Python+opencv实现人脸表情判别，口罩识别

一、dlib,face_recognition以及opencv-python库安装

介于我使用的是jupyter notebook，所以在安装dlib和opencv-python时是在

这个命令行安装的

dlib安装方法：

1.若可以，直接使用上图所示命令行输入以下命令：

pip install cmake

pip install boost

pip install dlib

若安装了visual studio2019应该就可以直接pip install dlib，至少我是这样

由于很多在执行第三句时都会报错，所以这里提供第二种办法

2.去dlib官网：http://dlib.net/ 或者 https://github.com/davisking/dlib 下载压缩包

下载完成后，解压缩

在安装dlib前需要安装Boost和Cmake，dlib19之后你需要安装vs2015以上的IDE，本人是安装的vs2019，（建议先安装好VS之后再安装Cmake和 boost）

Cmake安装

官网下载安装包：https://cmake.org/download/

我下的是

直接安装之后，配置环境变量

Boost下载

安装boost：下载地址：http://www.boost.org/

如果vs安装的是2015以上的版本，可以直接进行下一步，最好安装最新版本，不然会找不到b2命令

下载之后将其解压缩，进入boost_1_73_0文件夹中，找到bootstrap.bat批处理文件，双击运行，等待运行完成后（命令行自动消失）会生成两个文件b2.exe和bjam.exe

然后将这两个文件复制到boost_1_73_0根文件夹下：
同样开启一个命令行，定位到这个文件夹，运行命令：
b2 install

这个安装需要一段时间，耐心等候。

利用b2编译库文件：

b2 -a –with-python address-model=64 toolset=msvc runtime-link=static
之前你cmake下载的64位这里（address-model）写64，如果是32位的就把之前的64改成32

安装完成后配置boost环境变量

安装dlib

进入你的dlib解压路径，输入python setup.py install

成功之后会在文件夹中看见dlib和dlib.egg-info ，将这两个文件夹复制到你的python安装的目录下的Lib文件中：

—>例如我的python环境为python2.7，

—>所以将其放在python2-7文件夹的Python2-7\Lib\site-packages中

—>这时，就已经完成了dlib的配置

face_recognition安装方法

在Anaconda Prompt下输入以下命令

pip install face_recognition

opencv-python安装方法

在Anaconda Prompt下输入以下命令

pip install opencv-python

但如果一直失败，建议在Anaconda Prompt下输入以下命令或者换源

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple opencv-python

二、dlib的68点模型

dlib的68点模型，使用网络上大神训练好的特征预测器，用来进行python代码人脸识别的特征预测。

三、Python实现人脸识别&表情判别

import sys
import dlib  # 人脸识别的库dlib
import numpy as np  # 数据处理的库numpy
import cv2  # 图像处理的库OpenCv
 
class face_emotion():
    def __init__(self):
        # 使用特征提取器get_frontal_face_detector
        self.detector = dlib.get_frontal_face_detector()
        # dlib的68点模型，使用作者训练好的特征预测器
        self.predictor = dlib.shape_predictor("F:/face.dat")
 
        # 建cv2摄像头对象，这里使用电脑自带摄像头，如果接了外部摄像头，则自动切换到外部摄像头
        self.cap = cv2.VideoCapture(0)
        # 设置视频参数，propId设置的视频参数，value设置的参数值
        self.cap.set(3, 480)
        # 截图screenshoot的计数器
        self.cnt = 0
 
    def learning_face(self):
 
        # 眉毛直线拟合数据缓冲
        line_brow_x = []
        line_brow_y = []
 
        # cap.isOpened（） 返回true/false 检查初始化是否成功
        while (self.cap.isOpened()):
 
            # cap.read()
            # 返回两个值：
            #    一个布尔值true/false，用来判断读取视频是否成功/是否到视频末尾
            #    图像对象，图像的三维矩阵
            flag, im_rd = self.cap.read()
 
            # 每帧数据延时1ms，延时为0读取的是静态帧
            k = cv2.waitKey(1)
 
            # 取灰度
            img_gray = cv2.cvtColor(im_rd, cv2.COLOR_RGB2GRAY)
 
            # 使用人脸检测器检测每一帧图像中的人脸。并返回人脸数rects
            faces = self.detector(img_gray, 0)
 
            # 待会要显示在屏幕上的字体
            font = cv2.FONT_HERSHEY_SIMPLEX
 
            # 如果检测到人脸
            if (len(faces) != 0):
 
                # 对每个人脸都标出68个特征点
                for i in range(len(faces)):
                    # enumerate方法同时返回数据对象的索引和数据，k为索引，d为faces中的对象
                    for k, d in enumerate(faces):
                        # 用红色矩形框出人脸
                        cv2.rectangle(im_rd, (d.left(), d.top()), (d.right(), d.bottom()), (0, 0, 255))
                        # 计算人脸热别框边长
                        self.face_width = d.right() - d.left()
 
                        # 使用预测器得到68点数据的坐标
                        shape = self.predictor(im_rd, d)
                        # 圆圈显示每个特征点
                        for i in range(68):
                            cv2.circle(im_rd, (shape.part(i).x, shape.part(i).y), 2, (0, 255, 0), -1, 8)
                            # cv2.putText(im_rd, str(i), (shape.part(i).x, shape.part(i).y), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
                            #            (255, 255, 255))
 
                        # 分析任意n点的位置关系来作为表情识别的依据
                        mouth_width = (shape.part(54).x - shape.part(48).x) / self.face_width  # 嘴巴咧开程度
                        mouth_higth = (shape.part(66).y - shape.part(62).y) / self.face_width  # 嘴巴张开程度
                        
 
                        # 通过两个眉毛上的10个特征点，分析挑眉程度和皱眉程度
                        brow_sum = 0  # 高度之和
                        frown_sum = 0  # 两边眉毛距离之和
                        for j in range(17, 21):
                            brow_sum += (shape.part(j).y - d.top()) + (shape.part(j + 5).y - d.top())
                            frown_sum += shape.part(j + 5).x - shape.part(j).x
                            line_brow_x.append(shape.part(j).x)
                            line_brow_y.append(shape.part(j).y)
 
                        # self.brow_k, self.brow_d = self.fit_slr(line_brow_x, line_brow_y)  # 计算眉毛的倾斜程度
                        tempx = np.array(line_brow_x)
                        tempy = np.array(line_brow_y)
                        z1 = np.polyfit(tempx, tempy, 1)  # 拟合成一次直线
                        self.brow_k = -round(z1[0], 3)  # 拟合出曲线的斜率和实际眉毛的倾斜方向是相反的
 
                        brow_hight = (brow_sum / 10) / self.face_width  # 眉毛高度占比
                        brow_width = (frown_sum / 5) / self.face_width  # 眉毛距离占比
                        
 
                        # 眼睛睁开程度
                        eye_sum = (shape.part(41).y - shape.part(37).y + shape.part(40).y - shape.part(38).y +
                                   shape.part(47).y - shape.part(43).y + shape.part(46).y - shape.part(44).y)
                        eye_hight = (eye_sum / 4) / self.face_width
                        
 
                        # 分情况讨论
                        # 张嘴，可能是开心或者惊讶
                        if round(mouth_higth >= 0.03):
                            if eye_hight >= 0.056:
                                cv2.putText(im_rd, "amazing", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX,
                                            0.8,
                                            (0, 0, 255), 2, 4)
                            else:
                                cv2.putText(im_rd, "happy", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
                                            (0, 0, 255), 2, 4)
 
                        # 没有张嘴，可能是正常和生气
                        else:
                            if self.brow_k <= -0.3:
                                cv2.putText(im_rd, "angry", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
                                            (0, 0, 255), 2, 4)
                            else:
                                cv2.putText(im_rd, "nature", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
                                            (0, 0, 255), 2, 4)
 
                # 标出人脸数
                cv2.putText(im_rd, "Faces: " + str(len(faces)), (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
            else:
                # 没有检测到人脸
                cv2.putText(im_rd, "No Face", (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
 
            # 添加说明
            im_rd = cv2.putText(im_rd, "S: screenshot", (20, 400), font, 0.8, (0, 0, 255), 1, cv2.LINE_AA)
            im_rd = cv2.putText(im_rd, "Q: quit", (20, 450), font, 0.8, (0, 0, 255), 1, cv2.LINE_AA)
 
            # 按下s键截图保存
            if (k == ord('s')):
                self.cnt += 1
                cv2.imwrite("screenshoot" + str(self.cnt) + ".jpg", im_rd)
 
            # 按下q键退出
            if (k == ord('q')):
                break
 
            # 窗口显示
            cv2.imshow("camera", im_rd)
 
        # 释放摄像头
        self.cap.release()
 
        # 删除建立的窗口
        cv2.destroyAllWindows()
 
 
if __name__ == "__main__":
    my_face = face_emotion()
    my_face.learning_face()

四、基于CNN的表情识别

• 确定嘴唇的位置

# 显示嘴部特征点
# Draw the positions of someone's lip

import dlib         # 人脸识别的库 Dlib
import cv2          # 图像处理的库 OpenCv
from get_features import get_features   # return the positions of feature points

path_test_img = "F:/data/data_imgs/test_imgs/i064qa-mn.jpg"

detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('F:/shape_predictor_68_face_landmarks.dat')

# Get lip's positions of features points
positions_lip = get_features(path_test_img)

img_rd = cv2.imread(path_test_img)

# Draw on the lip points
for i in range(0, len(positions_lip), 2):
    print(positions_lip[i], positions_lip[i+1])
    cv2.circle(img_rd, tuple([positions_lip[i], positions_lip[i+1]]), radius=1, color=(0, 255, 0))

cv2.namedWindow("img_read", 2)
cv2.imshow("img_read", img_rd)
cv2.waitKey(0)

• 检测图片里面的人物是否微笑

# use the saved model
from sklearn.externals import joblib

from get_features import get_features
import ML_ways_sklearn

import cv2

# path of test img
path_test_img = "F:/data/data_imgs/test_imgs/i064qa-mn.jpg"

# 提取单张40维度特征
positions_lip_test = get_features(path_test_img)

# path of models
path_models = "F:/data/data_models/"

print("The result of"+path_test_img+":")
print('\n')

# #########  LR  ###########
LR = joblib.load(path_models+"model_LR.m")
ss_LR = ML_ways_sklearn.model_LR()
X_test_LR = ss_LR.transform([positions_lip_test])
y_predict_LR = str(LR.predict(X_test_LR)[0]).replace('0', "no smile").replace('1', "with smile")
print("LR:", y_predict_LR)

# #########  LSVC  ###########
LSVC = joblib.load(path_models+"model_LSVC.m")
ss_LSVC = ML_ways_sklearn.model_LSVC()
X_test_LSVC = ss_LSVC.transform([positions_lip_test])
y_predict_LSVC = str(LSVC.predict(X_test_LSVC)[0]).replace('0', "no smile").replace('1', "with smile")
print("LSVC:", y_predict_LSVC)

# #########  MLPC  ###########
MLPC = joblib.load(path_models+"model_MLPC.m")
ss_MLPC = ML_ways_sklearn.model_MLPC()
X_test_MLPC = ss_MLPC.transform([positions_lip_test])
y_predict_MLPC = str(MLPC.predict(X_test_MLPC)[0]).replace('0', "no smile").replace('1', "with smile")
print("MLPC:", y_predict_MLPC)

# #########  SGDC  ###########
SGDC = joblib.load(path_models+"model_SGDC.m")
ss_SGDC = ML_ways_sklearn.model_SGDC()
X_test_SGDC = ss_SGDC.transform([positions_lip_test])
y_predict_SGDC = str(SGDC.predict(X_test_SGDC)[0]).replace('0', "no smile").replace('1', "with smile")
print("SGDC:", y_predict_SGDC)

img_test = cv2.imread(path_test_img)

img_height = int(img_test.shape[0])
img_width = int(img_test.shape[1])

# show the results on the image
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img_test, "LR:    "+y_predict_LR,   (int(img_height/10), int(img_width/10)), font, 0.8, (84, 255, 159), 1, cv2.LINE_AA)
cv2.putText(img_test, "LSVC:  "+y_predict_LSVC, (int(img_height/10), int(img_width/10*2)), font, 0.8, (84, 255, 159), 1, cv2.LINE_AA)
cv2.putText(img_test, "MLPC:  "+y_predict_MLPC, (int(img_height/10), int(img_width/10)*3), font, 0.8, (84, 255, 159), 1, cv2.LINE_AA)
cv2.putText(img_test, "SGDC:  "+y_predict_SGDC, (int(img_height/10), int(img_width/10)*4), font, 0.8, (84, 255, 159), 1, cv2.LINE_AA)

cv2.namedWindow("img", 2)
cv2.imshow("img", img_test)
cv2.waitKey(0)

五、口罩识别

import cv2
detector= cv2.CascadeClassifier('E:/opencv/opencv/build/etc/haarcascades/haarcascade_frontalface_default.xml')
mask_detector=cv2.CascadeClassifier('F:/cascade.xml')
cap = cv2.VideoCapture(0)
while True:
    ret, img = cap.read()
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = detector.detectMultiScale(gray, 1.1, 3)
    for (x, y, w, h) in faces:
        #参数分别为 图片、左上角坐标，右下角坐标，颜色，厚度
        face=img[y:y+h,x:x+w]  # 裁剪坐标为[y0:y1, x0:x1]
        mask_face=mask_detector.detectMultiScale(gray, 1.1, 5)
        for (x2,y2,w2,h2) in mask_face:
            cv2.rectangle(img, (x2, y2), (x2 + w2, y2 + h2), (0, 0, 255), 2)
    cv2.imshow('mask', img)
    cv2.waitKey(3)
cap.release()
cv2.destroyAllWindows()

E:/opencv/opencv/build/etc/haarcascades/haarcascade_frontalface_default.xml是opencv中自带的人脸识别xml文件，opencv中还有其他的比如笑脸识别，猫脸识别，眼睛识别等等，可以参照我的安装路径去看看

cascade.xml是训练好的分类器，资源在这

自己训练模型进行识别

导入keras库，划分数据集

import keras
keras.__version__

‘2.3.1’

import os, shutil
# dataset was uncompressed
original_dataset_dir = 'F:/人工智能与机器学习/mask/test'
# original_dataset_dir2 = 'F:/人工智能与机器学习/mask/nomask'
# The directory where we will
# store our smaller dataset
base_dir = 'F:/人工智能与机器学习/mask/maskout'
os.mkdir(base_dir)

# Directories for our training,
# validation and test splits
train_dir = os.path.join(base_dir, 'train')
os.mkdir(train_dir)
validation_dir = os.path.join(base_dir, 'validation')
os.mkdir(validation_dir)
test_dir = os.path.join(base_dir, 'test')
os.mkdir(test_dir)

# Directory with our training cat pictures
train_mask_dir = os.path.join(train_dir, 'mask')
os.mkdir(train_mask_dir)

# Directory with our training dog pictures
train_unmask_dir = os.path.join(train_dir, 'unmask')
os.mkdir(train_unmask_dir)

# Directory with our validation cat pictures
validation_mask_dir = os.path.join(validation_dir, 'mask')
os.mkdir(validation_mask_dir)

# Directory with our validation dog pictures
validation_unmask_dir = os.path.join(validation_dir, 'unmask')
os.mkdir(validation_unmask_dir)

# Directory with our validation cat pictures
test_mask_dir = os.path.join(test_dir, 'mask')
os.mkdir(test_mask_dir)

# Directory with our validation dog pictures
test_unmask_dir = os.path.join(test_dir, 'unmask')
os.mkdir(test_unmask_dir)

# Copy first 1000 cat images to train_cats_dir
fnames = ['{}.jpg'.format(i) for i in range(561)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(train_mask_dir, fname)
    shutil.copyfile(src, dst)

# Copy next 500 cat images to validation_cats_dir
fnames = ['{}.jpg'.format(i) for i in range(561)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(validation_mask_dir, fname)
    shutil.copyfile(src, dst)
    
# Copy next 500 cat images to test_cats_dir
fnames = ['{}.jpg'.format(i) for i in range(561)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(test_mask_dir, fname)
    shutil.copyfile(src, dst)
    
# Copy first 1000 dog images to train_dogs_dir
fnames = ['{}.jpg'.format(i) for i in range(561,1591)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(train_unmask_dir, fname)
    shutil.copyfile(src, dst)
    
# Copy next 500 dog images to validation_dogs_dir
fnames = ['{}.jpg'.format(i) for i in range(561,1591)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(validation_unmask_dir, fname)
    shutil.copyfile(src, dst)
    
# Copy next 500 dog images to test_dogs_dir
fnames = ['{}.jpg'.format(i) for i in range(561,1591)]
for fname in fnames:
    src = os.path.join(original_dataset_dir, fname)
    dst = os.path.join(test_unmask_dir, fname)
    shutil.copyfile(src, dst)

print('total training mask images:', len(os.listdir(train_mask_dir)))
print('total training unmask images:', len(os.listdir(train_unmask_dir)))
print('total validation maskt images:', len(os.listdir(validation_mask_dir)))
print('total validation unmask images:', len(os.listdir(validation_unmask_dir)))
print('total test mask images:', len(os.listdir(test_mask_dir)))
print('total test unmask images:', len(os.listdir(test_unmask_dir)))

total training mask images: 561
total training unmask images: 1030
total validation maskt images: 561
total validation unmask images: 1030
total test mask images: 561
total test unmask images: 1030

构建网络

from keras import layers
from keras import models

model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',
                        input_shape=(150, 150, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

model.summary()

数据预处理

from keras import optimizers

model.compile(loss='binary_crossentropy',
              optimizer=optimizers.RMSprop(lr=1e-4),
              metrics=['acc'])

from keras.preprocessing.image import ImageDataGenerator

# All images will be rescaled by 1./255
train_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
        # This is the target directory
        train_dir,
        # All images will be resized to 150x150
        target_size=(150, 150),
        batch_size=20,
        # Since we use binary_crossentropy loss, we need binary labels
        class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
        validation_dir,
        target_size=(150, 150),
        batch_size=20,
        class_mode='binary')

Found 1591 images belonging to 2 classes.
Found 1591 images belonging to 2 classes.

for data_batch, labels_batch in train_generator:
    print('data batch shape:', data_batch.shape)
    print('labels batch shape:', labels_batch.shape)
    break

data batch shape: (20, 150, 150, 3)
labels batch shape: (20,)

history = model.fit_generator(
      train_generator,
      steps_per_epoch=100,
      epochs=30,
      validation_data=validation_generator,
      validation_steps=50)

model.save('mask_and_unmask_small_1.h5')

import matplotlib.pyplot as plt

acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(len(acc))

plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()

plt.figure()

plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()

plt.show()

数据增强

datagen = ImageDataGenerator(
      rotation_range=40,
      width_shift_range=0.2,
      height_shift_range=0.2,
      shear_range=0.2,
      zoom_range=0.2,
      horizontal_flip=True,
      fill_mode='nearest')

# This is module with image preprocessing utilities
from keras.preprocessing import image

fnames = [os.path.join(train_mask_dir, fname) for fname in os.listdir(train_mask_dir)]

# We pick one image to "augment"
img_path = fnames[5]

# Read the image and resize it
img = image.load_img(img_path, target_size=(150, 150))

# Convert it to a Numpy array with shape (150, 150, 3)
x = image.img_to_array(img)

# Reshape it to (1, 150, 150, 3)
x = x.reshape((1,) + x.shape)

# The .flow() command below generates batches of randomly transformed images.
# It will loop indefinitely, so we need to `break` the loop at some point!
i = 0
for batch in datagen.flow(x, batch_size=1):
    plt.figure(i)
    imgplot = plt.imshow(image.array_to_img(batch[0]))
    i += 1
    if i % 4 == 0:
        break

plt.show()

model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',
                        input_shape=(150, 150, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer=optimizers.RMSprop(lr=1e-4),
              metrics=['acc'])

训练模型

train_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=40,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,)

# Note that the validation data should not be augmented!
test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
        # This is the target directory
        train_dir,
        # All images will be resized to 150x150
        target_size=(150, 150),
        batch_size=32,
        # Since we use binary_crossentropy loss, we need binary labels
        class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
        validation_dir,
        target_size=(150, 150),
        batch_size=32,
        class_mode='binary')

history = model.fit_generator(
      train_generator,
      steps_per_epoch=100,
      epochs=30,
      validation_data=validation_generator,
      validation_steps=5)

保存模型

model.save('mask_and_unmask_small_2.h5')

acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(len(acc))

plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()

plt.figure()

plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()

plt.show()

使用模型进行识别

import cv2

from keras.preprocessing import image
from keras.models import load_model
import numpy as np
import dlib
from PIL import Image
model = load_model('mask_and_unmask_small_2.h5')
detector = dlib.get_frontal_face_detector()
video=cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_SIMPLEX
def rec(img):
    gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    dets=detector(gray,1)
    if dets is not None:
        for face in dets:
            left=face.left()
            top=face.top()
            right=face.right()
            bottom=face.bottom()
            cv2.rectangle(img,(left,top),(right,bottom),(0,255,0),2)
            img1=cv2.resize(img[top:bottom,left:right],dsize=(150,150))
            img1=cv2.cvtColor(img1,cv2.COLOR_BGR2RGB)
            img1 = np.array(img1)/255.
            img_tensor = img1.reshape(-1,150,150,3)
            prediction =model.predict(img_tensor)
            if prediction[0][0]>0.5:
                result='unmask'
            else:
                result='mask'
            cv2.putText(img, result, (left,top), font, 2, (0, 255, 0), 2, cv2.LINE_AA)
        cv2.imshow('mask', img)
while video.isOpened():
    res, img_rd = video.read()
    if not res:
        break
    rec(img_rd)
    if cv2.waitKey(5) & 0xFF == ord('q'):
        break
video.release()
cv2.destroyAllWindows()

六、参考文章

https://blog.csdn.net/qq_35723619/article/details/83042202
https://blog.csdn.net/cungudafa/article/details/93613135