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| import gradio as gr | |
| import torch | |
| from utils import get_image_from_url, colorize | |
| from PIL import Image | |
| import matplotlib.pyplot as plt | |
| title = "Interactive demo: ZoeDepth" | |
| description = "Unofficial Gradio Demo for using ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth. ZoeDepth is a technique that lets you perform metric depth estimation from a single image. For more information, please refer to the <a href='https://arxiv.org/abs/2302.12288' style='text-decoration: underline;' target='_blank'> paper</a> or the <a href='https://github.com/isl-org/ZoeDepth' style='text-decoration: underline;' target='_blank'> Github </a> implementation. </p> To use it, simply upload an image or use one of the examples below and click 'Submit'. Results will show up in a few seconds." | |
| examples = [["example.png"],["example_2.png"]] | |
| repo = "isl-org/ZoeDepth" | |
| # Zoe_N | |
| model_zoe_n = torch.hub.load(repo, "ZoeD_NK", pretrained=True) | |
| DEVICE = "cuda" if torch.cuda.is_available() else "cpu" | |
| zoe = model_zoe_n.to(DEVICE) | |
| def process_image(image): | |
| depth = zoe.infer_pil(image) # as numpy | |
| colored_depth = colorize(depth, cmap = 'magma_r') | |
| return colored_depth | |
| interface = gr.Interface(fn=process_image, | |
| inputs=[gr.Image(type="pil")], | |
| outputs=[gr.Image(type="pil", label ="Depth") | |
| ], | |
| title=title, | |
| description=description, | |
| examples = examples | |
| ) | |
| interface.launch(debug=True) |