[OpenCV-dlib]人脸识别功能拓展-通过随机要求头部动作实现活体检测
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
retinaface_new.py
import time
import cv2
import numpy as np
import torch
import torch.nn as nn
from PIL import Image, ImageDraw, ImageFont
from tqdm import tqdm
from 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
}
@classmethod
def 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_mnet
else:
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.net = 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.net.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.net = nn.DataParallel(self.net)
self.net = self.net.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.anchors
else:
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.net(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] * scale
boxes_conf_landms[:, 5:] = boxes_conf_landms[:, 5:] * scale_for_landmarks
# ---------------------------------------------------#
# 选取最大的人脸框。
# ---------------------------------------------------#
best_face_location = None
biggest_area = 0
for result in boxes_conf_landms:
left, top, right, bottom = result[0:4]
w = right - left
h = bottom - top
if w * h > biggest_area:
biggest_area = w * h
best_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]))) / 255
crop_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.anchors
else:
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.net(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] * scale
boxes_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]))) / 255
crop_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] + 12
cv2.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 = None
self.status = None
self.frame_counter = 0
self.blink_counter = 0
self.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 ear
def 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) / 2.0
if self.ear < 0.21:
self.frame_counter += 1
self.status = "Blinking"
else:
if self.frame_counter >= 2: # 改为2次算检测结束
self.blink_counter += 1
self.frame_counter = 0
self.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 = 0
self.MAR_THRESHOLD = 0.5
self.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 mar
def 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 += 1
self.mouth_open = True
else:
if self.mouth_open: # 从张开到闭上
self.mouth_open = False
return 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 = 0
self.right_counter = 0
self.nod_threshold = 10
self.low_threshold = -10
self.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 angle
def detect(self, shape):
angle = self.calculate_head_pose(shape)
if angle > self.nod_threshold:
self.head_status = "left"
self.left_counter += 1
return self.head_status, self.left_counter
elif angle < self.low_threshold:
self.head_status = "right"
self.right_counter += 1
return self.head_status, self.right_counter
else:
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_utils
class BlinkDetection:
def __init__(self):
self.ear = None
self.status = None
self.frame_counter = 0
self.blink_counter = 0
self.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 ear
def 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) / 2.0
if self.ear < 0.21:
self.frame_counter += 1
self.status = "Blinking"
else:
if self.frame_counter >= 2: # 改为2次算检测结束
self.blink_counter += 1
self.frame_counter = 0
self.status = "Open"
return self.blink_counter, self.status, self.ear
class MouthDetection:
def __init__(self):
self.mStart, self.mEnd = (48, 68)
self.mouth_counter = 0
self.MAR_THRESHOLD = 0.5
self.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 mar
def 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 += 1
self.mouth_open = True
else:
if self.mouth_open: # 从张开到闭上
self.mouth_open = False
return self.mouth_counter
class HeadPoseDetection:
def __init__(self):
self.left_counter = 0
self.right_counter = 0
self.nod_threshold = 10
self.low_threshold = -10
self.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 angle
def detect(self, shape):
angle = self.calculate_head_pose(shape)
if angle > self.nod_threshold:
self.head_status = "left"
self.left_counter += 1
return self.head_status, self.left_counter
elif angle < self.low_threshold:
self.head_status = "right"
self.right_counter += 1
return self.head_status, self.right_counter
else:
self.head_status = "neutral"
return self.head_status, 0
class FaceDetection:
def __init__(self, video_path, video_save_path="", video_fps=25.0, use_camera=False):
self.name = None
self.mouth_flag = False
self.head_flag = False
self.blink_flag = False
self.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_path
if 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 = 0.0
self.flag = 0
self.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 = 10
self.low_threshold = -10
self.head_status = "neutral"
self.blink_counter = 0
self.mouth_counter = 0
self.head_counter = 0
self.ear = None
self.status = None
self.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_counter
def 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_counter
def 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_counter
def process_frame(self):
t1 = time.time()
self.ref, self.frame = self.capture.read()
if not self.ref:
return None
gray = 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 = True
self.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 = True
self.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 = True
self.random_flag = random.randint(1, 3)
if self.blink_flag and self.mouth_flag and self.head_flag:
self.flag = 1
if self.video_save_path != "":
self.out.write(self.frame)
return self.ref, self.frame
def _largest_face(self, dets):
if len(dets) == 1:
return 0
face_areas = [(det.right() - det.left()) * (det.bottom() - det.top()) for det in dets]
largest_area = face_areas[0]
largest_index = 0
for index in range(1, len(dets)):
if face_areas[index] > largest_area:
largest_index = index
largest_area = face_areas[index]
print("largest_face index is {} in {} faces".format(largest_index, len(dets)))
return largest_index
def 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_counter
def get_mouth_counter(self):
return self.mouth_counter
def get_head_counter(self):
return self.head_counter
def get_flag(self):
return self.flag
def get_name(self):
return self.name
if __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:
break
cv2.imshow("Frame", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if flag == 1:
print(flag)
cv2.imwrite("last_frame.png", frame)
# print(fname)
break
detector.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：
-
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。
-
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。
-
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。
-
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。
-
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。