Adds basic integration test for subpixel matching (8e1b9850) · Commits · aflab / astrogeology / Autocnet

.github/workflows/continuous_integration.yml

+3 −0

Original line number	Diff line number	Diff line
		@@ -51,6 +51,9 @@ jobs:
		run: \|
		curl "${{ env.hirise-pds-url }}/PSP_010502_2090_RED5_0.IMG" -o $GITHUB_WORKSPACE/test-resources/PSP_010502_2090_RED5_0.img
		curl "${{ env.hirise-pds-url }}/PSP_010502_2090_RED5_1.IMG" -o $GITHUB_WORKSPACE/test-resources/PSP_010502_2090_RED5_1.img
		curl "https://asc-isisdata.s3.us-west-2.amazonaws.com/autocnet_test_data/B08_012650_1780_XN_02S046W.l1.cal.destriped.crop.cub" -o tests/test_subpixel_match/B08_012650_1780_XN_02S046W.l1.cal.destriped.crop.cub
		curl "https://asc-isisdata.s3.us-west-2.amazonaws.com/autocnet_test_data/D16_033458_1785_XN_01S046W.l1.cal.destriped.crop.cub" -o tests/test_subpixel_match/D16_033458_1785_XN_01S046W.l1.cal.destriped.crop.cub
		curl "https://asc-isisdata.s3.us-west-2.amazonaws.com/autocnet_test_data/J04_046447_1777_XI_02S046W.l1.cal.destriped.crop.cub" -o tests/test_subpixel_match/J04_046447_1777_XI_02S046W.l1.cal.destriped.crop.cub
		- name: Exit isis env
		run: conda deactivate
		- name: Checkout Code

.gitignore

+3 −0

Original line number	Diff line number	Diff line
		@@ -90,3 +90,6 @@ nodeids

		# VSCode
		launch.json

		# ASC Formats
		*.cub
		No newline at end of file

README.md

+4 −0

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		@@ -53,6 +53,10 @@ We suggest using Anaconda Python to install Autocnet within a virtual environmen
		2. Get the Postgresql with Postgis container and run it `docker run --name testdb -e POSTGRES_PASSOWRD='NotTheDefault' -e POSTGRES_USER='postgres' -p 5432:5432 -d mdillon/postgis`
		3. Run the test suite: `pytest autocnet`

		## How to skip long running tests

		The integration tests, inside the tests direcectory at the root of the project, can be long running. We have marked those as such and one can skip these long running integration tests by add `-m "not long"` to their pytest command.

		## Simple Network Examples:

autocnet/matcher/subpixel.py

+5 −86

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		@@ -36,6 +36,7 @@ from autocnet.spatial import isis
		from autocnet.io.db.model import Measures, Points, Images, JsonEncoder
		from autocnet.graph.node import NetworkNode
		from autocnet.transformation import roi
		from autocnet.transformation.affine import estimate_affine_transformation
		from autocnet import spatial
		from autocnet.utils.utils import bytescale

		@@ -771,7 +772,7 @@ def iterative_phase(sx, sy, dx, dy, s_img, d_img, size=(51, 51), reduction=11, c

		return dx, dy, metrics

		def estimate_affine_transformation(destination_coordinates, source_coordinates):
		'''def estimate_affine_transformation(destination_coordinates, source_coordinates):
		"""
		Given a set of destination control points compute the affine transformation
		required to project the source control points into the destination.
		@@ -793,7 +794,7 @@ def estimate_affine_transformation(destination_coordinates, source_coordinates):
		source_coordinates = np.asarray(source_coordinates)

		return tf.estimate_transform('affine', destination_coordinates, source_coordinates)

		'''
		def geom_match_simple(base_cube,
		input_cube,
		bcenter_x,
		@@ -1982,91 +1983,9 @@ def fourier_mellen(img1, img2, verbose=False, phase_kwargs={}):

		return newx, newy, error

		def estimate_affine_transformation(base_cube,
		input_cube,
		bcenter_x,
		bcenter_y,
		size_x=60,
		size_y=60):
		"""
		Using the a priori sensor model, project corner and center points from the base_cube into
		the input_cube and use these points to estimate an affine transformation.

		Parameters
		----------
		base_cube: plio.io.io_gdal.GeoDataset
		source image
		input_cube: plio.io.io_gdal.GeoDataset
		destination image; gets matched to the source image
		bcenter_x: int
		sample location of source measure in base_cube
		bcenter_y: int
		line location of source measure in base_cube
		size_x: int
		half-height of the subimage used in the affine transformation
		size_y: int
		half-width of the subimage used in affine transformation

		Returns
		-------
		affine : object
		The affine transformation object

		"""
		t1 = time.time()
		if not isinstance(input_cube, GeoDataset):
		raise Exception(f"Input cube must be a geodataset obj, but is type {type(input_cube)}.")
		if not isinstance(base_cube, GeoDataset):
		raise Exception(f"Match cube must be a geodataset obj, but is type {type(base_cube)}.")

		base_startx = int(bcenter_x - size_x)
		base_starty = int(bcenter_y - size_y)
		base_stopx = int(bcenter_x + size_x)
		base_stopy = int(bcenter_y + size_y)

		match_size = base_cube.raster_size

		# for now, require the entire window resides inside both cubes.
		if base_stopx > match_size[0]:
		raise Exception(f"Window: {base_stopx} > {match_size[0]}, center: {bcenter_x},{bcenter_y}")
		if base_startx < 0:
		raise Exception(f"Window: {base_startx} < 0, center: {bcenter_x},{bcenter_y}")
		if base_stopy > match_size[1]:
		raise Exception(f"Window: {base_stopy} > {match_size[1]}, center: {bcenter_x},{bcenter_y} ")
		if base_starty < 0:
		raise Exception(f"Window: {base_starty} < 0, center: {bcenter_x},{bcenter_y}")

		base_corners = [(base_startx,base_starty),
		(base_startx,base_stopy),
		(base_stopx,base_stopy),
		(base_stopx,base_starty),
		(bcenter_x, bcenter_y)]

		dst_corners = []
		passing_base_corners = []
		for x,y in base_corners:
		try:
		print('Processing: ', x,y)
		lon, lat = spatial.isis.image_to_ground(base_cube.file_name, x, y)
		dst_corners.append(
		spatial.isis.ground_to_image(input_cube.file_name, lon, lat)
		)
		passing_base_corners.append((x, y))
		except Exception as e:
		print(e)

		if len(dst_corners) < 3:
		raise ValueError(f'Unable to find enough points to compute an affine transformation. Found {len(dst_corners)} points, but need at least 3.')

		base_gcps = np.array([*passing_base_corners])

		dst_gcps = np.array([*dst_corners])

		affine = tf.estimate_transform('affine', np.array([base_gcps]), np.array([dst_gcps]))
		t2 = time.time()
		print(f'Estimation of the transformation took {t2-t1} seconds.')
		return affine

		# TODO: This func should be in transformation.affine with
		# signature (array_to_warp, affine, order=3).
		def affine_warp_image(base_cube, input_cube, affine, order=3):
		"""
		Given a base image, an input image, and an affine transformation, return

autocnet/spatial/isis.py

+11 −6

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		@@ -253,11 +253,15 @@ def point_info(
		# convert all pixels to PLIO pixels from ISIS
		r["Sample"] -= .5
		r["Line"] -= .5

		results.append(r)
		else:
		raise ValueError(
		f"ISIS campt completed, but reported an error: {r['Error']}"
		)
		r["Sample"] = None
		r["Line"] = None
		results.append(r)
		#raise ValueError(
		# f"ISIS campt completed, but reported an error: {r['Error']}"
		#)

		if isinstance(x, (abc.Sequence, np.ndarray)):
		return results
		@@ -271,6 +275,7 @@ def image_to_ground(
		line,
		lontype="PositiveEast360Longitude",
		lattype="PlanetocentricLatitude",
		allowoutside=False
		):
		"""
		Returns a two-tuple of numpy arrays or a two-tuple of floats, where
		@@ -306,7 +311,7 @@ def image_to_ground(
		are possible. Please see the campt or mappt documentation.

		"""
		res = point_info(cube_path, sample, line, "image")
		res = point_info(cube_path, sample, line, "image", allowoutside=allowoutside)

		if isinstance(sample, (abc.Sequence, np.ndarray)):
		lon_list = list()
		@@ -333,7 +338,7 @@ def _get_value(obj):
		return obj


		def ground_to_image(cube_path, lon, lat):
		def ground_to_image(cube_path, lon, lat, allowoutside=False):
		"""
		Returns a two-tuple of numpy arrays or a two-tuple of floats, where
		the first element of the tuple is the sample(s) and the second
		@@ -358,7 +363,7 @@ def ground_to_image(cube_path, lon, lat):
		Latitude coordinate(s).

		"""
		res = point_info(cube_path, lon, lat, "ground")
		res = point_info(cube_path, lon, lat, "ground", allowoutside=allowoutside)

		if isinstance(lon, (abc.Sequence, np.ndarray)):
		samples, lines = np.asarray([[r["Sample"], r["Line"]] for r in res]).T