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text_generation.py
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text_generation.py
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import os
import pandas as pd
import random
import csv
from data_generation import *
# from MMI.dataset import summary
# CK+ dataset
# 0 = neutral, 1 = anger, 2 = contempt, 3 = disgust, 4 = fear, 5 = happy, 6 = sadness, 7 = surprise
def textParam(dataframe):
# dfEmotion = pd.read_csv('Emotion.csv')
# dfIntensity = pd.read_csv('Intensity.csv')
# dfActionUnit = pd.read_csv('ActionUnit.csv')
dfEmotion = pd.read_csv('Emotion_mmi.csv')
dfIntensity = pd.read_csv('Intensity.csv')
dfActionUnit = pd.read_csv('ActionUnit_mmi.csv')
# gender
if int((set(dataframe['Gender'])).pop()) == 0:
Gender = 'man'
elif int((set(dataframe['Gender'])).pop()) == 1:
Gender = 'woman'
# emotion
numEmotion = int((set(dataframe['Emotion'])).pop())
listEmotion = list(dfEmotion[str(numEmotion)])
listEmotion = [Emo for Emo in listEmotion if str(Emo) != 'nan'] # remove nan
Emotion = random.choice(listEmotion)
# action unit with intensity
Intensity = []
AUnoun = []
AUadj = []
AUI = dataframe[['ActionUnit', 'Intensity']]
numau = [] ### for constraint
for i in range(len(AUI)):
aui_comb = dict(AUI.iloc[i])
AUAU = aui_comb['ActionUnit']
if not str(AUAU)[-1] == '0':
numActionUnit = aui_comb['ActionUnit']
else:
numActionUnit = int(aui_comb['ActionUnit'])
numIntensity = int(round(aui_comb['Intensity']))
if numIntensity < 0:
numIntensity = 0
numau.append(numActionUnit) ### for constraint
# action unit with noun and adjective(2 types)
AUnoun.append(dfActionUnit.iloc[2][str(numActionUnit)])
listAUadj = list(dfActionUnit.iloc[-2:][str(numActionUnit)])
if type(listAUadj[1]) == float:
AUadj.append(listAUadj[0])
else:
randAUadj = random.choice(listAUadj)
AUadj.append(randAUadj)
# intensity
if numIntensity == 0:
randIntensity = None
Intensity.append(randIntensity)
else:
listIntensity = list(dfIntensity[str(numIntensity)])
listIntensity = [Int for Int in listIntensity if str(Int) != 'nan'] # remove nan
randIntensity = random.choice(listIntensity)
Intensity.append(randIntensity)
return Gender, Emotion, Intensity, AUnoun, AUadj, numau
def generateText2(dataframe, numtext, orderS=False, orderG=False, rand=False):
'''
:param param: [Gender, Emotion, Intensity, AUnoun, AUadj]
:return: number of texts
'''
tlist = []
for t in range(numtext):
if rand == True:
pass
else:
Gender, Emotion, Intensity, AUnoun, AUadj, numau = textParam(dataframe)
if not orderS:
# change order of AU
shuffleAU = list(zip(Intensity, AUnoun, AUadj))
random.shuffle(shuffleAU)
Intensity, AUnoun, AUadj = zip(*shuffleAU)
Emotion = Emotion.lower()
if Gender == 'man':
pronoun = 'he'
pronoun2 = 'his'
else:
pronoun = 'she'
pronoun2 = 'her'
# text generation
txt = 'A ' + Gender + ' is ' + Emotion + ' because '
if not orderG:
for i in range(len(AUnoun)):
if random.random() > 0.5:
# if there is unknown intensity
if not Intensity[i]:
if len(AUnoun) == 1:
txt2 = pronoun + ' has ' + AUadj[i] + ' ' + AUnoun[i] + '.'
elif i == len(AUnoun) - 1 and len(AUnoun) != 1:
txt2 = 'and ' + pronoun + ' has ' + AUadj[i] + ' ' + AUnoun[i] + '.'
else:
txt2 = pronoun + ' has' + AUadj[i] + ' ' + AUnoun[i] + ', '
else:
if len(AUnoun) == 1:
txt2 = pronoun + ' has ' + Intensity[i] + ' ' + AUadj[i] + ' ' + AUnoun[i] + '.'
elif i == len(AUnoun) - 1 and len(AUnoun) != 1:
txt2 = 'and ' + pronoun + ' has ' + Intensity[i] + ' ' + AUadj[i] + ' ' + AUnoun[i] + '.'
else:
txt2 = pronoun + ' has ' + Intensity[i] + ' ' + AUadj[i] + ' ' + AUnoun[i] + ', '
txt = txt + txt2
else:
# if there is unknown intensity
if AUnoun[i][-1] == 's':
beverb = ' are '
else:
beverb = ' is '
if not Intensity[i]:
if len(AUnoun) == 1:
txt2 = pronoun2 + ' ' + AUnoun[i] + beverb + AUadj[i] + '.'
elif i == len(AUnoun) - 1 and len(AUnoun) != 1:
txt2 = 'and ' + pronoun2 + ' ' + AUnoun[i] + beverb + AUadj[i] + '.'
else:
txt2 = pronoun2 + ' ' + AUnoun[i] + beverb + AUadj[i] + ', '
else:
if random.random() > 0.5 or Intensity[i] == 'somewhat':
if len(AUnoun) == 1:
txt2 = pronoun2 + ' ' + AUnoun[i] + beverb + Intensity[i] + ' ' + AUadj[i] + '.'
elif i == len(AUnoun) - 1 and len(AUnoun) != 1:
txt2 = 'and ' + pronoun2 + ' ' + AUnoun[i] + beverb + Intensity[i] + ' ' + AUadj[i] + '.'
else:
txt2 = pronoun2 + ' ' + AUnoun[i] + beverb + Intensity[i] + ' ' + AUadj[i] + ', '
else:
if len(AUnoun) == 1:
txt2 = pronoun2 + ' ' + AUnoun[i] + beverb + AUadj[i] + ' ' + Intensity[i] + '.'
elif i == len(AUnoun) - 1 and len(AUnoun) != 1:
txt2 = 'and ' + pronoun2 + ' ' + AUnoun[i] + beverb + AUadj[i] + ' ' + Intensity[i] + '.'
else:
txt2 = pronoun2 + ' ' + AUnoun[i] + beverb + AUadj[i] + ' ' + Intensity[i] + ', '
txt = txt + txt2
else:
if random.random() > 0.5:
for i in range(len(AUnoun)):
# if there is unknown intensity
if not Intensity[i]:
if len(AUnoun) == 1:
txt2 = pronoun + ' has ' + AUadj[i] + ' ' + AUnoun[i] + '.'
elif i == len(AUnoun) - 1 and len(AUnoun) != 1:
txt2 = 'and ' + pronoun + ' has ' + AUadj[i] + ' ' + AUnoun[i] + '.'
else:
txt2 = pronoun + ' has' + AUadj[i] + ' ' + AUnoun[i] + ', '
else:
if len(AUnoun) == 1:
txt2 = pronoun + ' has ' + Intensity[i] + ' ' + AUadj[i] + ' ' + AUnoun[i] + '.'
elif i == len(AUnoun) - 1 and len(AUnoun) != 1:
txt2 = 'and ' + pronoun + ' has ' + Intensity[i] + ' ' + AUadj[i] + ' ' + AUnoun[
i] + '.'
else:
txt2 = pronoun + ' has ' + Intensity[i] + ' ' + AUadj[i] + ' ' + AUnoun[i] + ', '
txt = txt + txt2
else:
# if there is unknown intensity
if AUnoun[i][-1] == 's':
beverb = ' is '
else:
beverb = ' are '
if not Intensity[i]:
if len(AUnoun) == 1:
txt2 = pronoun2 + ' ' + AUnoun[i] + beverb + AUadj[i] + '.'
elif i == len(AUnoun) - 1 and len(AUnoun) != 1:
txt2 = 'and ' + pronoun2 + ' ' + AUnoun[i] + beverb + AUadj[i] + '.'
else:
txt2 = pronoun2 + ' ' + AUnoun[i] + beverb + AUadj[i] + ', '
else:
if random.random > 0.5:
if len(AUnoun) == 1:
txt2 = pronoun2 + ' ' + AUnoun[i] + beverb + Intensity[i] + ' ' + AUadj[i] + '.'
elif i == len(AUnoun) - 1 and len(AUnoun) != 1:
txt2 = 'and ' + pronoun2 + ' ' + AUnoun[i] + beverb + Intensity[i] + ' ' + AUadj[i] + '.'
else:
txt2 = pronoun2 + ' ' + AUnoun[i] + beverb + Intensity[i] + ' ' + AUadj[i] + ', '
else:
if len(AUnoun) == 1:
txt2 = pronoun2 + ' ' + AUnoun[i] + beverb + AUadj[i] + ' ' + Intensity[i] + '.'
elif i == len(AUnoun) - 1 and len(AUnoun) != 1:
txt2 = 'and ' + pronoun2 + ' ' + AUnoun[i] + beverb + AUadj[i] + ' ' + Intensity[i] + '.'
else:
txt2 = pronoun2 + ' ' + AUnoun[i] + beverb + AUadj[i] + ' ' + Intensity[i] + ', '
txt = txt + txt2
tlist.append(txt)
return tlist, numau
def generateText(dataframe, numtext, orderS=False, orderG=False, rand=False):
'''
:param param: [Gender, Emotion, Intensity, AUnoun, AUadj]
:return: number of texts
'''
preposition = [', subject with ', ' with ', ' subject with ']
tlist = []
for t in range(numtext):
if rand == True:
pass
else:
Gender, Emotion, Intensity, AUnoun, AUadj, numau = textParam(dataframe)
if not orderS:
# change order of AU
shuffleAU = list(zip(Intensity, AUnoun, AUadj))
random.shuffle(shuffleAU)
Intensity, AUnoun, AUadj = zip(*shuffleAU)
Emotion = Emotion.lower()
# text generation
if Emotion == 'neutral':
prep = random.choice(preposition)
txt = 'A ' + Gender + prep
else:
prep = random.choice(preposition)
if Emotion[0] in ('a', 'e', 'i', 'o', 'u'):
txt = 'An ' + Emotion + ' ' + Gender + prep
else:
txt = 'A ' + Emotion + ' ' + Gender + prep
if not orderG:
for i in range(len(AUnoun)):
if random.random() > 0.5:
# if there is unknown intensity
if not Intensity[i]:
if len(AUnoun) == 1:
txt2 = AUnoun[i] + ' ' + AUadj[i] + '.'
elif len(AUnoun) == 2 and i == 0:
txt2 = AUnoun[i] + ' ' + AUadj[i] + ' '
elif i == len(AUnoun) - 1 and len(AUnoun) != 1:
txt2 = 'and ' + AUnoun[i] + ' ' + AUadj[i] + '.'
else:
txt2 = AUnoun[i] + ' ' + AUadj[i] + ', '
else:
if len(AUnoun) == 1:
txt2 = AUnoun[i] + ' ' + Intensity[i] + ' ' + AUadj[i] + '.'
elif len(AUnoun) == 2 and i == 0:
txt2 = AUnoun[i] + ' ' + Intensity[i] + ' ' + AUadj[i] + ' '
elif i == len(AUnoun) - 1 and len(AUnoun) != 1:
txt2 = 'and ' + AUnoun[i] + ' ' + Intensity[i] + ' ' + AUadj[i] + '.'
else:
txt2 = AUnoun[i] + ' ' + Intensity[i] + ' ' + AUadj[i] + ', '
txt = txt + txt2
else:
# if there is unknown intensity
if not Intensity[i]:
if len(AUnoun) == 1:
txt2 = AUadj[i] + ' ' + AUnoun[i] + '.'
elif len(AUnoun) == 2 and i == 0:
txt2 = AUadj[i] + ' ' + AUnoun[i] + ' '
elif i == len(AUnoun) - 1 and len(AUnoun) != 1:
txt2 = 'and ' + AUadj[i] + ' ' + AUnoun[i] + '.'
else:
txt2 = AUadj[i] + ' ' + AUnoun[i] + ', '
else:
if len(AUnoun) == 1:
txt2 = Intensity[i] + ' ' + AUadj[i] + ' ' + AUnoun[i] + '.'
elif len(AUnoun) == 2 and i == 0:
txt2 = Intensity[i] + ' ' + AUadj[i] + ' ' + AUnoun[i] + ' '
elif i == len(AUnoun) - 1 and len(AUnoun) != 1:
txt2 = 'and ' + Intensity[i] + ' ' + AUadj[i] + ' ' + AUnoun[i] + '.'
else:
txt2 = Intensity[i] + ' ' + AUadj[i] + ' ' + AUnoun[i] + ', '
txt = txt + txt2
else:
if random.random() > 0.5:
for i in range(len(AUnoun)):
# if there is unknown intensity
if not Intensity[i]:
if len(AUnoun) == 1:
txt2 = AUnoun[i] + ' ' + AUadj[i] + '.'
elif i == len(AUnoun) - 1 and len(AUnoun) != 1:
txt2 = 'and ' + AUnoun[i] + ' ' + AUadj[i] + '.'
else:
txt2 = AUnoun[i] + ' ' + AUadj[i] + ', '
else:
if len(AUnoun) == 1:
txt2 = AUnoun[i] + ' ' + Intensity[i] + ' ' + AUadj[i] + '.'
elif i == len(AUnoun) - 1 and len(AUnoun) != 1:
txt2 = 'and ' + AUnoun[i] + ' ' + Intensity[i] + ' ' + AUadj[i] + '.'
else:
txt2 = AUnoun[i] + ' ' + Intensity[i] + ' ' + AUadj[i] + ', '
txt = txt + txt2
else:
for i in range(len(AUnoun)):
# if there is unknown intensity
if not Intensity[i]:
if i == len(AUnoun) - 1 and len(AUnoun) != 1:
txt2 = 'and ' + AUadj[i] + ' ' + AUnoun[i] + '.'
else:
txt2 = AUadj[i] + ' ' + AUnoun[i] + ', '
else:
if i == len(AUnoun) - 1 and len(AUnoun) != 1:
txt2 = 'and ' + Intensity[i] + ' ' + AUadj[i] + ' ' + AUnoun[i] + '.'
else:
txt2 = Intensity[i] + ' ' + AUadj[i] + ' ' + AUnoun[i] + ', '
txt = txt + txt2
tlist.append(txt)
return tlist, numau
def generateText3(dataframe, numtext, orderS=False, orderG=False, rand=False):
'''
:param param: [Gender, Emotion, Intensity, AUnoun, AUadj]
:return: number of texts
'''
tlist = []
for t in range(numtext):
if rand == True:
pass
else:
Gender, Emotion, Intensity, AUnoun, AUadj, numau = textParam(dataframe)
if not orderS:
# change order of AU
shuffleAU = list(zip(Intensity, AUnoun, AUadj))
random.shuffle(shuffleAU)
Intensity, AUnoun, AUadj = zip(*shuffleAU)
Emotion = Emotion.lower()
if Gender == 'man':
gender_pro = "man's"
pronoun = 'he'
pronoun2 = 'his'
else:
gender_pro = "woman's"
pronoun = 'she'
pronoun2 = 'her'
# text generation
txt = 'A ' + gender_pro
for i in range(len(AUnoun)):
# if there is unknown intensity
if AUnoun[i][-1] == 's':
beverb = ' are '
else:
beverb = ' is '
if not Intensity[i]:
if len(AUnoun) == 1:
txt2 = ' ' + AUnoun[i] + beverb + AUadj[i] + '.'
elif i == 0:
txt2 = ' ' + AUnoun[i] + beverb + AUadj[i] + ', '
elif i == 1:
txt2 = pronoun2 + ' ' + AUnoun[i] + beverb + AUadj[i] + ', '
elif i == len(AUnoun) - 1 and len(AUnoun) != 1:
txt2 = 'and ' + pronoun2 + ' ' + AUnoun[i] + beverb + AUadj[i] + '.'
else:
txt2 = AUnoun[i] + beverb + AUadj[i] + ', '
else:
if random.random() > 0.5 or Intensity[i] == 'somewhat' or Intensity[i] == 'kind of':
if len(AUnoun) == 1:
txt2 = ' ' + AUnoun[i] + beverb + Intensity[i] + ' ' + AUadj[i] + '.'
elif i == 0:
txt2 = ' ' + AUnoun[i] + beverb + Intensity[i] + ' ' + AUadj[i] + ', '
elif len(AUnoun) == 2:
txt2 = 'and ' + pronoun2 + ' ' + AUnoun[i] + beverb + Intensity[i] + ' ' + AUadj[i] + '.'
elif i == len(AUnoun) - 1 and len(AUnoun) != 1:
txt2 = 'and ' + pronoun2 + ' ' + AUnoun[i] + beverb + Intensity[i] + ' ' + AUadj[i] + '.'
elif i == 1:
txt2 = pronoun2 + ' ' + AUnoun[i] + beverb + Intensity[i] + ' ' + AUadj[i] + ', '
else:
txt2 = pronoun2 + ' ' + AUnoun[i] + beverb + Intensity[i] + ' ' + AUadj[i] + ', '
else:
if len(AUnoun) == 1:
txt2 = ' ' + AUnoun[i] + beverb + AUadj[i] + ' ' + Intensity[i] + '.'
elif i == 0:
txt2 = ' ' + AUnoun[i] + beverb + AUadj[i] + ' ' + Intensity[i] + ', '
elif len(AUnoun) == 2:
txt2 = 'and ' + pronoun2 + ' ' + AUnoun[i] + beverb + AUadj[i] + ' ' + Intensity[i] + '.'
elif i == len(AUnoun) - 1 and len(AUnoun) != 1:
txt2 = 'and ' + pronoun2 + ' ' + AUnoun[i] + beverb + AUadj[i] + ' ' + Intensity[i] + '.'
elif i ==1:
txt2 = pronoun2 + ' ' + AUnoun[i] + beverb + AUadj[i] + ' ' + Intensity[i] + ', '
else:
txt2 = pronoun2 + ' ' + AUnoun[i] + beverb + AUadj[i] + ' ' + Intensity[i] + ', '
txt = txt + txt2
tlist.append(txt)
return tlist, numau
if __name__ == '__main__':
dir = './CK+'
# dir = './MMI/mmi-facial-expression-database/mmi-images'
# data_directory = pd.read_csv('./DISFA+/annotation/cross_val2/data/0_test.csv')
#
# dictionary = {}
# txt, img, data=[], [], []
# for i in range(len(data_directory)):
# df = pd.read_csv(data_directory['data'][i])
# text, _ = generateText3(df, 1)
# for j in range(1):
# img.append(data_directory['image_file'][i])
# data.append(data_directory['data'][i])
# txt.append(text[j])
# dictionary['caption'] = txt
# dictionary['data'] = data
# dictionary['image_file'] = img
# dictionary = pd.DataFrame(dictionary)
# dictionary.to_csv('./DISFA+/annotation/cross_val2/0_test.csv')
# print('done.')
#
# data_directory = pd.read_csv('./DISFA+/annotation/cross_val2/data/1_test.csv')
#
# dictionary = {}
# txt, img, data=[], [], []
# for i in range(len(data_directory)):
# df = pd.read_csv(data_directory['data'][i])
# text, _ = generateText3(df, 1)
# for j in range(1):
# img.append(data_directory['image_file'][i])
# data.append(data_directory['data'][i])
# txt.append(text[j])
# dictionary['caption'] = txt
# dictionary['data'] = data
# dictionary['image_file'] = img
# dictionary = pd.DataFrame(dictionary)
# dictionary.to_csv('./DISFA+/annotation/cross_val2/1_test.csv')
# print('done.')
#
# data_directory = pd.read_csv('./DISFA+/annotation/cross_val2/data/2_test.csv')
#
# dictionary = {}
# txt, img, data=[], [], []
# for i in range(len(data_directory)):
# df = pd.read_csv(data_directory['data'][i])
# text, _ = generateText3(df, 1)
# for j in range(1):
# img.append(data_directory['image_file'][i])
# data.append(data_directory['data'][i])
# txt.append(text[j])
# dictionary['caption'] = txt
# dictionary['data'] = data
# dictionary['image_file'] = img
# dictionary = pd.DataFrame(dictionary)
# dictionary.to_csv('./DISFA+/annotation/cross_val2/2_test.csv')
# print('done.')
#
# data_directory = pd.read_csv('./DISFA+/annotation/cross_val2/data/3_test.csv')
#
# dictionary = {}
# txt, img, data=[], [], []
# for i in range(len(data_directory)):
# df = pd.read_csv(data_directory['data'][i])
# text, _ = generateText3(df, 1)
# for j in range(1):
# img.append(data_directory['image_file'][i])
# data.append(data_directory['data'][i])
# txt.append(text[j])
# dictionary['caption'] = txt
# dictionary['data'] = data
# dictionary['image_file'] = img
# dictionary = pd.DataFrame(dictionary)
# dictionary.to_csv('./DISFA+/annotation/cross_val2/3_test.csv')
# print('done.')
#
# data_directory = pd.read_csv('./DISFA+/annotation/cross_val2/data/4_test.csv')
#
# dictionary = {}
# txt, img, data=[], [], []
# for i in range(len(data_directory)):
# df = pd.read_csv(data_directory['data'][i])
# text, _ = generateText3(df, 1)
# for j in range(1):
# img.append(data_directory['image_file'][i])
# data.append(data_directory['data'][i])
# txt.append(text[j])
# dictionary['caption'] = txt
# dictionary['data'] = data
# dictionary['image_file'] = img
# dictionary = pd.DataFrame(dictionary)
# dictionary.to_csv('./DISFA+/annotation/cross_val2/4_test.csv')
# print('done.')
#
# data_directory = pd.read_csv('./DISFA+/annotation/cross_val2/data/5_test.csv')
#
# dictionary = {}
# txt, img, data=[], [], []
# for i in range(len(data_directory)):
# df = pd.read_csv(data_directory['data'][i])
# text, _ = generateText3(df, 1)
# for j in range(1):
# img.append(data_directory['image_file'][i])
# data.append(data_directory['data'][i])
# txt.append(text[j])
# dictionary['caption'] = txt
# dictionary['data'] = data
# dictionary['image_file'] = img
# dictionary = pd.DataFrame(dictionary)
# dictionary.to_csv('./DISFA+/annotation/cross_val2/5_test.csv')
# print('done.')
#
# data_directory = pd.read_csv('./DISFA+/annotation/cross_val2/data/6_test.csv')
#
# dictionary = {}
# txt, img, data=[], [], []
# for i in range(len(data_directory)):
# df = pd.read_csv(data_directory['data'][i])
# text, _ = generateText3(df, 1)
# for j in range(1):
# img.append(data_directory['image_file'][i])
# data.append(data_directory['data'][i])
# txt.append(text[j])
# dictionary['caption'] = txt
# dictionary['data'] = data
# dictionary['image_file'] = img
# dictionary = pd.DataFrame(dictionary)
# dictionary.to_csv('./DISFA+/annotation/cross_val2/6_test.csv')
# print('done.')
#
# data_directory = pd.read_csv('./DISFA+/annotation/cross_val2/data/7_test.csv')
#
# dictionary = {}
# txt, img, data=[], [], []
# for i in range(len(data_directory)):
# df = pd.read_csv(data_directory['data'][i])
# text, _ = generateText3(df, 1)
# for j in range(1):
# img.append(data_directory['image_file'][i])
# data.append(data_directory['data'][i])
# txt.append(text[j])
# dictionary['caption'] = txt
# dictionary['data'] = data
# dictionary['image_file'] = img
# dictionary = pd.DataFrame(dictionary)
# dictionary.to_csv('./DISFA+/annotation/cross_val2/7_test.csv')
# print('done.')
#
# data_directory = pd.read_csv('./DISFA+/annotation/cross_val2/data/8_test.csv')
#
# dictionary = {}
# txt, img, data=[], [], []
# for i in range(len(data_directory)):
# df = pd.read_csv(data_directory['data'][i])
# text, _ = generateText3(df, 1)
# for j in range(1):
# img.append(data_directory['image_file'][i])
# data.append(data_directory['data'][i])
# txt.append(text[j])
# dictionary['caption'] = txt
# dictionary['data'] = data
# dictionary['image_file'] = img
# dictionary = pd.DataFrame(dictionary)
# dictionary.to_csv('./DISFA+/annotation/cross_val2/8_test.csv')
# print('done.')
#### text classification task
# data_directory = pd.read_csv('directory.csv')
# au_dir = '/home/joannahong/PycharmProjects/FES-master/cnn-text-classification-pytorch-master/au.csv'
# text_dir = '/home/joannahong/PycharmProjects/FES-master/cnn-text-classification-pytorch-master/text.csv'
#
# # print(data_directory.index.get_values()) # index
# print(data_directory.shape[0])
# aus = []
# texts = []
# for i in range(data_directory.shape[0]):
# img_dir = data_directory.loc[i]['Image_aligned'], data_directory.loc[i]['Image_flipped']
# data_dir = data_directory.loc[i]['Gender'], data_directory.loc[i]['Emotion'], data_directory.loc[i]['Action Unit']
# df = summary(data_dir)
# # print(data_dir)
# # print(df)
# # print(img_dir)
# # print(data_dir)
# text, numau = generateText(df, numtext=100, orderS=False, orderG=False)
# for i in range(len(text)):
# aus.append([numau])
# texts.append([text[i]])
#
# with open(au_dir, "wb") as f:
# writer = csv.writer(f)
# writer.writerows(aus)
#
# with open(text_dir, "wb") as f:
# writer = csv.writer(f)
# writer.writerows(texts)
# # #print(len(text[i].split()))
# #### MMI
# dir = './MMI/mmi-facial-expression-database/mmi-images'
#
# fds = os.listdir(dir)
# fds.sort(key=int)
# num_text = 1
#
# data, img, txt = [], [], []
# a = {}
# for name in fds:
# img_dir = os.path.join(dir, name)
# files = sorted(os.listdir(img_dir))
#
#
# for i in files:
# if 'png' in i:
# data_file = os.path.join(img_dir, i[:-3]+'csv')
# img_file = os.path.join(img_dir, i)
# # print(data_file)
# # print(img_file)
# df = pd.read_csv(data_file)
#
# text, _ = generateText(df,num_text)
#
# for i in range(num_text):
# txt.append(text[i])
# data.append(data_file)
# img.append(img_file)
#
# # a['caption'] = txt
# a['image_file'] = img
# a['data'] = data
#
# df = pd.DataFrame.from_dict(a)
# df.to_csv('./annotation_mmi/directory.csv')
################### MMI
# print(len(data))
# print(len(img))
# print(len(txt))
# print(df)
#
gen, emo, au = extractAUdir(dir)
img_aligned, img_flip = extractImdir(dir)
for i in range(len(emo)):
print(len(emo))
image_dir = [img_aligned[i], img_flip[i]]
data_dir = [gen[i], emo[i], au[i]]
print(image_dir)
print(data_dir)
df = summary(data_dir)
print(df)
# print(int((set(df['Gender'])).pop()))
# print(int((set(df['Emotion'])).pop()))
#
# dfEmotion = pd.read_csv('Emotion.csv')
# dfIntensity = pd.read_csv('Intensity.csv')
# numEmotion = int((set(df['Emotion'])).pop())
# listEmotion = list(dfEmotion[str(numEmotion)])
# emo = random.choice(listEmotion)
# print(listEmotion)
# print(emo)
# auin = df[['ActionUnit','Intensity']]
# print(auin)
# print(len(auin))
# print(len(df))
# aui_comb = dict(auin.iloc[0])
# print(aui_comb['Intensity'])
#
#
# gender, emotion, intensity, noun, adj = textParam(df)
# print("{} {}, with {}".format(emotion, gender,intensity[0]))
# # todo
# # if None, do not print anything for intensity
# print(gender)
# print(emotion)
# print(intensity)
# print(noun)
# print(adj)
text = generateText(df, 3)
print(text)
print(text[0])
print(text[1])
print(text[2])
#
#
#
# full = []
# for i in range(len(emo)):
# print('Gender directory: {}'.format(gen[i]))
# print('Emotion directory: {}'.format(emo[i]))
# print('ActionUnit directory: {}'.format(au[i]))
#
# data_dir = [gen[i], emo[i], au[i]]
# df = summary(data_dir)
# print(df)
#
# full.append(df)
#
# # full_df = pd.concat(full)
#
# # print(full_df)
# # print(set(full_df['Gender']))
# # print(set(full_df['Emotion']))
# # print(set(full_df['ActionUnit']))
# # print(set(full_df['Intensity']))
#
# ############### Example ################
# # Gender Emotion ActionUnit Intensity
# # 0 0 4 1.0 4.0
# # 1 2.0 4.0
# # 2 4.0 2.0
# # 3 5.0 4.0
# # 4 20.0 3.0
# # 5 25.0 1.0