kopia lustrzana https://github.com/OpenDroneMap/ODM
Started writing GCP export functionality
Piero Toffanin
rodzic
4e9f53e0a2
commit
335802b563
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@ -4,11 +4,15 @@ OpenSfM related utils
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import os, shutil, sys, json, argparse
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import yaml
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import numpy as np
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import pyproj
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from pyproj import CRS
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from opendm import io
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from opendm import log
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from opendm import system
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from opendm import context
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from opendm import camera
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from opendm import location
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from opendm.utils import get_depthmap_resolution
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from opendm.photo import find_largest_photo_dim
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from opensfm.large import metadataset
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@ -18,6 +22,8 @@ from opensfm.dataset import DataSet
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from opensfm import report
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from opendm.multispectral import get_photos_by_band
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from opendm.gpu import has_gpus
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from opensfm import multiview
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from opensfm.actions.export_geocoords import _get_transformation
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class OSFMContext:
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def __init__(self, opensfm_project_path):
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@ -255,6 +261,10 @@ class OSFMContext:
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config_filename = self.get_config_file_path()
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with open(config_filename, 'w') as fout:
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fout.write("\n".join(config))
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# We impose our own reference_lla
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if reconstruction.is_georeferenced():
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self.write_reference_lla(reconstruction.georef.utm_east_offset, reconstruction.georef.utm_north_offset, reconstruction.georef.proj4())
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else:
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log.ODM_WARNING("%s already exists, not rerunning OpenSfM setup" % list_path)
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@ -429,6 +439,71 @@ class OSFMContext:
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else:
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log.ODM_WARNING("Report %s already exported" % report_path)
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def write_reference_lla(self, offset_x, offset_y, proj4):
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reference_lla = self.path("reference_lla.json")
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longlat = CRS.from_epsg("4326")
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lon, lat = location.transform2(CRS.from_proj4(proj4), longlat, offset_x, offset_y)
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with open(reference_lla, 'w') as f:
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f.write(json.dumps({
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'latitude': lat,
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'longitude': lon,
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'altitude': 0.0
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}, indent=4))
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log.ODM_INFO("Wrote reference_lla.json")
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def ground_control_points(self, proj4):
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"""
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Load ground control point information.
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Make sure this function is called *after* update_reference_lla()
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has been called.
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"""
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ds = DataSet(self.opensfm_project_path)
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gcps = ds.load_ground_control_points()
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if not gcps:
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return []
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reconstructions = ds.load_reconstruction()
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reference = ds.load_reference()
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projection = pyproj.Proj(proj4)
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t = _get_transformation(reference, projection, (0, 0))
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A, b = t[:3, :3], t[:3, 3]
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result = []
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for gcp in gcps:
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if not gcp.coordinates.has_value:
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continue
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triangulated = None
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for rec in reconstructions:
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triangulated = multiview.triangulate_gcp(gcp, rec.shots, 1.0, 0.1)
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if triangulated is None:
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continue
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else:
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break
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if triangulated is None:
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continue
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coordinates = np.array(gcp.coordinates.value)
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coordinates = np.dot(A, coordinates) + b
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triangulated = triangulated + b
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result.append({
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'id': gcp.id,
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'observations': [obs.shot_id for obs in gcp.observations],
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'triangulated': triangulated,
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'coordinates': coordinates,
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'error': np.abs(triangulated - coordinates)
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})
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return result
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def name(self):
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return os.path.basename(os.path.abspath(self.path("..")))
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@ -1,6 +1,9 @@
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import os
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import struct
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import pipes
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import fiona
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import fiona.crs
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from collections import OrderedDict
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from opendm import io
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from opendm import log
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@ -9,6 +12,7 @@ from opendm import system
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from opendm import context
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from opendm.cropper import Cropper
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from opendm import point_cloud
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from opendm.osfm import OSFMContext
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from opendm.multispectral import get_primary_band_name
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class ODMGeoreferencingStage(types.ODM_Stage):
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@ -16,58 +20,105 @@ class ODMGeoreferencingStage(types.ODM_Stage):
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tree = outputs['tree']
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reconstruction = outputs['reconstruction']
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if not io.file_exists(tree.odm_georeferencing_model_laz) or self.rerun():
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cmd = ('pdal translate -i "%s" -o \"%s\"' % (tree.filtered_point_cloud, tree.odm_georeferencing_model_laz))
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stages = ["ferry"]
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params = [
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'--filters.ferry.dimensions="views => UserData"',
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'--writers.las.compression="lazip"',
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]
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# if not io.file_exists(tree.odm_georeferencing_model_laz) or self.rerun():
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# cmd = ('pdal translate -i "%s" -o \"%s\"' % (tree.filtered_point_cloud, tree.odm_georeferencing_model_laz))
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# stages = ["ferry"]
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# params = [
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# '--filters.ferry.dimensions="views => UserData"',
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# '--writers.las.compression="lazip"',
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# ]
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if reconstruction.is_georeferenced():
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log.ODM_INFO("Georeferencing point cloud")
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# if reconstruction.is_georeferenced():
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# log.ODM_INFO("Georeferencing point cloud")
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stages.append("transformation")
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params += [
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'--filters.transformation.matrix="1 0 0 %s 0 1 0 %s 0 0 1 0 0 0 0 1"' % reconstruction.georef.utm_offset(),
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'--writers.las.offset_x=%s' % reconstruction.georef.utm_east_offset,
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'--writers.las.offset_y=%s' % reconstruction.georef.utm_north_offset,
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'--writers.las.offset_z=0',
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'--writers.las.a_srs="%s"' % reconstruction.georef.proj4()
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]
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# stages.append("transformation")
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# params += [
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# '--filters.transformation.matrix="1 0 0 %s 0 1 0 %s 0 0 1 0 0 0 0 1"' % reconstruction.georef.utm_offset(),
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# '--writers.las.offset_x=%s' % reconstruction.georef.utm_east_offset,
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# '--writers.las.offset_y=%s' % reconstruction.georef.utm_north_offset,
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# '--writers.las.offset_z=0',
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# '--writers.las.a_srs="%s"' % reconstruction.georef.proj4()
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# ]
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system.run(cmd + ' ' + ' '.join(stages) + ' ' + ' '.join(params))
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# system.run(cmd + ' ' + ' '.join(stages) + ' ' + ' '.join(params))
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self.update_progress(50)
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# self.update_progress(50)
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if args.crop > 0:
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log.ODM_INFO("Calculating cropping area and generating bounds shapefile from point cloud")
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cropper = Cropper(tree.odm_georeferencing, 'odm_georeferenced_model')
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# if args.crop > 0:
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# log.ODM_INFO("Calculating cropping area and generating bounds shapefile from point cloud")
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# cropper = Cropper(tree.odm_georeferencing, 'odm_georeferenced_model')
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if args.fast_orthophoto:
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decimation_step = 10
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# if args.fast_orthophoto:
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# decimation_step = 10
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# else:
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# decimation_step = 40
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# # More aggressive decimation for large datasets
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# if not args.fast_orthophoto:
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# decimation_step *= int(len(reconstruction.photos) / 1000) + 1
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# decimation_step = min(decimation_step, 95)
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# try:
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# cropper.create_bounds_gpkg(tree.odm_georeferencing_model_laz, args.crop,
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# decimation_step=decimation_step)
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# except:
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# log.ODM_WARNING("Cannot calculate crop bounds! We will skip cropping")
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# args.crop = 0
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# else:
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# log.ODM_INFO("Converting point cloud (non-georeferenced)")
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# system.run(cmd + ' ' + ' '.join(stages) + ' ' + ' '.join(params))
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# point_cloud.post_point_cloud_steps(args, tree, self.rerun())
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# else:
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# log.ODM_WARNING('Found a valid georeferenced model in: %s'
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# % tree.odm_georeferencing_model_laz)
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# Export GCP information if available
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gcp_export_file = tree.path("odm_georeferencing", "ground_control_points.gpkg")
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if (reconstruction.has_gcp() and not io.file_exists(gcp_export_file)) or self.rerun():
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octx = OSFMContext(tree.opensfm)
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gcps = octx.ground_control_points(reconstruction.georef.proj4())
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if len(gcps):
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gcp_schema = {
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'geometry': 'Point',
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'properties': OrderedDict([
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('id', 'str'),
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('observations_count', 'int'),
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('observations_list', 'str'),
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('triangulated_x', 'float'),
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('triangulated_y', 'float'),
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('triangulated_z', 'float'),
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('error_x', 'float'),
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('error_y', 'float'),
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('error_z', 'float'),
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])
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}
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# Write GeoPackage
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with fiona.open(gcp_export_file, 'w', driver="GPKG",
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crs=fiona.crs.from_string(reconstruction.georef.proj4()),
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schema=gcp_schema) as f:
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for gcp in gcps:
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f.write({
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'geometry': {
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'type': 'Point',
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'coordinates': gcp['coordinates'],
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},
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'properties': OrderedDict([
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('id', gcp['id']),
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('observations_count', len(gcp['observations'])),
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('observations_list', ",".join(gcp['observations'])),
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('triangulated_x', gcp['triangulated'][0]),
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('triangulated_y', gcp['triangulated'][1]),
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('triangulated_z', gcp['triangulated'][2]),
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('error_x', gcp['error'][0]),
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('error_y', gcp['error'][1]),
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('error_z', gcp['error'][2]),
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])
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})
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else:
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decimation_step = 40
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# More aggressive decimation for large datasets
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if not args.fast_orthophoto:
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decimation_step *= int(len(reconstruction.photos) / 1000) + 1
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decimation_step = min(decimation_step, 95)
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try:
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cropper.create_bounds_gpkg(tree.odm_georeferencing_model_laz, args.crop,
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decimation_step=decimation_step)
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except:
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log.ODM_WARNING("Cannot calculate crop bounds! We will skip cropping")
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args.crop = 0
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else:
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log.ODM_INFO("Converting point cloud (non-georeferenced)")
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system.run(cmd + ' ' + ' '.join(stages) + ' ' + ' '.join(params))
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point_cloud.post_point_cloud_steps(args, tree, self.rerun())
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else:
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log.ODM_WARNING('Found a valid georeferenced model in: %s'
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% tree.odm_georeferencing_model_laz)
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log.ODM_WARNING("GCPs could not be loaded for writing to %s" % gcp_export_file)
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if args.optimize_disk_space and io.file_exists(tree.odm_georeferencing_model_laz) and io.file_exists(tree.filtered_point_cloud):
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os.remove(tree.filtered_point_cloud)
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@ -55,18 +55,6 @@ class ODMOpenSfMStage(types.ODM_Stage):
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cleanup_disk_space()
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return
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# Stats are computed in the local CRS (before geoprojection)
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if not args.skip_report:
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# TODO: this will fail to compute proper statistics if
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# the pipeline is run with --skip-report and is subsequently
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# rerun without --skip-report a --rerun-* parameter (due to the reconstruction.json file)
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# being replaced below. It's an isolated use case.
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octx.export_stats(self.rerun())
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self.update_progress(75)
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# We now switch to a geographic CRS
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geocoords_flag_file = octx.path("exported_geocoords.txt")
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@ -79,6 +67,12 @@ class ODMOpenSfMStage(types.ODM_Stage):
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else:
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log.ODM_WARNING("Will skip exporting %s" % tree.opensfm_geocoords_reconstruction)
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self.update_progress(75)
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# Stats are computed in the geographic CRS
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if not args.skip_report:
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octx.export_stats(self.rerun())
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self.update_progress(80)
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updated_config_flag_file = octx.path('updated_config.txt')
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