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3_2d_to_3d_vectors.py
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# Import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
import open3d as o3d
import laspy
import pandas as pd
import alphashape as ash
import geopandas as gpd
import shapely as sh
# 2. Data Profiling
# Load the neighborhood point cloud (ensure lazrs or laszip is installed for .laz files)
try:
las = laspy.read(r'data/neighborhood.laz')
except laspy.LaspyException as e:
print(f"Error reading the LAS file: {e}")
# Display unique classifications in the dataset
print("Unique classifications in the dataset:", np.unique(las.classification))
# List available dimensions in the LAS file
print("Available dimensions in the LAS file:", [dimension.name for dimension in las.point_format.dimensions])
# Check and display CRS information if available
if len(las.vlrs) > 2:
crs = las.vlrs[2].string
print("Coordinate Reference System (CRS):", crs)
else:
print("No CRS information available in VLRs.")
# 3. Data Pre-Processing
# 3.1. Building points initialization
# Create a mask to filter points classified as buildings (classification code 6)
pts_mask = las.classification == 6
xyz_t = np.vstack((las.x[pts_mask], las.y[pts_mask], las.z[pts_mask]))
pcd_o3d = o3d.geometry.PointCloud()
pcd_o3d.points = o3d.utility.Vector3dVector(xyz_t.transpose())
pcd_center = pcd_o3d.get_center()
pcd_o3d.translate(-pcd_center)
# o3d.visualization.draw_geometries([pcd_o3d])
# Ground Plane
pts_mask = las.classification == 2
xyz_t = np.vstack((las.x[pts_mask], las.y[pts_mask], las.z[pts_mask]))
ground_pts = o3d.geometry.PointCloud()
ground_pts.points = o3d.utility.Vector3dVector(xyz_t.transpose())
ground_pts.translate(-pcd_center)
# o3d.visualization.draw_geometries([ground_pts])
# Get Average Distance between building points
nn_distance = np.mean(pcd_o3d.compute_nearest_neighbor_distance())
print("Average distance between building points:", nn_distance)
# 4. Unsupervised Segmentation
epsilon = 2
min_cluster_size = 100
labels = np.array(pcd_o3d.cluster_dbscan(eps=epsilon, min_points=min_cluster_size, print_progress=True))
max_label = labels.max()
print(f"point cloud has {max_label + 1} clusters")
colors = plt.get_cmap("tab20")(labels / (max_label if max_label > 0 else 1))
colors[labels < 0] = 0
pcd_o3d.colors = o3d.utility.Vector3dVector(colors[:, :3])
# o3d.visualization.draw_geometries([pcd_o3d])
# 5. Selecting a segment
sel = 1
segment = pcd_o3d.select_by_index(np.where(labels == sel)[0])
# o3d.visualization.draw_geometries([segment])
# 6. Extracting the segment
points_2D = np.asarray(segment.points)[:, :2]
building_vector = ash.alphashape(points_2D, alpha=0.5)
# 7. Store in a geodataframe the 2D polygon
building_gdf = gpd.GeoDataFrame(geometry=[building_vector], crs='EPSG:26910')
print(building_gdf.head(1))
# 7.1 Computing semantics and attributes
# Height of the building
altitude = np.asarray(segment.points)[:, 2]+pcd_center[2]
height_test = np.max(altitude) - np.min(altitude)
print("Height of the building (Initial):", height_test)
# Define Ground Plane
query_point = segment.get_center()
query_point[2] = segment.get_min_bound()[2]
pcd_tree = o3d.geometry.KDTreeFlann(ground_pts)
[k, idx, _] = pcd_tree.search_knn_vector_3d(query_point, 200)
sample = ground_pts.select_by_index(idx, invert=False)
sample.paint_uniform_color([0.5, 0.5, 0.5])
# o3d.visualization.draw_geometries([sample, ground_pts])
ground_zero = sample.get_center()[2]
height = segment.get_max_bound()[2] - ground_zero
print("Height of the building (Final):", height)
print("Difference in height:", height - height_test)
# 7.2 Computing parameters
building_gdf[['id']] = sel
building_gdf[['height']] = height
building_gdf[['area']] = building_vector.area
building_gdf[['perimeter']] = building_vector.length
building_gdf[['local_cx', 'local_cy', 'local_cz']] = np.asarray([building_vector.centroid.x, building_vector.centroid.y, ground_zero])
building_gdf[['transl_x', 'transl_y', 'transl_z']] = pcd_center
building_gdf[['pts_number']] = len(segment.points)
print(building_gdf.head(1))
# 8. 2D to 3D
# Generate the vertice list
vertices = list(building_vector.exterior.coords)
# Construct the Open3D Object
polygon_2d = o3d.geometry.LineSet()
polygon_2d.points = o3d.utility.Vector3dVector([point + (0,) for point in vertices])
polygon_2d.lines = o3d.utility.Vector2iVector([[i, (i + 1) % len(vertices)] for i in range(len(vertices))])
# o3d. visualization.draw_geometries([polygon_2d])
# 8.2 The top layer
extrusion = o3d.geometry.LineSet()
extrusion.points = o3d.utility.Vector3dVector([point + (height,) for point in vertices])
extrusion.lines = o3d.utility.Vector2iVector([[i, (i + 1) % len(vertices)] for i in range(len(vertices))])
# o3d. visualization.draw_geometries([polygon_2d, extrusion])
# Plot the vertices
temp = polygon_2d + extrusion
temp.points
temp_o3d = o3d.geometry.PointCloud()
temp_o3d.points = temp.points
o3d.visualization.draw_geometries([temp_o3d])