kopia lustrzana https://github.com/OpenDroneMap/ODM
Remove --use-opensfm-dense, add more stats data for report, diagram
Piero Toffanin
rodzic
72816ceaf5
commit
29a346c6aa
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@ -236,35 +236,6 @@ def config(argv=None, parser=None):
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'but produce denser point clouds. '
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'Default: %(default)s'))
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parser.add_argument('--opensfm-depthmap-min-consistent-views',
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metavar='<integer: 2 <= x <= 9>',
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action=StoreValue,
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type=int,
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default=3,
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help=('Minimum number of views that should reconstruct a point for it to be valid. Use lower values '
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'if your images have less overlap. Lower values result in denser point clouds '
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'but with more noise. '
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'Default: %(default)s'))
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parser.add_argument('--opensfm-depthmap-method',
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metavar='<string>',
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action=StoreValue,
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default='PATCH_MATCH',
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choices=['PATCH_MATCH', 'BRUTE_FORCE', 'PATCH_MATCH_SAMPLE'],
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help=('Raw depthmap computation algorithm. '
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'PATCH_MATCH and PATCH_MATCH_SAMPLE are faster, but might miss some valid points. '
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'BRUTE_FORCE takes longer but produces denser reconstructions. '
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'Default: %(default)s'))
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parser.add_argument('--opensfm-depthmap-min-patch-sd',
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metavar='<positive float>',
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action=StoreValue,
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type=float,
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default=1,
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help=('When using PATCH_MATCH or PATCH_MATCH_SAMPLE, controls the standard deviation threshold to include patches. '
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'Patches with lower standard deviation are ignored. '
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'Default: %(default)s'))
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parser.add_argument('--use-hybrid-bundle-adjustment',
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action=StoreTrue,
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nargs=0,
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@ -284,12 +255,6 @@ def config(argv=None, parser=None):
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default=False,
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help='Skip generation of a full 3D model. This can save time if you only need 2D results such as orthophotos and DEMs. Default: %(default)s')
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parser.add_argument('--use-opensfm-dense',
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action=StoreTrue,
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nargs=0,
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default=False,
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help='Use OpenSfM to compute the dense point cloud instead of OpenMVS. Default: %(default)s')
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parser.add_argument('--ignore-gsd',
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action=StoreTrue,
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nargs=0,
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@ -185,10 +185,6 @@ class OSFMContext:
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"processes: %s" % args.max_concurrency,
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"matching_gps_neighbors: %s" % args.matcher_neighbors,
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"matching_gps_distance: %s" % args.matcher_distance,
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"depthmap_method: %s" % args.opensfm_depthmap_method,
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"depthmap_resolution: %s" % depthmap_resolution,
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"depthmap_min_patch_sd: %s" % args.opensfm_depthmap_min_patch_sd,
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"depthmap_min_consistent_views: %s" % args.opensfm_depthmap_min_consistent_views,
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"optimize_camera_parameters: %s" % ('no' if args.use_fixed_camera_params or args.cameras else 'yes'),
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"undistorted_image_format: tif",
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"bundle_outlier_filtering_type: AUTO",
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@ -0,0 +1,7 @@
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# QGIS Generated Color Map Export File
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2,215,25,28,255,2
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3,246,201,5,255,3
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4,117,188,39,255,4
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5,26,150,65,255,5+
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inf,26,150,65,255,> 5
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nv 0 0 0 0
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Plik binarny nie jest wyświetlany.
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Po Szerokość: | Wysokość: | Rozmiar: 7.9 KiB |
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@ -243,7 +243,6 @@ class ODM_Tree(object):
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self.opensfm_image_list = os.path.join(self.opensfm, 'image_list.txt')
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self.opensfm_reconstruction = os.path.join(self.opensfm, 'reconstruction.json')
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self.opensfm_reconstruction_nvm = os.path.join(self.opensfm, 'undistorted/reconstruction.nvm')
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self.opensfm_model = os.path.join(self.opensfm, 'undistorted/depthmaps/merged.ply')
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self.opensfm_transformation = os.path.join(self.opensfm, 'geocoords_transformation.txt')
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# OpenMVS
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@ -135,22 +135,6 @@ class ODMLoadDatasetStage(types.ODM_Stage):
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log.ODM_INFO('Found %s usable images' % len(photos))
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# TODO: add support for masks in OpenMVS
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has_mask = False
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for p in photos:
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if p.mask is not None:
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has_mask = True
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break
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if has_mask and not args.use_opensfm_dense and not args.fast_orthophoto:
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log.ODM_WARNING("Image masks found, will use OpenSfM for dense reconstruction")
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args.use_opensfm_dense = True
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# Remove OpenMVS from pipeline. Yep.
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opensfm_stage = self.next_stage.next_stage.next_stage
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opensfm_stage.next_stage = opensfm_stage.next_stage.next_stage
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opensfm_stage.next_stage.prev_stage = opensfm_stage
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# Create reconstruction object
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reconstruction = types.ODM_Reconstruction(photos)
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@ -65,7 +65,7 @@ class ODMApp:
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.connect(merge) \
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.connect(opensfm)
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if args.use_opensfm_dense or args.fast_orthophoto:
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if args.fast_orthophoto:
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opensfm.connect(filterpoints)
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else:
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opensfm.connect(openmvs) \
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@ -18,8 +18,6 @@ class ODMFilterPoints(types.ODM_Stage):
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if not io.file_exists(tree.filtered_point_cloud) or self.rerun():
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if args.fast_orthophoto:
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inputPointCloud = os.path.join(tree.opensfm, 'reconstruction.ply')
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elif args.use_opensfm_dense:
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inputPointCloud = tree.opensfm_model
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else:
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inputPointCloud = tree.openmvs_model
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@ -1,4 +1,7 @@
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import os
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import json
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import math
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import shutil
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from opendm import log
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from opendm import io
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@ -8,7 +11,6 @@ from opendm.shots import get_geojson_shots_from_opensfm
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from opendm.osfm import OSFMContext
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from opendm import gsd
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from opendm.point_cloud import export_summary_json
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import json
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def hms(seconds):
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@ -23,6 +25,14 @@ def hms(seconds):
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return '{}s'.format(s)
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def generate_point_cloud_stats(input_point_cloud, pc_summary_file):
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if not os.path.exists(pc_summary_file):
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export_summary_json(input_point_cloud, pc_summary_file)
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if os.path.exists(pc_summary_file):
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with open(pc_summary_file, 'r') as f:
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return json.loads(f.read())
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class ODMReport(types.ODM_Stage):
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def process(self, args, outputs):
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tree = outputs['tree']
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@ -56,6 +66,8 @@ class ODMReport(types.ODM_Stage):
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octx = OSFMContext(tree.opensfm)
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osfm_stats_json = octx.path("stats", "stats.json")
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odm_stats = None
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point_cloud_file = None
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views_dimension = None
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if not os.path.exists(odm_stats_json) or self.rerun():
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if os.path.exists(osfm_stats_json):
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@ -63,15 +75,25 @@ class ODMReport(types.ODM_Stage):
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odm_stats = json.loads(f.read())
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# Add point cloud stats
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pc_summary_file = os.path.join(tree.odm_georeferencing, "odm_georeferenced_model.summary.json")
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# This should have been generated by cropper, but in case it hasn't..
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if not os.path.exists(pc_summary_file) and os.path.exists(tree.odm_georeferencing_model_laz):
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export_summary_json(tree.odm_georeferencing_model_laz, pc_summary_file)
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if os.path.exists(pc_summary_file):
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with open(pc_summary_file, 'r') as f:
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odm_stats['point_cloud_statistics'] = json.loads(f.read())
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if os.path.exists(tree.odm_georeferencing_model_laz):
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point_cloud_file = tree.odm_georeferencing_model_laz
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views_dimension = "UserData"
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# pc_summary_file should have been generated by cropper
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pc_summary_file = os.path.join(tree.odm_georeferencing, "odm_georeferenced_model.summary.json")
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odm_stats['point_cloud_statistics'] = generate_point_cloud_stats(tree.odm_georeferencing_model_laz, pc_summary_file)
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else:
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ply_pc = os.path.join(tree.odm_filterpoints, "point_cloud.ply")
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if os.path.exists(ply_pc):
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point_cloud_file = ply_pc
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views_dimension = "views"
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pc_summary_file = os.path.join(tree.odm_filterpoints, "point_cloud.summary.json")
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odm_stats['point_cloud_statistics'] = generate_point_cloud_stats(ply_pc, pc_summary_file)
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else:
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log.ODM_WARNING("No point cloud found")
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odm_stats['point_cloud_statistics']['dense'] = not args.fast_orthophoto
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# Add runtime stats
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odm_stats['odm_processing_statistics'] = {
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@ -89,4 +111,43 @@ class ODMReport(types.ODM_Stage):
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with open(odm_stats_json, 'r') as f:
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odm_stats = json.loads(f.read())
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# Generate overlap diagram
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if odm_stats.get('point_cloud_statistics') and point_cloud_file and views_dimension:
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bounds = odm_stats['point_cloud_statistics'].get('summary', {}).get('bounds')
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if bounds:
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diagram_target_size = 1600 # pixels
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osfm_stats_dir = os.path.join(tree.opensfm, "stats")
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diagram_tiff = os.path.join(osfm_stats_dir, "overlap.tif")
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diagram_png = os.path.join(osfm_stats_dir, "overlap.png")
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width = bounds.get('maxx') - bounds.get('minx')
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height = bounds.get('maxy') - bounds.get('miny')
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max_dim = max(width, height)
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resolution = float(max_dim) / float(diagram_target_size)
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radius = resolution * math.sqrt(2)
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# Larger radius for sparse point cloud diagram
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if not odm_stats['point_cloud_statistics']['dense']:
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radius *= 10
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system.run("pdal translate -i \"{}\" "
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"-o \"{}\" "
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"--writer gdal "
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"--writers.gdal.resolution={} "
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"--writers.gdal.data_type=uint8_t "
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"--writers.gdal.dimension={} "
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"--writers.gdal.output_type=max "
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"--writers.gdal.radius={} ".format(point_cloud_file, diagram_tiff,
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resolution, views_dimension, radius))
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report_assets = os.path.abspath(os.path.join(os.path.dirname(__file__), "../opendm/report"))
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overlap_color_map = os.path.join(report_assets, "overlap_color_map.txt")
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system.run("gdaldem color-relief \"{}\" \"{}\" \"{}\" -of PNG -alpha".format(diagram_tiff, overlap_color_map, diagram_png))
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# Copy legend
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shutil.copy(os.path.join(report_assets, "overlap_diagram_legend.png"), os.path.join(osfm_stats_dir, "overlap_diagram_legend.png"))
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else:
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log.ODM_WARNING("Cannot generate overlap diagram, cannot compute point cloud bounds")
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else:
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log.ODM_WARNING("Cannot generate overlap diagram, point cloud stats missing")
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octx.export_report(os.path.join(tree.odm_report, "report.pdf"), odm_stats, self.rerun())
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@ -55,6 +55,8 @@ class ODMOpenMVSStage(types.ODM_Stage):
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log.ODM_INFO("Running dense reconstruction. This might take a while.")
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# TODO: add support for image masks
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system.run('%s "%s" %s' % (context.omvs_densify_path,
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os.path.join(tree.openmvs, 'scene.mvs'),
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' '.join(config)))
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@ -193,14 +193,6 @@ class ODMOpenSfMStage(types.ODM_Stage):
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else:
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log.ODM_WARNING("Found a valid PLY reconstruction in %s" % output_file)
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elif args.use_opensfm_dense:
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output_file = tree.opensfm_model
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if not io.file_exists(output_file) or self.rerun():
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octx.run('compute_depthmaps')
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else:
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log.ODM_WARNING("Found a valid dense reconstruction in %s" % output_file)
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self.update_progress(90)
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if reconstruction.is_georeferenced() and (not io.file_exists(tree.opensfm_transformation) or self.rerun()):
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@ -222,7 +214,7 @@ class ODMOpenSfMStage(types.ODM_Stage):
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os.remove(f)
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# Keep these if using OpenMVS
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if args.fast_orthophoto or args.use_opensfm_dense:
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if args.fast_orthophoto:
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files = [octx.path("undistorted", "tracks.csv"),
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octx.path("undistorted", "reconstruction.json")
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]
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