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- README.md +1 -1
- app.py +0 -8
DESCRIPTION.md
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A demo for predicting the depth of an image and generating a 3D model of it.
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README.md
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---
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title: depth_estimation
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sdk: gradio
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---
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title: depth_estimation
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emoji: 🔥
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sdk: gradio
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app.py
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# URL: https://huggingface.co/spaces/radames/dpt-depth-estimation-3d-obj
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# DESCRIPTION: A demo for predicting the depth of an image and generating a 3D model of it.
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# imports
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import gradio as gr
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from transformers import DPTFeatureExtractor, DPTForDepthEstimation
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import torch
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from pathlib import Path
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import os
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# load the model
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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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# define the core and helper functions
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def process_image(image_path):
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image_path = Path(image_path)
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image_raw = Image.open(image_path)
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gltf_path, mesh_crop, write_triangle_uvs=True)
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return gltf_path
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# define the title, description and examples
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title = "Demo: zero-shot depth estimation with DPT + 3D Point Cloud"
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description = "This demo is a variation from the original <a href='https://huggingface.co/spaces/nielsr/dpt-depth-estimation' target='_blank'>DPT Demo</a>. It uses the DPT model to predict the depth of an image and then uses 3D Point Cloud to create a 3D object."
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examples = [["examples/1-jonathan-borba-CgWTqYxHEkg-unsplash.jpg"]]
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# define an interface with one Image input and 3 outputs: Image, Model3D and File
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iface = gr.Interface(fn=process_image,
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inputs=[gr.Image(
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type="filepath", label="Input Image")],
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allow_flagging="never",
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cache_examples=False)
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# launch
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iface.launch(debug=True, enable_queue=False)
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import gradio as gr
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from transformers import DPTFeatureExtractor, DPTForDepthEstimation
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import torch
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from pathlib import Path
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import os
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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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def process_image(image_path):
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image_path = Path(image_path)
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image_raw = Image.open(image_path)
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gltf_path, mesh_crop, write_triangle_uvs=True)
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return gltf_path
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title = "Demo: zero-shot depth estimation with DPT + 3D Point Cloud"
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description = "This demo is a variation from the original <a href='https://huggingface.co/spaces/nielsr/dpt-depth-estimation' target='_blank'>DPT Demo</a>. It uses the DPT model to predict the depth of an image and then uses 3D Point Cloud to create a 3D object."
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examples = [["examples/1-jonathan-borba-CgWTqYxHEkg-unsplash.jpg"]]
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iface = gr.Interface(fn=process_image,
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inputs=[gr.Image(
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type="filepath", label="Input Image")],
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allow_flagging="never",
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cache_examples=False)
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iface.launch(debug=True, enable_queue=False)
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