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| import gradio as gr | |
| def greet(name): | |
| return "Hello " + name + "!!" | |
| # Model to use | |
| net_path = 'fire.pth' | |
| # CPU / GPU | |
| device = 'cpu' | |
| # Images will be downscaled to this size prior processing with the network | |
| image_size = 1024 | |
| # Wrapper | |
| def generate_matching_superfeatures(im1, im2, scale=6): | |
| # Possible Scales for multiscale inference | |
| scales = [2.0, 1.414, 1.0, 0.707, 0.5, 0.353, 0.25] | |
| # GRADIO APP | |
| title = "Visualizing Super-features" | |
| description = "TBD" | |
| article = "<p style='text-align: center'><a href='https://github.com/naver/fire' target='_blank'>Original Github Repo</a></p>" | |
| iface = gr.Interface( | |
| fn=generate_matching_superfeatures, | |
| inputs=[ | |
| gr.inputs.Image(shape=(240, 240), type="pil"), | |
| gr.inputs.Image(shape=(240, 240), type="pil"), | |
| gr.inputs.Slider(minimum=1, maximum=7, step=1, default=2, label="Scale")], | |
| outputs="plot", | |
| enable_queue=True, | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=[["chateau_1.png", "chateau_2.png", 6]], | |
| ) | |
| iface.launch() | |