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| import gradio as gr | |
| from transformers import DPTImageProcessor, DPTForDepthEstimation | |
| import torch | |
| import numpy as np | |
| from PIL import Image | |
| torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg') | |
| processor = DPTImageProcessor.from_pretrained("Intel/dpt-large") | |
| model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large") | |
| def process_image(image): | |
| # prepare image for the model | |
| encoding = processor(image, return_tensors="pt") | |
| # forward pass | |
| with torch.no_grad(): | |
| outputs = model(**encoding) | |
| predicted_depth = outputs.predicted_depth | |
| # interpolate to original size | |
| prediction = torch.nn.functional.interpolate( | |
| predicted_depth.unsqueeze(1), | |
| size=image.size[::-1], | |
| mode="bicubic", | |
| align_corners=False, | |
| ).squeeze() | |
| output = prediction.cpu().numpy() | |
| formatted = (output * 255 / np.max(output)).astype('uint8') | |
| img = Image.fromarray(formatted) | |
| return img | |
| return result | |
| title = "Demo: zero-shot depth estimation with DPT" | |
| description = "Demo for Intel's DPT, a Dense Prediction Transformer for state-of-the-art dense prediction tasks such as semantic segmentation and depth estimation." | |
| examples =[['cats.jpg']] | |
| iface = gr.Interface(fn=process_image, | |
| inputs=gr.inputs.Image(type="pil"), | |
| outputs=gr.outputs.Image(type="pil", label="predicted depth"), | |
| title=title, | |
| description=description, | |
| examples=examples, | |
| enable_queue=True) | |
| iface.launch(debug=True) |