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import numpy as np
import open3d as o3d
def create_3d_obj(rgb_image, depth_image, depth=10, path='./image.gltf'):
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 = path
o3d.io.write_triangle_mesh(
gltf_path, mesh_crop, write_triangle_uvs=True)
return gltf_path
def create_3d_pc(rgb_image, depth_image, depth=10):
depth_image = depth_image.astype(np.float32) # Convert depth map to float32
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])
# Specify camera intrinsic parameters (modify based on actual camera)
fx = 500
fy = 500
cx = w / 2
cy = h / 2
camera_intrinsic = o3d.camera.PinholeCameraIntrinsic(w, h, fx, fy, cx, cy)
pcd = o3d.geometry.PointCloud.create_from_rgbd_image(
rgbd_image, camera_intrinsic)
print('Estimating 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.]))
# Save the point cloud as a PLY file
filename = "pc.pcd"
o3d.io.write_point_cloud(filename, pcd)
return filename # Return the file path where the PLY file is saved
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