如何在忘乎所以时察觉对方的情感变化
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如何在忘乎所以时察觉对方的情感变化
本项目用于对表情进行识别，可以利用表情来察觉情感。
一、项目背景
在学习了微表情心理学后，认识到读懂表情对认识一个人的情感变化的意义，从而诞生了做这一项目的想法。
载入所需库
import paddle
import numpy as np
import cv2
from paddle.vision.models import resnet50
from paddle.vision.datasets import DatasetFolder
import matplotlib.pylab as plt
import os
定义参数
train_file='train'
valid_file='valid'
test_file='test'
imagesize=32
batch_size=32
lr=1e-5
二、数据集介绍
本项目用fer2013数据集，事先数据集已完成对训练集和验证集的切分，同时已将不同表情放于不同文件夹中。具体表情对应的标签和中英文如下：0 anger 生气； 1 disgust 厌恶； 2 fear 恐惧； 3 happy 开心； 4 sad 伤心；5 surprised 惊讶； 6 normal 中性。
1.解压数据集
!unzip -oq /home/aistudio/work/image/fer2013.zip
2.对数据加载进行预处理
# 定义数据预处理
def load_image(img_path):
img = cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB)
#resize
img = cv2.resize(img,(imagesize,imagesize))
img = np.array(img).astype('float32')
# HWC to CHW
img = img.transpose((2,0,1))
#Normalize
img = img / 255
return img
# 构建Dataset
class Face(DatasetFolder):
def __init__(self, path):
super().__init__(path)
def __getitem__(self, index):
img_path, label = self.samples[index]
label = np.array(label).astype(np.int64)
return load_image(img_path), label
train_dataset = Face(train_file)
eval_dataset = Face(valid_file)
3.对数据集查看
plt.figure(figsize=(15, 15))
for i in range(5):
fundus_img, lab = train_dataset.__getitem__(i)
plt.subplot(2, 5, i+1)
plt.imshow(fundus_img.transpose(1, 2, 0))
plt.axis("off")
print(lab)
三、模型选择和开发
选择了resnet50,又用了两个全连接层，然后输出。
1.模型组网
class Network(paddle.nn.Layer):
def __init__(self):
super(Network, self).__init__()
self.resnet = resnet50(pretrained=True, num_classes=0)
self.flatten = paddle.nn.Flatten()
self.linear_1 = paddle.nn.Linear(2048, 512)
self.linear_2 = paddle.nn.Linear(512, 256)
self.linear_3 = paddle.nn.Linear(256, 8)
self.relu = paddle.nn.ReLU()
self.dropout = paddle.nn.Dropout(0.2)
def forward(self, inputs):
# print('input', inputs)
y = self.resnet(inputs)
y = self.flatten(y)
y = self.linear_1(y)
y = self.linear_2(y)
y = self.relu(y)
y = self.dropout(y)
y = self.linear_3(y)
y = paddle.nn.functional.sigmoid(y)
return y
2.实例化模型和模型可视化
inputs = paddle.static.InputSpec(shape=[None, 3, 32, 32], name='inputs')
labels = paddle.static.InputSpec(shape=[None, 2], name='labels')
model = paddle.Model(Network(), inputs, labels)
paddle.summary(Network(), (1, 3, 32, 32))
-------------------------------------------------------------------------------
Layer (type) Input Shape Output Shape Param #
===============================================================================
Conv2D-54 [[1, 3, 32, 32]] [1, 64, 16, 16] 9,408
BatchNorm2D-54 [[1, 64, 16, 16]] [1, 64, 16, 16] 256
ReLU-19 [[1, 64, 16, 16]] [1, 64, 16, 16] 0
MaxPool2D-2 [[1, 64, 16, 16]] [1, 64, 8, 8] 0
Conv2D-56 [[1, 64, 8, 8]] [1, 64, 8, 8] 4,096
BatchNorm2D-56 [[1, 64, 8, 8]] [1, 64, 8, 8] 256
ReLU-20 [[1, 256, 8, 8]] [1, 256, 8, 8] 0
Conv2D-57 [[1, 64, 8, 8]] [1, 64, 8, 8] 36,864
BatchNorm2D-57 [[1, 64, 8, 8]] [1, 64, 8, 8] 256
Conv2D-58 [[1, 64, 8, 8]] [1, 256, 8, 8] 16,384
BatchNorm2D-58 [[1, 256, 8, 8]] [1, 256, 8, 8] 1,024
Conv2D-55 [[1, 64, 8, 8]] [1, 256, 8, 8] 16,384
BatchNorm2D-55 [[1, 256, 8, 8]] [1, 256, 8, 8] 1,024
BottleneckBlock-17 [[1, 64, 8, 8]] [1, 256, 8, 8] 0
Conv2D-59 [[1, 256, 8, 8]] [1, 64, 8, 8] 16,384
BatchNorm2D-59 [[1, 64, 8, 8]] [1, 64, 8, 8] 256
ReLU-21 [[1, 256, 8, 8]] [1, 256, 8, 8] 0
Conv2D-60 [[1, 64, 8, 8]] [1, 64, 8, 8] 36,864
BatchNorm2D-60 [[1, 64, 8, 8]] [1, 64, 8, 8] 256
Conv2D-61 [[1, 64, 8, 8]] [1, 256, 8, 8] 16,384
BatchNorm2D-61 [[1, 256, 8, 8]] [1, 256, 8, 8] 1,024
BottleneckBlock-18 [[1, 256, 8, 8]] [1, 256, 8, 8] 0
Conv2D-62 [[1, 256, 8, 8]] [1, 64, 8, 8] 16,384
BatchNorm2D-62 [[1, 64, 8, 8]] [1, 64, 8, 8] 256
ReLU-22 [[1, 256, 8, 8]] [1, 256, 8, 8] 0
Conv2D-63 [[1, 64, 8, 8]] [1, 64, 8, 8] 36,864
BatchNorm2D-63 [[1, 64, 8, 8]] [1, 64, 8, 8] 256
Conv2D-64 [[1, 64, 8, 8]] [1, 256, 8, 8] 16,384
BatchNorm2D-64 [[1, 256, 8, 8]] [1, 256, 8, 8] 1,024
BottleneckBlock-19 [[1, 256, 8, 8]] [1, 256, 8, 8] 0
Conv2D-66 [[1, 256, 8, 8]] [1, 128, 8, 8] 32,768
BatchNorm2D-66 [[1, 128, 8, 8]] [1, 128, 8, 8] 512
ReLU-23 [[1, 512, 4, 4]] [1, 512, 4, 4] 0
Conv2D-67 [[1, 128, 8, 8]] [1, 128, 4, 4] 147,456
BatchNorm2D-67 [[1, 128, 4, 4]] [1, 128, 4, 4] 512
Conv2D-68 [[1, 128, 4, 4]] [1, 512, 4, 4] 65,536
BatchNorm2D-68 [[1, 512, 4, 4]] [1, 512, 4, 4] 2,048
Conv2D-65 [[1, 256, 8, 8]] [1, 512, 4, 4] 131,072
BatchNorm2D-65 [[1, 512, 4, 4]] [1, 512, 4, 4] 2,048
BottleneckBlock-20 [[1, 256, 8, 8]] [1, 512, 4, 4] 0
Conv2D-69 [[1, 512, 4, 4]] [1, 128, 4, 4] 65,536
BatchNorm2D-69 [[1, 128, 4, 4]] [1, 128, 4, 4] 512
ReLU-24 [[1, 512, 4, 4]] [1, 512, 4, 4] 0
Conv2D-70 [[1, 128, 4, 4]] [1, 128, 4, 4] 147,456
BatchNorm2D-70 [[1, 128, 4, 4]] [1, 128, 4, 4] 512
Conv2D-71 [[1, 128, 4, 4]] [1, 512, 4, 4] 65,536
BatchNorm2D-71 [[1, 512, 4, 4]] [1, 512, 4, 4] 2,048
BottleneckBlock-21 [[1, 512, 4, 4]] [1, 512, 4, 4] 0
Conv2D-72 [[1, 512, 4, 4]] [1, 128, 4, 4] 65,536
BatchNorm2D-72 [[1, 128, 4, 4]] [1, 128, 4, 4] 512
ReLU-25 [[1, 512, 4, 4]] [1, 512, 4, 4] 0
Conv2D-73 [[1, 128, 4, 4]] [1, 128, 4, 4] 147,456
BatchNorm2D-73 [[1, 128, 4, 4]] [1, 128, 4, 4] 512
Conv2D-74 [[1, 128, 4, 4]] [1, 512, 4, 4] 65,536
BatchNorm2D-74 [[1, 512, 4, 4]] [1, 512, 4, 4] 2,048
BottleneckBlock-22 [[1, 512, 4, 4]] [1, 512, 4, 4] 0
Conv2D-75 [[1, 512, 4, 4]] [1, 128, 4, 4] 65,536
BatchNorm2D-75 [[1, 128, 4, 4]] [1, 128, 4, 4] 512
ReLU-26 [[1, 512, 4, 4]] [1, 512, 4, 4] 0
Conv2D-76 [[1, 128, 4, 4]] [1, 128, 4, 4] 147,456
BatchNorm2D-76 [[1, 128, 4, 4]] [1, 128, 4, 4] 512
Conv2D-77 [[1, 128, 4, 4]] [1, 512, 4, 4] 65,536
BatchNorm2D-77 [[1, 512, 4, 4]] [1, 512, 4, 4] 2,048
BottleneckBlock-23 [[1, 512, 4, 4]] [1, 512, 4, 4] 0
Conv2D-79 [[1, 512, 4, 4]] [1, 256, 4, 4] 131,072
BatchNorm2D-79 [[1, 256, 4, 4]] [1, 256, 4, 4] 1,024
ReLU-27 [[1, 1024, 2, 2]] [1, 1024, 2, 2] 0
Conv2D-80 [[1, 256, 4, 4]] [1, 256, 2, 2] 589,824
BatchNorm2D-80 [[1, 256, 2, 2]] [1, 256, 2, 2] 1,024
Conv2D-81 [[1, 256, 2, 2]] [1, 1024, 2, 2] 262,144
BatchNorm2D-81 [[1, 1024, 2, 2]] [1, 1024, 2, 2] 4,096
Conv2D-78 [[1, 512, 4, 4]] [1, 1024, 2, 2] 524,288
BatchNorm2D-78 [[1, 1024, 2, 2]] [1, 1024, 2, 2] 4,096
BottleneckBlock-24 [[1, 512, 4, 4]] [1, 1024, 2, 2] 0
Conv2D-82 [[1, 1024, 2, 2]] [1, 256, 2, 2] 262,144
BatchNorm2D-82 [[1, 256, 2, 2]] [1, 256, 2, 2] 1,024
ReLU-28 [[1, 1024, 2, 2]] [1, 1024, 2, 2] 0
Conv2D-83 [[1, 256, 2, 2]] [1, 256, 2, 2] 589,824
BatchNorm2D-83 [[1, 256, 2, 2]] [1, 256, 2, 2] 1,024
Conv2D-84 [[1, 256, 2, 2]] [1, 1024, 2, 2] 262,144
BatchNorm2D-84 [[1, 1024, 2, 2]] [1, 1024, 2, 2] 4,096
BottleneckBlock-25 [[1, 1024, 2, 2]] [1, 1024, 2, 2] 0
Conv2D-85 [[1, 1024, 2, 2]] [1, 256, 2, 2] 262,144
BatchNorm2D-85 [[1, 256, 2, 2]] [1, 256, 2, 2] 1,024
ReLU-29 [[1, 1024, 2, 2]] [1, 1024, 2, 2] 0
Conv2D-86 [[1, 256, 2, 2]] [1, 256, 2, 2] 589,824
BatchNorm2D-86 [[1, 256, 2, 2]] [1, 256, 2, 2] 1,024
Conv2D-87 [[1, 256, 2, 2]] [1, 1024, 2, 2] 262,144
BatchNorm2D-87 [[1, 1024, 2, 2]] [1, 1024, 2, 2] 4,096
BottleneckBlock-26 [[1, 1024, 2, 2]] [1, 1024, 2, 2] 0
Conv2D-88 [[1, 1024, 2, 2]] [1, 256, 2, 2] 262,144
BatchNorm2D-88 [[1, 256, 2, 2]] [1, 256, 2, 2] 1,024
ReLU-30 [[1, 1024, 2, 2]] [1, 1024, 2, 2] 0
Conv2D-89 [[1, 256, 2, 2]] [1, 256, 2, 2] 589,824
BatchNorm2D-89 [[1, 256, 2, 2]] [1, 256, 2, 2] 1,024
Conv2D-90 [[1, 256, 2, 2]] [1, 1024, 2, 2] 262,144
BatchNorm2D-90 [[1, 1024, 2, 2]] [1, 1024, 2, 2] 4,096
BottleneckBlock-27 [[1, 1024, 2, 2]] [1, 1024, 2, 2] 0
Conv2D-91 [[1, 1024, 2, 2]] [1, 256, 2, 2] 262,144
BatchNorm2D-91 [[1, 256, 2, 2]] [1, 256, 2, 2] 1,024
ReLU-31 [[1, 1024, 2, 2]] [1, 1024, 2, 2] 0
Conv2D-92 [[1, 256, 2, 2]] [1, 256, 2, 2] 589,824
BatchNorm2D-92 [[1, 256, 2, 2]] [1, 256, 2, 2] 1,024
Conv2D-93 [[1, 256, 2, 2]] [1, 1024, 2, 2] 262,144
BatchNorm2D-93 [[1, 1024, 2, 2]] [1, 1024, 2, 2] 4,096
BottleneckBlock-28 [[1, 1024, 2, 2]] [1, 1024, 2, 2] 0
Conv2D-94 [[1, 1024, 2, 2]] [1, 256, 2, 2] 262,144
BatchNorm2D-94 [[1, 256, 2, 2]] [1, 256, 2, 2] 1,024
ReLU-32 [[1, 1024, 2, 2]] [1, 1024, 2, 2] 0
Conv2D-95 [[1, 256, 2, 2]] [1, 256, 2, 2] 589,824
BatchNorm2D-95 [[1, 256, 2, 2]] [1, 256, 2, 2] 1,024
Conv2D-96 [[1, 256, 2, 2]] [1, 1024, 2, 2] 262,144
BatchNorm2D-96 [[1, 1024, 2, 2]] [1, 1024, 2, 2] 4,096
BottleneckBlock-29 [[1, 1024, 2, 2]] [1, 1024, 2, 2] 0
Conv2D-98 [[1, 1024, 2, 2]] [1, 512, 2, 2] 524,288
BatchNorm2D-98 [[1, 512, 2, 2]] [1, 512, 2, 2] 2,048
ReLU-33 [[1, 2048, 1, 1]] [1, 2048, 1, 1] 0
Conv2D-99 [[1, 512, 2, 2]] [1, 512, 1, 1] 2,359,296
BatchNorm2D-99 [[1, 512, 1, 1]] [1, 512, 1, 1] 2,048
Conv2D-100 [[1, 512, 1, 1]] [1, 2048, 1, 1] 1,048,576
BatchNorm2D-100 [[1, 2048, 1, 1]] [1, 2048, 1, 1] 8,192
Conv2D-97 [[1, 1024, 2, 2]] [1, 2048, 1, 1] 2,097,152
BatchNorm2D-97 [[1, 2048, 1, 1]] [1, 2048, 1, 1] 8,192
BottleneckBlock-30 [[1, 1024, 2, 2]] [1, 2048, 1, 1] 0
Conv2D-101 [[1, 2048, 1, 1]] [1, 512, 1, 1] 1,048,576
BatchNorm2D-101 [[1, 512, 1, 1]] [1, 512, 1, 1] 2,048
ReLU-34 [[1, 2048, 1, 1]] [1, 2048, 1, 1] 0
Conv2D-102 [[1, 512, 1, 1]] [1, 512, 1, 1] 2,359,296
BatchNorm2D-102 [[1, 512, 1, 1]] [1, 512, 1, 1] 2,048
Conv2D-103 [[1, 512, 1, 1]] [1, 2048, 1, 1] 1,048,576
BatchNorm2D-103 [[1, 2048, 1, 1]] [1, 2048, 1, 1] 8,192
BottleneckBlock-31 [[1, 2048, 1, 1]] [1, 2048, 1, 1] 0
Conv2D-104 [[1, 2048, 1, 1]] [1, 512, 1, 1] 1,048,576
BatchNorm2D-104 [[1, 512, 1, 1]] [1, 512, 1, 1] 2,048
ReLU-35 [[1, 2048, 1, 1]] [1, 2048, 1, 1] 0
Conv2D-105 [[1, 512, 1, 1]] [1, 512, 1, 1] 2,359,296
BatchNorm2D-105 [[1, 512, 1, 1]] [1, 512, 1, 1] 2,048
Conv2D-106 [[1, 512, 1, 1]] [1, 2048, 1, 1] 1,048,576
BatchNorm2D-106 [[1, 2048, 1, 1]] [1, 2048, 1, 1] 8,192
BottleneckBlock-32 [[1, 2048, 1, 1]] [1, 2048, 1, 1] 0
AdaptiveAvgPool2D-2 [[1, 2048, 1, 1]] [1, 2048, 1, 1] 0
ResNet-2 [[1, 3, 32, 32]] [1, 2048, 1, 1] 0
Flatten-2 [[1, 2048, 1, 1]] [1, 2048] 0
Linear-4 [[1, 2048]] [1, 512] 1,049,088
Linear-5 [[1, 512]] [1, 256] 131,328
ReLU-36 [[1, 256]] [1, 256] 0
Dropout-2 [[1, 256]] [1, 256] 0
Linear-6 [[1, 256]] [1, 8] 2,056
===============================================================================
Total params: 24,743,624
Trainable params: 24,637,384
Non-trainable params: 106,240
-------------------------------------------------------------------------------
Input size (MB): 0.01
Forward/backward pass size (MB): 5.39
Params size (MB): 94.39
Estimated Total Size (MB): 99.79
-------------------------------------------------------------------------------
{'total_params': 24743624, 'trainable_params': 24637384}
3.模型训练
# 模型训练相关配置，准备损失计算方法，优化器和精度计算方法
model.prepare(paddle.optimizer.Adam(learning_rate=lr, parameters=model.parameters()),
paddle.nn.CrossEntropyLoss(),
paddle.metric.Accuracy())
# 模型训练
model.fit(train_data=train_dataset, #训练数据集
eval_data=eval_dataset, #测试数据集
batch_size=batch_size, #一个批次的样本数量
epochs=27, #迭代轮次
save_dir="/home/aistudio/lup", #把模型参数、优化器参数保存至自定义的文件夹
save_freq=3, #设定每隔多少个epoch保存模型参数及优化器参数
verbose=1
)
step 898/898 [==============================] - loss: 1.8075 - acc: 0.2449 - 53ms/step
save checkpoint at /home/aistudio/lup/0
Eval begin...
step 113/113 [==============================] - loss: 1.7850 - acc: 0.2530 - 23ms/step
Eval samples: 3589
4.模型评估测试
model.evaluate(eval_dataset, batch_size=5, verbose=1)
Eval begin...
step 718/718 [==============================] - loss: 1.9499 - acc: 0.4870 - 22ms/step
Eval samples: 3589
{'loss': [1.9498544], 'acc': 0.48704374477570356}
四、预测
1.对预测数据处理
def load_test(img_path):
img = cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB)
#resize
img = cv2.resize(img,(imagesize,imagesize))
img = np.array(img).astype('float32')
# HWC to CHW
img = img.transpose((2,0,1))
img = np.expand_dims(img, axis=0)
#Normalize
img = img / 255
return img
test_dataset=[]
for i in os.listdir(test_file):
test_dataset.append(load_test(test_file+'//'+i))
2.对模型预测
# 进行预测操作
result = model.predict(test_dataset)
# 定义产出数字与表情的对应关系
face={0:'anger',1:'disgust',2:'fear',3:'happy',4:'sad',5:'surprised',6:'normal'}
# 定义画图方法
def show_img(img, predict):
plt.figure()
plt.title('predict: {}'.format(face[predict]))
plt.imshow(img.reshape([3, 32, 32]).transpose(1,2,0))
plt.show()
# 抽样展示
indexs = [4, 15, 45,]
for idx in indexs:
show_img(test_dataset[idx][0], np.argmax(result[0][idx]))
Predict begin...
step 3589/3589 [==============================] - 21ms/step
Predict samples: 3589
五、效果展示
五、总结与升华
1.在图像分类中输出数要大于所分图像类数。
2.对于维度缺失问题可以用img = np.expand_dims(img, axis=0)增加维度。
六、个人介绍
太原理工大学 软件学院 软件工程专业 2020级 本科生 王志洲
AIstudio地址链接：https://aistudio.baidu.com/aistudio/personalcenter/thirdview/559770
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