create_csv.py

# import csv 
# import os

# set_1 = ['SN001','SN002','SN009','SN010','SN016','SN026','SN027','SN030','SN032']

# set_2 = ['SN006','SN011','SN012','SN013','SN018','SN021','SN024','SN028','SN031']

# set_3 = ['SN003','SN004','SN005','SN007','SN008','SN017','SN023','SN025','SN029']

# # print(os.getcwd())
# f = open("hi.txt")
# while True:
#     line = f.readline()
#     if line:
#         print (line)
#     else:
#         break
# f.close()


import os
import numpy as np
import pandas as pd
#You nead downloading DISFA including 'ActionUnit_Labels'
label_txt_path = '/home/ICT2000/dchang/DISFA_Data/DISFA/labels_txt'
label_path = '/home/ICT2000/dchang/DISFA_Data/DISFA/labels'
image_prefix = "/home/ICT2000/dchang/DISFA_Data/DISFA/aligned_images"
# list_path_prefix = '../data/DISFA/list/'

part1 = ['SN002','SN010','SN001','SN026','SN027','SN032','SN030','SN009','SN016']
part2 = ['SN013','SN018','SN011','SN028','SN012','SN006','SN031','SN021','SN024']
part3 = ['SN003','SN029','SN023','SN025','SN008','SN005','SN007','SN017','SN004']

# fold1:  train : part1+part2 test: part3
# fold2:  train : part1+part3 test: part2
# fold3:  train : part2+part3 test: part1

# au_idx = [1, 2, 4, 6, 9, 12, 25, 26]
# df = pd.DataFrame()
columns = ["image_path","au1","au2","au4","au5","au6","au9","au12","au15","au17","au20","au25","au26"]

# print(data_frame)
# part0 = ["SN001"]
for folder in part1:
    txt_path = os.listdir(os.path.join(label_txt_path,folder))
    for txt_file in txt_path:
        # file1 = open(os.path.join(label_txt_path,folder,txt_file), 'r')

        file = pd.read_csv(os.path.join(label_txt_path,folder,txt_file),sep = ",", header = None,index_col=0)
        # print(file.columns)# = file.set_index("0")
        # print(file):
        try:
            df = df.merge(file,left_index=True,right_index=True)
        except:
            df = file
    df = df.reset_index(drop = False)
    df.columns = columns
    df['image_path'] = df['image_path'].apply(lambda x : os.path.join(image_prefix,folder,str(x).zfill(5) + ".png"))
    # print(df.shape)
    try:
        df1 = pd.concat([df1,df], ignore_index=True).copy(deep=True)
        # print(df1)
        del df
    except:
        df1 = df.copy(deep=True)
        # print(df1)
        del df
df1.to_csv('/home/ICT2000/dchang/DISFA_Data/DISFA/labels/2/test.csv',index=False)



for folder in part2:
    txt_path = os.listdir(os.path.join(label_txt_path,folder))
    for txt_file in txt_path:
        # file1 = open(os.path.join(label_txt_path,folder,txt_file), 'r')

        file = pd.read_csv(os.path.join(label_txt_path,folder,txt_file),sep = ",", header = None,index_col=0)
        # print(file.columns)# = file.set_index("0")
        # print(file):
        try:
            df = df.merge(file,left_index=True,right_index=True)
        except:
            df = file
    df = df.reset_index(drop = False)
    df.columns = columns
    df['image_path'] = df['image_path'].apply(lambda x : os.path.join(image_prefix,folder,str(x).zfill(5) + ".png"))
    # print(df.shape)
    try:
        df2 = pd.concat([df2,df], ignore_index=True).copy(deep=True)
        # print(df1)
        del df
    except:
        df2 = df.copy(deep=True)
        # print(df1)
        del df
df2.to_csv('/home/ICT2000/dchang/DISFA_Data/DISFA/labels/1/test.csv',index=False)



for folder in part3:
    txt_path = os.listdir(os.path.join(label_txt_path,folder))
    for txt_file in txt_path:
        # file1 = open(os.path.join(label_txt_path,folder,txt_file), 'r')

        file = pd.read_csv(os.path.join(label_txt_path,folder,txt_file),sep = ",", header = None,index_col=0)
        # print(file.columns)# = file.set_index("0")
        # print(file):
        try:
            df = df.merge(file,left_index=True,right_index=True)
        except:
            df = file
    df = df.reset_index(drop = False)
    df.columns = columns
    df['image_path'] = df['image_path'].apply(lambda x : os.path.join(image_prefix,folder,str(x).zfill(5) + ".png"))
    # print(df.shape)
    try:
        df3 = pd.concat([df3,df], ignore_index=True).copy(deep=True)
        # print(df1)
        del df
    except:
        df3 = df.copy(deep=True)
        # print(df1)
        del df
df3.to_csv('/home/ICT2000/dchang/DISFA_Data/DISFA/labels/0/test.csv',index=False)

df_train1 = pd.concat([df2,df3])
df_train2 = pd.concat([df1,df3])
df_train3 = pd.concat([df1,df2])

df_train1.to_csv('/home/ICT2000/dchang/DISFA_Data/DISFA/labels/2/train.csv',index=False)
df_train2.to_csv('/home/ICT2000/dchang/DISFA_Data/DISFA/labels/1/train.csv',index=False)
df_train3.to_csv('/home/ICT2000/dchang/DISFA_Data/DISFA/labels/0/train.csv',index=False)

    # print(df1['image_path'])
        # Lines = file1.readlines()
        # for line in Lines:
        #     print(line)
# with open(list_path_prefix + 'DISFA_test_img_path_fold3.txt','w') as f:
#     u = 0

# part1_frame_list = []
# part1_numpy_list = []
# for fr in part1:
#     fr_path = os.path.join(label_path,fr)
#     au1_path = os.path.join(fr_path,fr+'_au1.txt')
#     with open(au1_path, 'r') as label:
#         total_frame = len(label.readlines())
#     au_label_array = np.zeros((total_frame,8),dtype=np.int)
#     for ai, au in enumerate(au_idx):
#         AULabel_path = os.path.join(fr_path,fr+'_au'+str(au) +'.txt')
#         if not os.path.isfile(AULabel_path):
#             continue
#         print("--Checking AU:" + str(au) + " ...")
#         with open(AULabel_path, 'r') as label:
#             for t, lines in enumerate(label.readlines()):
#                 frameIdx, AUIntensity = lines.split(',')
#                 frameIdx, AUIntensity = int(frameIdx), int(AUIntensity)
#                 if AUIntensity >= 2:
#                     AUIntensity = 1
#                 else:
#                     AUIntensity = 0
#                 au_label_array[t,ai] = AUIntensity
#     for i in range(total_frame):
#         frame_img_name = fr + '/' + str(i) + '.png'
#         part1_frame_list.append(frame_img_name)
#         with open(list_path_prefix + 'DISFA_test_img_path_fold3.txt', 'a+') as f:
#             f.write(frame_img_name+'\n')
#     part1_numpy_list.append(au_label_array)

# part1_numpy_list = np.concatenate(part1_numpy_list,axis=0)
# # part1 test for fold3
# np.savetxt(list_path_prefix + 'DISFA_test_label_fold3.txt', part1_numpy_list,fmt='%d', delimiter=' ')

# #################################################################################
# with open(list_path_prefix + 'DISFA_test_img_path_fold2.txt','w') as f:
#     u =0

# part2_frame_list = []
# part2_numpy_list = []
# for fr in part2:
#     fr_path = os.path.join(label_path,fr)
#     au1_path = os.path.join(fr_path,fr+'_au1.txt')
#     with open(au1_path, 'r') as label:
#         total_frame = len(label.readlines())
#     au_label_array = np.zeros((total_frame,8),dtype=np.int)
#     for ai, au in enumerate(au_idx):
#         AULabel_path = os.path.join(fr_path,fr+'_au'+str(au) +'.txt')
#         if not os.path.isfile(AULabel_path):
#             continue
#         print("--Checking AU:" + str(au) + " ...")
#         with open(AULabel_path, 'r') as label:
#             for t, lines in enumerate(label.readlines()):
#                 frameIdx, AUIntensity = lines.split(',')
#                 frameIdx, AUIntensity = int(frameIdx), int(AUIntensity)
#                 if AUIntensity >= 2:
#                     AUIntensity = 1
#                 else:
#                     AUIntensity = 0
#                 au_label_array[t,ai] = AUIntensity
#     for i in range(total_frame):
#         frame_img_name = fr + '/' + str(i) + '.png'
#         part2_frame_list.append(frame_img_name)
#         with open(list_path_prefix + 'DISFA_test_img_path_fold2.txt', 'a+') as f:
#             f.write(frame_img_name + '\n')
#     part2_numpy_list.append(au_label_array)

# part2_numpy_list = np.concatenate(part2_numpy_list,axis=0)
# # part2 test for fold2
# np.savetxt(list_path_prefix + 'DISFA_test_label_fold2.txt', part2_numpy_list,fmt='%d')

# #################################################################################
# with open(list_path_prefix + 'DISFA_test_img_path_fold1.txt','w') as f:
#     u =0

# part3_frame_list = []
# part3_numpy_list = []
# for fr in part3:
#     fr_path = os.path.join(label_path,fr)
#     au1_path = os.path.join(fr_path,fr+'_au1.txt')
#     with open(au1_path, 'r') as label:
#         total_frame = len(label.readlines())
#     au_label_array = np.zeros((total_frame,8),dtype=np.int)
#     for ai, au in enumerate(au_idx):
#         AULabel_path = os.path.join(fr_path,fr+'_au'+str(au) +'.txt')
#         if not os.path.isfile(AULabel_path):
#             continue
#         print("--Checking AU:" + str(au) + " ...")
#         with open(AULabel_path, 'r') as label:
#             for t, lines in enumerate(label.readlines()):
#                 frameIdx, AUIntensity = lines.split(',')
#                 frameIdx, AUIntensity = int(frameIdx), int(AUIntensity)
#                 if AUIntensity >= 2:
#                     AUIntensity = 1
#                 else:
#                     AUIntensity = 0
#                 au_label_array[t,ai] = AUIntensity
#     for i in range(total_frame):
#         frame_img_name = fr + '/' + str(i) + '.png'
#         part3_frame_list.append(frame_img_name)
#         with open(list_path_prefix + 'DISFA_test_img_path_fold1.txt', 'a+') as f:
#             f.write(frame_img_name + '\n')
#     part3_numpy_list.append(au_label_array)

# part3_numpy_list = np.concatenate(part3_numpy_list,axis=0)
# # part3 test for fold1
# np.savetxt(list_path_prefix + 'DISFA_test_label_fold1.txt', part3_numpy_list,fmt='%d')

# #################################################################################
# with open(list_path_prefix + 'DISFA_train_img_path_fold1.txt','w') as f:
#     u = 0
# train_img_label_fold1_list = part1_frame_list + part2_frame_list
# for frame_img_name in train_img_label_fold1_list:
# 	with open(list_path_prefix + 'DISFA_train_img_path_fold1.txt', 'a+') as f:
# 		f.write(frame_img_name + '\n')
# train_img_label_fold1_numpy_list = np.concatenate((part1_numpy_list, part2_numpy_list), axis=0)
# np.savetxt(list_path_prefix + 'DISFA_train_label_fold1.txt', train_img_label_fold1_numpy_list, fmt='%d')

# #################################################################################
# with open(list_path_prefix + 'DISFA_train_img_path_fold2.txt','w') as f:
#     u = 0
# train_img_label_fold2_list = part1_frame_list + part3_frame_list
# for frame_img_name in train_img_label_fold2_list:
# 	with open(list_path_prefix + 'DISFA_train_img_path_fold2.txt', 'a+') as f:
# 		f.write(frame_img_name + '\n')
# train_img_label_fold2_numpy_list = np.concatenate((part1_numpy_list, part3_numpy_list), axis=0)
# np.savetxt(list_path_prefix + 'DISFA_train_label_fold2.txt', train_img_label_fold2_numpy_list, fmt='%d')

# #################################################################################
# with open(list_path_prefix + 'DISFA_train_img_path_fold3.txt','w') as f:
#     u = 0
# train_img_label_fold3_list = part2_frame_list + part3_frame_list
# for frame_img_name in train_img_label_fold3_list:
# 	with open(list_path_prefix + 'DISFA_train_img_path_fold3.txt', 'a+') as f:
# 		f.write(frame_img_name + '\n')
# train_img_label_fold3_numpy_list = np.concatenate((part2_numpy_list, part3_numpy_list), axis=0)
# np.savetxt(list_path_prefix + 'DISFA_train_label_fold3.txt', train_img_label_fold3_numpy_list, fmt='%d')