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| import gradio as gr | |
| from transformers import DPTFeatureExtractor, DPTForDepthEstimation | |
| import torch | |
| import numpy as np | |
| from PIL import Image | |
| feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large") | |
| model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large") | |
| def process_image(image): | |
| # prepare image for the model | |
| encoding = feature_extractor(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 = "Depth Estimation" | |
| description = "Upload an image and get the depth visualization" | |
| examples =[['house.jpg'], ['plane.webp'], ['room.webp']] | |
| 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) | |
| iface.launch(debug=True) |