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| 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=None, # [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=12, # 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 = "Anime2Sketch" | |
| description = "demo for Anime2Sketch. To use it, simply upload your image, 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/2104.05703'>Adversarial Open Domain Adaption for Sketch-to-Photo Synthesis</a> | <a href='https://github.com/Mukosame/Anime2Sketch'>Github Repo</a></p>" | |
| gr.Interface( | |
| inference, | |
| gr.inputs.Video(label="Input"), | |
| gr.outputs.Video(label="Output"), | |
| title=title, | |
| description=description, | |
| article=article, | |
| enable_queue=True | |
| ).launch(debug=True) |