merge from upstream (2e755e24) · Commits · aflab / astrogeology / Autocnet

.travis.yml

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		language: generic # Since we use conda, just go generic
		sudo: false
		language: generic
		sudo: true

		branches:
		only:
		- dev
		- testing

		os:
		- linux
		- osx
		matrix:
		include:
		- os: linux
		env: PYTHON_VERSION=3.6
		- os: linux
		env: PYTHON_VERSION=3.7

		env:
		- PYTHON_VERSION=3.5
		- PYTHON_VERSION=3.6
		- PYTHON_VERSION=3.7

		# Setup a postgresql instance w/postgis
		services:
		- postgresql
		addons:
		postgresql: 9.6
		apt:
		packages:
		- postgresql-9.6-postgis-2.4

		before_install:
		# Create the template database that is used to instantiate the test DB
		- psql -c 'create database template_postgis;' -U postgres
		- psql template_postgis -U postgres -c "create extension postgis"
		- psql template_postgis -c "create extension postgis_topology"
		- psql -d template_postgis -c "GRANT ALL ON geometry_columns TO PUBLIC;"
		- psql -d template_postgis -c "GRANT ALL ON geography_columns TO PUBLIC;"
		- psql -d template_postgis -c "GRANT ALL ON spatial_ref_sys TO PUBLIC;"

		install:
		# We do this conditionally because it saves us some downloading if the
		# version is the same.
		- if [ "$TRAVIS_OS_NAME" == "linux" ]; then
		@@ -26,6 +44,7 @@ before_install:
		- export PATH="$HOME/miniconda/bin:$PATH"
		- hash -r
		- conda config --set always_yes yes --set changeps1 no
		- conda config --add channels conda-forge
		- conda update -q conda
		# Useful for debugging any issues with conda
		- conda info -a
		@@ -34,13 +53,11 @@ before_install:
		- source activate test

		# Install dependencies
		- conda config --add channels menpo
		- conda config --add channels usgs-astrogeology
		- conda config --add channels conda-forge
		- conda env update -n test -f environment.yml
		- export PROJ_LIB=$CONDA_PREFIX/share/proj

		script:
		- pytest autocnet tests
		- autocnet_config=config/test_config.yml pytest -v autocnet

		after_success:
		- coveralls

README.md

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		# AutoCNet

		[![Gitter Chat](https://badges.gitter.im/USGS-Astrogeology/autocnet.svg)](https://gitter.im/USGS-Astrogeology/autocnet?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

		[![Travis-CI](https://travis-ci.org/USGS-Astrogeology/autocnet.svg?branch=dev)](https://travis-ci.org/USGS-Astrogeology/autocnet)

		[![Coveralls](https://coveralls.io/repos/USGS-Astrogeology/autocnet/badge.svg?branch=dev&service=github)](https://coveralls.io/github/USGS-Astrogeology/autocnet?branch=dev)

		[![Docs](https://img.shields.io/badge/Docs-latest-green.svg)](https://usgs-astrogeology.github.io/autocnet/)

		Automated sparse control network generation to support photogrammetric control of planetary image data.

		## Documentation
		Is available at: https://usgs-astrogeology.github.io/autocnet/

		## Installation Instructions
		We suggest using Anaconda Python to install Autocnet within a virtual environment. These steps will walk you through the process.

		1. [Download](https://www.continuum.io/downloads) and install the Python 3.x Miniconda installer. Respond ``Yes`` when prompted to add conda to your BASH profile.
		2. Install the autocnet environment using the supplied environment.yml file: `conda env create -n autocnet -f environment.yml`
		3. Activate your environment: `conda activate autocnet`
		4. If you are doing to develop autocnet or would like to use the bleeding edge version: `python setup.py develop`. Otherwise, `conda install -c usgs-astrogeology` autocnet.

		## How to run the test suite locally

		1. Install Docker
		2. Get the Postgresql with Postgis container and run it `docker run --name testdb -e POSTGRES_PASSOWRD='' -e POSTGRES_USER='postgres' -p 5432:5432 -d mdillon/postgis`
		3. create database template_postgis: `docker exec testdb psql -c 'create database template_postgis;' -U postgres`
		4. Run the test suite: `autocnet_config=config/test_config.yml pytest autocnet`

README.rst

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		===============================
		AutoCNet
		===============================

		.. image:: https://badges.gitter.im/USGS-Astrogeology/autocnet.svg
		:alt: Join the chat at https://gitter.im/USGS-Astrogeology/autocnet
		:target: https://gitter.im/USGS-Astrogeology/autocnet?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge

		.. image:: https://img.shields.io/pypi/v/autocnet.svg
		:target: https://pypi.python.org/pypi/autocnet

		.. image:: https://travis-ci.org/USGS-Astrogeology/autocnet.svg?branch=master
		:target: https://travis-ci.org/USGS-Astrogeology/autocnet

		.. image:: https://coveralls.io/repos/USGS-Astrogeology/autocnet/badge.svg?branch=master&service=github
		:target: https://coveralls.io/github/USGS-Astrogeology/autocnet?branch=master

		.. image:: https://img.shields.io/badge/Docs-latest-green.svg
		:target: hhttps://usgs-astrogeology.github.io/autocnet/
		:alt: Documentation Status

		.. image:: https://badge.waffle.io/USGS-Astrogeology/autocnet.png?label=ready&title=Ready
		:target: https://waffle.io/USGS-Astrogeology/autocnet
		:alt: 'Stories in Ready'

		Automated sparse control network generation to support photogrammetric control of planetary image data.

		* Documentation: https://usgs-astrogeology.github.io/autocnet/

		Installation Instructions
		-------------------------
		We suggest using Anaconda Python to install Autocnet within a virtual environment. These steps will walk you through the process.

		#. [Download](https://www.continuum.io/downloads) and install the Python 3.x Miniconda installer. Respond ``Yes`` when
		prompted to add conda to your BASH profile.
		#. (Optional) We like to sequester applications in their own environments to avoid any dependency conflicts. To do this:

		* ``conda create -n <your_environment_name> python=3 && source activate <your_environment_name>``

		Note, that you might want to specify either ``python=3.5`` or ``python=3.6``, depending on your requirements. Both are currently supported by autocnet.
		#. Make the newly created environment the active one:

		* ``conda activate <your_environment_name>`` (or ``source activate`` on an older conda system)

		#. Bring up a command line and add three channels to your conda environment-specific config file:

		* ``conda config --env --add channels conda-forge``
		* ``conda config --env --add channels menpo``
		* ``conda config --env --add channels usgs-astrogeology``
		#. Finally, install autocnet: ``conda install autocnet``

autocnet/camera/camera.py

+2 −2

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		import numpy as np
		from autocnet.camera.utils import crossform
		import cv2
		from cv2 import triangulatePoints



		@@ -102,7 +102,7 @@ def triangulate(pt, pt1, p, p1):
		if pt1.shape[0] != 3:
		pt1 = pt1.T

		X = cv2.triangulatePoints(p, p1, pt[:2], pt1[:2])
		X = triangulatePoints(p, p1, pt[:2], pt1[:2])
		X /= X[3] # Homogenize
		return X

autocnet/graph/network.py

+0 −52

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		@@ -1363,9 +1363,6 @@ class NetworkCandidateGraph(CandidateGraph):
		onobj = options[on]

		res = []
		key = 1
		if isinstance(on, EdgeView):
		key = 2

		for job_counter, elem in enumerate(onobj.data('data')):
		# Determine if we are working with an edge or a node
		@@ -1428,54 +1425,6 @@ class NetworkCandidateGraph(CandidateGraph):
		for i, n in self.nodes(data='data'):
		n.generate_vrt(**kwargs)

		def compute_overlaps(self):
		"""
		For the candidate graph, compute the overlapping polygons that
		comprise the entire candidate graph / footprint map. Each overlap
		includes an 'overlaps' attribute/column that includes a list of the
		footprint polygons that have contributed to given overlap.

		"""
		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((ST_DUMP(footprint_latlon)).geom) AS the_geom
		FROM images) AS lines
		) AS noded_lines
		)
		)"""
		session = Session()
		oquery = session.query(Overlay)
		iquery = session.query(Images)

		rows = []
		srid = config['spatial']['srid']
		for q in engine.execute(query).fetchall():
		overlaps = []
		b = bytes(q['geom'])
		qgeom = shapely.wkb.loads(b)
		res = iquery.filter(Images.footprint_latlon.ST_Intersects(geoalchemy2.shape.from_shape(qgeom, srid=srid)))
		for i in res:
		fgeom = geoalchemy2.shape.to_shape(i.footprint_latlon)
		area = qgeom.intersection(fgeom).area
		if area < 1e-6:
		continue
		overlaps.append(i.id)
		o = Overlay(geom=f'SRID={srid};{qgeom.qkt}', overlaps=overlaps)
		res = oquery.filter(Overlay.overlaps == o.overlaps).first()
		if res is None:
		rows.append(o)

		session.bulk_save_objects(rows)
		session.commit()

		# If an overlap has only 1 entry, it is a sliver and we want to remove it.
		res = oquery.filter(sqlalchemy.func.array_length(Overlay.overlaps, 1) <= 1)
		res.delete(synchronize_session=False)
		session.commit()
		session.close()

		def to_isis(self, path, flistpath=None, sql = """
		SELECT points.id, measures.serial, points.pointtype, measures.sample, measures.line, measures.measuretype,
		measures.imageid
		@@ -1570,4 +1519,3 @@ AND i1.id < i2.id""".format(query_string)
		obj = cls.from_adjacency(adjacency, node_id_map=adjacency_lookup, config=config)

		return obj