[OpenCV

引言

在现代计算机视觉中，面部检测和姿势识别是一个重要的领域，它在各种应用中发挥着关键作用，包括人脸解锁、表情识别、虚拟现实等。本文将深入探讨一个使用Python编写的应用程序，该应用程序结合了多个库和技术，用于面部检测和姿势识别。

文章目录

• • 引言
  • 面部检测
  • • dlib库
    • OpenCV库
  • Retinaface-FaceNet实现人脸识别
  • 眨眼检测
  • 嘴部动作检测
  • 头部姿势检测
  • 完整代码
• 结尾与未来展望
• 下一步计划

面部检测

面部检测是任何面部识别任务的基础。在本应用程序中，我们使用了两个主要库来进行面部检测：dlib、OpenCV。

dlib库

dlib库是一个功能强大的面部检测和特征标定工具。它提供了用于检测人脸及面部特征的算法。在本应用程序中，dlib用于检测人脸的位置和特征点。
dlib库的跨平台安装：
全面横扫：dlib Python API在Linux和Windows的配置方案

【香橙派-OpenCV-Torch-dlib】TF损坏变成RAW格式解决方案及python环境配置

import dlib

OpenCV库

OpenCV是一个广泛用于图像处理和计算机视觉任务的库。在本应用程序中，OpenCV用于图像处理、显示和保存。

import cv2
import numpy as np

Retinaface-FaceNet实现人脸识别

代码基于人工智能领域大佬Bubbliiiing聪明的人脸识别4——Pytorch 利用Retinaface+Facenet搭建人脸识别平台微调

Retinaface+FaceNet人脸识别系统-Gradio界面设计
github:
Face-recognition-web-ui
recognition-dlib
retinaface_new.py

import timeimport cv2
import numpy as np
import torch
import torch.nn as nn
from PIL import Image, ImageDraw, ImageFont
from tqdm import tqdmfrom nets.facenet import Facenet
from nets_retinaface.retinaface import RetinaFace
from utils.anchors import Anchors
from utils.config import cfg_mnet, cfg_re50
from utils.utils import (Alignment_1, compare_faces, letterbox_image,preprocess_input)
from utils.utils_bbox import (decode, decode_landm, non_max_suppression,retinaface_correct_boxes)# --------------------------------------#
#   写中文需要转成PIL来写。
# --------------------------------------#def cv2ImgAddText(img, label, left, top, textColor=(255, 255, 255)):img = Image.fromarray(np.uint8(img))# ---------------##   设置字体# ---------------#font = ImageFont.truetype(font='model_data/simhei.ttf', size=20)draw = ImageDraw.Draw(img)label = label.encode('utf-8')draw.text((left, top), str(label, 'UTF-8'), fill=textColor, font=font)return np.asarray(img)# --------------------------------------#
#   一定注意backbone和model_path的对应。
#   在更换facenet_model后，
#   一定要注意重新编码人脸。
# --------------------------------------#
class Retinaface(object):_defaults = {# ----------------------------------------------------------------------##   retinaface训练完的权值路径# ----------------------------------------------------------------------#"retinaface_model_path": 'model_data/Retinaface_mobilenet0.25.pth',# ----------------------------------------------------------------------##   retinaface所使用的主干网络，有mobilenet和resnet50# ----------------------------------------------------------------------#"retinaface_backbone": "mobilenet",# ----------------------------------------------------------------------##   retinaface中只有得分大于置信度的预测框会被保留下来# ----------------------------------------------------------------------#"confidence": 0.5,# ----------------------------------------------------------------------##   retinaface中非极大抑制所用到的nms_iou大小# ----------------------------------------------------------------------#"nms_iou": 0.3,# ----------------------------------------------------------------------##   是否需要进行图像大小限制。#   输入图像大小会大幅度地影响FPS，想加快检测速度可以减少input_shape。#   开启后，会将输入图像的大小限制为input_shape。否则使用原图进行预测。#   会导致检测结果偏差，主干为resnet50不存在此问题。#   可根据输入图像的大小自行调整input_shape，注意为32的倍数，如[640, 640, 3]# ----------------------------------------------------------------------#"retinaface_input_shape": [640, 640, 3],# ----------------------------------------------------------------------##   是否需要进行图像大小限制。# ----------------------------------------------------------------------#"letterbox_image": True,# ----------------------------------------------------------------------##   facenet训练完的权值路径# ----------------------------------------------------------------------#"facenet_model_path": 'model_data/facenet_mobilenet.pth',# ----------------------------------------------------------------------##   facenet所使用的主干网络， mobilenet和inception_resnetv1# ----------------------------------------------------------------------#"facenet_backbone": "mobilenet",# ----------------------------------------------------------------------##   facenet所使用到的输入图片大小# ----------------------------------------------------------------------#"facenet_input_shape": [160, 160, 3],# ----------------------------------------------------------------------##   facenet所使用的人脸距离门限# ----------------------------------------------------------------------#"facenet_threhold": 0.9,# --------------------------------##   是否使用Cuda#   没有GPU可以设置成False# --------------------------------## "cuda": False"cuda": True}@classmethoddef get_defaults(cls, n):if n in cls._defaults:return cls._defaults[n]else:return "Unrecognized attribute name '" + n + "'"# ---------------------------------------------------##   初始化Retinaface# ---------------------------------------------------#def __init__(self, encoding=0, **kwargs):self.__dict__.update(self._defaults)for name, value in kwargs.items():setattr(self, name, value)# ---------------------------------------------------##   不同主干网络的config信息# ---------------------------------------------------#if self.retinaface_backbone == "mobilenet":self.cfg = cfg_mnetelse:self.cfg = cfg_re50# ---------------------------------------------------##   先验框的生成# ---------------------------------------------------#self.anchors = Anchors(self.cfg, image_size=(self.retinaface_input_shape[0], self.retinaface_input_shape[1])).get_anchors()self.generate()try:self.known_face_encodings = np.load("model_data/{backbone}_face_encoding.npy".format(backbone=self.facenet_backbone))self.known_face_names = np.load("model_data/{backbone}_names.npy".format(backbone=self.facenet_backbone))except:if not encoding:print("载入已有人脸特征失败，请检查model_data下面是否生成了相关的人脸特征文件。")pass# ---------------------------------------------------##   获得所有的分类# ---------------------------------------------------#def generate(self):# -------------------------------##   载入模型与权值# -------------------------------#self = RetinaFace(cfg=self.cfg, phase='eval', pre_train=False).eval()self.facenet = Facenet(backbone=self.facenet_backbone, mode="predict").eval()# torch.cuda.empty_cache()print('Loading weights into state dict...')# state_dict = torch.load(self.retinaface_model_path, map_location=torch.device('cpu'))state_dict = torch.load(self.retinaface_model_path)self.load_state_dict(state_dict)# state_dict = torch.load(self.facenet_model_path, map_location=torch.device('cpu'))state_dict = torch.load(self.facenet_model_path)self.facenet.load_state_dict(state_dict, strict=False)if self.cuda:self = nn.DataParallel(self)self = self.cuda()self.facenet = nn.DataParallel(self.facenet)self.facenet = self.facenet.cuda()print('Finished!')def encode_face_dataset(self, image_paths, names):face_encodings = []for index, path in enumerate(tqdm(image_paths)):# print('index,path',index,path)# ---------------------------------------------------##   打开人脸图片# ---------------------------------------------------#image = np.array(Image.open(path), np.float32)# ---------------------------------------------------##   对输入图像进行一个备份# ---------------------------------------------------#old_image = image.copy()# ---------------------------------------------------##   计算输入图片的高和宽# ---------------------------------------------------#im_height, im_width, _ = np.shape(image)# ---------------------------------------------------##   计算scale，用于将获得的预测框转换成原图的高宽# ---------------------------------------------------#scale = [np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0]]scale_for_landmarks = [np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],np.shape(image)[1], np.shape(image)[0]]if self.letterbox_image:image = letterbox_image(image, [self.retinaface_input_shape[1], self.retinaface_input_shape[0]])anchors = self.anchorselse:anchors = Anchors(self.cfg, image_size=(im_height, im_width)).get_anchors()# ---------------------------------------------------##   将处理完的图片传入Retinaface网络当中进行预测# ---------------------------------------------------#with torch.no_grad():# print(names[index], "here")# -----------------------------------------------------------##   图片预处理，归一化。# -----------------------------------------------------------#image = torch.from_numpy(preprocess_input(image).transpose(2, 0, 1)).unsqueeze(0).type(torch.FloatTensor)if self.cuda:image = image.cuda()anchors = anchors.cuda()loc, conf, landms = self(image)# -----------------------------------------------------------##   对预测框进行解码# -----------------------------------------------------------#boxes = decode(loc.data.squeeze(0), anchors, self.cfg['variance'])# -----------------------------------------------------------##   获得预测结果的置信度# -----------------------------------------------------------#conf = conf.data.squeeze(0)[:, 1:2]# -----------------------------------------------------------##   对人脸关键点进行解码# -----------------------------------------------------------#landms = decode_landm(landms.data.squeeze(0), anchors, self.cfg['variance'])# -----------------------------------------------------------##   对人脸检测结果进行堆叠# -----------------------------------------------------------#boxes_conf_landms = torch.cat([boxes, conf, landms], -1)boxes_conf_landms = non_max_suppression(boxes_conf_landms, self.confidence)if len(boxes_conf_landms) <= 0:print(names[index], "：未检测到人脸")continue# ---------------------------------------------------------##   如果使用了letterbox_image的话，要把灰条的部分去除掉。# ---------------------------------------------------------#if self.letterbox_image:boxes_conf_landms = retinaface_correct_boxes(boxes_conf_landms, \np.array([self.retinaface_input_shape[0],self.retinaface_input_shape[1]]),np.array([im_height, im_width]))boxes_conf_landms[:, :4] = boxes_conf_landms[:, :4] * scaleboxes_conf_landms[:, 5:] = boxes_conf_landms[:, 5:] * scale_for_landmarks# ---------------------------------------------------##   选取最大的人脸框。# ---------------------------------------------------#best_face_location = Nonebiggest_area = 0for result in boxes_conf_landms:left, top, right, bottom = result[0:4]w = right - lefth = bottom - topif w * h > biggest_area:biggest_area = w * hbest_face_location = result# ---------------------------------------------------##   截取图像# ---------------------------------------------------#crop_img = old_image[int(best_face_location[1]):int(best_face_location[3]),int(best_face_location[0]):int(best_face_location[2])]landmark = np.reshape(best_face_location[5:], (5, 2)) - np.array([int(best_face_location[0]), int(best_face_location[1])])crop_img, _ = Alignment_1(crop_img, landmark)crop_img = np.array(letterbox_image(np.uint8(crop_img), (self.facenet_input_shape[1], self.facenet_input_shape[0]))) / 255crop_img = crop_img.transpose(2, 0, 1)crop_img = np.expand_dims(crop_img, 0)# ---------------------------------------------------##   利用图像算取长度为128的特征向量# ---------------------------------------------------#with torch.no_grad():crop_img = torch.from_numpy(crop_img).type(torch.FloatTensor)if self.cuda:crop_img = crop_img.cuda()face_encoding = self.facenet(crop_img)[0].cpu().numpy()face_encodings.append(face_encoding)np.save("model_data/{backbone}_face_encoding.npy".format(backbone=self.facenet_backbone), face_encodings)np.save("model_data/{backbone}_names.npy".format(backbone=self.facenet_backbone), names)# ---------------------------------------------------##   检测图片# ---------------------------------------------------#def live_detect_image(self, image, flag):# ---------------------------------------------------##   对输入图像进行一个备份，后面用于绘图# ---------------------------------------------------#old_image = image.copy()# ---------------------------------------------------##   把图像转换成numpy的形式# ---------------------------------------------------#image = np.array(image, np.float32)# ---------------------------------------------------##   Retinaface检测部分-开始# ---------------------------------------------------## ---------------------------------------------------##   计算输入图片的高和宽# ---------------------------------------------------#im_height, im_width, _ = np.shape(image)# ---------------------------------------------------##   计算scale，用于将获得的预测框转换成原图的高宽# ---------------------------------------------------#scale = [np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0]]scale_for_landmarks = [np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],np.shape(image)[1], np.shape(image)[0]]# ---------------------------------------------------------##   letterbox_image可以给图像增加灰条，实现不失真的resize# ---------------------------------------------------------#if self.letterbox_image:image = letterbox_image(image, [self.retinaface_input_shape[1], self.retinaface_input_shape[0]])anchors = self.anchorselse:anchors = Anchors(self.cfg, image_size=(im_height, im_width)).get_anchors()# ---------------------------------------------------##   将处理完的图片传入Retinaface网络当中进行预测# ---------------------------------------------------#with torch.no_grad():# -----------------------------------------------------------##   图片预处理，归一化。# -----------------------------------------------------------#image = torch.from_numpy(preprocess_input(image).transpose(2, 0, 1)).unsqueeze(0).type(torch.FloatTensor)if self.cuda:anchors = anchors.cuda()image = image.cuda()# ---------------------------------------------------------##   传入网络进行预测# ---------------------------------------------------------#loc, conf, landms = self(image)# ---------------------------------------------------##   Retinaface网络的解码，最终我们会获得预测框#   将预测结果进行解码和非极大抑制# ---------------------------------------------------#boxes = decode(loc.data.squeeze(0), anchors, self.cfg['variance'])conf = conf.data.squeeze(0)[:, 1:2]landms = decode_landm(landms.data.squeeze(0), anchors, self.cfg['variance'])# -----------------------------------------------------------##   对人脸检测结果进行堆叠# -----------------------------------------------------------#boxes_conf_landms = torch.cat([boxes, conf, landms], -1)boxes_conf_landms = non_max_suppression(boxes_conf_landms, self.confidence)# ---------------------------------------------------##   如果没有预测框则返回原图# ---------------------------------------------------#if len(boxes_conf_landms) <= 0:return old_image, 'False'# ---------------------------------------------------------##   如果使用了letterbox_image的话，要把灰条的部分去除掉。# ---------------------------------------------------------#if self.letterbox_image:boxes_conf_landms = retinaface_correct_boxes(boxes_conf_landms, \np.array([self.retinaface_input_shape[0],self.retinaface_input_shape[1]]),np.array([im_height, im_width]))boxes_conf_landms[:, :4] = boxes_conf_landms[:, :4] * scaleboxes_conf_landms[:, 5:] = boxes_conf_landms[:, 5:] * scale_for_landmarks# ---------------------------------------------------##   Retinaface检测部分-结束# ---------------------------------------------------## -----------------------------------------------##   Facenet编码部分-开始# -----------------------------------------------#face_encodings = []for boxes_conf_landm in boxes_conf_landms:# ----------------------##   图像截取，人脸矫正# ----------------------#boxes_conf_landm = np.maximum(boxes_conf_landm, 0)crop_img = np.array(old_image)[int(boxes_conf_landm[1]):int(boxes_conf_landm[3]),int(boxes_conf_landm[0]):int(boxes_conf_landm[2])]landmark = np.reshape(boxes_conf_landm[5:], (5, 2)) - np.array([int(boxes_conf_landm[0]), int(boxes_conf_landm[1])])crop_img, _ = Alignment_1(crop_img, landmark)# ----------------------##   人脸编码# ----------------------#crop_img = np.array(letterbox_image(np.uint8(crop_img), (self.facenet_input_shape[1], self.facenet_input_shape[0]))) / 255crop_img = np.expand_dims(crop_img.transpose(2, 0, 1), 0)with torch.no_grad():crop_img = torch.from_numpy(crop_img).type(torch.FloatTensor)if self.cuda:crop_img = crop_img.cuda()# -----------------------------------------------##   利用facenet_model计算长度为128特征向量# -----------------------------------------------#face_encoding = self.facenet(crop_img)[0].cpu().numpy()face_encodings.append(face_encoding)# -----------------------------------------------##   Facenet编码部分-结束# -----------------------------------------------## -----------------------------------------------##   人脸特征比对-开始# -----------------------------------------------#face_names = []for face_encoding in face_encodings:# -----------------------------------------------------##   取出一张脸并与数据库中所有的人脸进行对比，计算得分# -----------------------------------------------------#matches, face_distances = compare_faces(self.known_face_encodings, face_encoding,tolerance=self.facenet_threhold)name = "Unknown"# -----------------------------------------------------##   取出这个最近人脸的评分#   取出当前输入进来的人脸，最接近的已知人脸的序号# -----------------------------------------------------#best_match_index = np.argmin(face_distances)if matches[best_match_index]:name = self.known_face_names[best_match_index]if flag == 0:name = "False"face_names.append(name)# -----------------------------------------------##   人脸特征比对-结束# -----------------------------------------------#for i, b in enumerate(boxes_conf_landms):text = "{:.4f}".format(b[4])b = list(map(int, b))# ---------------------------------------------------##   b[0]-b[3]为人脸框的坐标，b[4]为得分# ---------------------------------------------------#cv2.rectangle(old_image, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)cx = b[0]cy = b[1] + 12cv2.putText(old_image, text, (cx, cy),cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))# ---------------------------------------------------##   b[5]-b[14]为人脸关键点的坐标# ---------------------------------------------------#cv2.circle(old_image, (b[5], b[6]), 1, (0, 0, 255), 4)cv2.circle(old_image, (b[7], b[8]), 1, (0, 255, 255), 4)cv2.circle(old_image, (b[9], b[10]), 1, (255, 0, 255), 4)cv2.circle(old_image, (b[11], b[12]), 1, (0, 255, 0), 4)cv2.circle(old_image, (b[13], b[14]), 1, (255, 0, 0), 4)name = face_names[i]# font = cv2.FONT_HERSHEY_SIMPLEX# cv2.putText(old_image, name, (b[0] , b[3] - 15), font, 0.75, (255, 255, 255), 2)# --------------------------------------------------------------##   cv2不能写中文，加上这段可以，但是检测速度会有一定的下降。#   如果不是必须，可以换成cv2只显示英文。# --------------------------------------------------------------#old_image = cv2ImgAddText(old_image, name, b[0] + 5, b[3] - 25)# print('ff:', face_names[0])if face_names:return old_image, face_names[0]else:return old_image, 'False'```

眨眼检测

眨眼检测是本应用程序的一个重要功能。我们使用了眨眼检测算法来监测眨眼动作。在BlinkDetection类中，眨眼的EAR（眼睛纵横比）阈值被设置为0.2。当EAR低于这个阈值时，认为用户眨了眼睛。

class BlinkDetection:def __init__(self):self.ear = Noneself.status = Noneself.frame_counter = 0self.blink_counter = 0self.EAR_THRESHOLD = 0.2  # 眨眼的 EAR 阈值def eye_aspect_ratio(self, eye):A = np.linalg.norm(eye[1] - eye[5])B = np.linalg.norm(eye[2] - eye[4])C = np.linalg.norm(eye[0] - eye[3])ear = (A + B) / (2.0 * C)return eardef detect(self, landmarks):left_eye = landmarks[36:42]right_eye = landmarks[42:48]EAR_left = self.eye_aspect_ratio(left_eye)EAR_right = self.eye_aspect_ratio(right_eye)self.ear = (EAR_left + EAR_right) / 2.0if self.ear < 0.21:self.frame_counter += 1self.status = "Blinking"else:if self.frame_counter >= 2:  # 改为2次算检测结束self.blink_counter += 1self.frame_counter = 0self.status = "Open"return self.blink_counter, self.status, self.ear

嘴部动作检测

嘴部动作检测用于监测用户是否张嘴。在MouthDetection类中，我们计算了嘴巴的MAR（嘴巴纵横比），并将其与阈值0.5进行比较。当MAR大于0.5时，表示用户张嘴。

class MouthDetection:def __init__(self):self.mStart, self.mEnd = (48, 68)self.mouth_counter = 0self.MAR_THRESHOLD = 0.5self.mouth_open = False  # 嘴巴状态，初始为闭上def mouth_aspect_ratio(self, mouth):A = np.linalg.norm(mouth[2] - mouth[9])B = np.linalg.norm(mouth[4] - mouth[7])C = np.linalg.norm(mouth[0] - mouth[6])mar = (A + B) / (2.0 * C)return mardef detect(self, landmarks):mouth = landmarks[self.mStart:self.mEnd]mar = self.mouth_aspect_ratio(mouth)if mar > self.MAR_THRESHOLD:if not self.mouth_open:  # 从闭上到张开self.mouth_counter += 1self.mouth_open = Trueelse:if self.mouth_open:  # 从张开到闭上self.mouth_open = Falsereturn self.mouth_counter

头部姿势检测

头部姿势检测用于监测用户头部的旋转角度。在HeadPoseDetection类中，我们计算了头部的旋转角度，并根据阈值判断头部的方向（左、右、中）。

class HeadPoseDetection:def __init__(self):self.left_counter = 0self.right_counter = 0self.nod_threshold = 10self.low_threshold = -10self.head_status = "neutral"def calculate_head_pose(self, shape):x, y = zip(*shape)face_center = (int(np.mean(x)), int(np.mean(y)))left_eye_center = np.mean(shape[36:42], axis=0)right_eye_center = np.mean(shape[42:48], axis=0)dX = right_eye_center[0] - left_eye_center[0]dY = right_eye_center[1] - left_eye_center[1]angle = np.degrees(np.arctan2(dY, dX))return angledef detect(self, shape):angle = self.calculate_head_pose(shape)if angle > self.nod_threshold:self.head_status = "left"self.left_counter += 1return self.head_status, self.left_counterelif angle < self.low_threshold:self.head_status = "right"self.right_counter += 1return self.head_status, self.right_counterelse:self.head_status = "neutral"return self.head_status, 0

完整代码

在FaceDetection类中，我们将上述功能整合在一起，并使用摄像头或视频文件来进行面部检测和姿势识别。用户可以使用不同的动作来触发应用程序进入 “flag” 状态，例如眨眼、张嘴、或头部旋转。一旦触发，应用程序将采用Retinaface来检测面部特征，并在窗口中显示视频帧。
在这段代码中，首先我们通过随机选择一个顺序，包括眨眼、张嘴和头部姿势检测。每个动作检测都有其独立的计数器，例如眨眼计数器、张嘴计数器和头部计数器。只有在满足特定条件时，相关动作的计数器才会递增。一旦三个动作的计数器均达到阈值，应用程序的标志被设置为1，表示活体检测成功。接下来，我们使用Retinaface库检测面部特征，计算FPS，并在图像中显示检测结果。最后，当应用程序标志被设置为1时，我们可以执行人脸识别或其他相关操作，以确保在进行人脸识别之前已完成活体检测。这种随机动作顺序实现了更加严格的活体检测，提高了安全性和准确性。

"""
NAME : try_7
USER : admin
DATE : 9/10/2023
PROJECT_NAME : new_live_face
CSDN : friklogff
"""
import random
import time
import cv2
import numpy as np
from retinaface_new import Retinaface
import dlib
from imutils import face_utilsclass BlinkDetection:def __init__(self):self.ear = Noneself.status = Noneself.frame_counter = 0self.blink_counter = 0self.EAR_THRESHOLD = 0.2  # 眨眼的 EAR 阈值def eye_aspect_ratio(self, eye):A = np.linalg.norm(eye[1] - eye[5])B = np.linalg.norm(eye[2] - eye[4])C = np.linalg.norm(eye[0] - eye[3])ear = (A + B) / (2.0 * C)return eardef detect(self, landmarks):left_eye = landmarks[36:42]right_eye = landmarks[42:48]EAR_left = self.eye_aspect_ratio(left_eye)EAR_right = self.eye_aspect_ratio(right_eye)self.ear = (EAR_left + EAR_right) / 2.0if self.ear < 0.21:self.frame_counter += 1self.status = "Blinking"else:if self.frame_counter >= 2:  # 改为2次算检测结束self.blink_counter += 1self.frame_counter = 0self.status = "Open"return self.blink_counter, self.status, self.earclass MouthDetection:def __init__(self):self.mStart, self.mEnd = (48, 68)self.mouth_counter = 0self.MAR_THRESHOLD = 0.5self.mouth_open = False  # 嘴巴状态，初始为闭上def mouth_aspect_ratio(self, mouth):A = np.linalg.norm(mouth[2] - mouth[9])B = np.linalg.norm(mouth[4] - mouth[7])C = np.linalg.norm(mouth[0] - mouth[6])mar = (A + B) / (2.0 * C)return mardef detect(self, landmarks):mouth = landmarks[self.mStart:self.mEnd]mar = self.mouth_aspect_ratio(mouth)if mar > self.MAR_THRESHOLD:if not self.mouth_open:  # 从闭上到张开self.mouth_counter += 1self.mouth_open = Trueelse:if self.mouth_open:  # 从张开到闭上self.mouth_open = Falsereturn self.mouth_counterclass HeadPoseDetection:def __init__(self):self.left_counter = 0self.right_counter = 0self.nod_threshold = 10self.low_threshold = -10self.head_status = "neutral"def calculate_head_pose(self, shape):x, y = zip(*shape)face_center = (int(np.mean(x)), int(np.mean(y)))left_eye_center = np.mean(shape[36:42], axis=0)right_eye_center = np.mean(shape[42:48], axis=0)dX = right_eye_center[0] - left_eye_center[0]dY = right_eye_center[1] - left_eye_center[1]angle = np.degrees(np.arctan2(dY, dX))return angledef detect(self, shape):angle = self.calculate_head_pose(shape)if angle > self.nod_threshold:self.head_status = "left"self.left_counter += 1return self.head_status, self.left_counterelif angle < self.low_threshold:self.head_status = "right"self.right_counter += 1return self.head_status, self.right_counterelse:self.head_status = "neutral"return self.head_status, 0class FaceDetection:def __init__(self, video_path, video_save_path="", video_fps=25.0, use_camera=False):self.name = Noneself.mouth_flag = Falseself.head_flag = Falseself.blink_flag = Falseself.random_flag = random.randint(1, 3)if use_camera:self.capture = cv2.VideoCapture(0)else:self.capture = cv2.VideoCapture(video_path)self.video_save_path = video_save_pathif video_save_path != "":fourcc = cv2.VideoWriter_fourcc(*'XVID')size = (int(self.capture.get(cv2.CAP_PROP_FRAME_WIDTH)), int(self.capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))self.out = cv2.VideoWriter(video_save_path, fourcc, video_fps, size)self.ref, frame = self.capture.read()if not self.ref:raise ValueError("未能正确读取摄像头（视频），请注意是否正确安装摄像头（是否正确填写视频路径）。")self.fps = 0.0self.flag = 0self.detector = dlib.get_frontal_face_detector()self.predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")self.blink_detector = BlinkDetection()self.mouth_detector = MouthDetection()self.head_pose_detector = HeadPoseDetection()self.nod_threshold = 10self.low_threshold = -10self.head_status = "neutral"self.blink_counter = 0self.mouth_counter = 0self.head_counter = 0self.ear = Noneself.status = Noneself.retinaface = Retinaface()def detect_blink(self, frame, landmarks):self.blink_counter, self.status, self.ear = self.blink_detector.detect(landmarks)cv2.putText(frame, "Blinks: {}".format(self.blink_counter), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7,(0, 0, 255), 2)cv2.putText(frame, "EAR: {:.2f}".format(self.ear), (300, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)cv2.putText(frame, "Eyes Status: {}".format(self.status), (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0),2)return self.blink_counterdef detect_mouth(self, frame, landmarks):self.mouth_counter = self.mouth_detector.detect(landmarks)cv2.putText(frame, "Mouth Count: {}".format(self.mouth_counter), (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.7,(0, 0, 255), 2)return self.mouth_counterdef detect_head_pose(self, frame, gray, face_rectangle):shape = self.predictor(gray, face_rectangle)shape = face_utils.shape_to_np(shape)self.head_status, self.head_counter = self.head_pose_detector.detect(shape)cv2.putText(frame, "Head Status: {}".format(self.head_status), (10, 120), cv2.FONT_HERSHEY_SIMPLEX, 0.7,(0, 0, 255),2)cv2.putText(frame, "Head Count: {}".format(self.head_counter), (10, 150), cv2.FONT_HERSHEY_SIMPLEX, 0.7,(0, 0, 255),2)return self.head_counterdef process_frame(self):t1 = time.time()self.ref, self.frame = self.capture.read()if not self.ref:return Nonegray = cv2.cvtColor(self.frame, cv2.COLOR_BGR2GRAY)faces = self.detector(gray, 0)if self.flag == 1:self.frame = cv2.cvtColor(self.frame, cv2.COLOR_BGR2RGB)old_image, self.name = self.retinaface.live_detect_image(self.frame, self.flag)self.frame = np.array(old_image)self.frame = cv2.cvtColor(self.frame, cv2.COLOR_RGB2BGR)self.fps = (self.fps + (1. / (time.time() - t1))) / 2# print("fps= %.2f" % (self.fps))self.frame = cv2.putText(self.frame, "fps= %.2f" % self.fps, (200, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)elif len(faces) != 0:largest_index = self._largest_face(faces)face_rectangle = faces[largest_index]landmarks = np.matrix([[p.x, p.y] for p in self.predictor(self.frame, face_rectangle).parts()])if self.random_flag == 1:# 调用眨眼检测函数self.detect_blink(self.frame, landmarks)if self.blink_counter > 3:self.blink_flag = Trueself.random_flag = random.randint(1, 3)if self.random_flag == 2:# 调用嘴巴动作检测函数self.detect_mouth(self.frame, landmarks)if self.mouth_counter > 3:self.mouth_flag = Trueself.random_flag = random.randint(1, 3)if self.random_flag == 3:# 调用头部姿势检测函数self.detect_head_pose(self.frame, gray, face_rectangle)if self.head_counter == 0:self.head_flag = Trueself.random_flag = random.randint(1, 3)if self.blink_flag and self.mouth_flag and self.head_flag:self.flag = 1if self.video_save_path != "":self.out.write(self.frame)return self.ref, self.framedef _largest_face(self, dets):if len(dets) == 1:return 0face_areas = [(det.right() - det.left()) * (det.bottom() - det.top()) for det in dets]largest_area = face_areas[0]largest_index = 0for index in range(1, len(dets)):if face_areas[index] > largest_area:largest_index = indexlargest_area = face_areas[index]print("largest_face index is {} in {} faces".format(largest_index, len(dets)))return largest_indexdef release(self):print("Video Detection Done!")self.capture.release()if self.video_save_path != "":print("Save processed video to the path:" + self.video_save_path)self.out.release()def get_blink_counter(self):return self.blink_counterdef get_mouth_counter(self):return self.mouth_counterdef get_head_counter(self):return self.head_counterdef get_flag(self):return self.flagdef get_name(self):return self.nameif __name__ == "__main__":detector = FaceDetection('R.mp4')  # 使用摄像头，也可以指定视频文件路径# detector = FaceDetection(0)  # 使用摄像头，也可以指定视频文件路径while True:flag = detector.get_flag()ref, frame = detector.process_frame()if frame is None:breakcv2.imshow("Frame", frame)if cv2.waitKey(1) & 0xFF == ord('q'):breakif flag == 1:print(flag)cv2.imwrite("last_frame.png", frame)# print(fname)breakdetector.release()cv2.destroyAllWindows()

结尾与未来展望

面部检测和姿势识别是计算机视觉领域的重要研究方向之一，它们在各种应用中具有广泛的应用前景。未来，我们可以期待更多的创新，以提高这些技术的准确性和实用性。

在本文中，我们了解了如何使用Python和各种库来实现面部检测和姿势识别。我们看到了眨眼、张嘴和头部旋转等动作如何触发应用程序的不同功能。这只是开始，未来的应用将更加智能和多功能。

未来的展望包括：

1. 实时应用: 随着硬件性能的不断提高，实时面部检测和姿势识别将变得更加实用，用于虚拟现实、增强现实和交互式游戏。

2. 情感分析: 面部检测可用于情感分析，识别用户的情绪和情感状态，从而改进用户体验。

3. 生物识别: 面部识别技术正在被用于生物识别领域，例如人脸解锁和身份验证。

4. 医疗应用: 面部检测和姿势识别可以用于医疗应用，例如帮助监测病人的眼睛、嘴巴和头部动作，以提前识别疾病症状。

5. 人机交互: 进一步改进人机交互，包括手势控制和面部表情识别。

总的来说，面部检测和姿势识别技术将继续推动计算机视觉的发展，为各种应用提供更加智能和互动的功能。这个领域充满了机会，对于有兴趣深入研究的开发者和研究人员来说，有着无限的潜力。

本文中的示例应用程序仅仅是开始，你可以进一步扩展它，将这些技术应用到更多有趣的项目中。无论你是一个计算机视觉领域的专家，还是一个对新技术充满好奇心的初学者，这个领域都将为你提供无穷的探索和创新机会。希望本文能够激发你深入研究面部检测和姿势识别的兴趣，并在未来的项目中发挥作用。

下一步计划

本文活体检测算法安全性较差，接下来我会尝试学习活体模型训练算法，向大家分享我的学习历程。