L'algoritmo M3C2 viene utlizzato da Cloudcompare per effettuare il change detection ...volevo trovare una soluzione per rendere automatico il processing e la pubblicazione
Si parte da due LAS e tramite la libreria py4dgeo si ottiene un file las con i punti colorati secondo una scala colore basata sulla distanza tra i due LAS si origine
import numpy as np
import py4dgeo
import laspy
import sys
import math
py4dgeo.set_num_threads(4) # numero di threads attivi
epoch1, epoch2 = py4dgeo.read_from_las("2019.las", "2022.las")
corepoints = epoch1.cloud[::30]
m3c2 = py4dgeo.M3C2(epochs=(epoch1, epoch2),corepoints=corepoints,cyl_radii=(2.0,),normal_radii=(0.5, 1.0, 2.0),)
distances, uncertainties = m3c2.run()
dist_max = np.nanmax(distances)
dist_min = np.nanmin(distances)
reds = np.empty(corepoints.shape)
blues = np.empty(corepoints.shape)
greens = np.empty(corepoints.shape)
fattore =(dist_max-dist_min)/(dist_max+dist_min)
Red =0
Blue = 0
Green = 0
for i in range (distances.shape[0]):
if math.isnan(distances[i]):
t = 0
else:
#print(distances[i])
t = int(((distances[i]-dist_min)/(dist_max-dist_min)) * 16581375)
#print(t)
if math.isnan(t):
Blue = 0
Green = 0
Red = 0
else:
if (distances[i]< -1.5):
Red = 255
Green = 0
Blue = 0
if ((distances[i]>=-1.5) and (distances[i]<=-0.75)):
Red = 255
Green = 255
Blue = 0
if ((distances[i]>=-0.75) and (distances[i]<=0.75)):
Red = 0
Green = 255
Blue = 0
if ((distances[i]>=0.75) and (distances[i]<=1.5)):
Red = 0
Green = 255
Blue = 255
if (distances[i]> 1.5):
Red = 0
Green = 0
Blue = 255
#RGBint = t
#Red = RGBint & 255
#Blue = (RGBint >> 8) & 255
#Green = (RGBint >> 16) & 255'''
reds[i]= Red
greens[i]= Green
blues[i]=Blue
header = laspy.LasHeader(point_format=3, version="1.2")
header.add_extra_dim(laspy.ExtraBytesParams(name="distance", type=np.float32))
xmin = np.floor(np.min(corepoints[:,0]))
ymin = np.floor(np.min(corepoints[:,1]))
zmin = np.floor(np.min(corepoints[:,2]))
header.offsets = [xmin,ymin,zmin]
#header.scales = np.array([0.1, 0.1, 0.1])
las = laspy.LasData(header)
las.x = corepoints[:,0]
las.y = corepoints[:,1]
las.z = corepoints[:,2]
las.red = reds[:,0]
las.blue = blues[:,0]
las.green = greens[:,0]
las.distance = np.copy(distances)
las.write("m3c2_result.las")
#./PotreeConverter m3c2_result.las -o /var/www/html/m3c2/ --generate-page index
Per la pubblicazione su web il file viene convertito e vestito tramite
PoTree
Anders, K., Winiwarter, L., Lindenbergh, R., Williams, J. G., Vos, S. E., & Höfle, B. (2020). 4D objects-by-change: Spatiotemporal segmentation of geomorphic surface change from LiDAR time series. ISPRS Journal of Photogrammetry and Remote Sensing, 159, pp. 352-363. doi: 10.1016/j.isprsjprs.2019.11.025.
Anders, K., Winiwarter, L., Mara, H., Lindenbergh, R., Vos, S. E., & Höfle, B. (2021). Fully automatic spatiotemporal segmentation of 3D LiDAR time series for the extraction of natural surface changes. ISPRS Journal of Photogrammetry and Remote Sensing, 173, pp. 297-308. doi: 10.1016/j.isprsjprs.2021.01.015.
Truong, C., Oudre, L., Vayatis, N. (2018): ruptures: Change point detection in python. arXiv preprint: abs/1801.00826.
