卷积神经网络实现表情识别

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.shape
    img_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:
            continue
        num = 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
                continue
            for j in range(width):
                if d.left() + j >= img_width:  # 防止越界
                    continue
                img_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 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()  # 查看

归一化与数据增强

# 归一化
from tensorflow import optimizers

model.compile(loss='binary_crossentropy',
              optimizer=optimizers.RMSprop(lr=1e-4),
              metrics=['acc'])
from keras.preprocessing.image import ImageDataGenerator

train_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(
    '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 image

fnames = [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 += 1
    if 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 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(
    '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 Image

model = load_model('smileAndUnsmile1.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:
        break
    rec(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