Merge branch 'refactor_overlaps' into 'main' (821e8afb) · Commits · aflab / astrogeology / Autocnet

CHANGELOG.md

+3 −0

Changes for CHANGELOG.md: 3 added lines, 0 removed lines.
Original line number	Original line	Diff line number	Diff line
	@@ -42,6 +42,9 @@ release.
	- Functionality to add measure to a point		- Functionality to add measure to a point
	- Functionality to convert from sample, line (x,y) pixel coordinates to Body-Centered, Body-Fixed (BCBF) coordinates in meters.		- Functionality to convert from sample, line (x,y) pixel coordinates to Body-Centered, Body-Fixed (BCBF) coordinates in meters.
	- Functionality to test for valid input images.		- Functionality to test for valid input images.
			- Refactored place_points_in_overlap to make it easier to understand and work with
			- Created `sensor.py` that creates a class for either a 'csm' camera sensor or an 'isis' camera sensor. This removes confusing and wordy code. Now just need to create a sensor object based on the input 'csm' or 'isis'. Code inside classes will figure out the rest.
			- Fuctionality to convert between oc2xyz and xyz2oc in `spatial.py`

	### Fixed		### Fixed
	- string injection via format with sqlalchemy text() object.		- string injection via format with sqlalchemy text() object.

autocnet/io/db/model.py

+3 −4

Changes for autocnet/io/db/model.py: 3 added lines, 4 removed lines.
Original line number	Original line	Diff line number	Diff line
	@@ -584,7 +584,7 @@ class Points(Base, BaseMixin):
	choosername=choosername))		choosername=choosername))
	return point		return point

	def add_measures_to_point(self, candidates, choosername='autocnet'):		def add_measures_to_point(self, candidates, sensor, choosername='autocnet', **kwargs):
	"""		"""
	Attempt to add 1+ measures to a point from a list of candidate nodes. The		Attempt to add 1+ measures to a point from a list of candidate nodes. The
	function steps over each node and attempts to use the node's sensor model		function steps over each node and attempts to use the node's sensor model
	@@ -603,10 +603,9 @@ class Points(Base, BaseMixin):
	if not os.path.exists(node['image_path']):		if not os.path.exists(node['image_path']):
	log.info(f'Unable to find input image {node["image_path"]}')		log.info(f'Unable to find input image {node["image_path"]}')
	continue		continue

	try:		try:
	# ToDo: We want ot abstract away this to be a generic sensor model. No more 'isis' vs. 'csm' in the code		# TODO: Take this projection out of the CSM model and work it into the point
	sample, line = isis.ground_to_image(node["image_path"], self.geom.x, self.geom.y)		sample, line = sensor.calculate_sample_line(node, self.geom.x, self.geom.y, **kwargs)
	except:		except:
	log.info(f"{node['image_path']} failed ground_to_image. Likely due to being processed incorrectly or is just a bad image that failed campt.")		log.info(f"{node['image_path']} failed ground_to_image. Likely due to being processed incorrectly or is just a bad image that failed campt.")

autocnet/io/db/tests/test_points.py

+3 −1

Changes for autocnet/io/db/tests/test_points.py: 3 added lines, 1 removed line.
Original line number	Original line	Diff line number	Diff line
	@@ -3,6 +3,7 @@ import pytest

	import shapely		import shapely
	from autocnet.io.db.model import Points		from autocnet.io.db.model import Points
			from autocnet.spatial import sensor

	def test_points_exists(tables):		def test_points_exists(tables):
	assert Points.__tablename__ in tables		assert Points.__tablename__ in tables
	@@ -72,6 +73,7 @@ def test_create_point_with_reference_measure(session):
	def test_add_measures_to_point(session):		def test_add_measures_to_point(session):
	point = Points()		point = Points()
	point.adjusted = shapely.Point(0,0,0)		point.adjusted = shapely.Point(0,0,0)
			test_sensor = sensor.create_sensor('isis')

	node = MagicMock()		node = MagicMock()
	node.isis_serial = 'serial'		node.isis_serial = 'serial'
	@@ -81,7 +83,7 @@ def test_add_measures_to_point(session):

	with patch('autocnet.spatial.isis.ground_to_image') as mocked_call:		with patch('autocnet.spatial.isis.ground_to_image') as mocked_call:
	mocked_call.return_value = (0.5, 0.5)		mocked_call.return_value = (0.5, 0.5)
	point.add_measures_to_point(reference_nodes)		point.add_measures_to_point(reference_nodes, test_sensor)

	assert len(point.measures) == 4		assert len(point.measures) == 4
	assert point.measures[0].line == 0.5		assert point.measures[0].line == 0.5

autocnet/matcher/validation.py

0 → 100644

+72 −0

Changes for autocnet/matcher/validation.py: 72 added lines, 0 removed lines.
Original line number	Original line	Diff line number	Diff line
			import numpy as np

			def is_valid_lroc_polar_image(roi_array,
			include_var=True,
			include_mean=False,
			include_std=False):
			"""
			Checks if a numpy array representing an ROI from an lorc polar image is valid.
			Can check using variance, mean, and standard deviation, baed on user input.
			It is highly encouraged that at the very least the variance check is used.

			Parameters
			__________
			roi_array : np.array
			A numpy array representing a ROI from an image, meaning the values are pixels
			include_var : bool
			Choose whether to filter images based on variance. Default True.
			include_mean : bool
			Choose whether to filter images based on mean. Default True.
			Goal is to get rid of overally dark images.
			include_std : bool
			Choose whether to filter images based on standard deviation. Default True.
			Goal is to get rid of overally saturated images.

			Returns
			_______
			is_valid : bool
			Returns True is passes the checks, returns false otherwise.
			"""
			functions = []

			if include_var:
			# Get rid of super bad images
			var_func = lambda x : False if np.var(roi_array) == 0 else True
			functions.append(var_func)
			if include_mean:
			# Get rid of overally dark images
			mean_func = lambda x : False if np.mean(roi_array) < 0.0005 else True
			functions.append(mean_func)
			if include_std:
			# Get rid over overally saturated images
			std_func = lambda x : False if np.std(roi_array) > 0.001 else True
			functions.append(std_func)

			return all(func(roi_array) for func in functions)

			def is_valid_lroc_image(roi_array, include_var=True):
			"""
			Checks if a numpy array representing an ROI from an lroc image is valid.
			Can check using variance, based on user input.
			It is highly encouraged that the variance check is used.

			Parameters
			__________
			roi_array : np.array
			A numpy array representing a ROI from an image, meaning the values are pixels
			include_var : bool
			Choose whether to filter images based on variance. Default True.

			Returns
			_______
			is_valid : bool
			Returns True is passes the checks, returns false otherwise.
			"""
			functions = []

			if include_var:
			# Get rid of super bad images
			var_func = lambda x : False if np.var(roi_array) == 0 else True
			functions.append(var_func)

			return all(func(roi_array) for func in functions)
			No newline at end of file

autocnet/spatial/centroids.py

+2 −47

Changes for autocnet/spatial/centroids.py: 2 added lines, 47 removed lines.
Original line number	Original line	Diff line number	Diff line
	import json		import json
	import logging		import logging
	import math		import math
	import os

	import numpy as np		import numpy as np
	import shapely		import shapely

	from sqlalchemy import text
	from autocnet.cg.cg import create_points_along_line		from autocnet.cg.cg import create_points_along_line
	from autocnet.io.db.model import Images, Measures, Overlay, Points, JsonEncoder		from autocnet.io.db.model import Images, Points, JsonEncoder
	from autocnet.graph.node import NetworkNode		from autocnet.graph.node import NetworkNode
	from autocnet.spatial import isis		from autocnet.spatial import isis
	from autocnet.transformation import roi		from autocnet.transformation import roi
	from autocnet.matcher.cpu_extractor import extract_most_interesting		from autocnet.matcher.cpu_extractor import extract_most_interesting
			from autocnet.matcher.validation import is_valid_lroc_polar_image
	import time		import time

	# Set up logging file		# Set up logging file
	@@ -143,50 +142,6 @@ def find_points_in_centroids(radius,
	points.extend(line_points)		points.extend(line_points)
	return points		return points

	def is_valid_lroc_polar_image(roi_array,
	include_var=True,
	include_mean=False,
	include_std=False):
	"""
	Checks if a numpy array representing an ROI from an image is valid.
	Can check using variance, mean, and standard deviation, baed on unser input.
	It is highly encouraged that at the very least the variance check is used.

	Parameters
	__________
	roi_array : np.array
	A numpy array representing a ROI from an image, meaning the values are pixels
	include_var : bool
	Choose whether to filter images based on variance. Default True.
	include_mean : bool
	Choose whether to filter images based on mean. Default True.
	Goal is to get rid of overally dark images.
	include_std : bool
	Choose whether to filter images based on standard deviation. Default True.
	Goal is to get rid of overally saturated images.

	Returns
	_______
	is_valid : bool
	Returns True is passes the checks, returns false otherwise.
	"""
	functions = []

	if include_var:
	# Get rid of super bad images
	var_func = lambda x : False if np.var(roi_array) == 0 else True
	functions.append(var_func)
	if include_mean:
	# Get rid of overally dark images
	mean_func = lambda x : False if np.mean(roi_array) < 0.0005 else True
	functions.append(mean_func)
	if include_std:
	# Get rid over overally saturated images
	std_func = lambda x : False if np.std(roi_array) > 0.001 else True
	functions.append(std_func)

	return all(func(roi_array) for func in functions)

	def find_intresting_point(nodes, lon, lat, size=71):		def find_intresting_point(nodes, lon, lat, size=71):
	"""		"""
	Find an intresting point close the given lon, lat given a list data structure that contains		Find an intresting point close the given lon, lat given a list data structure that contains