FaceForensics++数据库下载（超详细版教程）
相信很多做deepfake相关研究的朋友，在对模型进行测试或者对潜前人的研究进行复现时，都需要下载一系列数据库并进行预处理等操作，而FaceForensics++数据库是一个由数千个使用不同DeepFake方法操纵的视频组成，并包含四个假子数据集，即DeepFake Detection (DFD)， DeepFake (DF)， Face2Face (F2F)和FaceSwap (FS)。
由于这是国外的数据集所以一下的操作都需要挂代理来实现：（如果小伙伴无法挂代理可以评论留言，我发给你）
获取下载脚本并并保存到本地
ondyari/FaceForensics: Github of the FaceForensics dataset
https://github.com/ondyari/FaceForensicsff++的官网如上，按照上面的要求填写谷歌的表格，他会通过邮件给你发脚本代码，这里不赘述，我会在文章最后附上这个代码，大家就可以不用填写表格了。
CMD窗口下载数据库
打开cmd窗口
将文后我给的代码粘贴并后缀改为.py文件，确保你的文件命名为FaceForensics++.py
cd you_dir # 转到这个.py文件所属的文件夹下接着cmd窗口会显示已经进入目标文件夹，接着输入下载命令：
python FaceForensics++.py
//前面的python指你的电脑本身的python.exe文件，注意并不一定是“python”,需要观察你自己下载的python中的pythin.exe文件的命名是什么，比如笔者下载的python运行文件的命名是python3.11.exe，所以这里就应该python3.11
<output path>
//这里意思是数据下载的地址，即你的数据集要放在哪里（注意存储空间要足够）
-d <dataset type, e.g., Face2Face, original or all>
//如果你要下载FaceForensics++全部直接-all即可，也可以选择FaceForensics++数据集其中的一项来下载
-c <compression quality, e.g., c23 or raw>
//这里指压缩参数选择，如果想要下载原始数据可以选-raw,笔者下载的是-23压缩版
-t <file type, e.g., videos, masks or models>
//文件下载的类型-video即可下载deepfake的video
比如，要在D盘上的FaceForensics++文件里下载FaceForensics++数据集全部视频，以C23参数压缩，命令可以是：
python3.11 FaceForensics.py E:/FaceForensics++ -d all -c c23 -t videos
注意，运行过程中如果出现“502 BadGateway”提示，可能是你的服务不能使用脚本默认的，而是需要更改，脚本里面提供了三个server可供选择，分别是EU,EU2和CA，对应了欧洲1，2和加拿大，默认使用的是EU，脚本这部分代码如下：
parser.add_argument('--server', type=str, default='EU',
help='Server to download the data from. If you '
'encounter a slow download speed, consider '
'changing the server.',
choices=SERVERS
)
args = parser.parse_args()
# URLs
server = args.server
if server == 'EU':
server_url = 'http://canis.vc.in.tum.de:8100/'
elif server == 'EU2':
server_url = 'http://kaldir.vc.in.tum.de/faceforensics/'
elif server == 'CA':
server_url = 'http://falas.cmpt.sfu.ca:8100/'
else:
raise Exception('Wrong server name. Choices: {}'.format(str(SERVERS)))
args.tos_url = server_url + 'webpage/FaceForensics_TOS.pdf'
args.base_url = server_url + 'v3/'
args.deepfakes_model_url = server_url + 'v3/manipulated_sequences/' + \
'Deepfakes/models/'
return args
所以，在你的CMD窗口可以指定以下server，笔者在出错以后，将server改为EU2就可以顺利下载，总体命令如下：
python3.11 FaceForensics.py E:/FaceForensics++ --server EU2 -d all -c c23 -t videos
PS:–server EU2即指定server为EU2
接下来数据就会开始下载（会有进度条显示下载情况）
由于挂代理，所以可能会出现不稳定的停止下载的情况，如果遇到程序中断，没有关系，确保网络、代理正确连接后重新输入下载命令即可，它会自动跳过已经下载好的文件，继续下载其他文件。
建议下载数据集时尽量保证网速较快，并且代理稳定（否则一中断就重新输入一遍命令很麻烦）。
脚本
附：邮件提供的FaceForensics++脚本如下：（自己复制粘贴做成.py文件）
#!/usr/bin/env python
""" Downloads FaceForensics++ and Deep Fake Detection public data release
Example usage:
see -h or https://github.com/ondyari/FaceForensics
"""
# -*- coding: utf-8 -*-
import argparse
import os
import urllib
import urllib.request
import tempfile
import time
import sys
import json
import random
from tqdm import tqdm
from os.path import join
# URLs and filenames
FILELIST_URL = 'misc/filelist.json'
DEEPFEAKES_DETECTION_URL = 'misc/deepfake_detection_filenames.json'
DEEPFAKES_MODEL_NAMES = ['decoder_A.h5', 'decoder_B.h5', 'encoder.h5',]
# Parameters
DATASETS = {
'original_youtube_videos': 'misc/downloaded_youtube_videos.zip',
'original_youtube_videos_info': 'misc/downloaded_youtube_videos_info.zip',
'original': 'original_sequences/youtube',
'DeepFakeDetection_original': 'original_sequences/actors',
'Deepfakes': 'manipulated_sequences/Deepfakes',
'DeepFakeDetection': 'manipulated_sequences/DeepFakeDetection',
'Face2Face': 'manipulated_sequences/Face2Face',
'FaceShifter': 'manipulated_sequences/FaceShifter',
'FaceSwap': 'manipulated_sequences/FaceSwap',
'NeuralTextures': 'manipulated_sequences/NeuralTextures'
}
ALL_DATASETS = ['original', 'DeepFakeDetection_original', 'Deepfakes',
'DeepFakeDetection', 'Face2Face', 'FaceShifter', 'FaceSwap',
'NeuralTextures']
COMPRESSION = ['raw', 'c23', 'c40']
TYPE = ['videos', 'masks', 'models']
SERVERS = ['EU', 'EU2', 'CA']
def parse_args():
parser = argparse.ArgumentParser(
description='Downloads FaceForensics v2 public data release.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument('output_path', type=str, help='Output directory.')
parser.add_argument('-d', '--dataset', type=str, default='all',
help='Which dataset to download, either pristine or '
'manipulated data or the downloaded youtube '
'videos.',
choices=list(DATASETS.keys()) + ['all']
)
parser.add_argument('-c', '--compression', type=str, default='raw',
help='Which compression degree. All videos '
'have been generated with h264 with a varying '
'codec. Raw (c0) videos are lossless compressed.',
choices=COMPRESSION
)
parser.add_argument('-t', '--type', type=str, default='videos',
help='Which file type, i.e. videos, masks, for our '
'manipulation methods, models, for Deepfakes.',
choices=TYPE
)
parser.add_argument('-n', '--num_videos', type=int, default=None,
help='Select a number of videos number to '
"download if you don't want to download the full"
' dataset.')
parser.add_argument('--server', type=str, default='EU',
help='Server to download the data from. If you '
'encounter a slow download speed, consider '
'changing the server.',
choices=SERVERS
)
args = parser.parse_args()
# URLs
server = args.server
if server == 'EU':
server_url = 'http://canis.vc.in.tum.de:8100/'
elif server == 'EU2':
server_url = 'http://kaldir.vc.in.tum.de/faceforensics/'
elif server == 'CA':
server_url = 'http://falas.cmpt.sfu.ca:8100/'
else:
raise Exception('Wrong server name. Choices: {}'.format(str(SERVERS)))
args.tos_url = server_url + 'webpage/FaceForensics_TOS.pdf'
args.base_url = server_url + 'v3/'
args.deepfakes_model_url = server_url + 'v3/manipulated_sequences/' + \
'Deepfakes/models/'
return args
def download_files(filenames, base_url, output_path, report_progress=True):
os.makedirs(output_path, exist_ok=True)
if report_progress:
filenames = tqdm(filenames)
for filename in filenames:
download_file(base_url + filename, join(output_path, filename))
def reporthook(count, block_size, total_size):
global start_time
if count == 0:
start_time = time.time()
return
duration = time.time() - start_time
progress_size = int(count * block_size)
speed = int(progress_size / (1024 * duration))
percent = int(count * block_size * 100 / total_size)
sys.stdout.write("\rProgress: %d%%, %d MB, %d KB/s, %d seconds passed" %
(percent, progress_size / (1024 * 1024), speed, duration))
sys.stdout.flush()
def download_file(url, out_file, report_progress=False):
out_dir = os.path.dirname(out_file)
if not os.path.isfile(out_file):
fh, out_file_tmp = tempfile.mkstemp(dir=out_dir)
f = os.fdopen(fh, 'w')
f.close()
if report_progress:
urllib.request.urlretrieve(url, out_file_tmp,
reporthook=reporthook)
else:
urllib.request.urlretrieve(url, out_file_tmp)
os.rename(out_file_tmp, out_file)
else:
tqdm.write('WARNING: skipping download of existing file ' + out_file)
def main(args):
# TOS
print('By pressing any key to continue you confirm that you have agreed '\
'to the FaceForensics terms of use as described at:')
print(args.tos_url)
print('***')
print('Press any key to continue, or CTRL-C to exit.')
_ = input('')
# Extract arguments
c_datasets = [args.dataset] if args.dataset != 'all' else ALL_DATASETS
c_type = args.type
c_compression = args.compression
num_videos = args.num_videos
output_path = args.output_path
os.makedirs(output_path, exist_ok=True)
# Check for special dataset cases
for dataset in c_datasets:
dataset_path = DATASETS[dataset]
# Special cases
if 'original_youtube_videos' in dataset:
# Here we download the original youtube videos zip file
print('Downloading original youtube videos.')
if not 'info' in dataset_path:
print('Please be patient, this may take a while (~40gb)')
suffix = ''
else:
suffix = 'info'
download_file(args.base_url + '/' + dataset_path,
out_file=join(output_path,
'downloaded_videos{}.zip'.format(
suffix)),
report_progress=True)
return
# Else: regular datasets
print('Downloading {} of dataset "{}"'.format(
c_type, dataset_path
))
# Get filelists and video lenghts list from server
if 'DeepFakeDetection' in dataset_path or 'actors' in dataset_path:
filepaths = json.loads(urllib.request.urlopen(args.base_url + '/' +
DEEPFEAKES_DETECTION_URL).read().decode("utf-8"))
if 'actors' in dataset_path:
filelist = filepaths['actors']
else:
filelist = filepaths['DeepFakesDetection']
elif 'original' in dataset_path:
# Load filelist from server
file_pairs = json.loads(urllib.request.urlopen(args.base_url + '/' +
FILELIST_URL).read().decode("utf-8"))
filelist = []
for pair in file_pairs:
filelist += pair
else:
# Load filelist from server
file_pairs = json.loads(urllib.request.urlopen(args.base_url + '/' +
FILELIST_URL).read().decode("utf-8"))
# Get filelist
filelist = []
for pair in file_pairs:
filelist.append('_'.join(pair))
if c_type != 'models':
filelist.append('_'.join(pair[::-1]))
# Maybe limit number of videos for download
if num_videos is not None and num_videos > 0:
print('Downloading the first {} videos'.format(num_videos))
filelist = filelist[:num_videos]
# Server and local paths
dataset_videos_url = args.base_url + '{}/{}/{}/'.format(
dataset_path, c_compression, c_type)
dataset_mask_url = args.base_url + '{}/{}/videos/'.format(
dataset_path, 'masks', c_type)
if c_type == 'videos':
dataset_output_path = join(output_path, dataset_path, c_compression,
c_type)
print('Output path: {}'.format(dataset_output_path))
filelist = [filename + '.mp4' for filename in filelist]
download_files(filelist, dataset_videos_url, dataset_output_path)
elif c_type == 'masks':
dataset_output_path = join(output_path, dataset_path, c_type,
'videos')
print('Output path: {}'.format(dataset_output_path))
if 'original' in dataset:
if args.dataset != 'all':
print('Only videos available for original data. Aborting.')
return
else:
print('Only videos available for original data. '
'Skipping original.\n')
continue
if 'FaceShifter' in dataset:
print('Masks not available for FaceShifter. Aborting.')
return
filelist = [filename + '.mp4' for filename in filelist]
download_files(filelist, dataset_mask_url, dataset_output_path)
# Else: models for deepfakes
else:
if dataset != 'Deepfakes' and c_type == 'models':
print('Models only available for Deepfakes. Aborting')
return
dataset_output_path = join(output_path, dataset_path, c_type)
print('Output path: {}'.format(dataset_output_path))
# Get Deepfakes models
for folder in tqdm(filelist):
folder_filelist = DEEPFAKES_MODEL_NAMES
# Folder paths
folder_base_url = args.deepfakes_model_url + folder + '/'
folder_dataset_output_path = join(dataset_output_path,
folder)
download_files(folder_filelist, folder_base_url,
folder_dataset_output_path,
report_progress=False) # already done
if __name__ == "__main__":
args = parse_args()
main(args)
