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| import gradio as gr | |
| from transformers import DPTFeatureExtractor, DPTForDepthEstimation | |
| import torch | |
| import numpy as np | |
| torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg') | |
| feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large") | |
| model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large") | |
| def compute_depth(depth, bits): | |
| depth_min = depth.min() | |
| depth_max = depth.max() | |
| max_val = (2 ** (8 * bits)) - 1 | |
| if depth_max - depth_min > np.finfo("float").eps: | |
| out = max_val * (depth - depth_min) / (depth_max - depth_min) | |
| else: | |
| out = np.zeros(depth.shape, dtype=depth.dtype) | |
| return out/65536 | |
| 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, | |
| ) | |
| prediction = prediction.squeeze().cpu().numpy() | |
| result = compute_depth(prediction, bits=2) | |
| return result | |
| title = "Interactive demo: 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(label="predicted depth"), | |
| title=title, | |
| description=description, | |
| examples=examples, | |
| enable_queue=True) | |
| iface.launch(debug=True) |