Merge branch 'master' of https://github.com/USGS-Astrogeology/autocnet (b0c676d0) · Commits · aflab / astrogeology / Autocnet

.travis.yml

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Changes for .travis.yml: 13 added lines, 9 removed lines.
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		@@ -33,12 +33,15 @@ install:

		# Install the non-conda packages if required, requirements.txt duplicates are ignored
		- conda install -c https://conda.anaconda.org/jlaura opencv3=3.0.0
		- conda install -c https://conda.anaconda.org/jlaura h5py gdal
		- conda install -c osgeo proj4
		- conda upgrade numpy
		- conda install -c menpo cyvlfeat
		- conda config --add channels conda-forge
		- conda install runipy
		- conda install -c https://conda.anaconda.org/conda-forge gdal h5py
		- pip install -r requirements.txt
		- pip install coverage
		# Development installation
		- conda install nose coverage
		- pip install coveralls
		- python runipynbs.py

		script:
		- nosetests --with-coverage --cover-package=autocnet
		@@ -49,15 +52,16 @@ after_success:
		notifications:
		webhooks:
		urls:
		- https://webhooks.gitter.im/e/6cbe4b65fff6764ed80f
		# - https://webhooks.gitter.im/e/6cbe4b65fff6764ed80f
		on_success: always
		on_failure: always
		on_start: never
		email:
		recipients:
		- kberry@usgs.gov
		- dmlytle@usgs.gov
		- jwbacker@usgs.gov
		- krodriguez@usgs.gov
		# - kberry@usgs.gov
		# - dmlytle@usgs.gov
		# - jwbacker@usgs.gov
		# - krodriguez@usgs.gov
		on_success: always
		on_failure: always

autocnet/spectral/init.py→autocnet/camera/init.py

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File moved.

autocnet/camera/camera.py

0 → 100644

+165 −0

Changes for autocnet/camera/camera.py: 165 added lines, 0 removed lines.
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		import numpy as np
		import cv2


		def compute_epipoles(f):
		"""
		Compute the epipole and epipolar prime

		Parameters
		----------
		f : ndarray
		(3,3) fundamental matrix or autocnet Fundamental Matrix object

		Returns
		-------
		e : ndarray
		(3,1) epipole

		e1 : ndarray
		(3,3) epipolar prime matrix
		"""
		u, _, _ = np.linalg.svd(f)
		e = u[:, -1]
		e1 = np.array([[0, -e[2], e[1]],
		[e[2], 0, -e[0]],
		[-e[1], e[0], 0]])

		return e, e1


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

		Returns
		-------
		: ndarray
		(3,4) with diagonal 1
		"""
		return np.eye(3, 4)


		def estimated_camera_from_f(f):
		"""
		Estimate a camera matrix using a fundamental matrix.


		Parameters
		----------
		f : ndarray
		(3,3) fundamental matrix or autocnet Fundamental Matrix object

		Returns
		-------
		p1 : ndarray
		Estimated camera matrix
		"""

		e, e1 = compute_epipoles(f)
		p1 = np.empty((3, 4))
		p1[:, :3] = e1.dot(f)
		p1[:, 3] = e

		return p1


		def triangulate(pt, pt1, p, p1):
		"""
		Given two sets of homogeneous coordinates and two camera matrices,
		triangulate the 3D coordinates. The image correspondences are
		assumed to be implicitly ordered.

		References
		----------
		.. [Hartley2003]

		Parameters
		----------
		pt : ndarray
		(n, 3) array of homogeneous correspondences

		pt1 : ndarray
		(n, 3) array of homogeneous correspondences

		p : ndarray
		(3, 4) camera matrix

		p1 : ndarray
		(3, 4) camera matrix

		Returns
		-------
		coords : ndarray
		(4, n) projection matrix

		"""
		pt = np.asarray(pt)
		pt1 = np.asarray(pt1)

		# Transpose for the openCV call if needed
		if pt.shape[0] != 3:
		pt = pt.T
		if pt1.shape[0] != 3:
		pt1 = pt1.T

		X = cv2.triangulatePoints(p, p1, pt[:2], pt1[:2])

		# Homogenize
		X /= X[3]

		return X


		def projection_error(p1, p, pt, pt1):
		"""
		Based on Hartley and Zisserman p.285 this function triangulates
		image correspondences and computes the reprojection error
		by back-projecting the points into the image.

		References
		----------
		.. [Hartley2003]

		Parameters
		-----------
		p1 : ndarray
		(3,4) camera matrix

		p : ndarray
		(3,4) idealized camera matrix in the form np.eye(3,4)

		pt : dataframe or ndarray
		of homogeneous coordinates in the form (x_{i}, y_{i}, 1)

		pt1 : dataframe or ndarray
		of homogeneous coordinates in the form (x_{i}, y_{i}, 1)

		Returns
		-------
		residuals : ndarray
		(n, 1) residuals for each correspondence

		cumulative_error : float
		sum of the residuals


		"""
		if p1.shape != (3,4):
		p1 = p1.reshape(3,4)

		# Triangulate the correspondences
		xw_est = triangulate(pt, pt1, p, p1)

		# Back project and homogenize
		xhat = np.dot(p, xw_est)
		xhat /= xhat[2]
		x2hat = np.dot(p1, xw_est)
		x2hat /= x2hat[2]

		# Compute residuals
		dist = (pt.T - xhat)2 + (pt1.T - x2hat)2
		residuals = np.sum(dist, axis=0)
		reproj_error = np.sum(dist)

		return residuals, reproj_error

autocnet/fileio/tests/test_moon_minerology_mapper.py→autocnet/camera/tests/init.py

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File moved.

autocnet/camera/tests/test_camera.py

0 → 100644

+66 −0

Changes for autocnet/camera/tests/test_camera.py: 66 added lines, 0 removed lines.
Original line number	Diff line number	Diff line
		import os
		import sys
		import unittest

		import numpy as np

		sys.path.append(os.path.abspath('..'))

		from .. import camera


		class TestCamera(unittest.TestCase):

		@classmethod
		def setUpClass(cls):
		cls.f = np.array([[3.27429392e-07, 8.31555374e-06, -1.37175583e-05],
		[2.33349759e-07, 5.99315297e-07, 1.15196332e-01],
		[-4.78065797e-03, -1.21448858e-01, 1.00000000e+00]])

		def test_compute_epipoles(self):
		e, e1 = camera.compute_epipoles(self.f)
		np.testing.assert_array_almost_equal(e, np.array([9.99999885e-01, -4.75272787e-04, 6.84672384e-05]))

		np.testing.assert_array_almost_equal(e1, np.array([[0.00000000e+00, -6.84672384e-05, -4.75272787e-04],
		[6.84672384e-05, 0.00000000e+00, -9.99999885e-01],
		[4.75272787e-04, 9.99999885e-01, 0.00000000e+00]]))

		def test_idealized_camera(self):
		np.testing.assert_array_equal(np.eye(3,4), camera.idealized_camera())

		def test_estimated_camera_from_f(self):
		p1 = camera.estimated_camera_from_f(self.f)
		np.testing.assert_array_almost_equal(p1, np.array([[2.27210066e-06, 5.77212964e-05, -4.83159962e-04, 9.99999885e-01],
		[4.78065745e-03, 1.21448845e-01, -9.99999886e-01, -4.75272787e-04],
		[2.33505351e-07, 6.03267385e-07, 1.15196312e-01, 6.84672384e-05]]))

		def test_triangulation_and_reprojection_error(self):
		p = np.eye(3,4)
		p1 = np.array([[2.27210066e-06, 5.77212964e-05, -4.83159962e-04, 9.99999885e-01],
		[4.78065745e-03, 1.21448845e-01, -9.99999886e-01, -4.75272787e-04],
		[2.33505351e-07, 6.03267385e-07, 1.15196312e-01, 6.84672384e-05]])

		coords1 = np.array([[260.12573242, 6.37760448, 1.],
		[539.05926514, 7.0553031 , 1.],
		[465.07751465, 16.02966881, 1.],
		[46.39139938, 16.96884346, 1.],
		[456.28939819, 23.12134743, 1.]])

		coords2 = np.array([[707.23968506, 8.2479744, 1.],
		[971.61566162, 18.7211895, 1.],
		[905.67974854, 25.06698608, 1.],
		[487.46420288, 11.28651524, 1.],
		[897.73425293, 32.06435013, 1.]])

		truth = np.array([[3.03655751, 4.49076221, 4.17705934, 0.7984432 , 4.13673958],
		[0.07447866, 0.05871216, 0.14393111, 0.29223435, 0.20962208],
		[0.01167342, 0.00833074, 0.00898143, 0.01721084, 0.00906604],
		[1., 1., 1., 1., 1. ]])

		c = camera.triangulate(coords1, coords2, p, p1)
		np.testing.assert_array_almost_equal(c, truth)

		truth = np.array([ 3.09866357e-02, 2.60295132e-01,
		8.12871690e-02, 5.57281224e-01, 4.72226586e-04])
		residuals, reproj_error = camera.projection_error(p1, p, coords1, coords2)
		np.testing.assert_array_almost_equal(residuals, truth)
		No newline at end of file