Loading autocnet/graph/network.py +67 −24 Original line number Diff line number Diff line Loading @@ -7,6 +7,7 @@ import os from shutil import copyfile from time import gmtime, strftime, time import warnings from itertools import combinations import networkx as nx import geopandas as gpd Loading @@ -14,6 +15,7 @@ import pandas as pd import numpy as np from redis import StrictRedis import shapely import scipy.special import geoalchemy2 from sqlalchemy.ext.declarative.api import DeclarativeMeta Loading Loading @@ -2222,18 +2224,12 @@ class NetworkCandidateGraph(CandidateGraph): """ self.redis_queue.flushdb() def empty_overlays(self, filters={'ignore': False}, size_threshold=0): def overlays(self, size_threshold=0): """ Find overlaps that do not contain valid points. By default, valid points include not ignored points, but additional point properties can be used to further define a valid point. For example, to look at not ignored, free (not ground) points; filters = {'ignored': False, 'pointtype': 2}. Return the overlays in a database Parameters ---------- filters: dict Points object properties for point filtering. size_threshold: float Minimum area requirment for returned overlaps. Units are determined by spatial reference system. Loading @@ -2241,30 +2237,22 @@ class NetworkCandidateGraph(CandidateGraph): Returns ------- overlays: list of Overlay objects Model information associated with overlaps that contain no valid points Model information associated with overlaps that contain one or more valid points See Also -------- autocnet.io.db.model.Overlay: for description of information associated with Overlay class autocnet.io.db.model.Points: for description of information associated with Points class """ with self.session_scope() as session: # Find overlap ids that contain one or more valid points sq = session.query(Overlay.id).join(Points, func.ST_Contains(Overlay.geom, Points.geom)) for attr, value in filters.items(): sq = sq.filter(getattr(Points, attr)==value) sq = sq.group_by(Overlay.id) # find overlap information not satisfying previous query q = session.query(Overlay).filter(Overlay.id.notin_(sq)).filter(func.ST_Area(Overlay.geom)>=size_threshold) with self.session_scope() as session: q = session.query(Overlay).filter(func.ST_Area(Overlay.geom)>=size_threshold) overlays = q.all() session.expunge_all() return overlays def connected_overlays(self, filters={'ignore': False}, size_threshold=0): def empty_overlays(self, filters={'ignore': False}, size_threshold=0): """ Find overlaps that contain valid points. By default, valid points Find overlaps that do not contain valid points. By default, valid points include not ignored points, but additional point properties can be used to further define a valid point. For example, to look at not ignored, free (not ground) points; filters = {'ignored': False, 'pointtype': 2}. Loading @@ -2281,7 +2269,7 @@ class NetworkCandidateGraph(CandidateGraph): Returns ------- overlays: list of Overlay objects Model information associated with overlaps that contain one or more valid points Model information associated with overlaps that contain no valid points See Also -------- Loading @@ -2295,12 +2283,67 @@ class NetworkCandidateGraph(CandidateGraph): sq = sq.filter(getattr(Points, attr)==value) sq = sq.group_by(Overlay.id) # find overlap information satisfying previous query q = session.query(Overlay).filter(Overlay.id.in_(sq)).filter(func.ST_Area(Overlay.geom)>=size_threshold) # find overlap information not satisfying previous query q = session.query(Overlay).filter(Overlay.id.notin_(sq)).filter(func.ST_Area(Overlay.geom)>=size_threshold) overlays = q.all() session.expunge_all() return overlays def overlay_connection(self, oid): """ Evaluate the connection status of an overlay. An overlap can be empty (no points), fully connected (all images are connected by points), or partially connected. The first two status return empty lists while partially connected overlaps will return a list of image pairs that are missing point connections. Parameters ---------- overlay: int Database id of overlay of interest. Returns ------- missing_edges: list of tuples tuples correspond to image ids that comprise an overlap but are not connected by a point. """ graph = nx.Graph() with self.session_scope() as session: # create graph nodes overlap = session.query(Overlay).filter(Overlay.id==oid).first() ointersections = overlap.intersections for ii in ointersections: graph.add_node(ii) # find measures in relevant overlap and images geom = geoalchemy2.shape.from_shape(overlap.geom, srid=self.config['spatial']['latitudinal_srid']) q = session.query(Measures).join(Points, Measures.pointid==Points.id).\ filter(func.ST_Contains(geom, Points.geom)).\ filter(Measures.imageid.in_(ointersections)) df = pd.read_sql(q.statement, session.bind) # TO DO: RETURN ALL EDGES if len(df) == 0: print(f'Overlap {oid} is empty') return [] # create graph edges for pid, g in df.groupby('pointid'): edge_pool = np.sort([row['imageid'] for i, row in g.iterrows()]) graph.add_edges_from(list(combinations(edge_pool, 2))) # evaluate connectivity of overlap fully_connected_number_of_edges = scipy.special.comb(graph.number_of_nodes(),2) all_edges = list(combinations(graph.nodes, 2)) if graph.number_of_edges() == fully_connected_number_of_edges: print(f'Overlap {oid} is fully connected') return [] # return missing image id pairs return [e for e in all_edges if e not in graph.edges] def cluster_propagate_control_network(self, base_cnet, walltime='00:20:00', Loading docs/users/tutorials/isis_ingestion/overlap_check.ipynb +297 −7 File changed.Preview size limit exceeded, changes collapsed. Show changes Loading
autocnet/graph/network.py +67 −24 Original line number Diff line number Diff line Loading @@ -7,6 +7,7 @@ import os from shutil import copyfile from time import gmtime, strftime, time import warnings from itertools import combinations import networkx as nx import geopandas as gpd Loading @@ -14,6 +15,7 @@ import pandas as pd import numpy as np from redis import StrictRedis import shapely import scipy.special import geoalchemy2 from sqlalchemy.ext.declarative.api import DeclarativeMeta Loading Loading @@ -2222,18 +2224,12 @@ class NetworkCandidateGraph(CandidateGraph): """ self.redis_queue.flushdb() def empty_overlays(self, filters={'ignore': False}, size_threshold=0): def overlays(self, size_threshold=0): """ Find overlaps that do not contain valid points. By default, valid points include not ignored points, but additional point properties can be used to further define a valid point. For example, to look at not ignored, free (not ground) points; filters = {'ignored': False, 'pointtype': 2}. Return the overlays in a database Parameters ---------- filters: dict Points object properties for point filtering. size_threshold: float Minimum area requirment for returned overlaps. Units are determined by spatial reference system. Loading @@ -2241,30 +2237,22 @@ class NetworkCandidateGraph(CandidateGraph): Returns ------- overlays: list of Overlay objects Model information associated with overlaps that contain no valid points Model information associated with overlaps that contain one or more valid points See Also -------- autocnet.io.db.model.Overlay: for description of information associated with Overlay class autocnet.io.db.model.Points: for description of information associated with Points class """ with self.session_scope() as session: # Find overlap ids that contain one or more valid points sq = session.query(Overlay.id).join(Points, func.ST_Contains(Overlay.geom, Points.geom)) for attr, value in filters.items(): sq = sq.filter(getattr(Points, attr)==value) sq = sq.group_by(Overlay.id) # find overlap information not satisfying previous query q = session.query(Overlay).filter(Overlay.id.notin_(sq)).filter(func.ST_Area(Overlay.geom)>=size_threshold) with self.session_scope() as session: q = session.query(Overlay).filter(func.ST_Area(Overlay.geom)>=size_threshold) overlays = q.all() session.expunge_all() return overlays def connected_overlays(self, filters={'ignore': False}, size_threshold=0): def empty_overlays(self, filters={'ignore': False}, size_threshold=0): """ Find overlaps that contain valid points. By default, valid points Find overlaps that do not contain valid points. By default, valid points include not ignored points, but additional point properties can be used to further define a valid point. For example, to look at not ignored, free (not ground) points; filters = {'ignored': False, 'pointtype': 2}. Loading @@ -2281,7 +2269,7 @@ class NetworkCandidateGraph(CandidateGraph): Returns ------- overlays: list of Overlay objects Model information associated with overlaps that contain one or more valid points Model information associated with overlaps that contain no valid points See Also -------- Loading @@ -2295,12 +2283,67 @@ class NetworkCandidateGraph(CandidateGraph): sq = sq.filter(getattr(Points, attr)==value) sq = sq.group_by(Overlay.id) # find overlap information satisfying previous query q = session.query(Overlay).filter(Overlay.id.in_(sq)).filter(func.ST_Area(Overlay.geom)>=size_threshold) # find overlap information not satisfying previous query q = session.query(Overlay).filter(Overlay.id.notin_(sq)).filter(func.ST_Area(Overlay.geom)>=size_threshold) overlays = q.all() session.expunge_all() return overlays def overlay_connection(self, oid): """ Evaluate the connection status of an overlay. An overlap can be empty (no points), fully connected (all images are connected by points), or partially connected. The first two status return empty lists while partially connected overlaps will return a list of image pairs that are missing point connections. Parameters ---------- overlay: int Database id of overlay of interest. Returns ------- missing_edges: list of tuples tuples correspond to image ids that comprise an overlap but are not connected by a point. """ graph = nx.Graph() with self.session_scope() as session: # create graph nodes overlap = session.query(Overlay).filter(Overlay.id==oid).first() ointersections = overlap.intersections for ii in ointersections: graph.add_node(ii) # find measures in relevant overlap and images geom = geoalchemy2.shape.from_shape(overlap.geom, srid=self.config['spatial']['latitudinal_srid']) q = session.query(Measures).join(Points, Measures.pointid==Points.id).\ filter(func.ST_Contains(geom, Points.geom)).\ filter(Measures.imageid.in_(ointersections)) df = pd.read_sql(q.statement, session.bind) # TO DO: RETURN ALL EDGES if len(df) == 0: print(f'Overlap {oid} is empty') return [] # create graph edges for pid, g in df.groupby('pointid'): edge_pool = np.sort([row['imageid'] for i, row in g.iterrows()]) graph.add_edges_from(list(combinations(edge_pool, 2))) # evaluate connectivity of overlap fully_connected_number_of_edges = scipy.special.comb(graph.number_of_nodes(),2) all_edges = list(combinations(graph.nodes, 2)) if graph.number_of_edges() == fully_connected_number_of_edges: print(f'Overlap {oid} is fully connected') return [] # return missing image id pairs return [e for e in all_edges if e not in graph.edges] def cluster_propagate_control_network(self, base_cnet, walltime='00:20:00', Loading
docs/users/tutorials/isis_ingestion/overlap_check.ipynb +297 −7 File changed.Preview size limit exceeded, changes collapsed. Show changes
