Visualize Spatial View

Overview

This example uses a simple obj file included in example models. For context of what the top-down view is based on, see the figure below.

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

from dhart.geometry import LoadOBJ
from dhart.raytracer import EmbreeBVH
from dhart.graphgenerator import GenerateGraph
from dhart.viewanalysis import SphericalViewAnalysis, SphericalViewAnalysisAggregate, AggregationType
import dhart as hfpy

# Get a sample model path
obj_path = hfpy.get_sample_model("VisibilityTestCases.obj")

# Load the obj file
obj = LoadOBJ(obj_path)

# Create a BVH
bvh = EmbreeBVH(obj, True)

# Set the graph parameters
# Test examples are failing on 1. Seems to be embree failing on edge intersection
start_point = (1.1 , 1.1, 20) #offset X by 60 for each new model
spacing = (1, 1, 5)
max_nodes = 10000
up_step, down_step = 0.1, 0.1
up_slope, down_slope = 1, 1
max_step_connections = 1

# Generate the Graph
graph = GenerateGraph(bvh, start_point, spacing, max_nodes,
                        up_step,up_slope,down_step,down_slope,
                        max_step_connections, cores=-1)

# Get the nodes of the graph as a list of x,y,z,type,id tuples
nodes = graph.getNodes()
print(len(nodes))

height = 1.7 # Set a height offset to cast rays from the points
ray_count = 1500 # Set the number of rays to use per node
scores = SphericalViewAnalysisAggregate(bvh, nodes, ray_count, height,
                                                    upward_fov = 20, downward_fov=20,
                                                    agg_type=AggregationType.AVERAGE)

# Plot the graph using visibility graph as the colors
fig = plt.figure(figsize=(6,6))
plt.scatter(nodes['x'], nodes['y'], c=scores)
plt.show()

(Source code)

We can see all of the example cases in the test file by regenerating the graph by our known model offset

for i in range(5):

    # Set the graph parameters
    # Test examples are failing on 1. Seems to be embree failing on edge intersection
    start_point = (1.1 + (60*i) , 1.1, 20) #offset X by 60 for each new model

    # Generate the Graph
    graph = GenerateGraph(bvh, start_point, spacing, max_nodes,
                            up_step,up_slope,down_step,down_slope,
                            max_step_connections, cores=-1)

    # Convert the graph to a CSR
    csr_graph = graph.CompressToCSR()

    # Get the nodes of the graph as a list of x,y,z,type,id tuples
    nodes = graph.getNodes()
    # Calculate view analysis on the new graph
    scores = SphericalViewAnalysisAggregate(bvh, nodes, ray_count, height,
                                            upward_fov = 20, downward_fov=20,
                                            agg_type=AggregationType.AVERAGE)

    # Plot the graph using visibility graph as the colors
    fig = plt.figure(figsize=(6,6))
    plt.scatter(nodes['x'], nodes['y'], c=scores)
    plt.show()

(Source code)