def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
if not os.path.exists(tree.odm_report): system.mkdir_p(tree.odm_report)
shots_geojson = os.path.join(tree.odm_report, "shots.geojson")
if not io.file_exists(shots_geojson) or self.rerun():
# Extract geographical camera shots
if reconstruction.is_georeferenced():
shots = get_geojson_shots_from_opensfm(
tree.opensfm_reconstruction, tree.opensfm_transformation,
reconstruction.get_proj_srs())
else:
# Pseudo geo
shots = get_geojson_shots_from_opensfm(
tree.opensfm_reconstruction,
pseudo_geotiff=tree.odm_orthophoto_tif)
if shots:
with open(shots_geojson, "w") as fout:
fout.write(json.dumps(shots))
log.ODM_INFO("Wrote %s" % shots_geojson)
else:
log.ODM_WARNING("Cannot extract shots")
else:
log.ODM_WARNING('Found a valid shots file in: %s' %
tree.shots_geojson)
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
if not os.path.exists(tree.odm_report): system.mkdir_p(tree.odm_report)
log.ODM_INFO("Exporting shots.geojson")
shots_geojson = os.path.join(tree.odm_report, "shots.geojson")
if not io.file_exists(shots_geojson) or self.rerun():
# Extract geographical camera shots
if reconstruction.is_georeferenced():
shots = get_geojson_shots_from_opensfm(
tree.opensfm_reconstruction,
utm_srs=reconstruction.get_proj_srs(),
utm_offset=reconstruction.georef.utm_offset())
else:
# Pseudo geo
shots = get_geojson_shots_from_opensfm(
tree.opensfm_reconstruction,
pseudo_geotiff=tree.odm_orthophoto_tif)
if shots:
with open(shots_geojson, "w") as fout:
fout.write(json.dumps(shots))
log.ODM_INFO("Wrote %s" % shots_geojson)
else:
log.ODM_WARNING("Cannot extract shots")
else:
log.ODM_WARNING('Found a valid shots file in: %s' % shots_geojson)
if args.skip_report:
# Stop right here
log.ODM_WARNING("Skipping report generation as requested")
return
# Augment OpenSfM stats file with our own stats
odm_stats_json = os.path.join(tree.odm_report, "stats.json")
octx = OSFMContext(tree.opensfm)
osfm_stats_json = octx.path("stats", "stats.json")
odm_stats = None
point_cloud_file = None
views_dimension = None
if not os.path.exists(odm_stats_json) or self.rerun():
if os.path.exists(osfm_stats_json):
with open(osfm_stats_json, 'r') as f:
odm_stats = json.loads(f.read())
# Add point cloud stats
if os.path.exists(tree.odm_georeferencing_model_laz):
point_cloud_file = tree.odm_georeferencing_model_laz
views_dimension = "UserData"
# pc_info_file should have been generated by cropper
pc_info_file = os.path.join(
tree.odm_georeferencing,
"odm_georeferenced_model.info.json")
odm_stats[
'point_cloud_statistics'] = generate_point_cloud_stats(
tree.odm_georeferencing_model_laz, pc_info_file,
self.rerun())
else:
ply_pc = os.path.join(tree.odm_filterpoints,
"point_cloud.ply")
if os.path.exists(ply_pc):
point_cloud_file = ply_pc
views_dimension = "views"
pc_info_file = os.path.join(tree.odm_filterpoints,
"point_cloud.info.json")
odm_stats[
'point_cloud_statistics'] = generate_point_cloud_stats(
ply_pc, pc_info_file, self.rerun())
else:
log.ODM_WARNING("No point cloud found")
odm_stats['point_cloud_statistics'][
'dense'] = not args.fast_orthophoto
# Add runtime stats
total_time = (system.now_raw() -
outputs['start_time']).total_seconds()
odm_stats['odm_processing_statistics'] = {
'total_time':
total_time,
'total_time_human':
hms(total_time),
'average_gsd':
gsd.opensfm_reconstruction_average_gsd(
octx.recon_file(),
use_all_shots=reconstruction.has_gcp()),
}
with open(odm_stats_json, 'w') as f:
f.write(json.dumps(odm_stats))
else:
log.ODM_WARNING(
"Cannot generate report, OpenSfM stats are missing")
else:
log.ODM_WARNING("Reading existing stats %s" % odm_stats_json)
with open(odm_stats_json, 'r') as f:
odm_stats = json.loads(f.read())
# Generate overlap diagram
if odm_stats.get('point_cloud_statistics'
) and point_cloud_file and views_dimension:
bounds = odm_stats['point_cloud_statistics'].get('stats', {}).get(
'bbox', {}).get('native', {}).get('bbox')
if bounds:
image_target_size = 1400 # pixels
osfm_stats_dir = os.path.join(tree.opensfm, "stats")
diagram_tiff = os.path.join(osfm_stats_dir, "overlap.tif")
diagram_png = os.path.join(osfm_stats_dir, "overlap.png")
width = bounds.get('maxx') - bounds.get('minx')
height = bounds.get('maxy') - bounds.get('miny')
max_dim = max(width, height)
resolution = float(max_dim) / float(image_target_size)
radius = resolution * math.sqrt(2)
# Larger radius for sparse point cloud diagram
if not odm_stats['point_cloud_statistics']['dense']:
radius *= 10
system.run("pdal translate -i \"{}\" "
"-o \"{}\" "
"--writer gdal "
"--writers.gdal.resolution={} "
"--writers.gdal.data_type=uint8_t "
"--writers.gdal.dimension={} "
"--writers.gdal.output_type=max "
"--writers.gdal.radius={} ".format(
point_cloud_file, diagram_tiff, resolution,
views_dimension, radius))
report_assets = os.path.abspath(
os.path.join(os.path.dirname(__file__),
"../opendm/report"))
overlap_color_map = os.path.join(report_assets,
"overlap_color_map.txt")
bounds_file_path = os.path.join(
tree.odm_georeferencing,
'odm_georeferenced_model.bounds.gpkg')
if (args.crop > 0
or args.boundary) and os.path.isfile(bounds_file_path):
Cropper.crop(bounds_file_path,
diagram_tiff,
get_orthophoto_vars(args),
keep_original=False)
system.run(
"gdaldem color-relief \"{}\" \"{}\" \"{}\" -of PNG -alpha".
format(diagram_tiff, overlap_color_map, diagram_png))
# Copy assets
for asset in [
"overlap_diagram_legend.png", "dsm_gradient.png"
]:
shutil.copy(os.path.join(report_assets, asset),
os.path.join(osfm_stats_dir, asset))
# Generate previews of ortho/dsm
if os.path.isfile(tree.odm_orthophoto_tif):
osfm_ortho = os.path.join(osfm_stats_dir, "ortho.png")
generate_png(tree.odm_orthophoto_tif, osfm_ortho,
image_target_size)
dems = []
if args.dsm:
dems.append("dsm")
if args.dtm:
dems.append("dtm")
for dem in dems:
dem_file = tree.path("odm_dem", "%s.tif" % dem)
if os.path.isfile(dem_file):
# Resize first (faster)
resized_dem_file = io.related_file_path(
dem_file, postfix=".preview")
system.run(
"gdal_translate -outsize {} 0 \"{}\" \"{}\" --config GDAL_CACHEMAX {}%"
.format(image_target_size, dem_file,
resized_dem_file, get_max_memory()))
log.ODM_INFO("Computing raster stats for %s" %
resized_dem_file)
dem_stats = get_raster_stats(resized_dem_file)
if len(dem_stats) > 0:
odm_stats[dem + '_statistics'] = dem_stats[0]
osfm_dem = os.path.join(osfm_stats_dir, "%s.png" % dem)
colored_dem, hillshade_dem, colored_hillshade_dem = generate_colored_hillshade(
resized_dem_file)
system.run(
"gdal_translate -outsize {} 0 -of png \"{}\" \"{}\" --config GDAL_CACHEMAX {}%"
.format(image_target_size, colored_hillshade_dem,
osfm_dem, get_max_memory()))
for f in [
resized_dem_file, colored_dem, hillshade_dem,
colored_hillshade_dem
]:
if os.path.isfile(f):
os.remove(f)
else:
log.ODM_WARNING(
"Cannot generate overlap diagram, cannot compute point cloud bounds"
)
else:
log.ODM_WARNING(
"Cannot generate overlap diagram, point cloud stats missing")
octx.export_report(os.path.join(tree.odm_report, "report.pdf"),
odm_stats, self.rerun())