使用Python完成口罩数据集训练，并实现摄像头口罩识别
使用Python完成口罩数据集训练，并实现摄像头口罩识别
- 一、准备数据集
- 二、导入Keras库，并划分数据集
- 三、构建网络
- 四、数据预处理
- 五、使用数据增强
- 六、使用摄像头读取人脸进行是否佩戴识别
一、准备数据集
下载地址
大家还可以自己找数据集进行训练。
二、导入Keras库，并划分数据集
import keras
keras.__version__
import os, shutil #复制文件
# 原始目录所在的路径
# 数据集未压缩
original_dataset_dir0 = 'D:\\Workspaces\\Jupyter-notebook\\datasets\\mldata\\人脸口罩数据集\\mask\\mask'
original_dataset_dir1 = 'D:\\Workspaces\\Jupyter-notebook\\datasets\\mldata\\人脸口罩数据集\\mask\\unmask'
# 我们将在其中的目录存储较小的数据集
base_dir = 'D:\\Workspaces\\Jupyter-notebook\\datasets\\mldata\\mask_small'
os.mkdir(base_dir)
# # 训练、验证、测试数据集的目录
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)
# 戴口罩训练图片所在目录
train_mask_dir = os.path.join(train_dir, 'mask')
os.mkdir(train_mask_dir)
# 不戴口罩训练图片所在目录
train_unmask_dir = os.path.join(train_dir, 'unmask')
os.mkdir(train_unmask_dir)
# 戴口罩验证图片所在目录
validation_mask_dir = os.path.join(validation_dir, 'mask')
os.mkdir(validation_mask_dir)
# 不戴口罩验证数据集所在目录
validation_unmask_dir = os.path.join(validation_dir, 'unmask')
os.mkdir(validation_unmask_dir)
# 戴口罩测试数据集所在目录
test_mask_dir = os.path.join(test_dir, 'mask')
os.mkdir(test_mask_dir)
# 不戴口罩测试数据集所在目录
test_unmask_dir = os.path.join(test_dir, 'unmask')
os.mkdir(test_unmask_dir)
# 将前600张戴口罩图像复制到train_mask_dir
fnames = ['mask{}.jpg'.format(i) for i in range(600)]
for fname in fnames:
src = os.path.join(original_dataset_dir0, fname)
dst = os.path.join(train_mask_dir, fname)
shutil.copyfile(src, dst)
# 将600张戴口罩图像复制到validation_mask_dir
fnames = ['mask{}.jpg'.format(i) for i in range(600)]
for fname in fnames:
src = os.path.join(original_dataset_dir0, fname)
dst = os.path.join(validation_mask_dir, fname)
shutil.copyfile(src, dst)
# 将600张戴口罩图像复制到test_mask_dir
fnames = ['mask{}.jpg'.format(i) for i in range(600)]
for fname in fnames:
src = os.path.join(original_dataset_dir0, fname)
dst = os.path.join(test_mask_dir, fname)
shutil.copyfile(src, dst)
# 将前1000张不戴口罩图像复制到train_unmask_dir
fnames = ['unmask{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:
src = os.path.join(original_dataset_dir1, fname)
dst = os.path.join(train_unmask_dir, fname)
shutil.copyfile(src, dst)
# 将500张不戴口罩图像复制到validation_unmask_dir
fnames = ['unmask{}.jpg'.format(i) for i in range(1000, 1500)]
for fname in fnames:
src = os.path.join(original_dataset_dir1, fname)
dst = os.path.join(validation_unmask_dir, fname)
shutil.copyfile(src, dst)
# 将500张不戴口罩图像复制到test_unmask_dir
fnames = ['unmask{}.jpg'.format(i) for i in range(1200, 1700)]
for fname in fnames:
src = os.path.join(original_dataset_dir1, fname)
dst = os.path.join(test_unmask_dir, fname)
shutil.copyfile(src, dst)
作为健全性检查，让我们计算一下每个训练分组（训练/验证/测试）中有多少张图片：
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)))
划分的图片数量与前面代码写的一致。
三、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'))
看一下特征贴图的尺寸如何随每个连续层变化：
model.summary()
四、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')
看一看这些生成器之一的输出：
for data_batch, labels_batch in train_generator:
print('data batch shape:', data_batch.shape)
print('labels batch shape:', labels_batch.shape)
break
``
## 五、训练模型
```python
history = model.fit_generator(
train_generator,
steps_per_epoch=100,
epochs=30,
validation_data=validation_generator,
validation_steps=50)
取部分训练轮数：
保存模型：
model.save('mask_and_unmask_small_1.h5')
在训练和验证数据上绘制模型的损失和准确性：
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()
五、使用数据增强
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')
看一下我们的增强图像：
# 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[3]
# 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()
在紧密连接的分类器之前为模型添加一个Dropout层：
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'])
使用数据增强和dropout来训练我们的网络：
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=100,
validation_data=validation_generator,
validation_steps=50)
部分训练结果：
把模型保存下来：
model.save('mask_and_unmask_small_2.h5')
在训练和验证数据上绘制模型的损失和准确性：
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()
六、使用摄像头读取人脸进行是否佩戴识别
#检测视频或者摄像头中的人脸
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('D:/Workspaces/Jupyter-notebook/人工智能与机器学习/人工智能大作业/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('Video', 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()
这里可以使用我们前面训练的两个模型。
测试结果展示：
