app.py

import os
os.system('pip install gradio --upgrade')
os.system('pip install torch')
os.system('pip freeze')
import torch
import gradio as gr
model = torch.hub.load("PeterL1n/RobustVideoMatting", "mobilenetv3") # or "resnet50"

convert_video = torch.hub.load("PeterL1n/RobustVideoMatting", "converter")

def inference(video):
 convert_video(
 model, # The loaded model, can be on any device (cpu or cuda).
 input_source=video, # A video file or an image sequence directory.
 input_resize=(400, 400), # [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= None, # [Optional] Output the raw alpha prediction.
 output_foreground= None, # [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'
 
title = "Robust Video Matting"
description = "Gradio demo for Robust Video Matting. To use it, simply upload your video, or click one of the examples to load them. 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>"

examples = [['pexels-darina-belonogova-7539228.mp4']]
gr.Interface(
 inference, 
 gr.inputs.Video(label="Input"), 
 gr.outputs.Video(label="Output"),
 title=title,
 description=description,
 article=article,
 enable_queue=True,
 examples=examples
 ).launch(debug=True)