一、用卷积神经网络实现，做笑脸、非笑脸等表情识别

1.数据集
2.将下载里面的datasets，放到D盘新建的smile中，

1.根据猫狗数据集训练的方法来训练笑脸数据集

1.首先将train_folder文件夹下俩个文件夹内的图片的名字做修改。（修改成猫狗的图片格式

#coding=gbk
import os
import sys
def rename():path=input("请输入路径(例如D:\\\\picture)：")name=input("请输入开头名:")startNumber=input("请输入开始数:")fileType=input("请输入后缀名（如 .jpg、.txt等等）:")print("正在生成以"+name+startNumber+fileType+"迭代的文件名")count=0filelist=os.listdir(path)for files in filelist:Olddir=os.path.join(path,files)if os.path.isdir(Olddir):continueNewdir=os.path.join(path,name+str(count+int(startNumber))+fileType)os.rename(Olddir,Newdir)count+=1print("一共修改了"+str(count)+"个文件")rename() 

2)图片分类

import os, shutil #复制文件
# 原始目录所在的路径
# 数据集未压缩
original_dataset_dir1 = 'D:\\smile\\datasets\\train_folder\\1'  ##笑脸
original_dataset_dir0 = 'D:\\smile\\datasets\\train_folder\\0'  ##非笑脸
# 我们将在其中的目录存储较小的数据集
base_dir = 'D:\\smile1'
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)# 猫训练图片所在目录
train_cats_dir = os.path.join(train_dir, 'smile')
os.mkdir(train_cats_dir)# 狗训练图片所在目录
train_dogs_dir = os.path.join(train_dir, 'unsmile')
os.mkdir(train_dogs_dir)# 猫验证图片所在目录
validation_cats_dir = os.path.join(validation_dir, 'smile')
os.mkdir(validation_cats_dir)# 狗验证数据集所在目录
validation_dogs_dir = os.path.join(validation_dir, 'unsmile')
os.mkdir(validation_dogs_dir)# 猫测试数据集所在目录
test_cats_dir = os.path.join(test_dir, 'smile')
os.mkdir(test_cats_dir)# 狗测试数据集所在目录
test_dogs_dir = os.path.join(test_dir, 'unsmile')
os.mkdir(test_dogs_dir)# 将前1000张笑脸图像复制到train_cats_dir
fnames = ['smile.{}.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_cats_dir, fname)shutil.copyfile(src, dst)# 将下500张笑脸图像复制到validation_cats_dir
fnames = ['smile.{}.jpg'.format(i) for i in range(500)]
for fname in fnames:src = os.path.join(original_dataset_dir1, fname)dst = os.path.join(validation_cats_dir, fname)shutil.copyfile(src, dst)# 将下500张笑脸图像复制到test_cats_dir
fnames = ['smile.{}.jpg'.format(i) for i in range(500)]
for fname in fnames:src = os.path.join(original_dataset_dir1, fname)dst = os.path.join(test_cats_dir, fname)shutil.copyfile(src, dst)# 将前1000张非笑脸图像复制到train_dogs_dir
fnames = ['unsmile.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:src = os.path.join(original_dataset_dir0, fname)dst = os.path.join(train_dogs_dir, fname)shutil.copyfile(src, dst)# 将下500张非笑脸图像复制到validation_dogs_dir
fnames = ['unsmile.{}.jpg'.format(i) for i in range(500)]
for fname in fnames:src = os.path.join(original_dataset_dir0, fname)dst = os.path.join(validation_dogs_dir, fname)shutil.copyfile(src, dst)# 将下500张非笑脸图像复制到test_dogs_dir
fnames = ['unsmile.{}.jpg'.format(i) for i in range(500)]
for fname in fnames:src = os.path.join(original_dataset_dir0, fname)dst = os.path.join(test_dogs_dir, fname)shutil.copyfile(src, dst)

3)作为健全性检查，让我们计算一下在每个训练分割中我们有多少图片（训练/验证/测试）：

print('total training cat images:', len(os.listdir(train_cats_dir)))
print('total training dog images:', len(os.listdir(train_dogs_dir)))
print('total validation cat images:', len(os.listdir(validation_cats_dir)))
print('total validation dog images:', len(os.listdir(validation_dogs_dir)))
print('total test cat images:', len(os.listdir(test_cats_dir)))
print('total test dog images:', len(os.listdir(test_dogs_dir)))

4)卷积网络模型搭建

from keras import layers
from keras import modelsmodel = 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()

5)图像生成器读取文件中数据，进行数据预处理。

from keras import optimizersmodel.compile(loss='binary_crossentropy',optimizer=optimizers.RMSprop(lr=1e-4),metrics=['acc'])
from keras.preprocessing.image import ImageDataGenerator# 所有图像将按1/255重新缩放
train_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)train_generator = train_datagen.flow_from_directory(# 这是目标目录train_dir,# 所有图像将调整为150x150target_size=(150, 150),batch_size=20,# 因为我们使用二元交叉熵损失，我们需要二元标签class_mode='binary')validation_generator = test_datagen.flow_from_directory(validation_dir,target_size=(150, 150),batch_size=20,class_mode='binary')

6)开始训练

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

7)保存训练模型`

model.save('D:\\smile1\\smiles_and_unsmiles_small_1.h5')

8)在培训和验证数据上绘制模型的损失和准确性（可视化界面）

import matplotlib.pyplot as pltacc = 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()

9)使用数据扩充

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')
# 这是带有图像预处理实用程序的模块
from keras.preprocessing import imagefnames = [os.path.join(train_cats_dir, fname) for fname in os.listdir(train_cats_dir)]# 我们选择一个图像来“增强”
img_path = fnames[3]# 读取图像并调整其大小
img = image.load_img(img_path, target_size=(150, 150))# 将其转换为具有形状的Numpy数组（150、150、3）
x = image.img_to_array(img)# 把它改成（1150150，3）
x = x.reshape((1,) + x.shape)# 下面的.flow（）命令生成一批随机转换的图像。
# 它将无限循环，所以我们需要在某个时刻“打破”循环！
i = 0
for batch in datagen.flow(x, batch_size=1):plt.figure(i)imgplot = plt.imshow(image.array_to_img(batch[0]))i += 1if i % 4 == 0:breakplt.show()

10)使用数据扩充和退出来训练我们的网络

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,)# 请注意，不应增加验证数据！
test_datagen = ImageDataGenerator(rescale=1./255)train_generator = train_datagen.flow_from_directory(# 这是目标目录train_dir,# 所有图像将调整为150x150target_size=(150, 150),batch_size=32,# 因为我们使用二元交叉熵损失，我们需要二元标签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)

11)保存模型

model.save('D:\\smile1\\smiles_and_unsmiles_small_2.h5')

12)在培训和验证数据上绘制模型的损失和准确性（可视化界面）

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()

二、完成一个摄像头采集自己人脸、并对表情（笑脸和非笑脸）的实时分类判读（输出分类文字）的程序；

1.基于上面卷积神经网络的笑脸识别

#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:\\smile1\\smiles_and_unsmiles_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='unsmile'else:result='smile'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:breakrec(img_rd)if cv2.waitKey(5) & 0xFF == ord('q'):break
video.release()
cv2.destroyAllWindows()