opencv directshow（通过Dlib和opencv实现人脸识别和活体检测）
导读：目录...
目录
一 、准备工作
1.1 需要的库
1.2准备需要的文件和图片
1.3 测试程序
二 、人脸识别开发
2.1 录入自己的人脸信息
2.2 提取录入的人脸特征
2.3 实时捕获人脸并进行识别
三 、活体检测
3.1 眨眼检测
3.2 张嘴检测
3.3 摇头检测
下面这些是我突发奇想想做来玩玩 ，就在github上下载了人脸识别的代码（网址下面有附上） ，用了之后突然想试试照片的识别效果 ，发现照片也会被识别成我 ，就查阅了相关资料 ，一般都是通过活体检测（比如眨眼 、转头之类的） ，想通过算法实现实时视频检测 ，区分真人和照片 。
一 、准备工作
开发环境：windows10+pycharm2022.1.3+python+3.9
1.1 需要的库
opencv-python==4.5.5.64
numpy==1.23.3
dlib==19.24.0
pandas=1.5.0
枕头=9.2.0
dlib库安装方法：
进入虚拟环境后输入：
anaconda search -t conda dlib
conda install -c https://conda.anaconda.org/conda-forge dlib
1.2准备需要的文件和图片
进入网址：http://dlib.net/files/
下载文件：shape_predictor_68_face_landmarks.dat.bz2 ，解压后存在data文件夹 ，该模型可以识别人脸68个关键点 。网址中还含有5关键点模型文件 。
准备人脸照片 ，存于img文件夹 。
1.3 测试程序
# -*- coding = utf-8 -*- # @Time : 2022/9/20 9:11 # @Author : 李昊芸 # @file : dlib_t.py # @Software : PyCharm import cv2 import dlib path = "img/lhy_1.jpeg" img = cv2.imread(path) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #人脸分类器 detector = dlib.get_frontal_face_detector() # 获取人脸检测器 predictor = dlib.shape_predictor( ".\\data\\shape_predictor_68_face_landmarks.dat" ) dets = detector(gray, 1) for face in dets: shape = predictor(img, face) # 寻找人脸的68个标定点 # 遍历所有点 ，打印出其坐标 ，并圈出来 for pt in shape.parts(): pt_pos = (pt.x, pt.y) cv2.circle(img, pt_pos, 2, (0, 255, 0), 1) cv2.imshow("image", img) cv2.waitKey(0) cv2.destroyAllWindows()68关键点分类器识别结果：
多人识别效果：
二 、人脸识别开发
原github地址：https://github.com/coneypo/Dlib_face_recognition_from_camera
2.1 录入自己的人脸信息
get_faces_from_camera.py # Copyright (C) 2018-2021 coneypo # SPDX-License-Identifier: MIT # Author: coneypo # Blog: http://www.cnblogs.com/AdaminXie # GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera # Mail: coneypo@foxmail.com # 进行人脸录入 / Face register import dlib import numpy as np import cv2 import os import shutil import time import logging # Dlib 正向人脸检测器 / Use frontal face detector of Dlib detector = dlib.get_frontal_face_detector() class Face_Register: def __init__(self): self.path_photos_from_camera = "data/data_faces_from_camera/" self.font = cv2.FONT_ITALIC self.existing_faces_cnt = 0 # 已录入的人脸计数器 / cnt for counting saved faces self.ss_cnt = 0 # 录入 personX 人脸时图片计数器 / cnt for screen shots self.current_frame_faces_cnt = 0 # 录入人脸计数器 / cnt for counting faces in current frame self.save_flag = 1 # 之后用来控制是否保存图像的 flag / The flag to control if save self.press_n_flag = 0 # 之后用来检查是否先按 n 再按 s / The flag to check if press n before s # FPS self.frame_time = 0 self.frame_start_time = 0 self.fps = 0 self.fps_show = 0 self.start_time = time.time() # 新建保存人脸图像文件和数据 CSV 文件夹 / Mkdir for saving photos and csv def pre_work_mkdir(self): # 新建文件夹 / Create folders to save face images and csv if os.path.isdir(self.path_photos_from_camera): pass else: os.mkdir(self.path_photos_from_camera) # 删除之前存的人脸数据文件夹 / Delete old face folders def pre_work_del_old_face_folders(self): # 删除之前存的人脸数据文件夹, 删除 "/data_faces_from_camera/person_x/"... folders_rd = os.listdir(self.path_photos_from_camera) for i in range(len(folders_rd)): shutil.rmtree(self.path_photos_from_camera+folders_rd[i]) if os.path.isfile("data/features_all.csv"): os.remove("data/features_all.csv") # 如果有之前录入的人脸, 在之前 person_x 的序号按照 person_x+1 开始录入 / Start from person_x+1 def check_existing_faces_cnt(self): if os.listdir("data/data_faces_from_camera/"): # 获取已录入的最后一个人脸序号 / Get the order of latest person person_list = os.listdir("data/data_faces_from_camera/") person_num_list = [] for person in person_list: person_num_list.append(int(person.split(_)[-1])) self.existing_faces_cnt = max(person_num_list) # 如果第一次存储或者没有之前录入的人脸, 按照 person_1 开始录入 / Start from person_1 else: self.existing_faces_cnt = 0 # 更新 FPS / Update FPS of Video stream def update_fps(self): now = time.time() # 每秒刷新 fps / Refresh fps per second if str(self.start_time).split(".")[0] != str(now).split(".")[0]: self.fps_show = self.fps self.start_time = now self.frame_time = now - self.frame_start_time self.fps = 1.0 / self.frame_time self.frame_start_time = now # 生成的 cv2 window 上面添加说明文字 / PutText on cv2 window def draw_note(self, img_rd): # 添加说明 / Add some notes cv2.putText(img_rd, "Face Register", (20, 40), self.font, 1, (255, 255, 255), 1, cv2.LINE_AA) cv2.putText(img_rd, "FPS: " + str(self.fps_show.__round__(2)), (20, 100), self.font, 0.8, (0, 255, 0), 1, cv2.LINE_AA) cv2.putText(img_rd, "Faces: " + str(self.current_frame_faces_cnt), (20, 140), self.font, 0.8, (0, 255, 0), 1, cv2.LINE_AA) cv2.putText(img_rd, "N: Create face folder", (20, 350), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA) cv2.putText(img_rd, "S: Save current face", (20, 400), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA) cv2.putText(img_rd, "Q: Quit", (20, 450), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA) # 获取人脸 / Main process of face detection and saving def process(self, stream): # 1. 新建储存人脸图像文件目录 / Create folders to save photos self.pre_work_mkdir() # 2. 删除 "/data/data_faces_from_camera" 中已有人脸图像文件 # / Uncomment if want to delete the saved faces and start from person_1 # if os.path.isdir(self.path_photos_from_camera): # self.pre_work_del_old_face_folders() # 3. 检查 "/data/data_faces_from_camera" 中已有人脸文件 self.check_existing_faces_cnt() while stream.isOpened(): flag, img_rd = stream.read() # Get camera video stream kk = cv2.waitKey(1) faces = detector(img_rd, 0) # Use Dlib face detector # 4. 按下 n 新建存储人脸的文件夹 / Press n to create the folders for saving faces if kk == ord(n): self.existing_faces_cnt += 1 current_face_dir = self.path_photos_from_camera + "person_" + str(self.existing_faces_cnt) os.makedirs(current_face_dir) logging.info("\n%-40s %s", "新建的人脸文件夹 / Create folders:", current_face_dir) self.ss_cnt = 0 # 将人脸计数器清零 / Clear the cnt of screen shots self.press_n_flag = 1 # 已经按下 n / Pressed n already # 5. 检测到人脸 / Face detected if len(faces) != 0: # 矩形框 / Show the ROI of faces for k, d in enumerate(faces): # 计算矩形框大小 / Compute the size of rectangle box height = (d.bottom() - d.top()) width = (d.right() - d.left()) hh = int(height/2) ww = int(width/2) # 6. 判断人脸矩形框是否超出 480x640 / If the size of ROI > 480x640 if (d.right()+ww) > 640 or (d.bottom()+hh > 480) or (d.left()-ww < 0) or (d.top()-hh < 0): cv2.putText(img_rd, "OUT OF RANGE", (20, 300), self.font, 0.8, (0, 0, 255), 1, cv2.LINE_AA) color_rectangle = (0, 0, 255) save_flag = 0 if kk == ord(s): logging.warning("请调整位置 / Please adjust your position") else: color_rectangle = (255, 255, 255) save_flag = 1 cv2.rectangle(img_rd, tuple([d.left() - ww, d.top() - hh]), tuple([d.right() + ww, d.bottom() + hh]), color_rectangle, 2) # 7. 根据人脸大小生成空的图像 / Create blank image according to the size of face detected img_blank = np.zeros((int(height*2), width*2, 3), np.uint8) if save_flag: # 8. 按下 s 保存摄像头中的人脸到本地 / Press s to save faces into local images if kk == ord(s): # 检查有没有先按n新建文件夹 / Check if you have pressed n if self.press_n_flag: self.ss_cnt += 1 for ii in range(height*2): for jj in range(width*2): img_blank[ii][jj] = img_rd[d.top()-hh + ii][d.left()-ww + jj] cv2.imwrite(current_face_dir + "/img_face_" + str(self.ss_cnt) + ".jpg", img_blank) logging.info("%-40s %s/img_face_%s.jpg", "写入本地 / Save into：", str(current_face_dir), str(self.ss_cnt)) else: logging.warning("请先按 N 来建文件夹, 按 S / Please press N and press S") self.current_frame_faces_cnt = len(faces) # 9. 生成的窗口添加说明文字 / Add note on cv2 window self.draw_note(img_rd) # 10. 按下 q 键退出 / Press q to exit if kk == ord(q): break # 11. Update FPS self.update_fps() cv2.namedWindow("camera", 1) cv2.imshow("camera", img_rd) def run(self): # cap = cv2.VideoCapture("video.mp4") # Get video stream from video file cap = cv2.VideoCapture(0) # Get video stream from camera self.process(cap) cap.release() cv2.destroyAllWindows() def main(): logging.basicConfig(level=logging.INFO) Face_Register_con = Face_Register() Face_Register_con.run() if __name__ == __main__: main()2.2 提取录入的人脸特征
features_extraction_to_csv.py # Copyright (C) 2018-2021 coneypo # SPDX-License-Identifier: MIT # Author: coneypo # Blog: http://www.cnblogs.com/AdaminXie # GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera # Mail: coneypo@foxmail.com # 从人脸图像文件中提取人脸特征存入 "features_all.csv" / Extract features from images and save into "features_all.csv" import os import dlib import csv import numpy as np import logging import cv2 # 要读取人脸图像文件的路径 / Path of cropped faces path_images_from_camera = "data/data_faces_from_camera/" # Dlib 正向人脸检测器 / Use frontal face detector of Dlib detector = dlib.get_frontal_face_detector() # Dlib 人脸 landmark 特征点检测器 / Get face landmarks predictor = dlib.shape_predictor(data/data_dlib/shape_predictor_68_face_landmarks.dat) # Dlib Resnet 人脸识别模型 ，提取 128D 的特征矢量 / Use Dlib resnet50 model to get 128D face descriptor face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat") # 返回单张图像的 128D 特征 / Return 128D features for single image # Input: path_img <class str> # Output: face_descriptor <class dlib.vector> def return_128d_features(path_img): img_rd = cv2.imread(path_img) faces = detector(img_rd, 1) logging.info("%-40s %-20s", "检测到人脸的图像 / Image with faces detected:", path_img) # 因为有可能截下来的人脸再去检测 ，检测不出来人脸了, 所以要确保是 检测到人脸的人脸图像拿去算特征 # For photos of faces saved, we need to make sure that we can detect faces from the cropped images if len(faces) != 0: shape = predictor(img_rd, faces[0]) face_descriptor = face_reco_model.compute_face_descriptor(img_rd, shape) else: face_descriptor = 0 logging.warning("no face") return face_descriptor # 返回 personX 的 128D 特征均值 / Return the mean value of 128D face descriptor for person X # Input: path_face_personX <class str> # Output: features_mean_personX <class numpy.ndarray> def return_features_mean_personX(path_face_personX): features_list_personX = [] photos_list = os.listdir(path_face_personX) if photos_list: for i in range(len(photos_list)): # 调用 return_128d_features() 得到 128D 特征 / Get 128D features for single image of personX logging.info("%-40s %-20s", "正在读的人脸图像 / Reading image:", path_face_personX + "/" + photos_list[i]) features_128d = return_128d_features(path_face_personX + "/" + photos_list[i]) # 遇到没有检测出人脸的图片跳过 / Jump if no face detected from image if features_128d == 0: i += 1 else: features_list_personX.append(features_128d) else: logging.warning("文件夹内图像文件为空 / Warning: No images in%s/", path_face_personX) # 计算 128D 特征的均值 / Compute the mean # personX 的 N 张图像 x 128D -> 1 x 128D if features_list_personX: features_mean_personX = np.array(features_list_personX, dtype=object).mean(axis=0) else: features_mean_personX = np.zeros(128, dtype=object, order=C) return features_mean_personX def main(): logging.basicConfig(level=logging.INFO) # 获取已录入的最后一个人脸序号 / Get the order of latest person person_list = os.listdir("data/data_faces_from_camera/") person_list.sort() with open("data/features_all.csv", "w", newline="") as csvfile: writer = csv.writer(csvfile) for person in person_list: # Get the mean/average features of face/personX, it will be a list with a length of 128D logging.info("%sperson_%s", path_images_from_camera, person) features_mean_personX = return_features_mean_personX(path_images_from_camera + person) if len(person.split(_, 2)) == 2: # "person_x" person_name = person else: # "person_x_tom" person_name = person.split(_, 2)[-1] features_mean_personX = np.insert(features_mean_personX, 0, person_name, axis=0) # features_mean_personX will be 129D, person name + 128 features writer.writerow(features_mean_personX) logging.info(\n) logging.info("所有录入人脸数据存入 / Save all the features of faces registered into: data/features_all.csv") if __name__ == __main__: main()2.3 实时捕获人脸并进行识别
face_reco_from_camera.py # Copyright (C) 2018-2021 coneypo # SPDX-License-Identifier: MIT # Author: coneypo # Blog: http://www.cnblogs.com/AdaminXie # GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera # Mail: coneypo@foxmail.com # 摄像头实时人脸识别 / Real-time face detection and recognition import dlib import numpy as np import cv2 import pandas as pd import os import time import logging from PIL import Image, ImageDraw, ImageFont # Dlib 正向人脸检测器 / Use frontal face detector of Dlib detector = dlib.get_frontal_face_detector() # Dlib 人脸 landmark 特征点检测器 / Get face landmarks predictor = dlib.shape_predictor(data/data_dlib/shape_predictor_68_face_landmarks.dat) # Dlib Resnet 人脸识别模型 ，提取 128D 的特征矢量 / Use Dlib resnet50 model to get 128D face descriptor face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat") class Face_Recognizer: def __init__(self): self.face_feature_known_list = [] # 用来存放所有录入人脸特征的数组 / Save the features of faces in database self.face_name_known_list = [] # 存储录入人脸名字 / Save the name of faces in database self.current_frame_face_cnt = 0 # 存储当前摄像头中捕获到的人脸数 / Counter for faces in current frame self.current_frame_face_feature_list = [] # 存储当前摄像头中捕获到的人脸特征 / Features of faces in current frame self.current_frame_face_name_list = [] # 存储当前摄像头中捕获到的所有人脸的名字 / Names of faces in current frame self.current_frame_face_name_position_list = [] # 存储当前摄像头中捕获到的所有人脸的名字坐标 / Positions of faces in current frame # Update FPS self.fps = 0 # FPS of current frame self.fps_show = 0 # FPS per second self.frame_start_time = 0 self.frame_cnt = 0 self.start_time = time.time() self.font = cv2.FONT_ITALIC self.font_chinese = ImageFont.truetype("simsun.ttc", 30) # 从 "features_all.csv" 读取录入人脸特征 / Read known faces from "features_all.csv" def get_face_database(self): if os.path.exists("data/features_all.csv"): path_features_known_csv = "data/features_all.csv" csv_rd = pd.read_csv(path_features_known_csv, header=None) for i in range(csv_rd.shape[0]): features_someone_arr = [] self.face_name_known_list.append(csv_rd.iloc[i][0]) for j in range(1, 129): if csv_rd.iloc[i][j] == : features_someone_arr.append(0) else: features_someone_arr.append(csv_rd.iloc[i][j]) self.face_feature_known_list.append(features_someone_arr) logging.info("Faces in Database：%d", len(self.face_feature_known_list)) return 1 else: logging.warning("features_all.csv not found!") logging.warning("Please run get_faces_from_camera.py " "and features_extraction_to_csv.py before face_reco_from_camera.py") return 0 # 计算两个128D向量间的欧式距离 / Compute the e-distance between two 128D features @staticmethod def return_euclidean_distance(feature_1, feature_2): feature_1 = np.array(feature_1) feature_2 = np.array(feature_2) dist = np.sqrt(np.sum(np.square(feature_1 - feature_2))) return dist # 更新 FPS / Update FPS of Video stream def update_fps(self): now = time.time() # 每秒刷新 fps / Refresh fps per second if str(self.start_time).split(".")[0] != str(now).split(".")[0]: self.fps_show = self.fps self.start_time = now self.frame_time = now - self.frame_start_time self.fps = 1.0 / self.frame_time self.frame_start_time = now # 生成的 cv2 window 上面添加说明文字 / PutText on cv2 window def draw_note(self, img_rd): cv2.putText(img_rd, "Face Recognizer", (20, 40), self.font, 1, (255, 255, 255), 1, cv2.LINE_AA) cv2.putText(img_rd, "Frame: " + str(self.frame_cnt), (20, 100), self.font, 0.8, (0, 255, 0), 1, cv2.LINE_AA) cv2.putText(img_rd, "FPS: " + str(self.fps_show.__round__(2)), (20, 130), self.font, 0.8, (0, 255, 0), 1, cv2.LINE_AA) cv2.putText(img_rd, "Faces: " + str(self.current_frame_face_cnt), (20, 160), self.font, 0.8, (0, 255, 0), 1, cv2.LINE_AA) cv2.putText(img_rd, "Q: Quit", (20, 450), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA) def draw_name(self, img_rd): # 在人脸框下面写人脸名字 / Write names under rectangle img = Image.fromarray(cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB)) draw = ImageDraw.Draw(img) for i in range(self.current_frame_face_cnt): # cv2.putText(img_rd, self.current_frame_face_name_list[i], self.current_frame_face_name_position_list[i], self.font, 0.8, (0, 255, 255), 1, cv2.LINE_AA) draw.text(xy=self.current_frame_face_name_position_list[i], text=self.current_frame_face_name_list[i], font=self.font_chinese, fill=(255, 255, 0)) img_rd = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) return img_rd # 修改显示人名 / Show names in chinese def show_chinese_name(self): # Default known name: person_1, person_2, person_3 if self.current_frame_face_cnt >= 1: # 修改录入的人脸姓名 / Modify names in face_name_known_list to chinese name self.face_name_known_list[0] = 张三.encode(utf-8).decode() # self.face_name_known_list[1] = 张四.encode(utf-8).decode() # 处理获取的视频流 ，进行人脸识别 / Face detection and recognition from input video stream def process(self, stream): # 1. 读取存放所有人脸特征的 csv / Read known faces from "features.all.csv" if self.get_face_database(): while stream.isOpened(): self.frame_cnt += 1 logging.debug("Frame %d starts", self.frame_cnt) flag, img_rd = stream.read() faces = detector(img_rd, 0) kk = cv2.waitKey(1) # 按下 q 键退出 / Press q to quit if kk == ord(q): break else: self.draw_note(img_rd) self.current_frame_face_feature_list = [] self.current_frame_face_cnt = 0 self.current_frame_face_name_position_list = [] self.current_frame_face_name_list = [] # 2. 检测到人脸 / Face detected in current frame if len(faces) != 0: # 3. 获取当前捕获到的图像的所有人脸的特征 / Compute the face descriptors for faces in current frame for i in range(len(faces)): shape = predictor(img_rd, faces[i]) self.current_frame_face_feature_list.append(face_reco_model.compute_face_descriptor(img_rd, shape)) # 4. 遍历捕获到的图像中所有的人脸 / Traversal all the faces in the database for k in range(len(faces)): logging.debug("For face %d in camera:", k+1) # 先默认所有人不认识 ，是 unknown / Set the default names of faces with "unknown" self.current_frame_face_name_list.append("unknown") # 每个捕获人脸的名字坐标 / Positions of faces captured self.current_frame_face_name_position_list.append(tuple( [faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)])) # 5. 对于某张人脸 ，遍历所有存储的人脸特征 # For every faces detected, compare the faces in the database current_frame_e_distance_list = [] for i in range(len(self.face_feature_known_list)): # 如果 person_X 数据不为空 if str(self.face_feature_known_list[i][0]) != 0.0: e_distance_tmp = self.return_euclidean_distance(self.current_frame_face_feature_list[k], self.face_feature_known_list[i]) logging.debug(" With person %s, the e-distance is %f", str(i + 1), e_distance_tmp) current_frame_e_distance_list.append(e_distance_tmp) else: # 空数据 person_X current_frame_e_distance_list.append(999999999) # 6. 寻找出最小的欧式距离匹配 / Find the one with minimum e-distance similar_person_num = current_frame_e_distance_list.index(min(current_frame_e_distance_list)) logging.debug("Minimum e-distance with %s: %f", self.face_name_known_list[similar_person_num], min(current_frame_e_distance_list)) if min(current_frame_e_distance_list) < 0.4: self.current_frame_face_name_list[k] = self.face_name_known_list[similar_person_num] logging.debug("Face recognition result: %s", self.face_name_known_list[similar_person_num]) else: logging.debug("Face recognition result: Unknown person") logging.debug("\n") # 矩形框 / Draw rectangle for kk, d in enumerate(faces): # 绘制矩形框 cv2.rectangle(img_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]), (255, 255, 255), 2) self.current_frame_face_cnt = len(faces) # 7. 在这里更改显示的人名 / Modify name if needed # self.show_chinese_name() # 8. 写名字 / Draw name img_with_name = self.draw_name(img_rd) else: img_with_name = img_rd logging.debug("Faces in camera now: %s", self.current_frame_face_name_list) cv2.imshow("camera", img_with_name) # 9. 更新 FPS / Update stream FPS self.update_fps() logging.debug("Frame ends\n\n") # OpenCV 调用摄像头并进行 process def run(self): # cap = cv2.VideoCapture("video.mp4") # Get video stream from video file cap = cv2.VideoCapture(0) # Get video stream from camera cap.set(3, 480) # 640x480 self.process(cap) cap.release() cv2.destroyAllWindows() def main(): # logging.basicConfig(level=logging.DEBUG) # Set log level to logging.DEBUG to print debug info of every frame logging.basicConfig(level=logging.INFO) Face_Recognizer_con = Face_Recognizer() Face_Recognizer_con.run() if __name__ == __main__: main()识别结果（我和我的无美颜照片）：
（自己的脸 ，马赛克一下）
可以看出 ， 识别效果还是很不错的 。
三 、活体检测
3.1 眨眼检测
经过查询资料 ，得到广为人使用的一个指标：眼睛纵横比（EAR）
分别得到两只眼睛的纵横比并取平均值 ，作为眨眼的指标 ，经过多次测试后 ，选取0.3作为阈值 。
在连续检测到两次EAR小于阈值 ，即眼睛一睁一闭时 ，我们将记录为一次眨眼 。
代码如下 ，实验结果如图：
from scipy.spatial import distance as dist from imutils.video import VideoStream from imutils import face_utils import imutils import time import dlib import cv2 def EAR(eye): # 计算眼睛的两组垂直关键点之间的欧式距离 A = dist.euclidean(eye[1], eye[5]) # 1,5是一组垂直关键点 B = dist.euclidean(eye[2], eye[4]) # 2,4是一组 # 计算眼睛的一组水平关键点之间的欧式距离 C = dist.euclidean(eye[0], eye[3]) # 0,3是一组水平关键点 return (A + B) / (2.0 * C) def main(): EAR_THRESH = 0.3 # 眨眼阈值 EYE_close = 2 # 闭眼次数阈值 # 初始化眨眼帧计数器和总眨眼次数 count_eye = 0 total = 0 detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor(shape_predictor_68_face_landmarks.dat) # 左右眼的索引 (lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"] (rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"] vs = VideoStream(src=0).start() time.sleep(1.0) while True: frame = vs.read() frame = imutils.resize(frame, width=600) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 在灰度框中检测人脸 rects = detector(gray, 0) # 进入循环 for rect in rects: shape = predictor(gray, rect) shape = face_utils.shape_to_np(shape) # 提取左眼和右眼坐标 ，然后使用该坐标计算两只眼睛的眼睛纵横比 leftEye = shape[lStart:lEnd] rightEye = shape[rStart:rEnd] ear = EAR(leftEye) + EAR(rightEye) / 2.0 # 判断眼睛纵横比是否低于眨眼阈值 if ear < EAR_THRESH: count_eye += 1 else: # 检测到一次闭眼 if count_eye >= EYE_close: total += 1 count_eye = 0 # 画出画框上眨眼的总次数以及计算出的帧的眼睛纵横比 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(1) & 0xFF if key == ord("q"): break cv2.destroyAllWindows() vs.stop() if __name__ == __main__: main()3.2 张嘴检测
同理 ，可以做张嘴检测：
from imutils.video import VideoStream from imutils import face_utils import imutils import time import dlib import cv2 import numpy as np def MAR(mouth): # 默认二范数：求特征值 ，然后求最大特征值得算术平方根 A = np.linalg.norm(mouth[2] - mouth[9]) # 51, 59（人脸68个关键点） B = np.linalg.norm(mouth[4] - mouth[7]) # 53, 57 C = np.linalg.norm(mouth[0] - mouth[6]) # 49, 55 return (A + B) / (2.0 * C) def main(): MAR_THRESH = 0.5 # 张嘴阈值 # 初始化 COUNTER_MOUTH = 0 TOTAL_MOUTH = 0 detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor(shape_predictor_68_face_landmarks.dat) # 嘴的索引 (mStart, mEnd) = face_utils.FACIAL_LANDMARKS_IDXS["mouth"] vs = VideoStream(src=0).start() time.sleep(1.0) while True: frame = vs.read() frame = imutils.resize(frame, width=600) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 在灰度框中检测人脸 rects = detector(gray, 0) # 进入循环 for rect in rects: shape = predictor(gray, rect) shape = face_utils.shape_to_np(shape) # 提取嘴唇坐标 ，然后使用该坐标计算嘴唇纵横比 Mouth = shape[mStart:mEnd] mar = MAR(Mouth) # 判断嘴唇纵横比是否高于张嘴阈值 ，如果是 ，则增加张嘴帧计数器 if mar > MAR_THRESH: COUNTER_MOUTH += 1 else: # 如果张嘴帧计数器不等于0 ，则增加张嘴的总次数 if COUNTER_MOUTH >= 2: TOTAL_MOUTH += 1 COUNTER_MOUTH = 0 cv2.putText(frame, "Mouth is open: {}".format(TOTAL_MOUTH), (10, 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.imshow("Frame", frame) key = cv2.waitKey(1) & 0xFF if key == ord("q"): break cv2.destroyAllWindows() vs.stop() if __name__ == __main__: main()3.3 摇头检测
在手机很多App如支付宝中 ，摇头也属于非常常见的活体检测手段 ，此项采用鼻子到左右脸的欧氏距离变化 ，判断是否摇头 。
代码如下：
from scipy.spatial import distance as dist from imutils.video import VideoStream from imutils import face_utils import imutils import time import dlib import cv2 def nose_jaw_distance(nose, jaw): # 计算鼻子上一点"27"到左右脸边界的欧式距离 face_left1 = dist.euclidean(nose[0], jaw[0]) # 27, 0 face_right1 = dist.euclidean(nose[0], jaw[16]) # 27, 16 # 计算鼻子上一点"30"到左右脸边界的欧式距离 face_left2 = dist.euclidean(nose[3], jaw[2]) # 30, 2 face_right2 = dist.euclidean(nose[3], jaw[14]) # 30, 14 # 创建元组 ，用以保存4个欧式距离值 face_distance = (face_left1, face_right1, face_left2, face_right2) return face_distance def main(): # 初始化眨眼帧计数器和总眨眼次数 distance_left = 0 distance_right = 0 TOTAL_FACE = 0 detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor(shape_predictor_68_face_landmarks.dat) (nStart, nEnd) = face_utils.FACIAL_LANDMARKS_IDXS["nose"] (jStart, jEnd) = face_utils.FACIAL_LANDMARKS_IDXS[jaw] vs = VideoStream(src=0).start() time.sleep(1.0) while True: frame = vs.read() frame = imutils.resize(frame, width=600) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 在灰度框中检测人脸 rects = detector(gray, 0) # 进入循环 for rect in rects: shape = predictor(gray, rect) shape = face_utils.shape_to_np(shape) # 提取鼻子和下巴的坐标 ，然后使用该坐标计算鼻子到左右脸边界的欧式距离 nose = shape[nStart:nEnd] jaw = shape[jStart:jEnd] NOSE_JAW_Distance = nose_jaw_distance(nose, jaw) # 移植鼻子到左右脸边界的欧式距离 face_left1 = NOSE_JAW_Distance[0] face_right1 = NOSE_JAW_Distance[1] face_left2 = NOSE_JAW_Distance[2] face_right2 = NOSE_JAW_Distance[3] # 根据鼻子到左右脸边界的欧式距离 ，判断是否摇头 # 左脸大于右脸 if face_left1 >= face_right1 + 2 and face_left2 >= face_right2 + 2: distance_left += 1 # 右脸大于左脸 if face_right1 >= face_left1 + 2 and face_right2 >= face_left2 + 2: distance_right += 1 # 左脸大于右脸 ，并且右脸大于左脸 ，判定摇头 if distance_left != 0 and distance_right != 0: TOTAL_FACE += 1 distance_right = 0 distance_left = 0 # 画出摇头次数 cv2.putText(frame, "shake ones head: {}".format(TOTAL_FACE), (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) cv2.imshow("Frame", frame) key = cv2.waitKey(1) & 0xFF if key == ord("q"): break cv2.destroyAllWindows() vs.stop() if __name__ == __main__: main()创心域SEO版权声明：以上内容作者已申请原创保护，未经允许不得转载，侵权必究！授权事宜、对本内容有异议或投诉，敬请联系网站管理员，我们将尽快回复您，谢谢合作！