caffe表情識別(一)：準備數據fer2013

數據集： Kaggle fer2013(

• 官網下載
• 百度雲下載（提取密碼: 9f05）

下載fer2013之後，解壓出的是csv格式的數據，我們需要先將數據轉換成圖片。

step 1: 從fer2013.csv中提取出訓練集、驗證集和測試集

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平臺中，需要在csv.writer()中加上lineterminator='\n'不然在生存的csv文件中，每行之間會有空行，影響後續操作。在Linux平臺中不需要這樣做。

step 2: 將csv中的數據轉化成圖片

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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生成的數據集目錄結構如下：

參考文獻：

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

第一篇文獻中有網絡結構圖，但根據我做實驗的情況來看，這篇論文水分較大，達不到論文中所說的分類精度。第二篇內容比第一篇詳細很多，很值得參考。

訓練結果：