转载请注明作者和出处：https://blog.csdn.net/qq_28810395
Python版本： Python3.x
运行平台： Windows 10
IDE： Pycharm profession 2019
如有需要数据集或整个工程文件的可以进行关注cai_5158046回复口罩识别进行获取。

一、前言

  在此只做简单利用Tensorflow进行口罩识别的程序实现，不做算法讲解，具体概念请移步下面链接。

https://blog.csdn.net/qq_28810395/article/details/107401125

二、前期准备

  数据集（200幅戴口罩图片，200幅没戴口罩图片）如下。

1. mask
2. nomask
   
   对于深度网络模型训练，仅仅200的训练集完全不足够进行训练，在此只是测试讲解，所以200样本集足够用于展示流程。

具体代码展示

  搭建网络，训练模型。

import os, shutil, random, glob
#添加tensorflow的库
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
#添加matplotlib的库进行界面可视化
import matplotlib.pyplot as plt

#变量
resize = 224  #输入图片尺寸参数
total_num = 200  #训练集总计和
train_num = 180  #训练集实际训练数字，20的验证集
epochs = 5       #迭代次数
batch_size = 8   #每次训练多少张

# -----------------------------------------------------------------------------
dir_data = './train/'            #训练集路径
dir_mask = os.path.join(dir_data, 'mask')     #戴口罩文件夹路径
dir_nomask = os.path.join(dir_data, 'nomask') #没带口罩文件夹路径
#健壮性的断言
assert os.path.exists(dir_mask), 'Could not find ' + dir_mask
assert os.path.exists(dir_nomask), 'Could not find ' + dir_nomask
#定义了文件指针对整个文件夹遍历一遍，将图像读出来
fpath_mask = [os.path.abspath(fp) for fp in glob.glob(os.path.join(dir_mask, '*.jpg'))]
fpath_nomask = [os.path.abspath(fp) for fp in glob.glob(os.path.join(dir_nomask, '*.jpg'))]
#文件数
num_mask = len(fpath_mask)
num_nomask = len(fpath_nomask)
#设置标签
label_mask = [0] * num_mask
label_nomask = [1] * num_nomask

print('#mask:   ', num_mask)
print('#nomask: ', num_nomask)
#划分多少为验证集
RATIO_TEST = 0.1

num_mask_test = int(num_mask * RATIO_TEST)
num_nomask_test = int(num_nomask * RATIO_TEST)

# train
fpath_train = fpath_mask[num_mask_test:] + fpath_nomask[num_nomask_test:]
label_train = label_mask[num_mask_test:] + label_nomask[num_nomask_test:]
# validation
fpath_vali = fpath_mask[:num_mask_test] + fpath_nomask[:num_nomask_test]
label_vali = label_mask[:num_mask_test] + label_nomask[:num_nomask_test]

num_train = len(fpath_train)
num_vali = len(fpath_vali)

print(num_train)
print(num_vali)
#预处理
def preproc(fpath, label):
    image_byte = tf.io.read_file(fpath)
    image = tf.io.decode_image(image_byte)
    image_resize = tf.image.resize_with_pad(image, 224, 224) #缩放到224*224
    image_norm = tf.cast(image_resize, tf.float32) / 255. #归一化
    
    label_onehot = tf.one_hot(label, 2)
    
    return image_norm, label_onehot

dataset_train = tf.data.Dataset.from_tensor_slices((fpath_train, label_train)) #将数据进行预处理
dataset_train = dataset_train.shuffle(num_train).repeat()  #打乱顺序
dataset_train = dataset_train.map(preproc, num_parallel_calls=tf.data.experimental.AUTOTUNE)
dataset_train = dataset_train.batch(batch_size).prefetch(tf.data.experimental.AUTOTUNE) #一批次处理多少份

dataset_vali = tf.data.Dataset.from_tensor_slices((fpath_vali, label_vali))
dataset_vali = dataset_vali.shuffle(num_vali).repeat()
dataset_vali = dataset_vali.map(preproc, num_parallel_calls=tf.data.experimental.AUTOTUNE)
dataset_vali = dataset_vali.batch(batch_size).prefetch(tf.data.experimental.AUTOTUNE)
# -----------------------------------------------------------------------------

# AlexNet  算法模型
model = keras.Sequential(name='Alexnet')
#第1层
model.add(layers.Conv2D(filters=96, kernel_size=(11,11),
                 strides=(4,4), padding='valid',
                 input_shape=(resize,resize,3),
                 activation='relu'))
model.add(layers.BatchNormalization())
#第1层池化
model.add(layers.MaxPooling2D(pool_size=(3,3), 
                       strides=(2,2), 
                       padding='valid'))
#第2层
model.add(layers.Conv2D(filters=256, kernel_size=(5,5), 
                 strides=(1,1), padding='same', 
                 activation='relu'))
model.add(layers.BatchNormalization())
#第2层池化
model.add(layers.MaxPooling2D(pool_size=(3,3), 
                       strides=(2,2), 
                       padding='valid'))
#第3层
model.add(layers.Conv2D(filters=384, kernel_size=(3,3), 
                 strides=(1,1), padding='same', 
                 activation='relu'))
#第4层
model.add(layers.Conv2D(filters=384, kernel_size=(3,3), 
                 strides=(1,1), padding='same', 
                 activation='relu'))
#第5层
model.add(layers.Conv2D(filters=256, kernel_size=(3,3), 
                 strides=(1,1), padding='same', 
                 activation='relu'))
model.add(layers.MaxPooling2D(pool_size=(3,3), 
                       strides=(2,2), padding='valid'))
#第6，7，8层
model.add(layers.Flatten())
model.add(layers.Dense(4096, activation='relu'))
model.add(layers.Dropout(0.5))

model.add(layers.Dense(4096, activation='relu'))
model.add(layers.Dropout(0.5))

model.add(layers.Dense(1000, activation='relu'))
model.add(layers.Dropout(0.5))

# Output Layer
model.add(layers.Dense(2, activation='softmax'))

# Training 优化器
model.compile(loss='categorical_crossentropy',
              optimizer='sgd',           #梯度下降法
              metrics=['accuracy'])


history = model.fit(dataset_train,
          steps_per_epoch = num_train//batch_size,
          epochs = epochs,         #迭代次数
          validation_data = dataset_vali,
          validation_steps = num_vali//batch_size,
          verbose = 1)
#评分标准
# scores = model.evaluate(train_data, train_label, verbose=1)
scores = model.evaluate(dataset_train, steps=num_train//batch_size, verbose=1)
print(scores)

# scores = model.evaluate(test_data, test_label, verbose=1)
scores = model.evaluate(dataset_vali, steps=num_vali//batch_size, verbose=1)
print(scores)
#保存模型
model.save('./model/mask.h5')

# Record loss and acc
history_dict = history.history
train_loss = history_dict['loss']
train_accuracy = history_dict['accuracy']
val_loss = history_dict['val_loss']
val_accuracy = history_dict['val_accuracy']

# Draw loss
plt.figure()
plt.plot(range(epochs), train_loss, label='train_loss')
plt.plot(range(epochs), val_loss, label='val_loss')
plt.legend()
plt.xlabel('epochs')
plt.ylabel('loss')

# Draw acc
plt.figure()
plt.plot(range(epochs), train_accuracy, label='train_accuracy')
plt.plot(range(epochs), val_accuracy, label='val_accuracy')
plt.legend()
plt.xlabel('epochs')
plt.ylabel('accuracy')

# Display
plt.show()

print('Task finished')

三、模型训练结果展示

  程序中只采用了5次迭代，具体工程也是远远不够的，在此也同样仅仅做流程说明，所以不做要求。

1. 显示loss
   
2. 显示acc
   

四、测试集

  简单测试，所以只采用了2张图片。

五、测试模型代码

import cv2

from tensorflow.keras.models import load_model

resize = 224
label = ('戴口罩!', '没戴口罩')

image = cv2.resize(cv2.imread('./test/test1.jpg'),(resize,resize))
image = image.astype("float") / 255.0

image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))

model = load_model('./model/mask.h5')
predict = model.predict(image)
i = predict.argmax(axis=1)[0]
print(label[i])

六、测试结果

七、参考信息

• http://edu.cstor.cn/#/