def create_network(self, nodes=[]):
cmds = 0
session = Session()
for res in session.query(Overlay):
msg = json.dumps({'oid': res.id, 'time': time.time()})
# If nodes are passed, process only those overlaps containing
# the provided node(s)
if nodes:
for r in res.overlaps:
if r in nodes:
self.redis_queue.rpush(
config['redis']['processing_queue'], msg)
cmds += 1
break
else:
self.redis_queue.rpush(config['redis']['processing_queue'],
msg)
cmds += 1
script = 'acn_create_network'
spawn_jobarr(script,
cmds,
mem=config['cluster']['processing_memory'],
queue=config['cluster']['queue'],
env=config['python']['env_name'])
session.close()
def from_database(cls, query_string='SELECT * FROM public.images'):
"""
This is a constructor that takes the results from an arbitrary query string,
uses those as a subquery into a standard polygon overlap query and
returns a NetworkCandidateGraph object. By default, an images
in the Image table will be used in the outer query.
Parameters
----------
query_string : str
A valid SQL select statement that targets the Images table
Usage
-----
Here, we provide usage examples for a few, potentially common use cases.
## Spatial Query
This example selects those images that intersect a given bounding polygon. The polygon is
specified as a Well Known Text LINESTRING with the first and last points being the same.
The query says, select the footprint_latlon (the bounding polygons in the database) that
intersect the user provided polygon (the LINESTRING) in the given spatial reference system
(SRID), 949900.
"SELECT * FROM Images WHERE ST_INTERSECTS(footprint_latlon, ST_Polygon(ST_GeomFromText('LINESTRING(159 10, 159 11, 160 11, 160 10, 159 10)'),949900)) = TRUE"
from_database
## Select from a specific orbit
This example selects those images that are from a particular orbit. In this case,
the regex string pulls all P##_* orbits and creates a graph from them. This method
does not guarantee that the graph is fully connected.
"SELECT * FROM Images WHERE (split_part(path, '/', 6) ~ 'P[0-9]+_.+') = True"
"""
composite_query = """WITH
i as ({})
SELECT i1.id as i1_id,i1.path as i1_path, i2.id as i2_id, i2.path as i2_path
FROM
i as i1, i as i2
WHERE ST_INTERSECTS(i1.footprint_latlon, i2.footprint_latlon) = TRUE
AND i1.id < i2.id""".format(query_string)
session = Session()
res = session.execute(composite_query)
adjacency = defaultdict(list)
adjacency_lookup = {}
for r in res:
sid, spath, did, dpath = r
adjacency_lookup[spath] = sid
adjacency_lookup[dpath] = did
if spath != dpath:
adjacency[spath].append(dpath)
session.close()
# Add nodes that do not overlap any images
obj = cls.from_adjacency(adjacency,
node_id_map=adjacency_lookup,
config=config)
return obj
def keypoints(self, kps):
session = Session()
io_keypoints.to_hdf(self.keypoint_file, keypoints=kps)
res = session.query(Keypoints).filter(getattr(Keypoints,'image_id') == self['node_id']).first()
if res is None:
_ = self.keypoint_file
res = self._from_db(Keypoints)
res.nkeypoints = len(kps)
session.commit()
def matches(self):
session = Session()
q = session.query(Matches)
qf = q.filter(Matches.source == self.source['node_id'],
Matches.destination == self.destination['node_id'])
odf = pd.read_sql(qf.statement, q.session.bind).set_index('id')
df = pd.DataFrame(odf.values, index=odf.index.values, columns=odf.columns.values)
df.index.name = 'id'
# Explicit close to get the session cleaned up
session.close()
return DbDataFrame(df,
parent=self,
name='matches')
def _from_db(self, table_obj):
"""
Generic database query to pull the row associated with this node
from an arbitrary table. We assume that the row id matches the node_id.
Parameters
----------
table_obj : object
The declared table class (from db.model)
"""
session = Session()
res = session.query(table_obj).\
filter(table_obj.source == self.source['node_id']).\
filter(table_obj.destination == self.destination['node_id'])
session.close()
return res
def footprint(self):
res = Session().query(Images).filter(Images.id == self['node_id']).first()
if res is None:
try:
footprint_latlon = generate_latlon_footprint(self.camera)
footprint_latlon = footprint_latlon.ExportToWkt()
footprint_latlon = WKTElement(footprint_latlon, srid=config['spatial']['srid'])
except:
footprint_latlon = None
else:
footprint_latlon = to_shape(res.footprint_latlon)
return footprint_latlon
def _from_db(self, table_obj, key='image_id'):
"""
Generic database query to pull the row associated with this node
from an arbitrary table. We assume that the row id matches the node_id.
Parameters
----------
table_obj : object
The declared table class (from db.model)
key : str
The name of the column to compare this object's node_id with. For
most tables this will be the default, 'image_id' because 'image_id'
is the foreign key in the DB. For the Images table (the parent table),
the key is simply 'id'.
"""
if 'node_id' not in self.keys():
return
session = Session()
res = session.query(table_obj).filter(getattr(table_obj,key) == self['node_id']).first()
session.close()
return res
def matches(self, v):
to_db_add = []
to_db_update = []
df = pd.DataFrame(v)
df.index.name = v.index.name
# Get the query obj
session = Session()
q = session.query(Matches)
for idx, row in df.iterrows():
# Determine if this is an update or the addition of a new row
if hasattr(row, 'id'):
res = q.filter(Matches.id == row.id).first()
match_id = row.id
elif v.index.name == 'id':
res = q.filter(Matches.id == row.name).first()
match_id = row.name
else:
res = None
if res:
# update
mapping = {}
mapping['id'] = match_id
for index in row.index:
row_val = row[index]
if isinstance(row_val, (np.int, )):
row_val = int(row_val)
elif isinstance(row_val, (np.float, )):
row_val = float(row_val)
elif isinstance(row_val, WKBElement):
continue
mapping[index] = row_val
to_db_update.append(mapping)
else:
match = Matches(source=int(row.source),
source_idx=int(row.source_idx),
destination=int(row.destination),
destination_idx=int(row.destination_idx))
to_db_add.append(match)
if to_db_add:
session.bulk_save_objects(to_db_add)
if to_db_update:
session.bulk_update_mappings(Matches, to_db_update)
session.commit()
def masks(self):
res = Session().query(Edges.masks).\
filter(Edges.source == self.source['node_id']).\
filter(Edges.destination == self.destination['node_id']).\
first()
if res:
df = pd.DataFrame.from_records(res[0])
df.index = df.index.map(int)
else:
ids = list(map(int, self.matches.index.values))
df = pd.DataFrame(index=ids)
df.index.name = 'match_id'
return DbDataFrame(df, parent=self, name='masks')
def fundamental_matrix(self, v):
session = Session()
res = session.query(table_obj).\
filter(table_obj.source == self.source['node_id']).\
filter(table_obj.destination == self.destination['node_id']).first()
if res:
res.fundamental = v
else:
edge = Edges(source=self.source['node_id'],
destination=self.destination['node_id'],
fundamental = v)
session.add(edge)
session.commit()
def costs(self):
# these are np.float coming out, sqlalchemy needs ints
ids = list(map(int, self.matches.index.values))
res = Session().query(Costs).filter(Costs.match_id.in_(ids)).all()
#qf = q.filter(Costs.match_id.in_(ids))
if res:
# Parse the JSON dicts in the cost field into a full dimension dataframe
costs = {r.match_id:r._cost for r in res}
df = pd.DataFrame.from_records(costs).T # From records is important because from_dict drops rows with empty dicts
else:
df = pd.DataFrame(index=ids)
df.index.name = 'match_id'
return DbDataFrame(df, parent=self, name='costs')
def ring(self, ring):
# Setters need a single session and so should not make use of the
# syntax sugar _from_db
session = Session()
res = session.query(Edges).\
filter(Edges.source == self.source['node_id']).\
filter(Edges.destination == self.destination['node_id']).first()
if res:
res.ring = ring
else:
edge = Edges(source=self.source['node_id'],
destination=self.destination['node_id'],
ring=ring)
session.add(edge)
session.commit()
return
def masks(self, v):
def dict_check(input):
for k, v in input.items():
if isinstance(v, dict):
dict_check(v)
elif v is None:
continue
elif np.isnan(v):
input[k] = None
df = pd.DataFrame(v)
session = Session()
res = session.query(Edges).\
filter(Edges.source == self.source['node_id']).\
filter(Edges.destination == self.destination['node_id']).first()
if res:
as_dict = df.to_dict()
dict_check(as_dict)
# Update the masks
res.masks = as_dict
session.add(res)
session.commit()
def compute_overlaps(self):
query = """
SELECT ST_AsEWKB(geom) AS geom FROM ST_Dump((
SELECT ST_Polygonize(the_geom) AS the_geom FROM (
SELECT ST_Union(the_geom) AS the_geom FROM (
SELECT ST_ExteriorRing(footprint_latlon) AS the_geom
FROM images) AS lines
) AS noded_lines
)
)"""
session = Session()
oquery = session.query(Overlay)
iquery = session.query(Images)
rows = []
for q in self._engine.execute(query).fetchall():
overlaps = []
b = bytes(q['geom'])
qgeom = shapely.wkb.loads(b)
res = iquery.filter(Images.footprint_latlon.ST_Intersects(from_shape(qgeom, srid=949900)))
for i in res:
fgeom = to_shape(i.footprint_latlon)
area = qgeom.intersection(fgeom).area
if area < 1e-6:
continue
overlaps.append(i.id)
o = Overlay(geom='SRID=949900;{}'.format(qgeom.wkt), overlaps=overlaps)
res = oquery.filter(Overlay.overlaps == o.overlaps).first()
if res is None:
rows.append(o)
session.bulk_save_objects(rows)
session.commit()
res = oquery.filter(sqlalchemy.func.cardinality(Overlay.overlaps) <= 1)
res.delete(synchronize_session=False)
session.commit()
session.close()
def __init__(self, *args, parent=None, **kwargs):
# If this is the first time that the image is seen, add it to the DB
if parent is None:
self.parent = Parent(config)
else:
self.parent = parent
# Create a session to work in
session = Session()
# For now, just use the PATH to determine if the node/image is in the DB
res = session.query(Images).filter(Images.path == kwargs['image_path']).first()
exists = False
if res:
exists = True
kwargs['node_id'] = res.id
session.close()
super(NetworkNode, self).__init__(*args, **kwargs)
if exists is False:
# Create the camera entry
try:
self._camera = create_camera(self.geodata)
serialized_camera = self._camera.getModelState()
cam = Cameras(camera=serialized_camera)
except:
cam = None
kpspath = io_keypoints.create_output_path(self.geodata.file_name)
# Create the keypoints entry
kps = Keypoints(path=kpspath, nkeypoints=0)
# Create the image
i = Images(name=kwargs['image_name'],
path=kwargs['image_path'],
footprint_latlon=self.footprint,
cameras=cam, keypoints=kps)
session = Session()
session.add(i)
session.commit()
session.close()
self.job_status = defaultdict(dict)
def costs(self, v):
to_db_add = []
# Get the query obj
session = Session()
q = session.query(Costs)
# Need the new instance here to avoid __setattr__ issues
df = pd.DataFrame(v)
for idx, row in df.iterrows():
# Now invert the expanded dict back into a single JSONB column for storage
res = q.filter(Costs.match_id == idx).first()
if res:
#update the JSON blob
costs_new_or_updated = row.to_dict()
for k, v in costs_new_or_updated.items():
if v is None:
continue
elif np.isnan(v):
v = None
res._cost[k] = v
sqlalchemy.orm.attributes.flag_modified(res, '_cost')
session.add(res)
session.commit()
else:
row = row.to_dict()
costs = row.pop('_costs', {})
for k, v in row.items():
if np.isnan(v):
v = None
costs[k] = v
cost = Costs(match_id=idx, _cost=costs)
to_db_add.append(cost)
if to_db_add:
session.bulk_save_objects(to_db_add)
session.commit()
def matches(self):
session = Session()
session.query(Matches).filter(Matches.source == self.source['node_id'], Matches.destination == self.destination['node_id']).delete()
session.commit()
session.close()
return
