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import os
os.system('pip install gradio --upgrade')
os.system('pip freeze')
import torch
import gradio as gr
from moviepy.editor import *
model = torch.hub.load("PeterL1n/RobustVideoMatting", "mobilenetv3") # or "resnet50"
convert_video = torch.hub.load("PeterL1n/RobustVideoMatting", "converter")
def inference(video):
#os.system('rm output.mp4')
#clip = VideoFileClip(video).subclip(0, 5)
#clip.write_videofile("output.mp4")
#os.system('ffmpeg -ss 00:00:00 -i '+ video +' -to 00:00:05 -c copy -y output.mp4')
clip = VideoFileClip(video)
print(clip.duration)
if clip.duration > 5:
return 'trim.mp4',"trim.mp4","trim.mp4"
convert_video(
model, # The loaded model, can be on any device (cpu or cuda).
input_source='output.mp4', # A video file or an image sequence directory.
input_resize=(500, 500), # [Optional] Resize the input (also the output).
downsample_ratio=0.25, # [Optional] If None, make downsampled max size be 512px.
output_type='video', # Choose "video" or "png_sequence"
output_composition='com.mp4', # File path if video; directory path if png sequence.
output_alpha="pha.mp4", # [Optional] Output the raw alpha prediction.
output_foreground="fgr.mp4", # [Optional] Output the raw foreground prediction.
output_video_mbps=4, # Output video mbps. Not needed for png sequence.
seq_chunk=7, # Process n frames at once for better parallelism.
num_workers=1, # Only for image sequence input. Reader threads.
progress=True # Print conversion progress.
)
return 'com.mp4',"pha.mp4","fgr.mp4"
title = "Robust Video Matting"
description = "Gradio demo for Robust Video Matting. To use it, simply upload your video, currently only mp4 and ogg formats are supported. Please trim video to 5 seconds or less. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2108.11515'>Robust High-Resolution Video Matting with Temporal Guidance</a> | <a href='https://github.com/PeterL1n/RobustVideoMatting'>Github Repo</a></p>"
gr.Interface(
inference,
gr.inputs.Video(label="Input"),
[gr.outputs.Video(label="Output Composition"),gr.outputs.Video(label="Output Alpha"),gr.outputs.Video(label="Output Foreground")],
title=title,
description=description,
article=article,
enable_queue=True).launch(debug=True) |