人脸表情预测

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可爱又积极 发表于 2021/07/18 09:55:38 2021/07/18
【摘要】 进入ModelArts点击如下链接:https://www.huaweicloud.com/product/modelarts.html , 进入ModelArts主页。点击“立即使用”按钮,输入用户名和密码登录,进入ModelArts使用页面。创建ModelArts notebook下面,我们在ModelArts中创建一个notebook开发环境,ModelArts notebook提供网...

进入ModelArts

点击如下链接:https://www.huaweicloud.com/product/modelarts.html , 进入ModelArts主页。点击“立即使用”按钮,输入用户名和密码登录,进入ModelArts使用页面。

创建ModelArts notebook

下面,我们在ModelArts中创建一个notebook开发环境,ModelArts notebook提供网页版的Python开发环境,可以方便的编写、运行代码,并查看运行结果。

第一步:在ModelArts服务主界面依次点击“开发环境”、“创建”

create_nb_create_button

第二步:填写notebook所需的参数:

jupyter

第三步:配置好notebook参数后,点击下一步,进入notebook信息预览。确认无误后,点击“立即创建”jupyter

第四步:创建完成后,返回开发环境主界面,等待Notebook创建完毕后,打开Notebook,进行下一步操作。modelarts_notebook_index

在ModelArts中创建开发环境

接下来,我们创建一个实际的开发环境,用于后续的实验步骤。

第一步:点击下图所示的“启动”按钮,加载后“打开”按钮变从灰色变为蓝色后点击“打开”进入刚刚创建的Notebookjupyterjupyter

第二步:创建一个Python3环境的的Notebook。点击右上角的"New",然后选择TensorFlow 1.13.1开发环境。

第三步:点击左上方的文件名"Untitled",并输入一个与本实验相关的名称,如"facial_expression"notebook_untitled_filenamenotebook_name_the_ipynb

在Notebook中编写并执行代码

在Notebook中,我们输入一个简单的打印语句,然后点击上方的运行按钮,可以查看语句执行的结果:run_helloworld

开发环境准备好啦,接下来可以愉快地写代码啦!

数据和代码下载

运行下面代码,进行数据和代码的下载和解压

In [1]:
import os
from modelarts.session import Session
sess = Session()

if sess.region_name == 'cn-north-1':
    bucket_path="modelarts-labs/notebook/DL_face_facial_expression/facial_expression.tar.gz"
elif sess.region_name == 'cn-north-4':
    bucket_path="modelarts-labs-bj4/notebook/DL_face_facial_expression/facial_expression.tar.gz"
else:
    print("请更换地区到北京一或北京四")

if not os.path.exists('./fer2013'):    
    sess.download_data(bucket_path=bucket_path, path="./facial_expression.tar.gz")

Successfully download file modelarts-labs-bj4/notebook/DL_face_facial_expression/facial_expression.tar.gz from OBS to local ./facial_expression.tar.gz

解压文件

In [2]:
if os.path.exists('./facial_expression.tar.gz'):
    # 使用tar命令解压资源包
    os.system("tar -xf ./facial_expression.tar.gz")
    # 清理压缩包
    os.system("rm ./facial_expression.tar.gz")

安装依赖

安装mtcnn工具库。安装后点击Kernel->Restart

In [3]:
!pip install mtcnn==0.0.8
!pip install numpy==1.16.2

Collecting mtcnn==0.0.8
  Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/0b/f5/d62ac2bdf1c683b7650268305db3126323a7b6a2f6390273038285fa9e3f/mtcnn-0.0.8.tar.gz (2.3MB)
    100% |████████████████████████████████| 2.3MB 44.2MB/s ta 0:00:01
Building wheels for collected packages: mtcnn
  Running setup.py bdist_wheel for mtcnn ... done
  Stored in directory: /home/ma-user/.cache/pip/wheels/5b/79/11/d14d6cffd223ad2ec9848799f86adc06c4973367bd9aa4fd61
Successfully built mtcnn
Installing collected packages: mtcnn
Successfully installed mtcnn-0.0.8
You are using pip version 9.0.1, however version 20.2.4 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
Collecting numpy==1.16.2
  Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/35/d5/4f8410ac303e690144f0a0603c4b8fd3b986feb2749c435f7cdbb288f17e/numpy-1.16.2-cp36-cp36m-manylinux1_x86_64.whl (17.3MB)
    100% |████████████████████████████████| 17.3MB 117.8MB/s ta 0:00:01
Installing collected packages: numpy
  Found existing installation: numpy 1.19.1
    Uninstalling numpy-1.19.1:
      Successfully uninstalled numpy-1.19.1
Successfully installed numpy-1.16.2
You are using pip version 9.0.1, however version 20.2.4 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.

引用

In [1]:
import numpy as np

import keras
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
from keras.layers import Dense, Activation, Dropout, Flatten
from keras.preprocessing.image import ImageDataGenerator

Using TensorFlow backend.

参数设置

定义表情的类别。

In [2]:
emotions = ('angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral')
In [3]:
num_classes = 7 # 类别数
batch_size = 16 # 批大小
epochs = 5 # 训练轮数

数据处理

读取表情识别数据集,文本文件的每行是一个人脸图片的向量。

In [4]:
with open("./fer2013/fer2013.csv") as f:
    content = f.readlines()
lines = np.array(content)

查看一个样本数据

In [5]:
emotion_1, img_1, usage_1 = lines[1].split(",")
In [6]:
val_1 = img_1.split(" ")
pixels_1 = np.array(val_1, 'float32')
print(emotion_1)
print(usage_1)
print(pixels_1.shape)

0
Training

(2304,)

打印整个数据集的样本数

In [7]:
num_of_instances = lines.size
num_of_instances
Out[7]:
35888

切分训练集和测试集

In [8]:
x_train, y_train, x_test, y_test = [], [], [], []

for i in range(1,num_of_instances):
    try:
        emotion, img, usage = lines[i].split(",")
        val = img.split(" ")
        pixels = np.array(val, 'float32')
        emotion = keras.utils.to_categorical(emotion, num_classes)
        if 'Training' in usage:
            y_train.append(emotion)
            x_train.append(pixels)
        elif 'PublicTest' in usage:
            y_test.append(emotion)
            x_test.append(pixels)
    except:
        print("",end="")

数据归一化,并对图像缩放

In [9]:
x_train = np.array(x_train, 'float32')
y_train = np.array(y_train, 'float32')
x_test = np.array(x_test, 'float32')
y_test = np.array(y_test, 'float32')

x_train /= 255 
x_test /= 255

x_train = x_train.reshape(x_train.shape[0], 48, 48, 1)
x_train = x_train.astype('float32')
x_test = x_test.reshape(x_test.shape[0], 48, 48, 1)
x_test = x_test.astype('float32')

print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

28709 train samples
3589 test samples

获取数据生成器

In [10]:
gen = ImageDataGenerator()
train_generator = gen.flow(x_train, y_train, batch_size=batch_size)

模型构建

使用Keras,自己手工搭建一个图像分类神经网络,用于表情分类。

In [11]:
def build_model():
    model = Sequential()

    #1st convolution layer
    model.add(Conv2D(64, (5, 5), activation='relu', input_shape=(48,48,1)))
    model.add(MaxPooling2D(pool_size=(5,5), strides=(2, 2)))

    #2nd convolution layer
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(AveragePooling2D(pool_size=(3,3), strides=(2, 2)))

    #3rd convolution layer
    model.add(Conv2D(128, (3, 3), activation='relu'))
    model.add(Conv2D(128, (3, 3), activation='relu'))
    model.add(AveragePooling2D(pool_size=(3,3), strides=(2, 2)))

    model.add(Flatten())

    #fully connected neural networks
    model.add(Dense(1024, activation='relu'))
    model.add(Dropout(0.2))
    model.add(Dense(1024, activation='relu'))
    model.add(Dropout(0.2))

    model.add(Dense(num_classes, activation='softmax'))
    
    return model
In [12]:
from keras.models import model_from_json

model = build_model()
model.compile(loss='categorical_crossentropy', optimizer=keras.optimizers.Adam(), metrics=['accuracy'])
model.load_weights('./model/facial_expression_model_weights.h5') # 加载预训练权重

WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.

查看模型结构

In [13]:
model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 44, 44, 64)        1664      
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 20, 20, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 18, 18, 64)        36928     
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 16, 16, 64)        36928     
_________________________________________________________________
average_pooling2d_1 (Average (None, 7, 7, 64)          0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 5, 5, 128)         73856     
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 3, 3, 128)         147584    
_________________________________________________________________
average_pooling2d_2 (Average (None, 1, 1, 128)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 128)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 1024)              132096    
_________________________________________________________________
dropout_1 (Dropout)          (None, 1024)              0         
_________________________________________________________________
dense_2 (Dense)              (None, 1024)              1049600   
_________________________________________________________________
dropout_2 (Dropout)          (None, 1024)              0         
_________________________________________________________________
dense_3 (Dense)              (None, 7)                 7175      
=================================================================
Total params: 1,485,831
Trainable params: 1,485,831
Non-trainable params: 0
_________________________________________________________________

进行训练

In [14]:
model.fit_generator(train_generator, steps_per_epoch=batch_size, epochs=epochs) 

WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
Epoch 1/5
16/16 [==============================] - 7s 432ms/step - loss: 0.5990 - acc: 0.7852
Epoch 2/5
16/16 [==============================] - 0s 5ms/step - loss: 0.6849 - acc: 0.7773
Epoch 3/5
16/16 [==============================] - 0s 5ms/step - loss: 0.6426 - acc: 0.7852
Epoch 4/5
16/16 [==============================] - 0s 5ms/step - loss: 0.5970 - acc: 0.7969
Epoch 5/5
16/16 [==============================] - 0s 5ms/step - loss: 0.7880 - acc: 0.7344
Out[14]:
<keras.callbacks.History at 0x7fc390105f28>

推理

打开一张测试图片

In [15]:
import cv2
img = cv2.cvtColor( cv2.imread("./test.jpg"), cv2.COLOR_BGR2RGB)

from PIL import Image
Image.fromarray(img)
Out[15]:

使用mtcnn算法检测出人脸区域

In [16]:
from mtcnn.mtcnn import MTCNN
detector = MTCNN()
result = detector.detect_faces(img)
result

WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/mtcnn/layer_factory.py:211: calling reduce_max_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/mtcnn/layer_factory.py:213: calling reduce_sum_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/mtcnn/layer_factory.py:214: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Deprecated in favor of operator or tf.math.divide.
Out[16]:
[{'box': [66, 68, 95, 123],
  'confidence': 0.9999872446060181,
  'keypoints': {'left_eye': (101, 111),
   'mouth_left': (98, 156),
   'mouth_right': (144, 156),
   'nose': (128, 137),
   'right_eye': (142, 111)}}]

打印检测出的人脸区域

In [17]:
x,y,w,h = result[0]["box"]
detected_face = img[int(y):int(y+h), int(x):int(x+w)]
detected_face = cv2.cvtColor(detected_face, cv2.COLOR_BGR2GRAY)
detected_face = cv2.resize(detected_face, (48, 48)) 
Image.fromarray(detected_face)
Out[17]:

使用表情分类模型预测出人脸的表情

In [18]:
from keras.preprocessing import image
import numpy as np
img_pixels = image.img_to_array(detected_face)
img_pixels = np.expand_dims(img_pixels, axis = 0)

img_pixels /= 255

predictions = model.predict(img_pixels) 
max_index = np.argmax(predictions[0])
result = emotions[max_index]
result
Out[18]:
'happy'
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