案例内容介绍
GAN 监督学习是一种联合端到端学习判别模型及其 GAN 生成的训练数据的方法。GANgealing将框架应用于密集视觉对齐问题。受经典 Congealing 方法的启发，GANgealing 算法训练空间变换器将随机样本从在未对齐数据上训练的 GAN 扭曲为共同的、联合学习的目标模式。目标模式已更新，以使空间转换器的工作“尽可能简单”。Spatial Transformer 专门针对 GAN 图像进行训练，并在测试时自动推广到真实图像。我们可以使用它来进行密集跟踪或创建物镜。例如，我们将给猫贴一张卡通脸，给 Elon Musk 贴上小胡子，给小狗贴上驯鹿角！

实验步骤
1.安装依赖包
安装完成之后需要重启Kernel，重启之后才会加载新安装的PyTorch库

!export CXX=g++
!pip install ninja==1.11.1 ray==2.1.0 plotly==4.14.3 torch==1.10.1 torchvision==0.11.2 moviepy==0.2.3.5 lmdb==0.99

2.下载代码

import os
import moxing as mox

if not os.path.exists('gangealing/'):
    mox.file.copy_parallel('obs://weilin/gangealing/', 'gangealing/')

3.进入案例文件夹

cd gangealing/gangealing

model：要检测的物体，celeba 代表人👩🏻、dog代表狗🐶、 cat代表猫🐱、 cub代表鸟🦜
pic：要添加的特效图片
video_name：要添加特效的视频

model = 'cat'     #@param ['celeba', 'dog', 'cat', 'cub']
pic = 'ModelArts.png'
video_name = 'demo.mp4'
os.environ['RAW_VIDEO_PATH'] = video_name
!chmod 777 ./ffmpeg
os.environ['FFMPEG_BINARY'] = os.path.join(os.getcwd(), 'ffmpeg')

4.对视频进行抽帧

from pathlib import Path
from utils.download import download_model, download_video
from applications.mixed_reality import run_gangealing_on_video
from applications import load_stn
from glob import glob
video_resolution = "512"       #@param [128, 256, 512, 1024, 2048, 4096, 8192]
pad_mode = 'center'            #@param ["center", "border"]
os.environ['FFMPEG_BINARY'] = os.path.join(os.getcwd(), 'ffmpeg')
os.environ['VIDEO_SIZE'] = video_size = str(video_resolution)
os.environ['PAD'] = pad_mode
video = Path(os.environ['RAW_VIDEO_PATH']).stem
os.environ['FRAME_PATH'] = f'data/video_frames/{video}'
os.environ['VIDEO_NAME'] = video
video_path = f'data/{video}'
!chmod 777 process_video.sh
!./process_video.sh "$RAW_VIDEO_PATH"
!python prepare_data.py --path "$FRAME_PATH" --out "data/$VIDEO_NAME" --pad "$PAD" --size "$VIDEO_SIZE"

5.为视频添加特效
根据视频的长度和硬件规格，运行此单元需要几分钟，您可以在下方监控进度。

fps = 30
batch_size = 1
use_flipping = False
memory_efficient_but_slower = False

if 'cutecat' in video_path:
    fps = 60

    
class MyDict(): 
    def __init__(self): pass


args = MyDict()
args.real_size = int(video_size)
args.real_data_path = video_path
args.fps = fps
args.batch = batch_size
args.transform = ['similarity', 'flow']
args.flow_size = 128
args.stn_channel_multiplier = 0.5
args.num_heads = 1
args.distributed = False  # Colab only uses 1 GPU
args.clustering = False
args.cluster = None
args.objects = True
args.no_flip_inference = not use_flipping
args.save_frames = memory_efficient_but_slower
args.overlay_congealed = False
args.ckpt = model
args.override = False
args.out = 'visuals'

if pic == 'dense tracking':
    args.label_path = f'assets/masks/{model}_mask.png'
    # Feel free to change the parameters below:
    args.resolution = 128
    args.sigma = 1.3
    args.opacity = 0.8
    args.objects = False
else:  # object lense
    args.label_path = f'assets/objects/{model}/{pic}'
    args.resolution = 4 * int(video_size)
    args.sigma = 0.3
    args.opacity = 1.0
    args.objects = True

stn = load_stn(args)
print('Running Spatial Transformer on frames...')
run_gangealing_on_video(args, stn, classifier=None)
print('Preparing videos to be displayed...')

from IPython.display import HTML
from base64 import b64encode

num = len(list(glob(f'{video}_compressed*')))
compressed_name = f'{video}_compressed{num}.mp4'
congealed_compressed_name = f'{video}_compressed_congealed{num}.mp4'
path = f'visuals/video_{video}/propagated.mp4'
congealed_path = f'visuals/video_{video}/congealed.mp4'
os.system(f"ffmpeg -i {path} -vcodec libx264 {compressed_name}")
os.system(f"ffmpeg -i {congealed_path} -vcodec libx264 {congealed_compressed_name}")

6.添加特效前的视频

mp4 = open(video_name,'rb').read()
data_url = "data:video/mp4;base64," + b64encode(mp4).decode()
HTML("""<video width=512 autoplay controls loop><source src="%s" type="video/mp4"></video>""" % (data_url))

7.添加特效后的视频

mp4_1 = open(compressed_name,'rb').read()
data_url_1 = "data:video/mp4;base64," + b64encode(mp4_1).decode()
HTML("""<video width=512 autoplay controls loop><source src="%s" type="video/mp4"></video>""" % (data_url_1))

8.制作自己的特效视频
上传自己的视频，将视频放在gangealing/gangealing/下面
上传自己的图片，将图片放在gangealing/gangealing/assets/objects/*/对应的种类的文件夹下面，自己制作的特效图片尺寸要是8192x8192
修改步骤3里的3个参数，重新运行一遍即可！