# -*- coding: utf-8 -*- # import the necessary packages from scipy.spatial import distance as dist from imutils.video import FileVideoStream from imutils.video import VideoStream from imutils import face_utils import numpy as np # 数据处理的库 numpy import argparse import imutils import time import dlib import cv2 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 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 # 定义两个常数 # 眼睛长宽比 # 闪烁阈值 EYE_AR_THRESH = 0.2 EYE_AR_CONSEC_FRAMES = 3 # 打哈欠长宽比 # 闪烁阈值 MAR_THRESH = 0.5 MOUTH_AR_CONSEC_FRAMES = 3 # 初始化帧计数器和眨眼总数 COUNTER = 0 TOTAL = 0 # 初始化帧计数器和打哈欠总数 mCOUNTER = 0 mTOTAL = 0 # 初始化DLIB的人脸检测器(HOG),然后创建面部标志物预测 print("[INFO] loading facial landmark predictor...") # 第一步:使用dlib.get_frontal_face_detector() 获得脸部位置检测器 detector = dlib.get_frontal_face_detector() # 第二步:使用dlib.shape_predictor获得脸部特征位置检测器 predictor = dlib.shape_predictor('./model/shape_predictor_68_face_landmarks.dat') # 第三步:分别获取左右眼面部标志的索引 (lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"] (rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"] (mStart, mEnd) = face_utils.FACIAL_LANDMARKS_IDXS["mouth"] # 第四步:打开cv2 本地摄像头 cap = cv2.VideoCapture(0) # 从视频流循环帧 while True: # 第五步:进行循环,读取图片,并对图片做维度扩大,并进灰度化 ret, frame = cap.read() frame = imutils.resize(frame, width=720) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 第六步:使用detector(gray, 0) 进行脸部位置检测 rects = detector(gray, 0) # 第七步:循环脸部位置信息,使用predictor(gray, rect)获得脸部特征位置的信息 for rect in rects: shape = predictor(gray, rect) # 第八步:将脸部特征信息转换为数组array的格式 shape = face_utils.shape_to_np(shape) # 第九步:提取左眼和右眼坐标 leftEye = shape[lStart:lEnd] rightEye = shape[rStart:rEnd] # 嘴巴坐标 mouth = shape[mStart:mEnd] # 第十步:构造函数计算左右眼的EAR值,使用平均值作为最终的EAR leftEAR = eye_aspect_ratio(leftEye) rightEAR = eye_aspect_ratio(rightEye) ear = (leftEAR + rightEAR) / 2.0 # 打哈欠 mar = mouth_aspect_ratio(mouth) # 第十一步:使用cv2.convexHull获得凸包位置,使用drawContours画出轮廓位置进行画图操作 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) mouthHull = cv2.convexHull(mouth) cv2.drawContours(frame, [mouthHull], -1, (0, 255, 0), 1) # 第十二步:进行画图操作,用矩形框标注人脸 left = rect.left() top = rect.top() right = rect.right() bottom = rect.bottom() cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 3) ''' 分别计算左眼和右眼的评分求平均作为最终的评分,如果小于阈值,则加1,如果连续3次都小于阈值,则表示进行了一次眨眼活动 ''' # 第十三步:循环,满足条件的,眨眼次数+1 if ear < EYE_AR_THRESH:# 眼睛长宽比:0.2 COUNTER += 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 COUNTER >= EYE_AR_CONSEC_FRAMES:# 阈值:3 TOTAL += 1 # 重置眼帧计数器 COUNTER = 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(rects)), (10, 30),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) cv2.putText(frame, "Blinks: {}".format(TOTAL), (150, 30),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) cv2.putText(frame, "COUNTER: {}".format(COUNTER), (300, 30),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) cv2.putText(frame, "EAR: {:.2f}".format(ear), (450, 30),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 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 > MAR_THRESH:# 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 >= MOUTH_AR_CONSEC_FRAMES:# 阈值:3 mTOTAL += 1 # 重置嘴帧计数器 mCOUNTER = 0 cv2.putText(frame, "Yawning: {}".format(mTOTAL), (150, 60),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) cv2.putText(frame, "mCOUNTER: {}".format(mCOUNTER), (300, 60),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) cv2.putText(frame, "MAR: {:.2f}".format(mar), (480, 60),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) # 第十五步:进行画图操作,68个特征点标识 for (x, y) in shape: cv2.circle(frame, (x, y), 1, (0, 0, 255), -1) print('嘴巴实时长宽比:{:.2f} '.format(mar)+"\t是否张嘴:"+str([False,True][mar > MAR_THRESH])) print('眼睛实时长宽比:{:.2f} '.format(ear)+"\t是否眨眼:"+str([False,True][COUNTER>=1])) # 确定疲劳提示 if TOTAL >= 50 or mTOTAL>=15: cv2.putText(frame, "SLEEP!!!", (100, 200),cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 3) # 按q退出 cv2.putText(frame, "Press 'q': Quit", (20, 500),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (84, 255, 159), 2) # 窗口显示 show with opencv cv2.imshow("Frame", frame) # if the `q` key was pressed, break from the loop if cv2.waitKey(1) & 0xFF == ord('q'): break # 释放摄像头 release camera cap.release() # do a bit of cleanup cv2.destroyAllWindows()