这篇教程人脸表情识别写得很实用，希望能帮到您。
人脸情绪识别与emoji转换(FaceEmotionClassifier)
用Keras做前端，tensorflow做后端训练模型识别人类的情绪。根据情绪选择相应的emoji匹配
博客地址
项目简介
项目源码https://github.com/zhouzaihang/FaceEmotionClassifier
https://github.com/zhouzaihang/FaceEmotionClassifier/blob/master/image/happyface.png
数据集介绍
FER2013
训练模型的数据集选用了kaggle挑战赛上的fer2013数据集
下载得到的csv格式可以通过Excel看到格式为：
| Emotion |
Pixels |
Usage |
| 0 |
4 0 170 118 101 88 88 75 78 82 66 74 68 59 63 64 65 90 89 73 80 80 85 88 95 117 … 129 |
Training |
| 2 |
200 197 149 139 156 89 111 58 62 95 113 117 116 116 112 111 96 86 99 113 120 1 … 116 |
Training |
所以首先打开csv文件，根据usage把数据集分为：训练集、测试集和验证集
with open(csv_file) as f:
csv_r = csv.reader(f)
header = next(csv_r) #Python中读取文件，指向的是文件的第一行，但是第一行是标题，所以用next()指向下一行，也就是从第二行开始
print(header)
rows = [row for row in csv_r]
trn = [row[:-1] for row in rows if row[-1] == 'Training']
csv.writer(open(train_csv, 'w+'), lineterminator='\n').writerows([header[:-1]] + trn)
print(len(trn))
val = [row[:-1] for row in rows if row[-1] == 'PublicTest']
csv.writer(open(val_csv, 'w+'), lineterminator='\n').writerows([header[:-1]] + val)
print(len(val))
tst = [row[:-1] for row in rows if row[-1] == 'PrivateTest']
csv.writer(open(test_csv, 'w+'), lineterminator='\n').writerows([header[:-1]] + tst)
print(len(tst))
如果直接用当前数据是一个扁平的向量，没有空间局部性。用这样的数据直接进行训练，就会失去空间结构和图像关系信息。卷积神经网络可以保留空间信息，并且更适合图像分类问题，所以要把数据转为图片方便下面采用卷积神经网络进行训练
https://github.com/zhouzaihang/FaceEmotionClassifier/blob/master/image/lable0-6.png
num = 1
with open(csv_file) as f:
csv_r = csv.reader(f)
header = next(csv_r)
for i, (label, pixel) in enumerate(csv_r):
# 0 - 6 文件夹内的图片label分别为：
# angry ，disgust ，fear ，happy ，sad ，surprise ，neutral
pixel = np.asarray([float(p) for p in pixel.split()]).reshape(48, 48)
sub_folder = os.path.join(save_path, label)
if not os.path.exists(sub_folder):
os.makedirs(sub_folder)
im = Image.fromarray(pixel).convert('L')
image_name = os.path.join(sub_folder, '{:05d}.jpg'.format(i))
print(image_name)
im.save(image_name)
顺便把图片灰度化处理（防止黑人和白人的肤色对模型造成影响 O(∩_∩)O哈哈哈）
Emoji表情集
替代人脸的卡通表情采用了Android 9的Emoji
深度卷积神经网络模型
构建模型
这里用到了很多神经网络层
这里图像使用tf（tensorflow）顺序，它在三个通道上的形状为（48,48），正常图片可以表示为(48, 48, 3)。只不过在刚刚生成图片的时候，已经做过灰度化处理，所以这个时候，只有一个通道了。
卷积阶段
使用keras添加一层二维滤波器，输出维度是32并且每个二维滤波器是1 * 1的卷积层
self.model.add(Conv2D(32, (1, 1), strides=1, padding='same', input_shape=(img_size, img_size, 1)))
padding='same'表示保留边界处的卷积计算结果。总共只有两种设置，这种表示输出和输入的大小相同，输入的区域边界填充为0；padding='valid'表示只对输入和滤波器完全叠加的部分做卷积运算，因而输出将会少于输入。不过讲道理，这里strides这个处理步幅已经是1了，不管设置什么都不会超过边界
使用ReLU激活函数
self.model.add(Activation('relu'))
然后给网络学习32个5 * 5的滤波器，也用ReLU激活。并且紧接着一个最大池化层方法
self.model.add(Conv2D(32, (5, 5), padding='same'))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
之后第二层卷积阶段和第三层卷积阶段都是用ReLU激活函数，后面再次跟着最大池化层方法。第二层仍然是32个3 3大小的滤波器，第三层滤波器增加到64个5 5，在更深的网络层增加滤波器数目是深度学习中一个普遍采用的技术
self.model.add(Conv2D(32, (3, 3), padding='same'))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(Conv2D(64, (5, 5), padding='same'))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
深度管道的下一个阶段
首先用Flatten()获得一个扁平的网络
self.model.add(Flatten())
用ReLU激活一个有2048个神经元的隐藏层，用Dropout丢弃到一半的网络，再添加一个1024个神经元的隐藏层，跟着一个关闭50%神经元的dropout层
self.model.add(Activation('relu'))
self.model.add(Dropout(0.5))
self.model.add(Dense(1024))
self.model.add(Activation('relu'))
self.model.add(Dropout(0.5))
输出层
添加作为输出7个类的softmax层，每个类对应一个类别
self.model.add(Dense(num_classes))
self.model.add(Activation('softmax'))
MODEL_SUMMARY
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 48, 48, 32) 64
_________________________________________________________________
activation_1 (Activation) (None, 48, 48, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 48, 48, 32) 25632
_________________________________________________________________
activation_2 (Activation) (None, 48, 48, 32) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 24, 24, 32) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 24, 24, 32) 9248
_________________________________________________________________
activation_3 (Activation) (None, 24, 24, 32) 0
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 12, 12, 32) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 12, 12, 64) 51264
_________________________________________________________________
activation_4 (Activation) (None, 12, 12, 64) 0
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 6, 6, 64) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 2304) 0
_________________________________________________________________
dense_1 (Dense) (None, 2048) 4720640
_________________________________________________________________
activation_5 (Activation) (None, 2048) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 2048) 0
_________________________________________________________________
dense_2 (Dense) (None, 1024) 2098176
_________________________________________________________________
activation_6 (Activation) (None, 1024) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 1024) 0
_________________________________________________________________
dense_3 (Dense) (None, 7) 7175
_________________________________________________________________
activation_7 (Activation) (None, 7) 0
=================================================================
Total params: 6,912,199
Trainable params: 6,912,199
Non-trainable params: 0
_________________________________________________________________
model built
Found 28709 images belonging to 7 classes.
训练模型
编译模型
这里选择随机梯度下降算法作为优化器
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
self.model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
通过数据增加改善性能
通常提高性能有两种方法，一种是定义一个更深、有更多卷积操作的网络，另一种训练更多的图片。这里用keras自带的ImageDataGenerator方法扩展数据集
# 自动扩充训练样本
train_datagen = ImageDataGenerator(
rescale=1. / 255, # 归一化处理
shear_range=0.2, # 随机缩放
zoom_range=0.2, # 放大
horizontal_flip=True) # 随机水平翻转
考虑到效率问题，keras提供了生成器针对模型的并发运行。我的理解就是CPU处理生成图像，GPU上并行进行训练
# 归一化验证集
val_datagen = ImageDataGenerator(
rescale=1. / 255)
eval_datagen = ImageDataGenerator(
rescale=1. / 255)
# 以文件分类名划分label
train_generator = train_datagen.flow_from_directory(
data_path + '/train',
target_size=(img_size, img_size),
color_mode='grayscale',
batch_size=batch_siz,
class_mode='categorical')
val_generator = val_datagen.flow_from_directory(
data_path + '/val',
target_size=(img_size, img_size),
color_mode='grayscale',
batch_size=batch_siz,
class_mode='categorical')
eval_generator = eval_datagen.flow_from_directory(
data_path + '/test',
target_size=(img_size, img_size),
color_mode='grayscale',
batch_size=batch_siz,
class_mode='categorical')
history_fit = self.model.fit_generator(
train_generator,
steps_per_epoch=800 / (batch_siz / 32), # 28709
nb_epoch=nb_epoch,
valIDAtion_data=val_generator,
validation_steps=2000,
)
保存模型结构及权重
把结构保存为JSON字串，把权重保存到HDF5文件
model_json = self.model.to_json()
with open(model_path + "/model_json.json", "w") as json_file:
json_file.write(model_json)
self.model.save_weights(model_path + '/model_weight.h5')
self.model.save(model_path + '/model.h5')
识别模块
加载权重及模型结构
# 从json中加载模型
json_file = open(model_path + 'model_json.json')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# 加载模型权重
model.load_weights(model_path + 'model_weight.h5')
使用OPENCV-PYTHON识别人脸
用opencv打开摄像头，使用opencv提供的一个训练好的模型识别人脸人类器
# 创建VideoCapture对象
capture = cv2.VideoCapture(0)
# 使用opencv的人脸分类器
cascade = cv2.CascadeClassifier(model_path + 'haarcascade_frontalface_alt.xml')
# 实时获得摄像头数据
ret, frame = capture.read()
# 灰度化处理
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 识别人脸位置
faceLands = cascade.detectMultiScale(gray, scaleFactor=1.1,
minNeighbors=1, minSize=(120, 120))
识别人脸情绪
根据识别出的脸部特征点，裁剪出脸部图像，然后调用模型预测情绪
if len(faceLands) > 0:
for faceLand in faceLands:
x, y, w, h = faceLand
images = []
result = np.array([0.0] * num_class)
# 裁剪出脸部图像
image = cv2.resize(gray[y:y + h, x:x + w], (img_size, img_size))
image = image / 255.0
image = image.reshape(1, img_size, img_size, 1)
# 调用模型预测情绪
predict_lists = model.predict_proba(image, batch_size=32, verbose=1)
result += np.array([predict for predict_list in predict_lists
for predict in predict_list])
emotion = emotion_labels[int(np.argmax(result))]
print("Emotion:", emotion)
根据识别结果，用cv的rectangle在视频流上框出脸部并且用putText打上标签
# 框出脸部并且写上标签
cv2.rectangle(frame, (x - 20, y - 20), (x + w + 20, y + h + 20),
(0, 255, 255), thickness=10)
cv2.putText(frame, '%s' % emotion, (x, y - 50),
cv2.FONT_HERSHEY_DUPLEX, 2, (255, 255, 255), 2, 30)
cv2.imshow('Face', frame)
用EMOJI盖住人脸
先在第一次获取视频画面的时候就copy一个没有灰度化处理的视频画面
# 呈现用emoji替代后的画面
emoji_show = frame.copy()
直接把emoji图片遮盖人脸会出现emoji背景变为黑色盖上去了。所以这里要蒙版处理一下，也就是保持emoji透明背景的特性，当然，这里所有图像都要归一化处理
def face2emoji(face, emotion_index, position):
x, y, w, h = position
emotion_image = cv2.resize(emotion_images[emotion_index], (w, h))
overlay_img = emotion_image[:, :, :3]/255.0
overlay_bg = emotion_image[:, :, 3:]/255.0
background = (1.0 - overlay_bg)
face_part = (face[y:y + h, x:x + w]/255.0) * background
overlay_part = overlay_img * overlay_bg
face[y:y + h, x:x + w] = cv2.addWeighted(face_part, 255.0, overlay_part, 255.0, 0.0)
return face
首先非常感谢 zhouzaihang：https://www.52pojie.cn/forum.php?mod=viewthread&tid=863608
环境和数据集
环境：python、python-opencv、keras、tensorflow
其他库，可以安装anaconda，差不多的库都装好了的。
训练数据：fer2013.csv
下载地址：链接：https://pan.baidu.com/s/1Ac5XBue0ahLOkIXwa7W77g 提取码：qrue
总流程：
第一步：数据预处理：fer2013.csv = train.csv +test.csv +val.csv ;同时还原出图像数据。
标签emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']对应0-6命名的文件夹。
代码：
import csv
import os
from PIL import Image
import numpy as np
# 读、写数据的地址
data_path = os.getcwd() + "/data/"
csv_file = data_path + 'fer2013.csv' # 读数据集地址
train_csv = data_path + 'train.csv' # 拆数据集保存地址
val_csv = data_path + 'val.csv'
test_csv = data_path + 'test.csv'
# csv文件像素保存为图像的文件夹名称
train_set = os.path.join(data_path, 'train')
val_set = os.path.join(data_path, 'val')
test_set = os.path.join(data_path, 'test')
# 开始整理数据集：读
with open(csv_file) as f:
csv_r = csv.reader(f)
header = next(csv_r)
print(header)
rows = [row for row in csv_r]
trn = [row[:-1] for row in rows if row[-1] == 'Training']
csv.writer(open(train_csv, 'w+'), lineterminator='\n').writerows([header[:-1]] + trn)
print(len(trn))
val = [row[:-1] for row in rows if row[-1] == 'PublicTest']
csv.writer(open(val_csv, 'w+'), lineterminator='\n').writerows([header[:-1]] + val)
print(len(val))
tst = [row[:-1] for row in rows if row[-1] == 'PrivateTest']
csv.writer(open(test_csv, 'w+'), lineterminator='\n').writerows([header[:-1]] + tst)
print(len(tst))
for save_path, csv_file in [(train_set, train_csv), (val_set, val_csv), (test_set, test_csv)]:
if not os.path.exists(save_path):
os.makedirs(save_path)
num = 1
with open(csv_file) as f:
csv_r = csv.reader(f)
header = next(csv_r)
for i, (label, pixel) in enumerate(csv_r):
# 0 - 6 文件夹分别label为：
# angry ，disgust ，fear ，happy ，sad ，surprise ，neutral
pixel = np.asarray([float(p) for p in pixel.split()]).reshape(48, 48)
sub_folder = os.path.join(save_path, label)
if not os.path.exists(sub_folder):
os.makedirs(sub_folder)
im = Image.fromarray(pixel).convert('L')
image_name = os.path.join(sub_folder, '{:05d}.jpg'.format(i))
print(image_name)
im.save(image_name)
第二部：训练网络，得到分类器模型。
定义Model：深度卷积神经网络的构建和训练。
卷积层conv2D +激活层activation-relu +conv2D + activation-relu +池化层MaxPooling2D +
conv2D + activation-relu + MaxPooling2D +
conv2D + activation-relu + MaxPooling2D +
扁平Flaten + 全连接层Dense + activation-relu +
丢失部分特征Dropout + Dense + activation-relu +Dropout +
softmax:Dense + activation-relu
``` python
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 48, 48, 32) 64
_________________________________________________________________
activation_1 (Activation) (None, 48, 48, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 48, 48, 32) 25632
_________________________________________________________________
activation_2 (Activation) (None, 48, 48, 32) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 24, 24, 32) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 24, 24, 32) 9248
_________________________________________________________________
activation_3 (Activation) (None, 24, 24, 32) 0
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 12, 12, 32) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 12, 12, 64) 51264
_________________________________________________________________
activation_4 (Activation) (None, 12, 12, 64) 0
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 6, 6, 64) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 2304) 0
_________________________________________________________________
dense_1 (Dense) (None, 2048) 4720640
_________________________________________________________________
activation_5 (Activation) (None, 2048) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 2048) 0
_________________________________________________________________
dense_2 (Dense) (None, 1024) 2098176
_________________________________________________________________
activation_6 (Activation) (None, 1024) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 1024) 0
_________________________________________________________________
dense_3 (Dense) (None, 7) 7175
_________________________________________________________________
activation_7 (Activation) (None, 7) 0
=================================================================
Total params: 6,912,199
Trainable params: 6,912,199
Non-trainable params: 0
_________________________________________________________________
model built
Found 28709 images belonging to 7 classes.
保存网络.json和 模型.h5
流程：
train.py
from keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import SGD
batch_siz = 128
num_classes = 7
nb_epoch = 100
img_size = 48
data_path = './data'
model_path = './model'
class Model:
def __init__(self):
self.model = None
def build_model(self):
self.model = Sequential()
self.model.add(Conv2D(32, (1, 1), strides=1, padding='same', input_shape=(img_size, img_size, 1)))
self.model.add(Activation('relu'))
self.model.add(Conv2D(32, (5, 5), padding='same'))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2))) #池化，每个块只留下max
self.model.add(Conv2D(32, (3, 3), padding='same'))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(Conv2D(64, (5, 5), padding='same'))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(Flatten()) # 扁平，折叠成一维的数组
self.model.add(Dense(2048)) # 全连接神经网络层
self.model.add(Activation('relu'))
self.model.add(Dropout(0.5)) # 忽略一半的特征检测器
self.model.add(Dense(1024))
self.model.add(Activation('relu'))
self.model.add(Dropout(0.5))
self.model.add(Dense(num_classes))
self.model.add(Activation('softmax'))
self.model.summary() # 参数输出
def train_model(self):
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) #随机梯度下降的方向训练权重
self.model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
# 自动扩充训练样本
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
# 归一化验证集
val_datagen = ImageDataGenerator(
rescale=1. / 255)
eval_datagen = ImageDataGenerator(
rescale=1. / 255)
# 以文件分类名划分label
train_generator = train_datagen.flow_from_directory(
data_path + '/train',
target_size=(img_size, img_size),
color_mode='grayscale',
batch_size=batch_siz,
class_mode='categorical')
val_generator = val_datagen.flow_from_directory(
data_path + '/val',
target_size=(img_size, img_size),
color_mode='grayscale',
batch_size=batch_siz,
class_mode='categorical')
eval_generator = eval_datagen.flow_from_directory(
data_path + '/test',
target_size=(img_size, img_size),
color_mode='grayscale',
batch_size=batch_siz,
class_mode='categorical')
# early_stopping = EarlyStopping(monitor='loss', patience=3)
history_fit = self.model.fit_generator(
train_generator,
steps_per_epoch=800 / (batch_siz / 32), # 28709
nb_epoch=nb_epoch,
validation_data=val_generator,
validation_steps=2000,
# callbacks=[early_stopping]
)
# history_eval=self.model.evaluate_generator(
# eval_generator,
# steps=2000)
history_predict = self.model.predict_generator(
eval_generator,
steps=2000)
with open(model_path + '/model_fit_log', 'w') as f:
f.write(str(history_fit.history))
with open(model_path + '/model_predict_log', 'w') as f:
f.write(str(history_predict))
# 保存训练的模型文件
def save_model(self):
model_json = self.model.to_json()
with open(model_path + "/model_json.json", "w") as json_file:
json_file.write(model_json)
self.model.save_weights(model_path + '/model_weight.h5')
self.model.save(model_path + '/model.h5')
if __name__ == '__main__':
model = Model()
model.build_model()
print('model built')
model.train_model()
print('model trained')
model.save_model()
print('model saved')
第三步：使用模型，预测表情。
predictFER.py
#!/usr/bin/python
# -*- coding = utf-8 -*-
#author:thy
#date:20191230
#version:1.0
import cv2
import numpy as np
from keras.models import model_from_json
model_path = './model/'
img_size = 48
emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
num_class = len(emotion_labels)
# 从json中加载模型
json_file = open(model_path + 'model_json.json')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# 加载模型权重
model.load_weights(model_path + 'model_weight.h5')
# 创建VideoCapture对象
capture = cv2.VideoCapture(0)
# 使用opencv的人脸分类器
cascade = cv2.CascadeClassifier(model_path + 'haarcascade_frontalface_alt.xml')
while True:
ret, frame = capture.read()
# 灰度化处理
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 呈现用emoji替代后的画面
emoji_show = frame.copy()
# 识别人脸位置
faceLands = cascade.detectMultiScale(gray, scaleFactor=1.1,
minNeighbors=1, minSize=(120, 120))
if len(faceLands) > 0:
for faceLand in faceLands:
x, y, w, h = faceLand
images = []
result = np.array([0.0] * num_class)
# 裁剪出脸部图像
image = cv2.resize(gray[y:y + h, x:x + w], (img_size, img_size))
image = image / 255.0
image = image.reshape(1, img_size, img_size, 1)
# 调用模型预测情绪
predict_lists = model.predict_proba(image, batch_size=32, verbose=1)
# print(predict_lists)
result += np.array([predict for predict_list in predict_lists
for predict in predict_list])
# print(result)
emotion = emotion_labels[int(np.argmax(result))]
print("Emotion:", emotion)
# 框出脸部并且写上标签
cv2.rectangle(frame, (x - 20, y - 20), (x + w + 20, y + h + 20),
(0, 255, 255), thickness=10)
cv2.putText(frame, '%s' % emotion, (x, y - 50),
cv2.FONT_HERSHEY_DUPLEX, 2, (255, 255, 255), 2, 30)
cv2.imshow('Face', frame)
if cv2.waitKey(60) == ord('q'):
break
# 释放摄像头并销毁所有窗口
capture.release()
cv2.destroyAllWindows()
结论：
实现摄像头检测到的人脸的表情标记。
开放源码：https://github.com/beauthy/FER_model2pb
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版权声明：本文为CSDN博主「燕芝沛然」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/weixin_44205550/article/details/103901113
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