博客汇总:Python | 人脸识别系统 — 博客索引
GitHub地址:Su-Face-Recognition
注:阅读本博客前请先参考
工具安装、环境配置:Python | 人脸识别系统 — 简介
UI界面设计:Python | 人脸识别系统 — UI界面设计
UI事件处理:Python | 人脸识别系统 — UI事件处理
摄像头画面展示:Python | 人脸识别系统 — 摄像头画面展示
一、基本思路
代码使用 静默活体检测+交互活体检测 结合判断。
静默活体检测使用百度API,通过接口返回的置信度,判断是否通过。
交互活体检测通过要求用户完成一定动作,判断是否通过。
二、初始化
初始化 isFaceRecognition_flag 标志判断当前人脸识别状态。
按钮绑定人脸识别判断器 recognize_face_judge 方法。
其余属性会在后面讲到。
def __init__(self, parent=None):
super(UserMainWindow, self).__init__(parent)
self.setupUi(self)
self.isFaceDetection_flag = False # 是否打开活体检测标志
self.biopsy_testing_button.clicked.connect(self.detect_face_judge) # 活体检测
self.detector = None # 人脸检测器
self.predictor = None # 特征点检测器
# 闪烁阈值
self.EAR_THRESH = None
self.MOUTH_THRESH = None
# 总闪烁次数
self.eye_flash_counter = None
self.mouth_open_counter = None
self.turn_left_counter = None
self.turn_right_counter = None
# 连续帧数阈值
self.EAR_CONSTANT_FRAMES = None
self.MOUTH_CONSTANT_FRAMES = None
self.LEFT_CONSTANT_FRAMES = None
self.RIGHT_CONSTANT_FRAMES = None
# 连续帧计数器
self.eye_flash_continuous_frame = 0
self.mouth_open_continuous_frame = 0
self.turn_left_continuous_frame = 0
self.turn_right_continuous_frame = 0
# 字体颜色
self.text_color = (255, 0, 0)
# 百度API
self.api = BaiduApiUtil
三、判断器
# 活体检测判断器
def detect_face_judge(self):
if not self.cap.isOpened():
QMessageBox.information(self, "提示", self.tr("请先打开摄像头"))
else:
if not self.isFaceDetection_flag:
self.isFaceDetection_flag = True
self.biopsy_testing_button.setText("关闭活体检测")
self.detect_face()
self.biopsy_testing_button.setText("活体检测")
self.isFaceDetection_flag = False
elif self.isFaceDetection_flag:
self.isFaceDetection_flag = False
self.remind_label.setText("")
self.biopsy_testing_button.setText("活体检测")
self.show_camera()
四、检测器
首先判断当前环境是否联网(联网检测代码在BaiduApiUtil工具类中,工具类的代码在下面),联网进行静默活体检测+交互活体检测(联网检测),否则进行单独的交互活体检测(本地检测)。
# 百度API
self.api = BaiduApiUtil
... ...
# 整体活体检测
def detect_face(self):
if self.api.network_connect_judge():
if not self.detect_face_network():
return False
if not self.detect_face_local():
return False
return True
# 联网活体检测
def detect_face_network(self):
... ...
# 本地活体检测
def detect_face_local(self):
... ...
1、静默活体检测
静默活体检测用到了百度智能云接口,我们创建一个工具类 BaiduApiUtil,在工具类中编写网络连接、请求、解析结果等的代码。然后在用户界面逻辑代码中进行使用。
接口详情请参考 百度智能云-接口详情
代码示例请参考 百度智能云-代码示例
注意:使用前需要注册百度智能云账号,申请接口(接口免费),获取自己的API_KEY以及SECRET_KEY
(1)工具类 BaiduApiUtil
a.联网判断
def network_connect_judge():
"""
联网判断
:return: 是否联网
"""
ret = os.system("ping baidu.com -n 1")
return True if ret == 0 else False
b. 获取访问令牌
将申请百度接口的API_KEY等参数保存到.conf配置文件中(配置文件在当前项目的conf目录下),然后在使用ConfigParser读取并使用。
[baidu_config] app_id = XXXXXXXXXXXXXXXXXXXXXXXX secret_key = XXXXXXXXXXXXXXXXXXXXXXXX
def get_access_token():
"""
获取访问令牌
:return: 访问令牌
"""
conf = ConfigParser()
path = os.path.join(os.path.dirname(__file__))
conf.read(path[:path.rindex('util')] + "conf\\setting.conf", encoding='gbk')
API_KEY = conf.get('baidu_config', 'app_id')
SECRET_KEY = conf.get('baidu_config', 'secret_key')
url = "https://aip.baidubce.com/oauth/2.0/token"
params = {"grant_type": "client_credentials", "client_id": API_KEY, "client_secret": SECRET_KEY}
return str(requests.post(url, params=params).json().get("access_token"))
c.接口调用
注意:进行API请求时,上传的图片格式为base64格式,我们传入的图片为jpg格式,故需要进行格式转换。通过base64.b64encode()方法进行转换
def face_api_invoke(path):
"""
人脸 API 调用
:param path: 待检测的图片路径
:return: 是否通过静默人脸识别
"""
with open(path, 'rb') as f:
img_data = f.read()
base64_data = base64.b64encode(img_data)
base64_str = base64_data.decode('utf-8')
url = "https://aip.baidubce.com/rest/2.0/face/v3/faceverify?access_token=" + get_access_token()
headers = {'Content-Type': 'application/json'}
payload = json.dumps(([{
"image": base64_str,
"image_type": "BASE64"
}]))
response = requests.request("POST", url, headers=headers, data=payload)
print(response)
result = json.loads(response.text)
if result["error_msg"] == "SUCCESS":
frr_1e_4 = result["result"]["thresholds"]["frr_1e-4"]
frr_1e_3 = result["result"]["thresholds"]["frr_1e-3"]
frr_1e_2 = result["result"]["thresholds"]["frr_1e-2"]
face_liveness = result["result"]["face_liveness"]
if face_liveness >= frr_1e_2:
return True
elif frr_1e_3 <= face_liveness <= frr_1e_2:
return True
elif face_liveness <= frr_1e_4:
return False
(2)用户主界面逻辑调用
# 文件目录
curPath = os.path.abspath(os.path.dirname(__file__))
# 项目根路径
rootPath = curPath[:curPath.rindex('logic')] # logic为存放用户界面逻辑代码的文件夹名
# 配置文件夹路径
CONF_FOLDER_PATH = rootPath + 'conf\\'
# 图片文件夹路径
PHOTO_FOLDER_PATH = rootPath + 'photo\\'
# 数据文件夹路径
DATA_FOLDER_PATH = rootPath + 'data\\'
... ...
# 联网活体检测
def detect_face_network(self):
while self.cap.isOpened():
ret, frame = self.cap.read()
frame_location = face_recognition.face_locations(frame)
if len(frame_location) == 0:
QApplication.processEvents()
self.remind_label.setText("未检测到人脸")
else:
global PHOTO_FOLDER_PATH
shot_path = PHOTO_FOLDER_PATH + datetime.now().strftime("%Y%m%d%H%M%S") + ".jpg"
self.show_image.save(shot_path)
QApplication.processEvents()
self.remind_label.setText("正在初始化\n请稍后")
# 百度API进行活体检测
QApplication.processEvents()
if not self.api.face_api_invoke(shot_path):
os.remove(shot_path)
QMessageBox.about(self, '警告', '未通过活体检测')
self.remind_label.setText("")
return False
else:
os.remove(shot_path)
return True
show_video = cv2.cvtColor(cv2.resize(frame, (self.WIN_WIDTH, self.WIN_HEIGHT)), cv2.COLOR_BGR2RGB)
self.show_image = QImage(show_video.data, show_video.shape[1], show_video.shape[0], QImage.Format_RGB888)
self.camera_label.setPixmap(QPixmap.fromImage(self.show_image))
2、交互活体检测
(1)基本原理
采用开源框架dlib的shape_predictor_68_face_landmarks模型,对人脸的68个特征点进行检测定位。本系统活体检测主要检测人脸左摇头、右摇头、眨眼、张嘴、点头等多个动作,故需要鼻子[32,36]、左眼[37,42]、右眼[43,48]、上嘴唇内边缘[66,68]等多个部分的特征点集合。
眨眼检测的基本原理是计算眼睛长宽比EAR(Eye Aspect Ratio)值。当人眼睁开时,EAR在某个值上下波动。当人眼闭合时,EAR将迅速下降,理论上接近于零,实际上一般波动于0.25上下,故本系统设置阈值在0.25。
EAR的计算公式如下:
其中,p1~p5为当前眼睛的6个标记点,图示如下:
(2)实现原理
不断读取摄像头传回的每一帧,对该帧的EAR值进行计算。当EAR低于阈值时,自动将当前帧计数加一。当帧连续计数超过2帧后,EAR值大于阈值,则将该次动作视为一次眨眼
同理,对张嘴、左摇头、右摇头的处理也是类似的。首先通过dlib获取当前器官的标记点,计算其长宽比,与系统预先指定的阈值进行比较。当长宽比小于阈值时,连续帧计数器自动加一。当连续帧计数器值超过指定值时,判断本次动作为一次有效的动作,进行记录。
由于需要用户进行各种动作的完成,纸质或者电子照片基本上无法再通过本次活体检测。
但对于视频,攻击者有可能使用预先录制的完成一定顺序动作的视频,以此欺骗系统。对于该情况,本团队的应对措施如下:
系统需要用户完成左摇头、右摇头、眨眼、张嘴动作,其中张嘴以及眨眼指定的次数为指定数目。系统对上述动作进行随机打乱,并且张嘴以及眨眼指定的次数也为随机数。
通过以上方式,用户在进行每一次的交互活体检测时,需要完成的方案都是完全不相同的,且完成的张嘴、眨眼次数也是不同的。当用户超过系统的指定时间未完成检测,则自动判断为活体检测失效。当用户超过3次进行登录的活体检测失败,系统将判断当前用户存在风险,并锁死当前登录的用户。被锁死的用户需要经过管理员通过管理员系统方可以解除锁定。
通过以上的方式,对视频的欺骗攻击,本系统也有能力进行抵御阻挡。
(3)代码详解
a.初始化
需要初始化的参数包括:特征点检测器 self.predictor、self.detector、闪烁阈值、总闪烁次数、连续帧数阈值、连续帧计数器、当前总帧数、检测随机值、面部特征点索引。
特征点检测器:通过dlib的shape_predictor_68_face_landmarks模型,对人脸的68个特征点进行检测定位,首先需要进行模型的加载。由于模型加载时间较长,设置逻辑判断。当不是第一次使用活体检测时,使用已经加载好的属性,提高初始化时间。
面部特征点索引:当前用户面部特征点的索引序号
闪烁阈值、连续帧计数器:设置眨眼、张嘴的EAR、MAR阈值,当前帧用户的动作超过阈值时连续帧计数器加一。
连续帧数阈值:当帧连续计数超过阈值设置的帧数后,EAR值大于阈值,则将该次动作视为一次眨眼或张嘴动作。
总闪烁次数:用户需要完成的动作的次数。
当前总帧数:从开始到当前时间 活体检测的帧数,超过系统指定帧数时则判断活体检测失败。
检测随机值:包括随机次数的眨眼、张嘴次数,以及随机的动作集合,如(右转头-眨眼-张嘴-左转头)、(右转头-眨眼-左转头-张嘴)、(眨眼-张嘴-右转头-左转头)等
项目结构如下:
其中 shape_predictor_68_face_landmarks.dat 文件在项目的data目录下。
# 本地活体检测
def detect_face_local(self):
self.detect_start_time = time()
QApplication.processEvents()
self.remind_label.setText("正在初始化\n请稍后")
# 特征点检测器首次加载比较慢,通过判断减少后面加载的速度
if self.detector is None:
self.detector = dlib.get_frontal_face_detector()
if self.predictor is None:
self.predictor = dlib.shape_predictor('../data/shape_predictor_68_face_landmarks.dat')
# 闪烁阈值
self.EAR_THRESH = 0.25
self.MOUTH_THRESH = 0.7
# 总闪烁次数
self.eye_flash_counter = 0
self.mouth_open_counter = 0
self.turn_left_counter = 0
self.turn_right_counter = 0
# 连续帧数阈值
self.EAR_CONSTANT_FRAMES = 2
self.MOUTH_CONSTANT_FRAMES = 2
self.LEFT_CONSTANT_FRAMES = 4
self.RIGHT_CONSTANT_FRAMES = 4
# 连续帧计数器
self.eye_flash_continuous_frame = 0
self.mouth_open_continuous_frame = 0
self.turn_left_continuous_frame = 0
self.turn_right_continuous_frame = 0
print("活体检测 初始化时间:", time() - self.detect_start_time)
# 当前总帧数
total_frame_counter = 0
# 设置随机值
now_flag = 0
random_type = [0, 1, 2, 3]
random.shuffle(random_type)
random_eye_flash_number = random.randint(4, 6)
random_mouth_open_number = random.randint(2, 4)
QMessageBox.about(self, '提示', '请按照指示执行相关动作')
self.remind_label.setText("")
# 抓取面部特征点的索引
(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"]
b.EAR、MAR等值的计算
以眼睛为例,获取眼睛的,套用计算EAR值的公式,计算得到EAR值。
# 计算眼长宽比例 EAR值
@staticmethod
def count_EAR(eye):
A = dist.euclidean(eye[1], eye[5])
B = dist.euclidean(eye[2], eye[4])
C = dist.euclidean(eye[0], eye[3])
EAR = (A + B) / (2.0 * C)
return EAR
# 计算嘴长宽比例 MAR值
@staticmethod
def count_MAR(mouth):
A = dist.euclidean(mouth[1], mouth[11])
B = dist.euclidean(mouth[2], mouth[10])
C = dist.euclidean(mouth[3], mouth[9])
D = dist.euclidean(mouth[4], mouth[8])
E = dist.euclidean(mouth[5], mouth[7])
F = dist.euclidean(mouth[0], mouth[6]) # 水平欧几里德距离
ratio = (A + B + C + D + E) / (5.0 * F)
return ratio
# 计算左右脸转动比例 FR值
@staticmethod
def count_FR(face):
rightA = dist.euclidean(face[0], face[27])
rightB = dist.euclidean(face[2], face[30])
rightC = dist.euclidean(face[4], face[48])
leftA = dist.euclidean(face[16], face[27])
leftB = dist.euclidean(face[14], face[30])
leftC = dist.euclidean(face[12], face[54])
ratioA = rightA / leftA
ratioB = rightB / leftB
ratioC = rightC / leftC
ratio = (ratioA + ratioB + ratioC) / 3
return ratio
c.用户动作判断
def check_eye_flash(self, average_EAR):
if average_EAR < self.EAR_THRESH:
self.eye_flash_continuous_frame += 1
else:
if self.eye_flash_continuous_frame >= self.EAR_CONSTANT_FRAMES:
self.eye_flash_counter += 1
self.eye_flash_continuous_frame = 0
def check_mouth_open(self, mouth_MAR):
if mouth_MAR > self.MOUTH_THRESH:
self.mouth_open_continuous_frame += 1
else:
if self.mouth_open_continuous_frame >= self.MOUTH_CONSTANT_FRAMES:
self.mouth_open_counter += 1
self.mouth_open_continuous_frame = 0
def check_right_turn(self, leftRight_FR):
if leftRight_FR <= 0.5:
self.turn_right_continuous_frame += 1
else:
if self.turn_right_continuous_frame >= self.RIGHT_CONSTANT_FRAMES:
self.turn_right_counter += 1
self.turn_right_continuous_frame = 0
def check_left_turn(self, leftRight_FR):
if leftRight_FR >= 2.0:
self.turn_left_continuous_frame += 1
else:
if self.turn_left_continuous_frame >= self.LEFT_CONSTANT_FRAMES:
self.turn_left_counter += 1
self.turn_left_continuous_frame = 0
d.活体检测判断
当摄像头打开时,进行活体检测判断。当用户 活体检测成功 或者 超时 时才退出循环。
while self.cap.isOpened():
ret, frame = self.cap.read()
total_frame_counter += 1
frame = imutils.resize(frame)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
rects = self.detector(gray, 0)
if len(rects) == 1:
QApplication.processEvents()
shape = self.predictor(gray, rects[0])
shape = face_utils.shape_to_np(shape)
# 提取面部坐标
left_eye = shape[lStart:lEnd]
right_eye = shape[rStart:rEnd]
mouth = shape[mStart:mEnd]
# 计算长宽比
left_EAR = self.count_EAR(left_eye)
right_EAR = self.count_EAR(right_eye)
mouth_MAR = self.count_MAR(mouth)
leftRight_FR = self.count_FR(shape)
average_EAR = (left_EAR + right_EAR) / 2.0
# 计算左眼、右眼、嘴巴的凸包
left_eye_hull = cv2.convexHull(left_eye)
right_eye_hull = cv2.convexHull(right_eye)
mouth_hull = cv2.convexHull(mouth)
# 可视化
cv2.drawContours(frame, [left_eye_hull], -1, (0, 255, 0), 1)
cv2.drawContours(frame, [right_eye_hull], -1, (0, 255, 0), 1)
cv2.drawContours(frame, [mouth_hull], -1, (0, 255, 0), 1)
if now_flag >= 4:
self.remind_label.setText("")
QMessageBox.about(self, '提示', '已通过活体检测')
self.turn_right_counter = 0
self.mouth_open_counter = 0
self.eye_flash_counter = 0
return True
if random_type[now_flag] == 0:
if self.turn_left_counter > 0:
now_flag += 1
else:
self.remind_label.setText("请向左摇头")
self.check_left_turn(leftRight_FR)
self.turn_right_counter = 0
self.mouth_open_counter = 0
self.eye_flash_counter = 0
elif random_type[now_flag] == 1:
if self.turn_right_counter > 0:
now_flag += 1
else:
self.remind_label.setText("请向右摇头")
self.check_right_turn(leftRight_FR)
self.turn_left_counter = 0
self.mouth_open_counter = 0
self.eye_flash_counter = 0
elif random_type[now_flag] == 2:
if self.mouth_open_counter >= random_mouth_open_number:
now_flag += 1
else:
self.remind_label.setText("已张嘴{}次\n还需张嘴{}次".format(self.mouth_open_counter, (
random_mouth_open_number - self.mouth_open_counter)))
self.check_mouth_open(mouth_MAR)
self.turn_right_counter = 0
self.turn_left_counter = 0
self.eye_flash_counter = 0
elif random_type[now_flag] == 3:
if self.eye_flash_counter >= random_eye_flash_number:
now_flag += 1
else:
self.remind_label.setText("已眨眼{}次\n还需眨眼{}次".format(self.eye_flash_counter, (
random_eye_flash_number - self.eye_flash_counter)))
self.check_eye_flash(average_EAR)
self.turn_right_counter = 0
self.turn_left_counter = 0
self.mouth_open_counter = 0
elif len(rects) == 0:
QApplication.processEvents()
self.remind_label.setText("没有检测到人脸!")
elif len(rects) > 1:
QApplication.processEvents()
self.remind_label.setText("检测到超过一张人脸!")
show_video = cv2.cvtColor(cv2.resize(frame, (self.WIN_WIDTH, self.WIN_HEIGHT)), cv2.COLOR_BGR2RGB)
self.show_image = QImage(show_video.data, show_video.shape[1], show_video.shape[0], QImage.Format_RGB888)
self.camera_label.setPixmap(QPixmap.fromImage(self.show_image))
if total_frame_counter >= 1000.0:
QMessageBox.about(self, '警告', '已超时,未通过活体检测')
self.remind_label.setText("")
return False
(4)全部代码
# 计算眼长宽比例 EAR值
@staticmethod
def count_EAR(eye):
A = dist.euclidean(eye[1], eye[5])
B = dist.euclidean(eye[2], eye[4])
C = dist.euclidean(eye[0], eye[3])
EAR = (A + B) / (2.0 * C)
return EAR
# 计算嘴长宽比例 MAR值
@staticmethod
def count_MAR(mouth):
A = dist.euclidean(mouth[1], mouth[11])
B = dist.euclidean(mouth[2], mouth[10])
C = dist.euclidean(mouth[3], mouth[9])
D = dist.euclidean(mouth[4], mouth[8])
E = dist.euclidean(mouth[5], mouth[7])
F = dist.euclidean(mouth[0], mouth[6]) # 水平欧几里德距离
ratio = (A + B + C + D + E) / (5.0 * F)
return ratio
# 计算左右脸转动比例 FR值
@staticmethod
def count_FR(face):
rightA = dist.euclidean(face[0], face[27])
rightB = dist.euclidean(face[2], face[30])
rightC = dist.euclidean(face[4], face[48])
leftA = dist.euclidean(face[16], face[27])
leftB = dist.euclidean(face[14], face[30])
leftC = dist.euclidean(face[12], face[54])
ratioA = rightA / leftA
ratioB = rightB / leftB
ratioC = rightC / leftC
ratio = (ratioA + ratioB + ratioC) / 3
return ratio
# 本地活体检测
def detect_face_local(self):
self.detect_start_time = time()
QApplication.processEvents()
self.remind_label.setText("正在初始化\n请稍后")
# 特征点检测器首次加载比较慢,通过判断减少后面加载的速度
if self.detector is None:
self.detector = dlib.get_frontal_face_detector()
if self.predictor is None:
global DATA_FOLDER_PATH
self.predictor = dlib.shape_predictor('../data/shape_predictor_68_face_landmarks.dat')
# 闪烁阈值
self.EAR_THRESH = 0.25
self.MOUTH_THRESH = 0.7
# 总闪烁次数
self.eye_flash_counter = 0
self.mouth_open_counter = 0
self.turn_left_counter = 0
self.turn_right_counter = 0
# 连续帧数阈值
self.EAR_CONSTANT_FRAMES = 2
self.MOUTH_CONSTANT_FRAMES = 2
self.LEFT_CONSTANT_FRAMES = 4
self.RIGHT_CONSTANT_FRAMES = 4
# 连续帧计数器
self.eye_flash_continuous_frame = 0
self.mouth_open_continuous_frame = 0
self.turn_left_continuous_frame = 0
self.turn_right_continuous_frame = 0
print("活体检测 初始化时间:", time() - self.detect_start_time)
# 当前总帧数
total_frame_counter = 0
# 设置随机值
now_flag = 0
random_type = [0, 1, 2, 3]
random.shuffle(random_type)
random_eye_flash_number = random.randint(4, 6)
random_mouth_open_number = random.randint(2, 4)
QMessageBox.about(self, '提示', '请按照指示执行相关动作')
self.remind_label.setText("")
# 抓取面部特征点的索引
(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"]
while self.cap.isOpened():
ret, frame = self.cap.read()
total_frame_counter += 1
frame = imutils.resize(frame)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
rects = self.detector(gray, 0)
if len(rects) == 1:
QApplication.processEvents()
shape = self.predictor(gray, rects[0])
shape = face_utils.shape_to_np(shape)
# 提取面部坐标
left_eye = shape[lStart:lEnd]
right_eye = shape[rStart:rEnd]
mouth = shape[mStart:mEnd]
# 计算长宽比
left_EAR = self.count_EAR(left_eye)
right_EAR = self.count_EAR(right_eye)
mouth_MAR = self.count_MAR(mouth)
leftRight_FR = self.count_FR(shape)
average_EAR = (left_EAR + right_EAR) / 2.0
# 计算左眼、右眼、嘴巴的凸包
left_eye_hull = cv2.convexHull(left_eye)
right_eye_hull = cv2.convexHull(right_eye)
mouth_hull = cv2.convexHull(mouth)
# 可视化
cv2.drawContours(frame, [left_eye_hull], -1, (0, 255, 0), 1)
cv2.drawContours(frame, [right_eye_hull], -1, (0, 255, 0), 1)
cv2.drawContours(frame, [mouth_hull], -1, (0, 255, 0), 1)
if now_flag >= 4:
self.remind_label.setText("")
QMessageBox.about(self, '提示', '已通过活体检测')
self.turn_right_counter = 0
self.mouth_open_counter = 0
self.eye_flash_counter = 0
return True
if random_type[now_flag] == 0:
if self.turn_left_counter > 0:
now_flag += 1
else:
self.remind_label.setText("请向左摇头")
self.check_left_turn(leftRight_FR)
self.turn_right_counter = 0
self.mouth_open_counter = 0
self.eye_flash_counter = 0
elif random_type[now_flag] == 1:
if self.turn_right_counter > 0:
now_flag += 1
else:
self.remind_label.setText("请向右摇头")
self.check_right_turn(leftRight_FR)
self.turn_left_counter = 0
self.mouth_open_counter = 0
self.eye_flash_counter = 0
elif random_type[now_flag] == 2:
if self.mouth_open_counter >= random_mouth_open_number:
now_flag += 1
else:
self.remind_label.setText("已张嘴{}次\n还需张嘴{}次".format(self.mouth_open_counter, (
random_mouth_open_number - self.mouth_open_counter)))
self.check_mouth_open(mouth_MAR)
self.turn_right_counter = 0
self.turn_left_counter = 0
self.eye_flash_counter = 0
elif random_type[now_flag] == 3:
if self.eye_flash_counter >= random_eye_flash_number:
now_flag += 1
else:
self.remind_label.setText("已眨眼{}次\n还需眨眼{}次".format(self.eye_flash_counter, (
random_eye_flash_number - self.eye_flash_counter)))
self.check_eye_flash(average_EAR)
self.turn_right_counter = 0
self.turn_left_counter = 0
self.mouth_open_counter = 0
elif len(rects) == 0:
QApplication.processEvents()
self.remind_label.setText("没有检测到人脸!")
elif len(rects) > 1:
QApplication.processEvents()
self.remind_label.setText("检测到超过一张人脸!")
show_video = cv2.cvtColor(cv2.resize(frame, (self.WIN_WIDTH, self.WIN_HEIGHT)), cv2.COLOR_BGR2RGB)
self.show_image = QImage(show_video.data, show_video.shape[1], show_video.shape[0], QImage.Format_RGB888)
self.camera_label.setPixmap(QPixmap.fromImage(self.show_image))
if total_frame_counter >= 1000.0:
QMessageBox.about(self, '警告', '已超时,未通过活体检测')
self.remind_label.setText("")
return False
def check_eye_flash(self, average_EAR):
if average_EAR < self.EAR_THRESH:
self.eye_flash_continuous_frame += 1
else:
if self.eye_flash_continuous_frame >= self.EAR_CONSTANT_FRAMES:
self.eye_flash_counter += 1
self.eye_flash_continuous_frame = 0
def check_mouth_open(self, mouth_MAR):
if mouth_MAR > self.MOUTH_THRESH:
self.mouth_open_continuous_frame += 1
else:
if self.mouth_open_continuous_frame >= self.MOUTH_CONSTANT_FRAMES:
self.mouth_open_counter += 1
self.mouth_open_continuous_frame = 0
def check_right_turn(self, leftRight_FR):
if leftRight_FR <= 0.5:
self.turn_right_continuous_frame += 1
else:
if self.turn_right_continuous_frame >= self.RIGHT_CONSTANT_FRAMES:
self.turn_right_counter += 1
self.turn_right_continuous_frame = 0
def check_left_turn(self, leftRight_FR):
if leftRight_FR >= 2.0:
self.turn_left_continuous_frame += 1
else:
if self.turn_left_continuous_frame >= self.LEFT_CONSTANT_FRAMES:
self.turn_left_counter += 1
self.turn_left_continuous_frame = 0
继续阅读:
用户端逻辑:
- 人脸识别:Python | 人脸识别系统 — 人脸识别
- 背景模糊:Python | 人脸识别系统 — 背景模糊
- 姿态检测:Python | 人脸识别系统 — 姿态检测
- 人脸比对:Python | 人脸识别系统 — 人脸比对
- 用户操作:Python | 人脸识别系统 — 简介
管理员端逻辑:文章来源:https://www.toymoban.com/news/detail-434205.html
- 管理员操作:Python | 人脸识别系统 —— 管理员操作
注:以上代码仅为参考,若需要运行,请参考项目GitHub完整源代码: Python | 人脸识别系统 —— 管理员操作文章来源地址https://www.toymoban.com/news/detail-434205.html
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