Commit 72a4fd6b authored by Adam Paquette's avatar Adam Paquette
Browse files

Updated to current version of dev.

parents a8db400e 1187045c

.coveragerc

+2 −12
Original line number Diff line number Diff line
@@ -2,19 +2,9 @@ 
source = autocnet

[report]

omit =

    autocnet/fileio/ControlNetFileV0002_pb2.py

    autocnet/vis/graph_view.py

    autocnet/fileio/sqlalchemy_json/*

    autocnet/fileio/io_multibandimager.py

    autocnet/fileio/io_moon_minerology_mapper.py

    autocnet/fileio/header_parser.py

    autocnet/fileio/io_ccs.py

    autocnet/fileio/io_jsc.py

    autocnet/fileio/io_edr.py

    autocnet/fileio/lookup.py

    autocnet/fileio/utils.py

    autocnet/utils/folds.py

    autocnet/spectral/*

    autocnet/__init__.py

    autocnet/matcher/cuda_*

    bin/*

    */tests/*

exclude_lines =

.travis.yml

+6 −8
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@@ -14,8 +14,6 @@ os: 
  - linux

  - osx



before_install:



install:

  # We do this conditionally because it saves us some downloading if the

  # version is the same.
@@ -44,18 +42,18 @@ install: 
  - conda config --add channels conda-forge

  - conda config --add channels menpo

  - conda config --add channels jlaura

  - conda install -c conda-forge numpy

  - conda install -c jlaura plio opencv3=3.0.0

  - 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

  - conda install -c menpo cyvlfeat

  - pip install pillow pysal

  - conda install scipy networkx numexpr dill cython pyyaml matplotlib runipy



  # Development installation

  - conda install nose coverage sh anaconda-client

  - conda install pytest pytest-cov coverage sh anaconda-client

  - pip install coveralls

  - python runipynbs.py





    # Straight from the menpo team

  - if [["$TRAVIS_OS_NAME" == "osx"]]; then
@@ -67,7 +65,7 @@ install: 
  - python condaci.py setup



script:

  - nosetests --with-coverage --cover-package=autocnet

  - pytest --cov=autocnet



after_success:

  # Upload to anaconda and push to coveralls

README.rst

+2 −2
Original line number Diff line number Diff line
@@ -25,7 +25,7 @@ AutoCNet 


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



* Documentation: https://autocnet.readthedocs.org.

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



Installation Instructions

-------------------------
@@ -37,6 +37,6 @@ We suggest using Anaconda Python to install Autocnet within a virtual environmen 
  * ``conda create -n <your_environment_name> python=3 && source activate <your_environment_name>``

1. Bring up a command line and add three channels to your conda config (``~/condarc``):

  * ``conda config --add channels conda-forge``

  * ``conda condig --add channels jlaura``

  * ``conda config --add channels jlaura``

  * ``conda config --add channels menpo``

1. Finally, install autocnet: ``conda install -c jlaura autocnet-dev``

autocnet/__init__.py

+44 −8
Original line number Diff line number Diff line 
import os

import autocnet



__version__ = "0.1.0"

import warnings



def get_data(filename):

    packagdir = autocnet.__path__[0]

    dirname = os.path.join(os.path.dirname(packagdir), 'data')

    fullname = os.path.join(dirname, filename)

    return fullname

import autocnet



import autocnet.examples

import autocnet.camera
@@ -18,3 +12,45 @@ import autocnet.matcher 
import autocnet.transformation

import autocnet.utils

import autocnet.utils



__version__ = "0.1.0"



def get_data(filename):

    packagdir = autocnet.__path__[0]

    dirname = os.path.join(os.path.dirname(packagdir), 'data')

    fullname = os.path.join(dirname, filename)

    return fullname



def cuda(enable=False, gpu=0):

    # Classes/Methods that can vary if GPU is available

    from autocnet.graph.node import Node

    from autocnet.graph.edge import Edge

    if enable:

        try:

            import cudasift as cs

            cs.PyInitCuda(gpu)



            # Here is where the GPU methods get patched into the class

            from autocnet.matcher.cuda_extractor import extract_features

            Node._extract_features = staticmethod(extract_features)



            from autocnet.matcher.cuda_matcher import match

            Edge.match = match



            from autocnet.matcher.cuda_decompose import decompose_and_match

            Edge.decompose_and_match = decompose_and_match

        except Exception:

            warning.warn('Failed to enable Cuda')

        return



    # Here is where the CPU methods get patched into the class

    from autocnet.matcher.feature_extractor import extract_features

    Node._extract_features = staticmethod(extract_features)



    from autocnet.matcher.feature_matcher import match

    Edge.match = match



    from autocnet.matcher.cpu_decompose import decompose_and_match

    Edge.decompose_and_match = decompose_and_match



cuda()

autocnet/camera/camera.py

+41 −31
Original line number Diff line number Diff line 
import numpy as np

from autocnet.camera.utils import crossform

try:

    import cv2



except:

    cv2 = None



def compute_epipoles(f):

    """
@@ -21,9 +24,7 @@ def compute_epipoles(f): 
    """

    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]])

    e1 = crossform(e)



    return e, e1
@@ -102,24 +103,37 @@ def triangulate(pt, pt1, p, p1): 
        pt = pt.T

    if pt1.shape[0] != 3:

        pt1 = pt1.T



    #if cv2:

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



    # Homogenize

    X /= X[3]



    X /= X[3] # Homogenize

    return X





    """

    # Stubbed in for a ticket addressing making OpenCV an optional dependency

    else:

        npts = len(pt)

        a = np.zeros((4, 4))

        coords = np.empty((npts, 4))

        coords[:] = 1

        for i in range(npts):

            # Compute AX = 0

            a[0] = pt[i][0] * p[2] - p[0]

            a[1] = pt[i][1] * p[2] - p[1]

            a[2] = pt1[i][0] * p1[2] - p1[0]

            a[3] = pt1[i][1] * p1[2] - p1[1]

            # v.T is a least squares solution that minimizes the error residual

            u, s, vh = np.linalg.svd(a)

            v = vh.T

            coords[i] = v[:,3] / (v[:,3][-1])

        return coords.T

    """

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]

    This is the classic cost function (minimization problem) into

    the gold standard method for fundamental matrix estimation.



    Parameters

    -----------
@@ -137,29 +151,25 @@ def projection_error(p1, p, pt, pt1): 


    Returns

    -------

    residuals : ndarray

                (n, 1) residuals for each correspondence



    cumulative_error : float

                       sum of the residuals

    reproj_error : ndarray

                   (n, 1) vector of reprojection errors





    """

    # SciPy least squares solver needs a vector, so reshape back to a 3x4 c

    # camera matrix at each iteration



    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]

    xhat = triangulate(pt, pt1, p, p1)

    xhat1 = xhat[:3] / xhat[2]

    xhat2 = p1.dot(xhat)

    xhat2 /= xhat2[2]



    # Compute residuals

    dist = (pt.T - xhat)**2 + (pt1.T - x2hat)**2

    residuals = np.sum(dist, axis=0)

    reproj_error = np.sum(dist)

    # Compute error

    cost = (pt - xhat1)**2 + (pt1 - xhat2)**2

    cost = np.sqrt(np.sum(cost, axis=0))



    return residuals, reproj_error
 
    return cost