#sleeping.py
from ear import eye_aspect_ratio
from mar import mouth_aspect_ratio
from head import get_head_pose
import head
import cv2
import dlib
from imutils import face_utils
from scipy.spatial import distance as dist
import numpy as np
import math
eye1 = 0.25
eye2 = 2
ecounter = 0
etotal = 0
mouth1=0.5
mouth2=3
mcounter=0
mtotal=0
head1=0.3
head2=3
hcounter=0
htotal=0
line_pairs = [[0, 1], [1, 2], [2, 3], [3, 0],
[4, 5], [5, 6], [6, 7], [7, 4],
[0, 4], [1, 5], [2, 6], [3, 7]]
print("loading~请稍后")
# 打开摄像头，0表示打开电脑内置摄像头，也可以是视频文件的路径？？？cap
cap = cv2.VideoCapture(0)
# 加载人脸检测模块，获得脸部位置检测器(画框)
detector = dlib.get_frontal_face_detector()
# 使用dlib.shape_predictor获得脸部特征位置检测器
predictor = dlib.shape_predictor('F:/fatigue1/shape_predictor_68_face_landmarks.dat')
# 建cv2摄像头对象，这里使用电脑自带摄像头，如果接了外部摄像头，则自动切换到外部摄像头
# 左开右闭,获得的值是（a,b）
(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_68_IDXS["left_eye"]
(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_68_IDXS["right_eye"]
(mStart, mEnd) = face_utils.FACIAL_LANDMARKS_68_IDXS["mouth"]
while True:
# 读取图片并存在frame中。ret 为True 或者False,代表有没有读取到图片
ret, frame = cap.read()
# 把frame进行灰度处理
gray = cv2.cvtColor(frame, code=cv2.COLOR_BGR2GRAY)
# 识别人脸，第二个参数越大，代表讲原图放大多少倍在进行检测，提高小人脸的检测效果。
faces = detector(gray, 1)
# 画人脸识别的框
for face in faces:
x1 = face.left()
y1 = face.top()
x2 = face.right()
y2 = face.bottom()
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255))
# 在gray中识别图片的face中提取特征点
landmarks = predictor(gray, face)
# 68个特征点的坐标
landmark = face_utils.shape_to_np(landmarks)
# 获取68个特征点的坐标
for n in range(0, 68):
x = landmarks.part(n).x
y = landmarks.part(n).y
cv2.circle(frame, (x, y), 1, (0, 0, 255))
leftEye = landmark[lStart:lEnd]
rightEye = landmark[rStart:rEnd]
leftEAR = eye_aspect_ratio(leftEye)
rightEAR = eye_aspect_ratio(rightEye)
ear = (leftEAR + rightEAR) / 2.0
mouth = landmark[mStart:mEnd]
mar = mouth_aspect_ratio(mouth)
print("ear is {},mar is {}".format(ear, mar))
if ear < eye1: # 眼睛长宽比：0.2
ecounter += 1
else:
# 如果连续3次都小于阈值，则表示进行了一次眨眼活动
if ecounter >= eye2: # 阈值：3
etotal += 1
# 重置眼帧计数器
ecounter = 0
# 第十四步：进行画图操作，同时使用cv2.putText将眨眼次数进行显示
cv2.putText(frame, "Faces: {}".format(len(faces)), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.putText(frame, "COUNTER: {}".format(ecounter), (150, 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.putText(frame, "Blinks: {}".format(etotal), (450, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 0), 2)
'''
计算张嘴评分，如果小于阈值，则加1，如果连续3次都小于阈值，则表示打了一次哈欠，同一次哈欠大约在3帧
'''
# 同理，判断是否打哈欠
if mar > mouth1: # 张嘴阈值0.5
mcounter += 1
cv2.putText(frame, "Yawning!", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
else:
# 如果连续3次都小于阈值，则表示打了一次哈欠
if mcounter >= mouth2: # 阈值：3
mtotal += 1
# 重置嘴帧计数器
mcounter = 0
cv2.putText(frame, "COUNTER: {}".format(mcounter), (150, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.putText(frame, "MAR: {:.2f}".format(mar), (300, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.putText(frame, "Yawning: {}".format(mtotal), (450, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 0), 2)
"""
瞌睡点头
"""
reprojectdst, euler_angle = get_head_pose(landmark)
har = euler_angle[0, 0]
if har > head1: # 点头阈值0.3
hcounter += 1
else:
# 如果连续3次都小于阈值，则表示瞌睡点头一次
if hcounter >= head2: # 阈值：3
htotal += 1
# 重置点头帧计数器
hcounter = 0
# 绘制正方体12轴
for start, end in line_pairs:
starts = (int(reprojectdst[start][0]), int(reprojectdst[start][1]))
ends = (int(reprojectdst[end][0]), int(reprojectdst[end][1]))
cv2.line(frame, starts, ends, (0, 0, 255))
# 显示角度结果
cv2.putText(frame, "X: " + "{:7.2f}".format(euler_angle[0, 0]), (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.75,
(0, 255, 0), thickness=2) # GREEN
cv2.putText(frame, "Y: " + "{:7.2f}".format(euler_angle[1, 0]), (150, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.75,
(255, 0, 0), thickness=2) # BLUE
cv2.putText(frame, "Z: " + "{:7.2f}".format(euler_angle[2, 0]), (300, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.75,
(0, 0, 255), thickness=2) # RED
cv2.putText(frame, "Nod: {}".format(htotal), (450, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 0), 2)
print('嘴巴实时长宽比:{:.2f} '.format(mar) + "\t是否张嘴：" + str([False, True][mar >= 3]))
print('眼睛实时长宽比:{:.2f} '.format(ear) + "\t是否眨眼：" + str([False, True][ecounter >= 1]))
cv2.imshow("img", frame)
key = cv2.waitKey(500)
if key == ord('q'):
break
cap.release()
cv2.destroyAllWindows
#eye.py
from scipy.spatial import distance as dist
def eye_aspect_ratio(eye):
# 垂直眼标志（X，Y）坐标
A = dist.euclidean(eye[1], eye[5]) # 计算两个集合之间的欧式距离
B = dist.euclidean(eye[2], eye[4])
# 计算水平之间的欧几里得距离
# 水平眼标志（X，Y）坐标
C = dist.euclidean(eye[0], eye[3])
# 眼睛长宽比的计算
ear = (A + B) / (2.0 * C)
# 返回眼睛的长宽比
return ear#head.py
import numpy as np
import cv2
import math
object_pts = np.float32([[6.825897, 6.760612, 4.402142], # 33左眉左上角
[1.330353, 7.122144, 6.903745], # 29左眉右角
[-1.330353, 7.122144, 6.903745], # 34右眉左角
[-6.825897, 6.760612, 4.402142], # 38右眉右上角
[5.311432, 5.485328, 3.987654], # 13左眼左上角
[1.789930, 5.393625, 4.413414], # 17左眼右上角
[-1.789930, 5.393625, 4.413414], # 25右眼左上角
[-5.311432, 5.485328, 3.987654], # 21右眼右上角
[2.005628, 1.409845, 6.165652], # 55鼻子左上角
[-2.005628, 1.409845, 6.165652], # 49鼻子右上角
[2.774015, -2.080775, 5.048531], # 43嘴左上角
[-2.774015, -2.080775, 5.048531], # 39嘴右上角
[0.000000, -3.116408, 6.097667], # 45嘴中央下角
[0.000000, -7.415691, 4.070434]]) # 6下巴角
# 相机坐标系(XYZ)：添加相机内参
K = [6.5308391993466671e+002, 0.0, 3.1950000000000000e+002,
0.0, 6.5308391993466671e+002, 2.3950000000000000e+002,
0.0, 0.0, 1.0] # 等价于矩阵[fx, 0, cx; 0, fy, cy; 0, 0, 1]
# 图像中心坐标系(uv)：相机畸变参数[k1, k2, p1, p2, k3]
D = [7.0834633684407095e-002, 6.9140193737175351e-002, 0.0, 0.0, -1.3073460323689292e+000]
# 像素坐标系(xy)：填写凸轮的本征和畸变系数
cam_matrix = np.array(K).reshape(3, 3).astype(np.float32)
dist_coeffs = np.array(D).reshape(5, 1).astype(np.float32)
reprojectsrc = np.float32([[10.0, 10.0, 10.0],
[10.0, 10.0, -10.0],
[10.0, -10.0, -10.0],
[10.0, -10.0, 10.0],
[-10.0, 10.0, 10.0],
[-10.0, 10.0, -10.0],
[-10.0, -10.0, -10.0],
[-10.0, -10.0, 10.0]])
line_pairs = [[0, 1], [1, 2], [2, 3], [3, 0],
[4, 5], [5, 6], [6, 7], [7, 4],
[0, 4], [1, 5], [2, 6], [3, 7]]
def get_head_pose(shape): # 头部姿态估计
# （像素坐标集合）填写2D参考点，注释遵循https://ibug.doc.ic.ac.uk/resources/300-W/
# 17左眉左上角/21左眉右角/22右眉左上角/26右眉右上角/36左眼左上角/39左眼右上角/42右眼左上角/
# 45右眼右上角/31鼻子左上角/35鼻子右上角/48左上角/54嘴右上角/57嘴中央下角/8下巴角
image_pts = np.float32([shape[17], shape[21], shape[22], shape[26], shape[36],
shape[39], shape[42], shape[45], shape[31], shape[35],
shape[48], shape[54], shape[57], shape[8]])
# solvePnP计算姿势——求解旋转和平移矩阵：
# rotation_vec表示旋转矩阵，translation_vec表示平移矩阵，cam_matrix与K矩阵对应，dist_coeffs与D矩阵对应。
_, rotation_vec, translation_vec = cv2.solvePnP(object_pts, image_pts, cam_matrix, dist_coeffs)
# projectPoints重新投影误差：原2d点和重投影2d点的距离（输入3d点、相机内参、相机畸变、r、t，输出重投影2d点）
reprojectdst, _ = cv2.projectPoints(reprojectsrc, rotation_vec, translation_vec, cam_matrix, dist_coeffs)
reprojectdst = tuple(map(tuple, reprojectdst.reshape(8, 2))) # 以8行2列显示
# 计算欧拉角calc euler angle
# 参考https://docs.opencv.org/2.4/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html#decomposeprojectionmatrix
rotation_mat, _ = cv2.Rodrigues(rotation_vec) # 罗德里格斯公式（将旋转矩阵转换为旋转向量）
pose_mat = cv2.hconcat((rotation_mat, translation_vec)) # 水平拼接，vconcat垂直拼接
# decomposeProjectionMatrix将投影矩阵分解为旋转矩阵和相机矩阵
_, _, _, _, _, _, euler_angle = cv2.decomposeProjectionMatrix(pose_mat)
pitch, yaw, roll = [math.radians(_) for _ in euler_angle]
pitch = math.degrees(math.asin(math.sin(pitch)))
roll = -math.degrees(math.asin(math.sin(roll)))
yaw = math.degrees(math.asin(math.sin(yaw)))
print('pitch:{}, yaw:{}, roll:{}'.format(pitch, yaw, roll))
return reprojectdst, euler_angle # 投影误差，欧拉角
#mar.py
import numpy as np
def mouth_aspect_ratio(mouth):
A = np.linalg.norm(mouth[2] - mouth[9]) # 51, 59
B = np.linalg.norm(mouth[4] - mouth[7]) # 53, 57
C = np.linalg.norm(mouth[0] - mouth[6]) # 49, 55
mar = (A + B) / (2.0 * C)
return mar差可视化