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import numpy as np | |
import open3d as o3d | |
def create_3d_obj(rgb_image, depth_image, depth=10): | |
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.orient_normals_towards_camera_location( | |
camera_location=np.array([0., 0., 1000.])) | |
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=depth, 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) | |
gltf_path = './image.gltf' | |
o3d.io.write_triangle_mesh( | |
gltf_path, mesh_crop, write_triangle_uvs=True) | |
return gltf_path |