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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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import numpy as np |
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from PIL import Image |
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import open3d as o3d |
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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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image = image_raw.resize( |
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(800, int(800 * image_raw.size[1] / image_raw.size[0])), |
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Image.Resampling.LANCZOS, |
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) |
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encoding = feature_extractor(image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**encoding) |
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predicted_depth = outputs.predicted_depth |
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prediction = torch.nn.functional.interpolate( |
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predicted_depth.unsqueeze(1), |
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size=image.size[::-1], |
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mode="bicubic", |
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align_corners=False, |
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).squeeze() |
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output = prediction.cpu().numpy() |
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depth_image = (output * 255 / np.max(output)).astype("uint8") |
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try: |
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gltf_path = create_3d_obj(np.array(image), depth_image, image_path) |
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img = Image.fromarray(depth_image) |
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return [img, gltf_path, gltf_path] |
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except Exception as e: |
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gltf_path = create_3d_obj(np.array(image), depth_image, image_path, depth=8) |
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img = Image.fromarray(depth_image) |
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return [img, gltf_path, gltf_path] |
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except: |
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print("Error reconstructing 3D model") |
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raise Exception("Error reconstructing 3D model") |
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def create_3d_obj(rgb_image, depth_image, image_path, depth=10): |
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depth_o3d = o3d.geometry.Image(depth_image) |
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image_o3d = o3d.geometry.Image(rgb_image) |
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rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth( |
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image_o3d, depth_o3d, convert_rgb_to_intensity=False |
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) |
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w = int(depth_image.shape[1]) |
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h = int(depth_image.shape[0]) |
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camera_intrinsic = o3d.camera.PinholeCameraIntrinsic() |
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camera_intrinsic.set_intrinsics(w, h, 500, 500, w / 2, h / 2) |
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pcd = o3d.geometry.PointCloud.create_from_rgbd_image(rgbd_image, camera_intrinsic) |
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print("normals") |
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pcd.normals = o3d.utility.Vector3dVector( |
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np.zeros((1, 3)) |
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) |
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pcd.estimate_normals( |
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search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.01, max_nn=30) |
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) |
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pcd.orient_normals_towards_camera_location( |
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camera_location=np.array([0.0, 0.0, 1000.0]) |
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) |
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pcd.transform([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]]) |
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pcd.transform([[-1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]) |
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print("run Poisson surface reconstruction") |
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with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug) as cm: |
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mesh_raw, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson( |
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pcd, depth=depth, width=0, scale=1.1, linear_fit=True |
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) |
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voxel_size = max(mesh_raw.get_max_bound() - mesh_raw.get_min_bound()) / 256 |
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print(f"voxel_size = {voxel_size:e}") |
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mesh = mesh_raw.simplify_vertex_clustering( |
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voxel_size=voxel_size, |
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contraction=o3d.geometry.SimplificationContraction.Average, |
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) |
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bbox = pcd.get_axis_aligned_bounding_box() |
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mesh_crop = mesh.crop(bbox) |
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gltf_path = f"./{image_path.stem}.gltf" |
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o3d.io.write_triangle_mesh(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/" + 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=[gr.Image(type="filepath", label="Input Image")], |
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outputs=[ |
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gr.Image(label="predicted depth", type="pil"), |
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gr.Model3D(label="3d mesh reconstruction", clear_color=[1.0, 1.0, 1.0, 1.0]), |
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gr.File(label="3d gLTF"), |
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], |
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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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) |
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iface.launch(debug=True, show_api=False) |
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