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data_find.py
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data_find.py
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import os
import json
import random
import sys
import glob
from random import shuffle
import numpy as np
import argparse
parser = argparse.ArgumentParser(description='manual to this script')
parser.add_argument("--data_path", type=str, default=None)
args = parser.parse_args()
path_list = []
def msu_process(data_dir, label_save_dir):
test_list = []
# data_label for msu
for line in open(data_dir + 'test_sub_list.txt', 'r'):
test_list.append(line[0:2])
train_list = []
for line in open(data_dir + 'train_sub_list.txt', 'r'):
train_list.append(line[0:2])
print(test_list)
print(train_list)
train_final_json = []
test_final_json = []
all_final_json = []
real_final_json = []
fake_final_json = []
print_final_json = []
video_final_json = []
# label_save_dir = './labels/msu/'
if not os.path.exists(label_save_dir):
os.makedirs(label_save_dir)
f_train = open(label_save_dir + 'train_label.json', 'w')
f_test = open(label_save_dir + 'test_label.json', 'w')
f_all = open(label_save_dir + 'all_label.json', 'w')
f_real = open(label_save_dir + 'real_label.json', 'w')
f_fake = open(label_save_dir + 'fake_label.json', 'w')
f_print = open(label_save_dir + 'print_label.json', 'w')
f_video = open(label_save_dir + 'video_label.json', 'w')
dataset_path = data_dir
path_list = glob.glob(dataset_path + '**/*.jpg', recursive=True)
path_list.sort()
for i in range(len(path_list)):
flag = path_list[i].find('/real/')
if(flag != -1):
label = 1
else:
label = 0
dict = {}
dict['photo_path'] = path_list[i]
dict['photo_label'] = label
video_num = path_list[i].split('/')[-2].split('_')[1][-2:]
if (video_num in train_list):
train_final_json.append(dict)
else:
test_final_json.append(dict)
all_final_json.append(dict)
if(label == 1):
real_final_json.append(dict)
else:
fake_final_json.append(dict)
if path_list[i].split('/')[-2].find('video') != -1:
video_final_json.append(dict)
elif path_list[i].split('/')[-2].find('printed') != -1:
print_final_json.append(dict)
elif label == 1:
rand = random.random()
if rand > 0.5:
video_final_json.append(dict)
else:
print_final_json.append(dict)
print('\nMSU: ', len(path_list))
print('MSU(train): ', len(train_final_json))
print('MSU(test): ', len(test_final_json))
print('MSU(all): ', len(all_final_json))
print('MSU(real): ', len(real_final_json))
print('MSU(fake): ', len(fake_final_json))
print('MSU(video): ', len(video_final_json))
print('MSU(print): ', len(print_final_json))
json.dump(train_final_json, f_train, indent=4)
f_train.close()
json.dump(test_final_json, f_test, indent=4)
f_test.close()
json.dump(all_final_json, f_all, indent=4)
f_all.close()
json.dump(real_final_json, f_real, indent=4)
f_real.close()
json.dump(fake_final_json, f_fake, indent=4)
f_fake.close()
json.dump(video_final_json, f_video, indent=4)
f_video.close()
json.dump(print_final_json, f_print, indent=4)
f_print.close()
def casia_process(data_dir, label_save_dir):
train_final_json = []
test_final_json = []
dev_final_json = []
all_final_json = []
real_final_json = []
fake_final_json = []
print_final_json = []
video_final_json = []
other_final_json = []
# label_save_dir = './labels/casia/'
if not os.path.exists(label_save_dir):
os.makedirs(label_save_dir)
f_train = open(label_save_dir + 'train_label.json', 'w')
f_test = open(label_save_dir + 'test_label.json', 'w')
f_dev = open(label_save_dir + 'dev_label.json', 'w')
f_all = open(label_save_dir + 'all_label.json', 'w')
f_real = open(label_save_dir + 'real_label.json', 'w')
f_fake = open(label_save_dir + 'fake_label.json', 'w')
f_print = open(label_save_dir + 'print_label.json', 'w')
f_video = open(label_save_dir + 'video_label.json', 'w')
dataset_path = data_dir
path_list = glob.glob(dataset_path + '**/*.jpg', recursive=True)
path_list.sort()
for i in range(len(path_list)):
flag = path_list[i].split('/')[-2]
if (flag == '1' or flag == '2' or flag == 'HR_1'):
label = 1
else:
label = 0
dict = {}
dict['photo_path'] = path_list[i]
dict['photo_label'] = label
if path_list[i].find('/train_release/')!=-1:
train_final_json.append(dict)
elif path_list[i].find('/test_release/')!=-1:
test_final_json.append(dict)
all_final_json.append(dict)
if (label == 1):
real_final_json.append(dict)
else:
fake_final_json.append(dict)
flag = path_list[i].split('/')[-2]
if label == 0:
if flag == '3' or flag == '4' or flag == '5' or flag == '6' or flag == 'HR_2' or flag == 'HR_3':
print_final_json.append(dict)
elif flag == '7' or flag == '8' or flag == 'HR_4':
video_final_json.append(dict)
else:
# print(other_final_json)
other_final_json.append(dict)
else:
prob = random.random()
if prob>=0.5:
print_final_json.append(dict)
else:
video_final_json.append(dict)
print('\nCasia: ', len(path_list))
print('Casia(train): ', len(train_final_json))
print('Casia(test): ', len(test_final_json))
print('Casia(dev): ', len(dev_final_json))
print('Casia(all): ', len(all_final_json))
print('Casia(real): ', len(real_final_json))
print('Casia(fake): ', len(fake_final_json))
print('Casia(print): ', len(print_final_json))
print('Casia(video): ', len(video_final_json))
json.dump(train_final_json, f_train, indent=4)
f_train.close()
json.dump(test_final_json, f_test, indent=4)
f_test.close()
json.dump(dev_final_json, f_dev, indent=4)
f_dev.close()
json.dump(all_final_json, f_all, indent=4)
f_all.close()
json.dump(real_final_json, f_real, indent=4)
f_real.close()
json.dump(fake_final_json, f_fake, indent=4)
f_fake.close()
json.dump(print_final_json, f_print, indent=4)
f_print.close()
json.dump(video_final_json, f_video, indent=4)
f_video.close()
def idiap_process(data_dir, label_save_dir):
train_final_json = []
valid_final_json = []
test_final_json = []
all_final_json = []
real_final_json = []
fake_final_json = []
print_final_json = []
video_final_json = []
# label_save_dir = './labels/idiap/'
if not os.path.exists(label_save_dir):
os.makedirs(label_save_dir)
f_train = open(label_save_dir + 'train_label.json', 'w')
f_valid = open(label_save_dir + 'valid_label.json', 'w')
f_test = open(label_save_dir + 'test_label.json', 'w')
f_all = open(label_save_dir + 'all_label.json', 'w')
f_real = open(label_save_dir + 'real_label.json', 'w')
f_fake = open(label_save_dir + 'fake_label.json', 'w')
f_print = open(label_save_dir + 'print_label.json', 'w')
f_video = open(label_save_dir + 'video_label.json', 'w')
dataset_path = data_dir
path_list = glob.glob(dataset_path + '**/*.jpg', recursive=True)
path_list.sort()
for i in range(len(path_list)):
flag = path_list[i].find('/real/')
if (flag != -1):
label = 1
elif path_list[i].find('/enroll/') != -1:
label = 1
else:
label = 0
dict = {}
dict['photo_path'] = path_list[i]
dict['photo_label'] = label
if (path_list[i].find('/train/') != -1):
train_final_json.append(dict)
elif(path_list[i].find('/devel/') != -1):
valid_final_json.append(dict)
train_final_json.append(dict)
else:
test_final_json.append(dict)
all_final_json.append(dict)
if (label == 1):
real_final_json.append(dict)
else:
fake_final_json.append(dict)
if label == 0:
if path_list[i].split('/')[-2].split('_')[1] == 'highdef' or path_list[i].split('/')[-2].split('_')[1] == 'mobile':
video_final_json.append(dict)
elif path_list[i].split('/')[-2].split('_')[1] == 'print':
print_final_json.append(dict)
else:
print(path_list[i])
else:
rand = random.random()
if rand >= 0.5:
video_final_json.append(dict)
else:
print_final_json.append(dict)
print('\nReplay: ', len(path_list))
print('Replay(train): ', len(train_final_json))
print('Replay(valid): ', len(valid_final_json))
print('Replay(test): ', len(test_final_json))
print('Replay(all): ', len(all_final_json))
print('Replay(real): ', len(real_final_json))
print('Replay(fake): ', len(fake_final_json))
print('Replay(video): ', len(video_final_json))
print('Replay(print): ', len(print_final_json))
json.dump(train_final_json, f_train, indent=4)
f_train.close()
json.dump(valid_final_json, f_valid, indent=4)
f_valid.close()
json.dump(test_final_json, f_test, indent=4)
f_test.close()
json.dump(all_final_json, f_all, indent=4)
f_all.close()
json.dump(real_final_json, f_real, indent=4)
f_real.close()
json.dump(fake_final_json, f_fake, indent=4)
f_fake.close()
json.dump(video_final_json, f_video, indent=4)
f_video.close()
json.dump(print_final_json, f_print, indent=4)
f_print.close()
def oulu_process(data_dir, label_save_dir):
train_final_json = []
valid_final_json = []
test_final_json = []
all_final_json = []
real_final_json = []
fake_final_json = []
print_final_json = []
video_final_json = []
# label_save_dir = './labels/oulu/'
if not os.path.exists(label_save_dir):
os.makedirs(label_save_dir)
f_train = open(label_save_dir + 'train_label.json', 'w')
f_valid = open(label_save_dir + 'valid_label.json', 'w')
f_test = open(label_save_dir + 'test_label.json', 'w')
f_all = open(label_save_dir + 'all_label.json', 'w')
f_real = open(label_save_dir + 'real_label.json', 'w')
f_fake = open(label_save_dir + 'fake_label.json', 'w')
f_print = open(label_save_dir + 'print_label.json', 'w')
f_video = open(label_save_dir + 'video_label.json', 'w')
dataset_path = data_dir
path_list = glob.glob(dataset_path + '**/*.jpg', recursive=True)
path_list.sort()
for i in range(len(path_list)):
flag = int(path_list[i].split('/')[-2].split('_')[-1])
if (flag == 1):
label = 1
else:
label = 0
dict = {}
dict['photo_path'] = path_list[i]
dict['photo_label'] = label
all_final_json.append(dict)
if (path_list[i].find('/Train_files/') != -1):
train_final_json.append(dict)
elif(path_list[i].find('/Dev_files/') != -1):
valid_final_json.append(dict)
train_final_json.append(dict)
else:
test_final_json.append(dict)
if (label == 1):
real_final_json.append(dict)
else:
fake_final_json.append(dict)
if flag == 2 or flag == 3:
print_final_json.append(dict)
elif flag == 4 or flag == 5:
video_final_json.append(dict)
elif flag == 1:
prob = random.random()
if prob >= 0.5:
print_final_json.append(dict)
else:
video_final_json.append(dict)
print('\nOulu: ', len(path_list))
print('Oulu(train): ', len(train_final_json))
print('Oulu(valid): ', len(valid_final_json))
print('Oulu(test): ', len(test_final_json))
print('Oulu(all): ', len(all_final_json))
print('Oulu(real): ', len(real_final_json))
print('Oulu(fake): ', len(fake_final_json))
print('Oulu(print): ', len(print_final_json))
print('Oulu(video): ', len(video_final_json))
json.dump(train_final_json, f_train, indent=4)
f_train.close()
json.dump(valid_final_json, f_valid, indent=4)
f_valid.close()
json.dump(test_final_json, f_test, indent=4)
f_test.close()
json.dump(all_final_json, f_all, indent=4)
f_all.close()
json.dump(real_final_json, f_real, indent=4)
f_real.close()
json.dump(fake_final_json, f_fake, indent=4)
f_fake.close()
json.dump(print_final_json, f_print, indent=4)
f_print.close()
json.dump(video_final_json, f_video, indent=4)
f_video.close()
if __name__=="__main__":
"""
change your data path
path_list: [msu_path, casia_path, idiap_path, oulu_path]
"""
path_list = args.data_path
msu_process(path_list[0], './labels/msu/')
casia_process(path_list[1], './labels/casia/')
idiap_process(path_list[2], './labels/idiap/')
oulu_process(path_list[3], './labels/oulu/')