Jeu de traces simulées à partir d’un réseau#
Nous présentons ici un exemple simple et complet reposant sur une collection de traces simulées à partir d’un réseau. Le réseau est un extrait de la BDTOPO situé sur un versant de montagne en face de la ville de Chamonix, il représente un cas non complexe de tronçons.
Ce pipeline est exécuté en une seule itération.
Import des librairies#
Les deux librairies socles : tracklib et footprint2graph
Pour visualiser les résultats : matplotlib
[1]:
import os
import sys
import matplotlib.pyplot as plt
# Ajout dans la variable PATH du système du chemin où est installée la librairie tracklib
module_path = os.path.abspath(os.path.join('../../../../tracklib'))
if module_path not in sys.path:
sys.path.append(module_path)
# Alias pour tracklib
import tracklib as tkl
# Ajout dans la variable PATH du système du chemin où est installée la librairie footprint2graph
module_path = os.path.abspath(os.path.join('../../..'))
if module_path not in sys.path:
sys.path.append(module_path)
Import des données : réseau puis traces simulées#
[2]:
# WKT;link_id;source;target;direction;wkt_source;wkt_target
fmt = tkl.NetworkFormat({
"pos_edge_id": 1,
"pos_source": 2,
"pos_target": 3,
"pos_wkt": 0,
"srid": "ENU",
"separator": ";",
"header": 1})
netpath = os.path.abspath(os.path.join('../../../data/network2.csv'))
network = tkl.NetworkReader.readFromFile(netpath, fmt, verbose=False)
plt.figure(figsize=(8, 6))
network.plot('k-', '', 'g-', 'r-', 0.5, plt)
print ('Number of edges=', len(network.EDGES))
print ('Number of nodes=', len(network.NODES))
print ('')
Number of edges= 7
Number of nodes= 8
[3]:
tkl.stochastics.seed(333)
#
noiser = tkl.NoiseProcess(amps=2.5, kernels=tkl.ExponentialKernel(80))
# generate simulated trajectories from the network
collection = tkl.generateTracksOnNetwork(network, N=1000, p_round_trip=0.05, p_cplx_trip=0.10, resolution=1, noiser=noiser)
# add 3 attributes
for idx, track in enumerate(collection):
track.createAnalyticalFeature('TID', idx+1)
#
plt.figure(figsize=(8, 5))
collection.plot(append=plt)
100% (1000 of 1000) |####################| Elapsed Time: 0:00:18 Time: 0:00:180001
------------------------------------------------------------
877 (87.7 %) tracks generated on network
------------------------------------------------------------
Dossier de stockage des résultats#
[4]:
from footprint2graph import prepareEnv, setupEnv, logEnv
RESPATH = r'/home/md_vandamme/7_LIB/Footprint2graph-pipeline/test/result11/'
import os
import shutil
# Suppression des répertoires
prepareEnv(RESPATH)
# Création des répertoires
iteration_index = 1
setupEnv(RESPATH, iteration_index)
# Log environment information
logEnv(RESPATH)
# On définit un format pour le stockage des traces modifiées dans le pipeline
fmt = tkl.TrackFormat({'ext': 'CSV',
'srid': 'ENU',
'id_E': 1, 'id_N': 0, 'id_U': 3, 'id_T': 2,
'time_fmt': '2D/2M/4Y 2h:2m:2s',
'separator': ';',
'header': 0,
'cmt': '#',
'read_all': True})
pipeline_idx = 1
Step 1 : segmentation and resampling#
[5]:
from footprint2graph import segmentation_resample
# Paramètre : Nombre de points minimum pour un morceau de trace au moment du découpage
# si le nombre n'est pas atteint, le morceau de trace est oublié
NB_OBS_MIN = 10
# Paramètre : Distance en mètres entre 2 points, si supérieure au seuil on coupe la trace
DIST_MAX_2OBS = 50
RESAMPLE_SIZE_GRID = 1
RESAMPLE_SIZE_FUSION = 5
# =============================================================================
# On définit un format pour le stockage des traces modifiées dans le pipeline
segmentation_resample(RESPATH, collection, fmt, NB_OBS_MIN, DIST_MAX_2OBS,
RESAMPLE_SIZE_GRID, RESAMPLE_SIZE_FUSION)
Starting segmentation and resampling...
Starting segmentation ...
500 / 877
Number of tracks after segmentation: 877
Finished saving segmented tracks.
Starting resampling ...
Number of tracks to resample: 877
Number of tracks after resampling: 877
Number of tracks after resampling: 877
Finished saving resampled tracks.
Stage 1 finished: segmentation and resampling.
[6]:
print ('affiche des résultats ??')
affiche des résultats ??
Step 2 : calculs des cartes de densités, de constraste et binaire#
[7]:
from footprint2graph import density_polygonize
# Définition des grilles géométrique et contraste
G1_SIZE = 2
G2_SIZE = 30
SEUIL_DENSITE = 25 # pas d'unité
SEUIL_SURFACE = 1000 # m2
cut_factor = 5
interp_dist = 5
clean_dist = 0
density_polygonize(RESPATH, G1_SIZE, G2_SIZE, SEUIL_DENSITE, SEUIL_SURFACE,
pipeline_idx, cut_factor=cut_factor, interp_dist=interp_dist, clean_dist=clean_dist)
Starting rasterization and vectorization (iteration 1)
Loading tracks from : resample_grid
Number of tracks to load: 877
Building high-resolution geometry density grid G1 : 2 m ...
Building low-resolution contextual density grid G2 : 30 m ...
Assigning track points to the G1 and G2 grids
500 / 877
Computing G1 ...
Computing G2 ...
Number of neighboring cells to consider: 7
Building contrast grid : 2 m
Execution time (seconds): 7.25739049911499
Finished heatmap computation.
Starting morphological closingImage ...
100% (291 of 291) |######################| Elapsed Time: 0:00:00 Time: 0:00:00
100% (287 of 287) |######################| Elapsed Time: 0:00:00 Time: 0:00:00
0% (0 of 1417) | | Elapsed Time: 0:00:00 ETA: --:--:--
Execution time (seconds): 0.24849605560302734
Finished morphological opening.
Vectorizing cleaned image ...
Extracting road surface vector features ...
Number of polygonize features: 3
Number of polygonize features copied: 2
Execution time (seconds): 0.024357318878173828
Vectorization completed.
Smoothing polygon to remove stair-step artifacts ...
Execution time (seconds): 0.08345317840576172
Road surface smoothing completed.
Starting centerline computation ...
100% (1417 of 1417) |####################| Elapsed Time: 0:00:00 Time: 0:00:00
Execution time (seconds): 0.18981075286865234
Centerline computed.
Stage 2 completed: rasterization and vectorization.
[8]:
from footprint2graph.util.PlotRes import matPlotShapefile, matPlotRasterShp, maPlotRasterTiff
plt.figure(figsize=(15, 12))
# ----------------------------------------------------------------------------------------------------------
ax1 = plt.subplot2grid((3, 2), (0, 0))
rasterG1 = tkl.RasterReader.readFromAscFile(RESPATH + 'image/G1_1.asc', name='G1', separator='\t')
mapDensity = rasterG1.getAFMap('G1')
mapDensity.plotAsImage(cmap='jet', vmin=0, append=ax1)
# ----------------------------------------------------------------------------------------------------------
ax2 = plt.subplot2grid((3, 2), (0, 1))
rasterK = tkl.RasterReader.readFromAscFile(RESPATH + 'image/K_1.asc', name='K', separator='\t')
mapContraste = rasterK.getAFMap('K')
mapContraste.plotAsImage(cmap='jet', append=ax2)
# ----------------------------------------------------------------------------------------------------------
ax3 = plt.subplot2grid((3, 2), (1, 0))
rasterB = tkl.RasterReader.readFromAscFile(RESPATH + 'image/B_1.asc', name='B', separator='\t')
mapBinaire = rasterB.getAFMap('B')
mapBinaire.plotAsImage(append=ax3)
# ----------------------------------------------------------------------------------------------------------
ax4 = plt.subplot2grid((3, 2), (1, 1))
#if closing:
maPlotRasterTiff(RESPATH, 'image/erosion_1.tif', ax4)
ax4.set_title('Closing')
#else:
# maPlotRasterTiff(RESPATH, 'image/imageclean_1.tif', ax4)
# ax4.set_title('Comble trou')
# ----------------------------------------------------------------------------------------------------------
ax5 = plt.subplot2grid((3, 2), (2, 0))
matPlotRasterShp(RESPATH, 'image/road_surface_1.shp', ax5)
ax5.set_title('Road surface')
# ----------------------------------------------------------------------------------------------------------
ax6 = plt.subplot2grid((3, 2), (2, 1))
matPlotRasterShp(RESPATH, 'image/road_surface_lissee_1.shp', ax6)
ax6.set_title('Road surface lissée')
/home/md_vandamme/.local/lib/python3.10/site-packages/pyogrio/raw.py:200: RuntimeWarning: /home/md_vandamme/7_LIB/Footprint2graph-pipeline/test/result11/image/road_surface_lissee_1.shp contains polygon(s) with rings with invalid winding order. Autocorrecting them, but that shapefile should be corrected using ogr2ogr for example.
return ogr_read(
[8]:
Text(0.5, 1.0, 'Road surface lissée')
Step 3 : création de la center line#
[9]:
from footprint2graph import addTopologyToNetwork
SEARCH = 25
h = 5
addTopologyToNetwork(RESPATH, SEARCH, h, pipeline_idx)
fmt = tkl.NetworkFormat({
"pos_edge_id": 0,
"pos_source": 1,
"pos_target": 2,
"pos_wkt": 4,
"srid": "ENU",
"separator": ",",
"header": 1})
networkpath = RESPATH + 'network/reseau_1.csv'
squelette = tkl.NetworkReader.readFromFile(networkpath, fmt, verbose=False)
print ('-----------------------------------------------------------------')
100% (16 of 16) |########################| Elapsed Time: 0:00:00 Time: 0:00:000:00
Starting topology creation for the network
Number of edges in the skeleton: 279
Finished loaded skeleton.
Finished removing hooked parts of the skeleton.
Finished simplification of the skeleton.
Number of edges in the simplified skeleton: 15
Number of nodes: 16
Conflation cannot be performed for node 0 ; the three incident edges are too long: 33 64 122
Conflation cannot be performed for node 11 ; the three incident edges are too long: 33 191 167
Edge count after conflation: 5
Stage 3 completed: adding topology to the skeleton.
-----------------------------------------------------------------
[10]:
from footprint2graph.util.PlotRes import plotSqueletteTopo
plt.figure(figsize=(18, 8))
ax1 = plt.subplot2grid((2, 3), (0, 0))
matPlotRasterShp(RESPATH, 'image/road_surface_lissee_1.shp', ax1)
ax1.set_title('Road surface lissée')
ax2 = plt.subplot2grid((2, 3), (0, 1))
matPlotShapefile(RESPATH + 'network/', 'squelette_1.shp', ax2)
ax2.set_title('squelette brut')
ax3 = plt.subplot2grid((2, 3), (0, 2))
plotSqueletteTopo(RESPATH, ax3)
ax3.set_title('squelette avec topologie')
ax4 = plt.subplot2grid((2, 3), (1, 0))
network.plot(edges='b-', size=1.0, append=ax4)
ax4.set_title('BDTOPO')
ax5 = plt.subplot2grid((2, 3), (1, 1))
squelette.plot('k-', nodes='ko', size=0.8, append=ax5)
ax5.set_title('squelette simplifié avec topologie')
print ('')
/home/md_vandamme/.local/lib/python3.10/site-packages/pyogrio/raw.py:200: RuntimeWarning: /home/md_vandamme/7_LIB/Footprint2graph-pipeline/test/result11/image/road_surface_lissee_1.shp contains polygon(s) with rings with invalid winding order. Autocorrecting them, but that shapefile should be corrected using ogr2ogr for example.
return ogr_read(
Step 4 : Construction des géométries agrégées pour chaque arc du squelette#
Attribue les points des traces brutes à chaque arc de la topologie
Recalage avec l’algorithme de Newson and Krumm (2009)
Reconstruit les bons morceaux de traces candidats pour chaque arc de la topologie
Agrégation des morceaux de traces
Conflation des traces fusionnées afin d’obtenir un réseau de mobilité
[11]:
from footprint2graph import createNetworkGeom
SEARCH = 25
BUFFER = 20
createNetworkGeom(RESPATH, SEARCH, BUFFER, pipeline_idx)
Starting map-matching, aggregation, and conflation of GNSS trajectories.
Loading network (1) ...
Number of edges = 5
Number of nodes = 6
Total segment length of the network = 765.9881721625043
Loading collection of tracks ...
100% (6 of 6) |##########################| Elapsed Time: 0:00:00 Time: 0:00:00
Number of tracks: 877
Execution time (seconds): 0.6212308406829834
Starting map-matching ...
Map-matching preparation...
PARAMETER search_radius: 25
Map-matching ended.
Execution time (seconds): 12.402592897415161
Map-matching results exported.
Number of map-matched points = 49111 (89.82 %)
Map-matching results restructuring completed.
Starting construction of candidate trajectory segments for each topology edge ...
265 candidates for edge 15
571 candidates for edge 12
264 candidates for edge 16
189 candidates for edge 17
235 candidates for edge 21
Number of processed edges: 5
Minimum number of candidate tracks per edge: 189
Maximum number of candidate traces per edge: 571
Average number of candidate tracks per edge: 305
Segment construction completed.
Execution time (seconds): 4.6723151206970215
Starting aggregation ...
Aggregation for arc number: 15
Number of candidate tracks / number of sampled tracks 265 / 30
Aggregation for arc number: 12
Number of candidate tracks / number of sampled tracks 569 / 30
Aggregation for arc number: 16
Number of candidate tracks / number of sampled tracks 264 / 30
Aggregation for arc number: 17
Number of candidate tracks / number of sampled tracks 189 / 30
Aggregation for arc number: 21
Number of candidate tracks / number of sampled tracks 235 / 30
Number of aggregations: 5
Number of aggregations with 30 traces: 5
Number of aggregations with fewer than 30 traces: 0
Minimum number of traces in aggregation: 189
Average number of traces in aggregation: 304
Aggregation process finished.
Execution time (seconds): 3.1148173809051514
Starting conflation ...
Conflation process finished.
Execution time (seconds): 0.008690595626831055
Stage 4 completed: map-matching, aggregation, and conflation.
Visualisation du recalage sur le réseau#
[12]:
from footprint2graph.util.PlotRes import plotMM
plotMM(RESPATH, squelette)
plt.show()
Visualisation des candidats pour l’agrégation#
[13]:
from footprint2graph.util.PlotRes import plotSegmentsConstruction
plt.figure(figsize=(8, 8))
ax1 = plt.subplot2grid((1, 1), (0, 0))
plotSegmentsConstruction(RESPATH, ax1, squelette)
Visualisation de l’agrégation et de la conflation#
[14]:
from footprint2graph.util.PlotRes import plotAggregation, plotConflation
import os
plt.figure(figsize=(18, 15))
# ---------------------------------------------------
ax1 = plt.subplot2grid((2, 2), (0, 0))
network.plot('k-', '', 'g-', 'r-', 0.5, ax1)
ax1.set_title('BDTOPO')
# ---------------------------------------------------
ax2 = plt.subplot2grid((2, 2), (0, 1))
squelette.plot('k-', nodes='ko', size=0.8, append=ax2)
ax2.set_title('squelette simplifié avec topologie')
# ---------------------------------------------------
ax3 = plt.subplot2grid((2, 2), (1, 0))
plotAggregation(RESPATH, ax3)
ax3.set_title('Agrégation des morceaux de traces')
# ---------------------------------------------------
ax4 = plt.subplot2grid((2, 2), (1, 1))
plotConflation(RESPATH, ax4)
ax4.set_title('CONFLATION')
[14]:
Text(0.5, 1.0, 'CONFLATION')
Visualisation du résultat final#
[15]:
plt.figure(figsize=(10, 10))
ax = plt.subplot2grid((1, 1), (0, 0))
network.plot('b-', nodes='', size=1.2, append=plt)
plotConflation(RESPATH, ax, size=1.5, label='Outdoor Footprint Network')
plt.legend()
[15]:
<matplotlib.legend.Legend at 0x77ca210ef010>
