Geological Map on Topography

Texture mapping for a GeoTIFF on a topography surface.

To overlay an image/map from a GeoTIFF on to a topography surface, it’s necessary to have texture coordinates (“texture mapping”) matching the proper extends of the mesh/surface you’d like to drape the texture (GeoTIFF) on.

We can do this by using the spatial reference of the GeoTIFF itself, as this allows you to preserve the entire mesh that the texture is being draped on without having to clip out the parts where you don’t have imagery. In this example, we explicitly set the texture extents onto a topography surface where the texture/GeoTIFF has a much larger extent than the topography surface.

Originally posted here: pyvista/pyvista-support#14

import os
import tempfile

import numpy as np
import pyvista as pv
from pyvista import examples
import requests

path = examples.download_file("topo_clean.vtk")
topo = pv.read(path)
topo

UnstructuredGrid	Information
N Cells	824278
N Points	413250
X Bounds	3.299e+05, 3.442e+05
Y Bounds	4.253e+06, 4.271e+06
Z Bounds	1.494e+03, 2.723e+03
N Arrays	0

Load the GeoTIFF/texture (this could take a minute to download) https://dl.dropbox.com/s/bp9j3fl3wbi0fld/downsampled_Geologic_map_on_air_photo.tif?dl=0

url = "https://dl.dropbox.com/s/bp9j3fl3wbi0fld/downsampled_Geologic_map_on_air_photo.tif?dl=0"

response = requests.get(url)
filename = os.path.join(tempfile.gettempdir(), "downsampled_Geologic_map_on_air_photo.tif")
open(filename, "wb").write(response.content)

8175934

In the block below, we can use the get_gcps function to get the Ground Control Points of the raster, however this depends on GDAL. For this tutorial, we are going to hard code the GCPs to avoid having users install GDAL.

def get_gcps(filename):
    """This helper function retrieves the Ground Control
    Points of a GeoTIFF. Note that this requires gdal"""
    import rasterio

    get_point = lambda gcp: np.array([gcp.x, gcp.y, gcp.z])
    # Load a raster
    src = rasterio.open(filename)
    # Grab the Groung Control Points
    points = np.array([get_point(gcp) for gcp in src.gcps[0]])
    # Now Grab the three corners of their bounding box
    # -- This guarantees we grab the right points
    bounds = pv.PolyData(points).bounds
    origin = [bounds[0], bounds[2], bounds[4]]  # BOTTOM LEFT CORNER
    point_u = [bounds[1], bounds[2], bounds[4]]  # BOTTOM RIGHT CORNER
    point_v = [bounds[0], bounds[3], bounds[4]]  # TOP LEFT CORNER
    return origin, point_u, point_v

# Fetch the GCPs
# origin, point_u, point_v = get_gcps(filename)

# Hard code GCPs
origin = [310967.75148705335, 4238841.045453942, 0.0]
point_u = [358682.9364281533, 4238841.045453942, 0.0]
point_v = [310967.75148705335, 4276281.98755258, 0.0]

# Use the GCPs to map the texture coordinates onto the topography surface
topo.texture_map_to_plane(origin, point_u, point_v, inplace=True)

Header	Data Arrays
UnstructuredGrid Information N Cells 824278 N Points 413250 X Bounds 3.299e+05, 3.442e+05 Y Bounds 4.253e+06, 4.271e+06 Z Bounds 1.494e+03, 2.723e+03 N Arrays 1	Name Field Type N Comp Min Max Texture Coordinates Points float32 2 3.737e-01 8.576e-01

Show GCPs in relation to topo surface with texture coordinates displayed

p = pv.Plotter()
p.add_point_labels(
    np.array(
        [
            origin,
            point_u,
            point_v,
        ]
    ),
    ["Origin", "Point U", "Point V"],
    point_size=5,
)

p.add_mesh(topo)
p.show(cpos="xy")

Read the GeoTIFF as a Texture in PyVista:

texture = pv.read_texture(filename)

# Now plot the topo surface with the texture draped over it
# And make window size large for a high-res screenshot
p = pv.Plotter(window_size=np.array([1024, 768]) * 3)
p.add_mesh(topo, texture=texture)
p.camera_position = [
    (337461.4124956896, 4257141.430658634, 2738.4956020899253),
    (339000.40935731295, 4260394.940646875, 1724.0720826501868),
    (0.10526647627366331, 0.2502863297360612, 0.962432190920575),
]
p.show()