤：
- 首先对对检测到的人脸进行关键点定位并锁定眼睛部分的关键点，然后，对视频的每一帧图像用实时计算的EAR值来统计眨眼次数。
(1)锁定眼睛部分关键点
detector = dlib.get_frontal_face_detector()#dlib正面人脸检测
predictor = dlib.shape_predictor(args["shape_predictor"])#关键点定位，传入68关键点模型
# 分别取两个眼睛区域
(lStart, lEnd) = FACIAL_LANDMARKS_68_IDXS["left_eye"]#返回左眼开始索引，结束索引
(rStart, rEnd) = FACIAL_LANDMARKS_68_IDXS["right_eye"]
FACIAL_LANDMARKS_68_IDXS = OrderedDict([
("mouth", (48, 68)),
("right_eyebrow", (17, 22)),
("left_eyebrow", (22, 27)),
("right_eye", (36, 42)),
("left_eye", (42, 48)),
("nose", (27, 36)),
("jaw", (0, 17))
])
(2)EAR
根据上图可知，眼睛睁和闭，关键点的位置会发生变化，这里定义一个EAR=（|p2-p6|+|p3-p5|）/2*|p1-p4|,由此判断眼睛睁和闭
#根据眼睛部分的关键点，计算ear值判断眼睛睁闭
def eye_aspect_ratio(eye):
# 计算距离，竖直的
A = dist.euclidean(eye[1], eye[5])#相当于图中的|p2-p6|,scipy工具包中的euclidean欧式距离
B = dist.euclidean(eye[2], eye[4])#相当于图中的|p3-p5|
# 计算距离，水平的
C = dist.euclidean(eye[0], eye[3])#相当于图中的|p1-p4|
# ear值
ear = (A + B) / (2.0 * C)
return ear
(2)设置参数
# 设置判断参数
EYE_AR_THRESH = 0.3#阈值，闭眼时EAR值较小，眨眼是一个过程，涉及多帧图像，出现有一个过程小于阈值的EAR情况就统计为眨眼
EYE_AR_CONSEC_FRAMES = 3#EAR小于0.3持续了3帧判断为一次眨眼
# 初始化计数器
COUNTER = 0#小于0.3，COUNTER++
TOTAL = 0#当COUNTER为3时，表示连续3帧阈值小于0.3，则统计为一次眨眼TOTAL++
(3)对视频的每一帧进行判断
# 读取视频
print("[INFO] starting video stream thread...")
vs = cv2.VideoCapture(args["video"])
#vs = FileVideoStream(args["video"]).start()
time.sleep(1.0)
# 遍历每一帧
while True:
# 预处理
frame = vs.read()[1]
if frame is None:
break
(h, w) = frame.shape[:2]
width=1200#根据不同场景设置，保证人脸够大能被检测到
r = width / float(w)
dim = (width, int(h * r))
frame = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 检测人脸
rects = detector(gray, 0)
# 遍历每一个检测到的人脸
for rect in rects:
# 获取坐标
shape = predictor(gray, rect)
shape = shape_to_np(shape)#shape_to_np把关键点转换成坐标值
# 分别计算左眼和右眼的ear值
leftEye = shape[lStart:lEnd]
rightEye = shape[rStart:rEnd]
leftEAR = eye_aspect_ratio(leftEye)
rightEAR = eye_aspect_ratio(rightEye)
# 算一个平均的，(左眼加右眼)/2
ear = (leftEAR + rightEAR) / 2.0
# 绘制眼睛区域
leftEyeHull = cv2.convexHull(leftEye)
rightEyeHull = cv2.convexHull(rightEye)
cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)
cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)
# 检查是否满足阈值
if ear < EYE_AR_THRESH:
COUNTER += 1
else:
# 如果连续几帧都是闭眼的，总数算一次
if COUNTER >= EYE_AR_CONSEC_FRAMES:
TOTAL += 1
# 重置，等待下一次眨眼
COUNTER = 0
# 显示
cv2.putText(frame, "Blinks: {}".format(TOTAL), (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.putText(frame, "EAR: {:.2f}".format(ear), (300, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.imshow("Frame", frame)
key = cv2.waitKey(10) & 0xFF
if key == 27:
break
vs.release()
cv2.destroyAllWindows()
