Spaces:
Running
Running
Update z axis code
Browse files- .gitignore +46 -45
- README.md +11 -3
- app.py +28 -14
- pre-requirements.txt +1 -0
- requirements.txt +9 -5
- web-ui.bat +1 -1
.gitignore
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# Python build
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.eggs/
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gradio.egg-info/*
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!gradio.egg-info/requires.txt
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!gradio.egg-info/PKG-INFO
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dist/
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*.pyc
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__pycache__/
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*.py[cod]
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*$py.class
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build/
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# JS build
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gradio/templates/frontend
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# Secrets
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.env
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# Gradio run artifacts
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*.db
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*.sqlite3
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gradio/launches.json
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flagged/
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gradio_cached_examples/
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# Tests
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.coverage
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coverage.xml
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test.txt
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# Demos
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demo/tmp.zip
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demo/files/*.avi
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demo/files/*.mp4
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*.db-journal
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/.vs
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# Python build
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.eggs/
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gradio.egg-info/*
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!gradio.egg-info/requires.txt
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!gradio.egg-info/PKG-INFO
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dist/
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*.pyc
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__pycache__/
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*.py[cod]
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*$py.class
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build/
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# JS build
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gradio/templates/frontend
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# Secrets
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.env
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# Gradio run artifacts
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*.db
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*.sqlite3
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gradio/launches.json
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flagged/
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gradio_cached_examples/
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# Tests
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.coverage
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coverage.xml
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test.txt
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# Demos
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demo/tmp.zip
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demo/files/*.avi
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demo/files/*.mp4
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models/
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# Etc
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.idea/*
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.DS_Store
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*.bak
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workspace.code-workspace
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*.h5
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.vscode/
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# log files
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.pnpm-debug.log
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venv/
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*.db-journal
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/.vs
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README.md
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---
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title: DPT Depth Estimation + 3D
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emoji: ⚡
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colorTo: red
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sdk: gradio
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sdk_version: 5.16.1
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app_file: app.py
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
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---
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title: DPT Depth Estimation + 3D
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emoji: ⚡
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short_description: Image to 3D with DPT + 3D Point Cloud
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colorFrom: yellow
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colorTo: red
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python_version: 3.10.13
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sdk: gradio
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sdk_version: 5.16.1
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app_file: app.py
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license: apache-2.0
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tags:
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- depth
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- 3d
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hf_oauth: true
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fullWidth: false
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thumbnail: >-
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https://cdn-uploads.huggingface.co/production/uploads/6346595c9e5f0fe83fc60444/s0fQvcoiSBlH36AXpVwPi.png
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
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app.py
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@@ -12,7 +12,9 @@ from transformers import DPTForDepthEstimation, DPTImageProcessor
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image_processor = DPTImageProcessor.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, resized_width=800, z_scale=208):
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"""
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Processes the input image to generate a depth map and a 3D mesh reconstruction.
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predicted_depth.unsqueeze(1),
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size=(image.height, image.width),
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mode="bicubic",
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align_corners=
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).squeeze()
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# Normalize the depth image to 8-bit
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depth_min, depth_max = prediction.min(), prediction.max()
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depth_image = ((prediction - depth_min) / (depth_max - depth_min) * 255).astype("uint8")
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gltf_path = create_3d_obj(np.array(image), prediction, image_path, depth=8, z_scale=z_scale)
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img = Image.fromarray(depth_image)
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return [img, gltf_path, gltf_path]
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def create_3d_obj(rgb_image, raw_depth, image_path, depth=10, z_scale=200):
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"""
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Creates a 3D object from RGB and depth images.
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camera_intrinsic = o3d.camera.PinholeCameraIntrinsic(
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width,
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height,
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fx=
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fy=
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cx=width / 2.0,
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cy=height / 2.0,
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)
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# Scale the Z dimension
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points = np.asarray(pcd.points)
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depth_scaled = ((raw_depth - raw_depth.min()) / (raw_depth.max() - raw_depth.min())) * z_scale
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z_values = depth_scaled.flatten()[:len(points)]
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points[:, 2] *= z_values
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pcd.points = o3d.utility.Vector3dVector(points)
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# Estimate and orient normals
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pcd.estimate_normals(
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search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.01, max_nn=
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)
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pcd.orient_normals_towards_camera_location(camera_location=np.array([0.0, 0.0,
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# Apply transformations
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pcd.transform([[1, 0, 0, 0],
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)
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# Create Gradio sliders for resized_width and z_scale
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resized_width_slider = gr.Slider(
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minimum=
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maximum=
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step=16,
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value=800,
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label="Resized Width",
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)
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z_scale_slider = gr.Slider(
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minimum=
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maximum=
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step=
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value=
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label="Z-Scale",
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info="Adjust the scaling factor for the Z-axis in the 3D model."
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)
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examples = [["examples/" + img] for img in os.listdir("examples/")]
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iface = gr.Interface(
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fn=process_image,
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inputs=[
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title=title,
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description=description,
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examples=examples,
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allow_flagging="never",
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cache_examples=False,
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theme="Surn/Beeuty"
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)
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image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large")
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model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
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import spaces
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@spaces.GPU(duration=90,progress=gr.Progress(track_tqdm=True))
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def process_image(image_path, resized_width=800, z_scale=208):
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"""
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Processes the input image to generate a depth map and a 3D mesh reconstruction.
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predicted_depth.unsqueeze(1),
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size=(image.height, image.width),
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mode="bicubic",
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align_corners=False,
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).squeeze()
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# Normalize the depth image to 8-bit
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if torch.cuda.is_available():
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prediction = prediction.numpy()
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else:
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prediction = prediction.cpu().numpy()
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depth_min, depth_max = prediction.min(), prediction.max()
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depth_image = ((prediction - depth_min) / (depth_max - depth_min) * 255).astype("uint8")
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gltf_path = create_3d_obj(np.array(image), prediction, image_path, depth=8, z_scale=z_scale)
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img = Image.fromarray(depth_image)
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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return [img, gltf_path, gltf_path]
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@spaces.GPU()
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def create_3d_obj(rgb_image, raw_depth, image_path, depth=10, z_scale=200):
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"""
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Creates a 3D object from RGB and depth images.
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camera_intrinsic = o3d.camera.PinholeCameraIntrinsic(
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width,
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height,
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fx=z_scale,
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fy=z_scale,
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cx=width / 2.0,
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cy=height / 2.0,
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)
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# Scale the Z dimension
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points = np.asarray(pcd.points)
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depth_scaled = ((raw_depth - raw_depth.min()) / (raw_depth.max() - raw_depth.min())) * (z_scale*100)
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z_values = depth_scaled.flatten()[:len(points)]
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points[:, 2] *= z_values
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pcd.points = o3d.utility.Vector3dVector(points)
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# Estimate and orient normals
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pcd.estimate_normals(
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search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.01, max_nn=60)
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)
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pcd.orient_normals_towards_camera_location(camera_location=np.array([0.0, 0.0, 1.5 ]))
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# Apply transformations
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pcd.transform([[1, 0, 0, 0],
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)
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# Create Gradio sliders for resized_width and z_scale
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resized_width_slider = gr.Slider(
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minimum=256,
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maximum=1760,
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step=16,
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value=800,
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label="Resized Width",
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)
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z_scale_slider = gr.Slider(
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minimum=0.2,
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maximum=3.0,
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step=0.01,
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value=0.5,
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label="Z-Scale",
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info="Adjust the scaling factor for the Z-axis in the 3D model."
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)
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examples = [["examples/" + img] for img in os.listdir("examples/")]
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process_image.zerogpu = True
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gr.set_static_paths(paths=["models/","examples/"])
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iface = gr.Interface(
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fn=process_image,
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inputs=[
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title=title,
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description=description,
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examples=examples,
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examples_per_page=15,
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flagging_mode=None,
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allow_flagging="never",
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cache_examples=False,
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delete_cache=(86400,86400),
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theme="Surn/Beeuty"
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)
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pre-requirements.txt
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pip>=25.0.1
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requirements.txt
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transformers
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numpy
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Pillow
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jinja2
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open3d
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git+https://github.com/huggingface/diffusers.git
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git+https://github.com/huggingface/transformers.git
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safetensors
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sentencepiece
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git+https://github.com/huggingface/peft.git
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numpy
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Pillow>=11.1.0
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torch>=2.4.1
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jinja2
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open3d
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spaces
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web-ui.bat
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python311 -m app
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pause
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python311 -m app
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pause
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