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 = "

Original Github Repo

" 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()