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import os
import shutil
import gradio as gr
from PIL import Image
import subprocess
#os.chdir('Restormer')
examples = [['project/cartoon2.jpg','project/video1.mp4'],
['project/cartoon3.jpg','project/video2.mp4'],
['project/celeb1.jpg','project/video1.mp4'],
['project/celeb2.jpg','project/video2.mp4'],
]
title = "DaGAN"
description = """
Gradio demo for <b>Depth-Aware Generative Adversarial Network for Talking Head Video Generation</b>, CVPR 2022. <a href='https://arxiv.org/abs/2203.06605'>[Paper]</a><a href='https://github.com/harlanhong/CVPR2022-DaGAN'>[Github Code]</a>\n Read more at the links below. Upload a video file (cropped to face), a facial image and have fun :D. Please note that your video will be trimmed to first 8 seconds.
"""
##With Restormer, you can perform: (1) Image Denoising, (2) Defocus Deblurring, (3) Motion Deblurring, and (4) Image Deraining.
##To use it, simply upload your own image, or click one of the examples provided below.
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2203.06605'>Depth-Aware Generative Adversarial Network for Talking Head Video Generation</a> | <a href='https://github.com/harlanhong/CVPR2022-DaGAN'>Github Repo</a></p>"
def inference(img, video):
if not os.path.exists('temp'):
os.system('mkdir temp')
# trim video to 8 seconds
cmd = f"ffmpeg -y -ss 00:00:00 -i {video} -to 00:00:08 -c copy video_input.mp4"
subprocess.run(cmd.split())
video = "video_input.mp4"
#### Resize the longer edge of the input image
# os.system("ffmpeg -y -ss 00:00:00 -i {video} -to 00:00:08 -c copy temp/driving_video.mp4")
# driving_video = "video_input.mp4"
os.system("python demo_dagan.py --source_image {} --driving_video {} --output 'temp/rst.mp4'".format(img,video))
return f'temp/rst.mp4'
gr.Interface(
inference,
[
gr.inputs.Image(type="filepath", label="Source Image"),
gr.inputs.Video(type='mp4',label="Driving Video"),
],
gr.outputs.Video(type="mp4", label="Output Video"),
title=title,
description=description,
article=article,
theme ="huggingface",
examples=examples,
allow_flagging=False,
).launch(debug=False,enable_queue=True)
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