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| import numpy as np | |
| import open3d as o3d | |
| import open3d as o3d | |
| import plotly.express as px | |
| import numpy as np | |
| import pandas as pd | |
| 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 | |
| def point_cloud(rgb_image, depth_image): | |
| # Step 2: Create an RGBD image from the RGB and depth image | |
| 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) | |
| # Step 3: Create a PointCloud from the RGBD image | |
| pcd = o3d.geometry.PointCloud.create_from_rgbd_image(rgbd_image, o3d.camera.PinholeCameraIntrinsic(o3d.camera.PinholeCameraIntrinsicParameters.PrimeSenseDefault)) | |
| # Step 4: Convert PointCloud data to a NumPy array | |
| points = np.asarray(pcd.points) | |
| colors = np.asarray(pcd.colors) | |
| # Step 5: Create a DataFrame from the NumPy arrays | |
| data = {'x': points[:, 0], 'y': points[:, 1], 'z': points[:, 2], | |
| 'red': colors[:, 0], 'green': colors[:, 1], 'blue': colors[:, 2]} | |
| df = pd.DataFrame(data) | |
| # Step 6: Create a 3D scatter plot using Plotly Express | |
| fig = px.scatter_3d(df, x='x', y='y', z='z', color='red', size_max=0.1) | |
| return fig | |