caffe表情识别(一)：准备数据fer2013

数据集： Kaggle fer2013(

• 官网下载
• 百度云下载（提取密码: 9f05）

下载fer2013之后，解压出的是csv格式的数据，我们需要先将数据转换成图片。

step 1: 从fer2013.csv中提取出训练集、验证集和测试集

convert_fer2013.py:

# -*- coding: utf-8 -*-
import csv
import os

database_path = r'F:\Datasets\fer2013'
datasets_path = r'.\datasets'
csv_file = os.path.join(database_path, 'fer2013.csv')
train_csv = os.path.join(datasets_path, 'train.csv')
val_csv = os.path.join(datasets_path, 'val.csv')
test_csv = os.path.join(datasets_path, 'test.csv')


with open(csv_file) as f:
    csvr = csv.reader(f)
    header = next(csvr)
    rows = [row for row in csvr]

    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))

  
  

   
   
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注意：在Windows平台中，需要在csv.writer()中加上lineterminator='\n'不然在生存的csv文件中，每行之间会有空行，影响后续操作。在Linux平台中不需要这样做。

step 2: 将csv中的数据转化成图片

convert_csv2gray:

# -*- coding: utf-8 -*-
import csv
import os
from PIL import Image
import numpy as np


datasets_path = r'.\datasets'
train_csv = os.path.join(datasets_path, 'train.csv')
val_csv = os.path.join(datasets_path, 'val.csv')
test_csv = os.path.join(datasets_path, 'test.csv')

train_set = os.path.join(datasets_path, 'train')
val_set = os.path.join(datasets_path, 'val')
test_set = os.path.join(datasets_path, 'test')

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:
        csvr = csv.reader(f)
        header = next(csvr)
        for i, (label, pixel) in enumerate(csvr):
            pixel = np.asarray([float(p) for p in pixel.split()]).reshape(48, 48)
            subfolder = os.path.join(save_path, label)
            if not os.path.exists(subfolder):
                os.makedirs(subfolder)
            im = Image.fromarray(pixel).convert('L')
            image_name = os.path.join(subfolder, '{:05d}.jpg'.format(i))
            print(image_name)
            im.save(image_name)

  
  

   
   
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生成的数据集目录结构如下：

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参考文献：

• A Real-time Facial Expression Recongnizer using Deep Neural Network
• Touchy Feely: An Emotion Recognition Challenge
  

第一篇文献中有网络结构图，但根据我做实验的情况来看，这篇论文水分较大，达不到论文中所说的分类精度。第二篇内容比第一篇详细很多，很值得参考。