Multiple updates - separated by commit message (#527) (d8e91957) · Commits · aflab / astrogeology / Autocnet

autocnet/cg/cg.py

+20 −8

Original line number	Diff line number	Diff line
		@@ -313,8 +313,7 @@ def xy_in_polygon(x,y, geom):
		"""
		return geom.contains(Point(x, y))


		def distribute_points_classic(geom, nspts, ewpts, **kwargs):
		def distribute_points_classic(geom, nspts, ewpts, use_mrr=True, **kwargs):
		"""
		This is a decision tree that attempts to perform a
		very simplistic approximation of the shape
		@@ -334,13 +333,20 @@ def distribute_points_classic(geom, nspts, ewpts, **kwargs):
		The number of points to attempt to place
		in the E/W (right/left) direction

		use_mrr : boolean
		If True (default) compute the minimum rotated rectangle bounding
		the geometry

		Returns
		-------
		valid : list
		of point coordinates in the form [(x1,y1), (x2,y2), ..., (xn, yn)]
		"""
		original_geom = geom
		if use_mrr:
		geom = geom.minimum_rotated_rectangle

		geom_coords = np.column_stack(geom.envelope.exterior.xy)
		geom_coords = np.column_stack(geom.exterior.xy)
		coords = np.array(list(zip(*geom.envelope.exterior.xy))[:-1])

		ll = coords[0]
		@@ -371,7 +377,7 @@ def distribute_points_classic(geom, nspts, ewpts, **kwargs):

		points = np.vstack(points)
		# Perform a spatial intersection check to eject points that are not valid
		valid = [p for p in points if xy_in_polygon(p[0], p[1], geom)]
		valid = [p for p in points if xy_in_polygon(p[0], p[1], original_geom)]
		return valid

		def distribute_points_new(geom, nspts, ewpts, Session):
		@@ -434,7 +440,8 @@ def distribute_points_new(geom, nspts, ewpts, Session):
		def distribute_points_in_geom(geom, method="classic",
		nspts_func=lambda x: ceil(round(x,1)*10),
		ewpts_func=lambda x: ceil(round(x,1)*5),
		Session=None):
		Session=None,
		**kwargs):
		"""
		Given a geometry, attempt a basic classification of the shape.
		RIght now, this simply attempts to determine if the bounding box
		@@ -477,6 +484,9 @@ def distribute_points_in_geom(geom, method="classic",
		point_distribution_func = point_funcs[method]

		coords = list(zip(*geom.envelope.exterior.xy))


		# This logic is kwarg swapping - need to trace this logic.
		short = np.inf
		long = -np.inf
		shortid = 0
		@@ -490,9 +500,11 @@ def distribute_points_in_geom(geom, method="classic",
		long = d
		longid = i
		ratio = short/long

		ns = False
		ew = False
		valid = []

		# The polygons should be encoded with a lower left origin in counter-clockwise direction.
		# Therefore, if the 'bottom' is the short edge it should be id 0 and modulo 2 == 0.
		if shortid % 2 == 0:
		@@ -515,7 +527,7 @@ def distribute_points_in_geom(geom, method="classic",
		if nspts == 1 and ewpts == 1:
		valid = single_centroid(geom)
		else:
		valid = point_distribution_func(geom, nspts, ewpts, Session=Session)
		valid = point_distribution_func(geom, nspts, ewpts, Session=Session, **kwargs)
		elif ew == True:
		# Since this is an LS, we should place these diagonally from the 'lower left' to the 'upper right'
		nspts = ewpts_func(short)
		@@ -523,7 +535,7 @@ def distribute_points_in_geom(geom, method="classic",
		if nspts == 1 and ewpts == 1:
		valid = single_centroid(geom)
		else:
		valid = point_distribution_func(geom, nspts, ewpts, Session=Session)
		valid = point_distribution_func(geom, nspts, ewpts, Session=Session, **kwargs)
		else:
		print('WTF Willy')
		return valid

autocnet/graph/network.py

+46 −16

Original line number	Diff line number	Diff line
		@@ -1268,7 +1268,6 @@ class CandidateGraph(nx.Graph):
		"""
		return self.controlnetwork.groupby('point_id').apply(lambda g: g if len(g) > 1 else None)


		def to_isis(self, outname, flistpath=None, target="Mars"): # pragma: no cover
		"""
		Write the control network out to the ISIS3 control network format.
		@@ -1310,13 +1309,6 @@ class CandidateGraph(nx.Graph):
		cnet.to_isis(df, outname, targetname=target)
		cnet.write_filelist(self.files, path=flistpath)

		def to_bal(self):
		"""
		Write the control network out to the Bundle Adjustment in the Large
		(BAL) file format. For more information see:
		http://grail.cs.washington.edu/projects/bal/
		"""
		pass

		class NetworkCandidateGraph(CandidateGraph):
		node_factory = NetworkNode
		@@ -1468,14 +1460,18 @@ class NetworkCandidateGraph(CandidateGraph):
		port=conf['port'],
		db=0)
		self.processing_queue = conf['processing_queue']
		self.completed_queue = conf['completed_queue']
		self.working_queue = conf['working_queue']

		def empty_queues(self):
		def clear_queues(self):
		"""
		Delete all messages from the redis queue. This a convenience method.
		The `redis_queue` object is a redis-py StrictRedis object with API
		documented at: https://redis-py.readthedocs.io/en/latest/#redis.StrictRedis
		"""
		return self.redis_queue.flushall()
		queues = [self.processing_queue, self.completed_queue, self.working_queue]
		for q in queues:
		self.redis_queue.delete(q)

		def _execute_sql(self, sql):
		"""
		@@ -1586,6 +1582,7 @@ class NetworkCandidateGraph(CandidateGraph):
		query_string='',
		reapply=False,
		log_dir=None,
		queue=None,
		**kwargs):
		"""
		A mirror of the apply function from the standard CandidateGraph object. This implementation
		@@ -1647,6 +1644,10 @@ class NetworkCandidateGraph(CandidateGraph):
		kwargs : dict
		Of keyword arguments passed to the function being applied

		queue : str
		The processing queue to use. If None (default), use the processing queue from
		the config file.

		Examples
		--------
		Apply a function to the overlay table omitting those overlay rows that already have
		@@ -1708,16 +1709,19 @@ class NetworkCandidateGraph(CandidateGraph):
		isisroot = env['ISISROOT']
		isisdata = env['ISISDATA']

		isissetup = f'export ISISROOT={isisroot} && export ISIS3DATA={isisdata} && export ISISDATA={isisdata}'
		isissetup = f'export ISISROOT={isisroot} && export ISISDATA={isisdata}'
		condasetup = f'conda activate {condaenv}'
		job = f'acn_submit -r={rhost} -p={rport} {processing_queue}'
		command = f'{condasetup} && {isissetup} && {job}'

		if queue == None:
		queue = self.config['cluster']['queue']

		submitter = Slurm(command,
		job_name='AutoCNet',
		mem_per_cpu=self.config['cluster']['processing_memory'],
		time=walltime,
		partition=self.config['cluster']['queue'],
		partition=queue,
		output=log_dir+f'/autocnet.{function}-%j')
		submitter.submit(array='1-{}%{}'.format(job_counter,arraychunk), chunksize=chunksize)
		return job_counter
		@@ -2217,12 +2221,18 @@ class NetworkCandidateGraph(CandidateGraph):
		llen = self.redis_queue.llen(self.config['redis']['processing_queue'])
		return llen

		def queue_flushdb(self):
		@property
		def union(self):
		"""
		Clear the processing queue of any left over jobs from a previous cluster
		job cancellation or hanging jobs.
		The boundary formed by unioning (or merging) all of the input footprints. The result
		will likely be a multipolygon, likely with holes where data were not collected.

		Returns
		"""
		self.redis_queue.flushdb()
		if not hasattr(self, '_union'):
		with self.session_scope() as session:
		self._union = Images.union(session)
		return self._union

		def overlays(self, size_threshold=0):
		"""
		@@ -2413,3 +2423,23 @@ class NetworkCandidateGraph(CandidateGraph):
		self.dem,
		nodes,
		**kwargs)
		def distribute_ground(self, distribute_points_kwargs={}):
		"""
		Distribute candidate ground points into the union of the image footprints. This
		function returns a list of 2d nd-arrays where the first element is the longitude
		and the second element is the latitude.

		Parameters
		----------
		distirbute_points_kwargs : dict
		Of arguments that are passed on the the
		distribute_points_in_geom argument in autocnet.cg.cg

		Returns
		-------
		valid : list
		of nd-arrays in the form [array([lon, lat]), array([lon, lat])]
		"""
		geom = self.union
		valid = cg.distribute_points_in_geom(geom, **distribute_points_kwargs)
		return valid
		No newline at end of file

autocnet/io/db/controlnetwork.py

+1 −0

Original line number	Diff line number	Diff line
		@@ -11,6 +11,7 @@ SELECT measures."pointid",
		points."apriori",
		points."adjusted",
		points."pointIgnore",
		points."referenceIndex",
		measures."id",
		measures."serialnumber",
		measures."sample",

autocnet/io/db/model.py

+21 −1

Original line number	Diff line number	Diff line
		@@ -222,6 +222,20 @@ class Images(BaseMixin, Base):
		else:
		self._geom = from_shape(newgeom, srid=self.latitudinal_srid)

		@classmethod
		def union(cls, session):
		"""
		The boundary formed by unioning (or merging) all of the input footprints. The result
		will likely be a multipolygon, likely with holes where data were not collected.

		Returns
		-------
		: obj
		A shapely MULTIPOLYGON object
		"""
		res = session.query(cls.geom.ST_Union()).one()[0]
		return to_shape(res)

		class Overlay(BaseMixin, Base):
		__tablename__ = 'overlay'
		latitudinal_srid = -1
		@@ -289,7 +303,8 @@ class Points(BaseMixin, Base):
		ignore = Column("pointIgnore", Boolean, default=False)
		_apriori = Column("apriori", Geometry('POINTZ', srid=rectangular_srid, dimension=3, spatial_index=False))
		_adjusted = Column("adjusted", Geometry('POINTZ', srid=rectangular_srid, dimension=3, spatial_index=False))
		measures = relationship('Measures')
		measures = relationship('Measures', back_populates="point")
		reference_index = Column("referenceIndex", Integer, default=0)

		@hybrid_property
		def geom(self):
		@@ -385,6 +400,7 @@ class Measures(BaseMixin, Base):
		linesigma = Column(Float)
		weight = Column(Float, default=None)
		rms = Column(Float)
		point = relationship("Points", back_populates="measures")

		@hybrid_property
		def measuretype(self):
		@@ -396,6 +412,10 @@ class Measures(BaseMixin, Base):
		v = MeasureType(v)
		self._measuretype = v

		@property
		def reference_index(self):
		return self.point.reference_index

		def try_db_creation(engine, config):
		from autocnet.io.db.triggers import valid_point_function, valid_point_trigger, valid_geom_function, valid_geom_trigger, ignore_image_function, ignore_image_trigger

autocnet/matcher/cpu_extractor.py

+3 −1

Original line number	Diff line number	Diff line
		@@ -83,7 +83,7 @@ def extract_features(array, extractor_method='sift', extractor_parameters={}):
		keypoints = pd.DataFrame(keypoints, columns=['x', 'y', 'response', 'size',
		'angle', 'octave', 'layer'])

		if descriptors.dtype != np.float32:
		if hasattr(descriptors, 'dtype') and descriptors.dtype != np.float32:
		descriptors = descriptors.astype(np.float32)

		return keypoints, descriptors
		@@ -115,6 +115,8 @@ def extract_most_interesting(image, extractor_method='orb', extractor_parameters
		extractor_method=extractor_method,
		extractor_parameters=extractor_parameters)

		if len(kps) == 0:
		return None
		# Naively assume that the maximum variance is the most unique feature
		vari = np.var(desc, axis=1)
		return kps.iloc[np.argmax(vari)]