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Update app.py
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app.py
CHANGED
@@ -4,7 +4,7 @@ import torch
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import numpy as np
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from PIL import Image
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torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg')
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feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
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model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
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@@ -34,13 +34,12 @@ def process_image(image):
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title = "Demo: zero-shot depth estimation with DPT"
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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."
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iface = gr.Interface(fn=process_image,
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inputs=gr.inputs.Image(type="pil"),
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outputs=gr.outputs.Image(type="pil", label="predicted depth"),
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title=title,
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description=description,
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examples=examples,
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enable_queue=True)
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iface.launch(debug=True)
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import numpy as np
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from PIL import Image
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#torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg')
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feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")
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model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
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title = "Demo: zero-shot depth estimation with DPT"
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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."
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iface = gr.Interface(fn=process_image,
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inputs=gr.inputs.Image(type="pil"),
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outputs=gr.outputs.Image(type="pil", label="predicted depth"),
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title=title,
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description=description,
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enable_queue=True)
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iface.launch(debug=True)
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