CNN人脸表情识别

图片预处理

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
import os# dlib预测器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('D:\\Project\\AIpack\\shape_predictor_68_face_landmarks.dat')# 读取图像的路径
path_read = "D:\\Project\\AIpack\\genki4k\\files"
num = 0
for file_name in os.listdir(path_read):# aa是图片的全路径aa = (path_read + "/" + file_name)# 读入的图片的路径中含非英文img = cv2.imdecode(np.fromfile(aa, dtype=np.uint8), cv2.IMREAD_UNCHANGED)# 获取图片的宽高img_shape = img.shapeimg_height = img_shape[0]img_width = img_shape[1]# 用来存储生成的单张人脸的路径path_save = "D:\\Project\\AIpack\\genki4k\\files1"# dlib检测dets = detector(img, 1)print("人脸数：", len(dets))for k, d in enumerate(dets):if len(dets) > 1:continuenum = num + 1# 计算矩形大小# (x,y), (宽度width, 高度height)pos_start = tuple([d.left(), d.top()])pos_end = tuple([d.right(), d.bottom()])# 计算矩形框大小height = d.bottom() - d.top()width = d.right() - d.left()# 根据人脸大小生成空的图像img_blank = np.zeros((height, width, 3), np.uint8)for i in range(height):if d.top() + i >= img_height:  # 防止越界continuefor j in range(width):if d.left() + j >= img_width:  # 防止越界continueimg_blank[i][j] = img[d.top() + i][d.left() + j]img_blank = cv2.resize(img_blank, (200, 200), interpolation=cv2.INTER_CUBIC)cv2.imencode('.jpg', img_blank)[1].tofile(path_save + "\\" + "file" + str(num) + ".jpg")  # 正确方法

原本效果

处理后效果

图片数据集

import os
import shutil# 原始数据集路径
original_dataset_dir = 'D:\\Project\\AIpack\\genki4k\\files1'# 新的数据集
base_dir = 'D:\\Project\\AIpack\\genki4k\\files2'
os.mkdir(base_dir)# 训练图像、验证图像、测试图像的目录
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)train_cats_dir = os.path.join(train_dir, 'smile')
os.mkdir(train_cats_dir)train_dogs_dir = os.path.join(train_dir, 'unsmile')
os.mkdir(train_dogs_dir)validation_cats_dir = os.path.join(validation_dir, 'smile')
os.mkdir(validation_cats_dir)validation_dogs_dir = os.path.join(validation_dir, 'unsmile')
os.mkdir(validation_dogs_dir)test_cats_dir = os.path.join(test_dir, 'smile')
os.mkdir(test_cats_dir)test_dogs_dir = os.path.join(test_dir, 'unsmile')
os.mkdir(test_dogs_dir)# 复制1000张笑脸图片到train_c_dir
fnames = ['file{}.jpg'.format(i) for i in range(1, 900)]
for fname in fnames:src = os.path.join(original_dataset_dir, fname)dst = os.path.join(train_cats_dir, fname)shutil.copyfile(src, dst)fnames = ['file{}.jpg'.format(i) for i in range(900, 1350)]
for fname in fnames:src = os.path.join(original_dataset_dir, fname)dst = os.path.join(validation_cats_dir, fname)shutil.copyfile(src, dst)# Copy next 500 cat images to test_cats_dir
fnames = ['file{}.jpg'.format(i) for i in range(1350, 1800)]
for fname in fnames:src = os.path.join(original_dataset_dir, fname)dst = os.path.join(test_cats_dir, fname)shutil.copyfile(src, dst)fnames = ['file{}.jpg'.format(i) for i in range(2127, 3000)]
for fname in fnames:src = os.path.join(original_dataset_dir, fname)dst = os.path.join(train_dogs_dir, fname)shutil.copyfile(src, dst)# Copy next 500 dog images to validation_dogs_dir
fnames = ['file{}.jpg'.format(i) for i in range(3000, 3878)]
for fname in fnames:src = os.path.join(original_dataset_dir, fname)dst = os.path.join(validation_dogs_dir, fname)shutil.copyfile(src, dst)# Copy next 500 dog images to test_dogs_dir
fnames = ['file{}.jpg'.format(i) for i in range(3000, 3878)]
for fname in fnames:src = os.path.join(original_dataset_dir, fname)dst = os.path.join(test_dogs_dir, fname)shutil.copyfile(src, dst)

效果

CNN人脸识别

创建模型

# 创建模型
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()  # 查看

归一化与数据增强

# 归一化
from tensorflow import optimizersmodel.compile(loss='binary_crossentropy',optimizer=optimizers.RMSprop(lr=1e-4),metrics=['acc'])
from keras.preprocessing.image import ImageDataGeneratortrain_datagen = ImageDataGenerator(rescale=1. / 255)
validation_datagen = ImageDataGenerator(rescale=1. / 255)
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(# 目标文件目录'D:\\Project\\AIpack\\genki4k\\files2\\train',# 所有图片的size必须是150x150target_size=(150, 150),batch_size=20,# Since we use binary_crossentropy loss, we need binary labelsclass_mode='binary')
validation_generator = test_datagen.flow_from_directory('D:\\Project\\AIpack\\genki4k\\files2\\validation',target_size=(150, 150),batch_size=20,class_mode='binary')
test_generator = test_datagen.flow_from_directory('D:\\Project\\AIpack\\genki4k\\files2\\test',target_size=(150, 150),batch_size=20,class_mode='binary')
for data_batch, labels_batch in train_generator:print('data batch shape:', data_batch.shape)print('labels batch shape:', labels_batch)break
# 'smile': 0, 'unsmile': 1# 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')
# 数据增强后图片变化
import matplotlib.pyplot as plt
# This is module with image preprocessing utilities
from keras.preprocessing import imagefnames = [os.path.join('D:\\Project\\AIpack\\genki4k\\files2\\train\\smile', fname) for fname in os.listdir('D:\\Project\\AIpack\\genki4k\\files2\\train\\smile')]
img_path = fnames[3]
img = image.load_img(img_path, target_size=(150, 150))
x = image.img_to_array(img)
x = x.reshape((1,) + x.shape)
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:break
plt.show()

创建网络

from keras import layers
from keras import models
from tensorflow import optimizers
import matplotlib.pyplot as plt
from keras.preprocessing.image import ImageDataGenerator# 创建网络
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'])
# 归一化处理
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, )test_datagen = ImageDataGenerator(rescale=1. / 255)train_generator = train_datagen.flow_from_directory(# This is the target directory'D:\\Project\\AIpack\\genki4k\\files2\\train\\',# All images will be resized to 150x150target_size=(150, 150),batch_size=32,# Since we use binary_crossentropy loss, we need binary labelsclass_mode='binary')validation_generator = test_datagen.flow_from_directory('D:\\Project\\AIpack\\genki4k\\files2\\validation\\',target_size=(150, 150),batch_size=32,class_mode='binary')history = model.fit_generator(train_generator,steps_per_epoch=100,epochs=200,validation_data=validation_generator,validation_steps=50)
model.save('smileAndUnsmile1.h5')# 数据增强过后的训练集与验证集的精确度与损失度的图形
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()

摄像头人脸识别

# 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 Imagemodel = load_model('smileAndUnsmile1.h5')
detector = dlib.get_frontal_face_detector()
video = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_SIMPLEXdef 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(1) & 0xFF == ord('q'):break
video.release()
cv2.destroyAllWindows()

图片识别

# 单张图片进行判断  是笑脸还是非笑脸
import cv2
from keras.preprocessing import image
from keras.models import load_model
import numpy as np# 加载模型
model = load_model('smileAndUnsmile1.h5')
# 本地图片路径
img_path = './115.png'
img = image.load_img(img_path, target_size=(150, 150))img_tensor = image.img_to_array(img) / 255.0
img_tensor = np.expand_dims(img_tensor, axis=0)
prediction = model.predict(img_tensor)
print(prediction)
if prediction[0][0] > 0.5:result = '非笑脸'
else:result = '笑脸'
print(result)

参考

Python-人脸识别并判断表情 笑脸或非笑脸 使用笑脸数据集genki4k