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| import os | |
| os.system('pip install gradio --upgrade') | |
| 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=(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. 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) |