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| import gradio as gr | |
| from transformers import DPTFeatureExtractor, DPTForDepthEstimation | |
| import torch | |
| import numpy as np | |
| from PIL import Image | |
| import open3d as o3d | |
| from pathlib import Path | |
| import os | |
| feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large") | |
| model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large") | |
| def process_image(image_path): | |
| image_path = Path(image_path) | |
| image_raw = Image.open(image_path) | |
| image = image_raw.resize( | |
| (800, int(800 * image_raw.size[1] / image_raw.size[0])), | |
| Image.Resampling.LANCZOS) | |
| # 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, | |
| ).squeeze() | |
| output = prediction.cpu().numpy() | |
| depth_image = (output * 255 / np.max(output)).astype('uint8') | |
| gltf_path = create_3d_obj(np.array(image), depth_image, image_path) | |
| img = Image.fromarray(depth_image) | |
| return [img, gltf_path, gltf_path] | |
| def create_3d_obj(rgb_image, depth_image, image_path): | |
| depth_o3d = o3d.geometry.Image(depth_image) | |
| image_o3d = o3d.geometry.Image(rgb_image) | |
| rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth( | |
| image_o3d, depth_o3d, convert_rgb_to_intensity=False) | |
| w = int(depth_image.shape[1]) | |
| h = int(depth_image.shape[0]) | |
| camera_intrinsic = o3d.camera.PinholeCameraIntrinsic() | |
| camera_intrinsic.set_intrinsics(w, h, 500, 500, w/2, h/2) | |
| pcd = o3d.geometry.PointCloud.create_from_rgbd_image( | |
| rgbd_image, camera_intrinsic) | |
| print('normals') | |
| pcd.normals = o3d.utility.Vector3dVector( | |
| np.zeros((1, 3))) # invalidate existing normals | |
| pcd.estimate_normals( | |
| search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.01, max_nn=30)) | |
| pcd.transform([[1, 0, 0, 0], | |
| [0, -1, 0, 0], | |
| [0, 0, -1, 0], | |
| [0, 0, 0, 1]]) | |
| pcd.transform([[-1, 0, 0, 0], | |
| [0, 1, 0, 0], | |
| [0, 0, 1, 0], | |
| [0, 0, 0, 1]]) | |
| print('run Poisson surface reconstruction') | |
| with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug) as cm: | |
| mesh_raw, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson( | |
| pcd, depth=10, width=0, scale=1.1, linear_fit=True) | |
| voxel_size = max(mesh_raw.get_max_bound() - mesh_raw.get_min_bound()) / 256 | |
| print(f'voxel_size = {voxel_size:e}') | |
| mesh = mesh_raw.simplify_vertex_clustering( | |
| voxel_size=voxel_size, | |
| contraction=o3d.geometry.SimplificationContraction.Average) | |
| # vertices_to_remove = densities < np.quantile(densities, 0.001) | |
| # mesh.remove_vertices_by_mask(vertices_to_remove) | |
| bbox = pcd.get_axis_aligned_bounding_box() | |
| mesh_crop = mesh.crop(bbox) | |
| print(mesh) | |
| gltf_path = f'./{image_path.stem}.gltf' | |
| o3d.io.write_triangle_mesh( | |
| gltf_path, mesh_crop, write_triangle_uvs=True) | |
| return gltf_path | |
| title = "Demo: zero-shot depth estimation with DPT + 3D Point Cloud" | |
| 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." | |
| examples = [["examples/" + img] for img in os.listdir("examples/")] | |
| iface = gr.Interface(fn=process_image, | |
| inputs=[gr.inputs.Image( | |
| type="filepath", label="Input Image")], | |
| outputs=[gr.outputs.Image(label="predicted depth", type="pil"), | |
| gr.outputs.Image3D(label="3d mesh reconstruction", clear_color=[ | |
| 1.0, 1.0, 1.0, 1.0]), | |
| gr.outputs.File(label="3d gLTF")], | |
| title=title, | |
| description=description, | |
| examples=examples, | |
| allow_flagging="never") | |
| iface.launch(debug=True, enable_queue=True, cache_examples=True) | |