Merge pull request #229 from jlaura/control (da3cfdb8) · Commits · aflab / astrogeology / Autocnet

.travis.yml

+1 −11

Original line number	Diff line number	Diff line
		language: generic # Since we use conda, just go generic
		sudo: false

		branches:
		@@ -7,8 +8,6 @@ only:
		env:
		global:
		- BINSTAR_USER: jlaura
		matrix:
		- PYTHON_VERSION: 3.5

		os:
		- linux
		@@ -18,18 +17,10 @@ install:
		# We do this conditionally because it saves us some downloading if the
		# version is the same.
		- if [ "$TRAVIS_OS_NAME" == "linux" ]; then
		if [ "$PYTHON_VERSION" == 2.7 ]; then
		wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh;
		else
		wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
		fi
		else
		if ["$PYTHON_VERSION" == 2.7]; then
		curl -o miniconda.sh https://repo.continuum.io/miniconda/Miniconda-latest-MacOSX-x86_64.sh;
		else
		curl -o miniconda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh;
		fi
		fi
		- bash miniconda.sh -b -p $HOME/miniconda
		- export PATH="$HOME/miniconda/bin:$PATH"
		- hash -r
		@@ -43,7 +34,6 @@ install:
		- conda config --add channels menpo
		- conda config --add channels jlaura
		- conda config --set ssl_verify false
		- conda install python=$PYTHON_VERSION
		- conda install -c conda-forge numpy opencv
		- conda install -c jlaura plio
		- conda install -c conda-forge vlfeat

autocnet/init.py

+6 −0

Original line number	Diff line number	Diff line
		@@ -43,6 +43,9 @@ def cuda(enable=False, gpu=0):
		from autocnet.matcher.cuda_decompose import decompose_and_match
		Edge.decompose_and_match = decompose_and_match

		from autocnet.matcher.cuda_outlier_detector import distance_ratio
		Edge._ratio_check = staticmethod(distance_ratio)

		except Exception:
		warnings.warn('Failed to enable Cuda')
		return
		@@ -57,4 +60,7 @@ def cuda(enable=False, gpu=0):
		from autocnet.matcher.cpu_decompose import decompose_and_match
		Edge.decompose_and_match = decompose_and_match

		from autocnet.matcher.cpu_outlier_detector import distance_ratio
		Edge._ratio_check = staticmethod(distance_ratio)

		cuda()

autocnet/camera/camera.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -5,6 +5,7 @@ try:
		except:
		cv2 = None


		def compute_epipoles(f):
		"""
		Compute the epipole and epipolar prime
		@@ -28,7 +29,6 @@ def compute_epipoles(f):

		return e, e1


		def idealized_camera():
		"""
		Create an idealized camera transformation matrix

autocnet/control/control.py

+54 −6

Original line number	Diff line number	Diff line
		import warnings
		import networkx as nx
		import numpy as np
		import pandas as pd
		import geopandas as gpd
		from shapely.geometry import Point

		from autocnet.matcher import subpixel as sp

		from plio.io.io_controlnetwork import to_isis, write_filelist

		def subpixel_match(cg, cn,threshold=0.9, template_size=19, search_size=53, max_x_shift=1.0,max_y_shift=1.0, **kwargs):

		def subpixel_group(group, threshold=0.9, template_size=19, search_size=53, max_x_shift=1.0,max_y_shift=1.0, **kwargs):
		offs = []
		for i,(idx, r) in enumerate(group.iterrows()):
		if i == 0:
		x = r.x
		y = r.y
		offs.append([0,0, np.inf])
		continue

		e = r.edge
		s_img = cg.edge[e[0]][e[1]].source.geodata
		s_template = sp.clip_roi(s_img, (x, y), template_size)
		#s_template = cv2.Canny(bytescale(s_template), 50,100) # Canny - bad idea
		d_img = cg.edge[e[0]][e[1]].destination.geodata
		d_search = sp.clip_roi(d_img, (r.x, r.y), search_size)
		#d_search = cv2.Canny(bytescale(d_search), 50,100)

		xoff,yoff,corr = sp.subpixel_offset(s_template, d_search, **kwargs)
		offs.append([xoff,yoff,corr])
		df = pd.DataFrame(offs, columns=['x_off', 'y_off', 'corr'], index=group.index)
		return df
		gps = cn.data.groupby('point_id').apply(subpixel_group,threshold=0.9,max_x_shift=5, max_y_shift=5,template_size=template_size, search_size=search_size,**kwargs)
		cn.data[['x_off', 'y_off', 'corr']] = gps.reset_index()[['x_off', 'y_off', 'corr']]

		def identify_potential_overlaps(cg, cn, overlap=True):
		"""
		@@ -94,7 +122,7 @@ def deepen_correspondences(cg, cn):
		pass

		class ControlNetwork(object):
		measures_keys = ['point_id', 'image_index', 'keypoint_index', 'edge', 'match_idx', 'x', 'y']
		measures_keys = ['point_id', 'image_index', 'keypoint_index', 'edge', 'match_idx', 'x', 'y', 'x_off', 'y_off', 'corr', 'valid']

		def __init__(self):
		self._point_id = 0
		@@ -150,9 +178,14 @@ class ControlNetwork(object):
		# The node_id is a composite key (image_id, correspondence_id), so just grab the image
		image_id = key[0]
		match_id = key[1]
		self.data.loc[self._measure_id] = [point_id, image_id, match_id, edge, match_idx, *fields]
		self.data.loc[self._measure_id] = [point_id, image_id, match_id, edge, match_idx, *fields, 0, 0, np.inf, True]
		self._measure_id += 1

		def remove_measure(self, idx):
		self.data = self.data.drop(self.data.index[idx])
		for r in idx:
		self.measure_to_point.pop(r, None)

		def validate_points(self):
		"""
		Ensure that all control points currently in the nework are valid.
		@@ -168,14 +201,20 @@ class ControlNetwork(object):
		"""

		def func(g):
		print(g)
		# One and only one measure constraint
		if not g.image_index.duplicated().any():
		if g.image_index.duplicated().any():
		return True
		else: return False

		return self.data.groupby('point_id').apply(func)

		def clean_singles(self):
		"""
		Take the `data` dataframe and return only those points with
		at least two measures. This is automatically called before writing
		as functions such as subpixel matching can result in orphaned measures.
		"""
		return self.data.groupby('point_id').apply(lambda g: g if len(g) > 1 else None)

		def to_isis(self, outname, serials, olist, args, *kwargs): #pragma: no cover
		"""
		Write the control network out to the ISIS3 control network format.
		@@ -185,9 +224,18 @@ class ControlNetwork(object):
		warnings.warn('Control Network is not ISIS3 compliant. Please run the validate_points method on the control network.')
		return

		to_isis(outname + '.net', self.data, serials, args, *kwargs)
		# Apply the subpixel shift
		self.data.x += self.data.x_off
		self.data.y += self.data.y_off

		to_isis(outname + '.net', self.data.query('valid == True'),
		serials, args, *kwargs)
		write_filelist(olist, outname + '.lis')

		# Back out the subpixel shift
		self.data.x -= self.data.x_off
		self.data.y -= self.data.y_off

		def to_bal(self):
		"""
		Write the control network out to the Bundle Adjustment in the Large

autocnet/control/tests/test_control.py

+4 −4

Original line number	Diff line number	Diff line
		@@ -12,7 +12,7 @@ from .. import control
		def test_fromcandidategraph(candidategraph, controlnetwork_data):#, controlnetwork):
		matches = candidategraph.get_matches()
		cn = control.ControlNetwork.from_candidategraph(matches)
		assert cn.data.equals(controlnetwork_data)
		assert cn.data[['point_id', 'image_index']].equals(controlnetwork_data[['point_id', 'image_index']])

		def test_add_measure():
		cn = control.ControlNetwork()
		@@ -36,11 +36,11 @@ def test_add_measure():
		assert cn.measure_to_point[key] == 0

		def test_validate_points(controlnetwork):
		assert controlnetwork.validate_points().any()
		assert not controlnetwork.validate_points().any()

		def test_bad_validate_points(bad_controlnetwork):
		assert bad_controlnetwork.validate_points().iloc[0] == False
		assert bad_controlnetwork.validate_points().iloc[1:].all()
		assert bad_controlnetwork.validate_points().iloc[0] == True
		assert not bad_controlnetwork.validate_points().iloc[1:].all()

		def test_identify_potential_overlaps(controlnetwork, candidategraph):
		res = control.identify_potential_overlaps(candidategraph,