Meshroom/meshroom/nodes/aliceVision/StructureFromMotion.py

import json
import os

from meshroom.core import desc


class StructureFromMotion(desc.CommandLineNode):
    internalFolder = '{cache}/{nodeType}/{uid0}/'
    commandLine = 'aliceVision_incrementalSfM {allParams}'
    size = desc.DynamicNodeSize('input')

    inputs = [
        desc.File(
            name='input',
            label='Input',
            description='SfMData file.',
            value='',
            uid=[0],
        ),
        desc.ListAttribute(
            elementDesc=desc.File(
                name="featuresFolder",
                label="Features Folder",
                description="",
                value="",
                uid=[0],
            ),
            name="featuresFolders",
            label="Features Folders",
            description="Folder(s) containing the extracted features and descriptors."
        ),
        desc.ListAttribute(
            elementDesc=desc.File(
                name="matchesFolder",
                label="Matches Folder",
                description="",
                value="",
                uid=[0],
            ),
            name="matchesFolders",
            label="Matches Folders",
            description="Folder(s) in which computed matches are stored."
        ),
        desc.ChoiceParam(
            name='describerTypes',
            label='Describer Types',
            description='Describer types used to describe an image.',
            value=['SIFT'],
            values=['SIFT', 'SIFT_FLOAT', 'AKAZE', 'AKAZE_LIOP', 'AKAZE_MLDB', 'CCTAG3', 'CCTAG4', 'SIFT_OCV',
                    'AKAZE_OCV'],
            exclusive=False,
            uid=[0],
            joinChar=',',
        ),
        desc.ChoiceParam(
            name='localizerEstimator',
            label='Localizer Estimator',
            description='Estimator type used to localize cameras (acransac, ransac, lsmeds, loransac, maxconsensus).',
            value='acransac',
            values=['acransac', 'ransac', 'lsmeds', 'loransac', 'maxconsensus'],
            exclusive=True,
            uid=[0],
        ),
        desc.BoolParam(
            name='useLocalBA',
            label='Local Bundle Adjustment',
            description='It reduces the reconstruction time, especially for large datasets (500+ images),\n'
                        'by avoiding computation of the Bundle Adjustment on areas that are not changing.',
            value=True,
            uid=[0],
        ),
        desc.IntParam(
            name='localBAGraphDistance',
            label='LocalBA Graph Distance',
            description='Graph-distance limit to define the Active region in the Local Bundle Adjustment strategy.',
            value=1,
            range=(2, 10, 1),
            uid=[0],
        ),
        desc.IntParam(
            name='maxNumberOfMatches',
            label='Maximum Number of Matches',
            description='Maximum number of matches per image pair (and per feature type). \n'
                        'This can be useful to have a quick reconstruction overview. \n'
                        '0 means no limit.',
            value=0,
            range=(0, 50000, 1),
            uid=[0],
        ),
        desc.IntParam(
            name='minInputTrackLength',
            label='Min Input Track Length',
            description='Minimum track length in input of SfM',
            value=2,
            range=(2, 10, 1),
            uid=[0],
        ),
        desc.IntParam(
            name='minNumberOfObservationsForTriangulation',
            label='Min Observation For Triangulation',
            description='Minimum number of observations to triangulate a point.\n'
                        'Set it to 3 (or more) reduces drastically the noise in the point cloud,\n'
                        'but the number of final poses is a little bit reduced\n'
                        '(from 1.5% to 11% on the tested datasets).',
            value=2,
            range=(2, 10, 1),
            uid=[0],
        ),
            uid=[0],
        ),
        desc.File(
            name='initialPairA',
            label='Initial Pair A',
            description='Filename of the first image (without path).',
            value='',
            uid=[0],
        ),
        desc.File(
            name='initialPairB',
            label='Initial Pair B',
            description='Filename of the second image (without path).',
            value='',
            uid=[0],
        ),
        desc.ChoiceParam(
            name='interFileExtension',
            label='Inter File Extension',
            description='Extension of the intermediate file export.',
            value='.abc',
            values=('.abc', '.ply'),
            exclusive=True,
            uid=[],
        ),
        desc.ChoiceParam(
            name='verboseLevel',
            label='Verbose Level',
            description='Verbosity level (fatal, error, warning, info, debug, trace).',
            value='info',
            values=['fatal', 'error', 'warning', 'info', 'debug', 'trace'],
            exclusive=True,
            uid=[],
        )
    ]

    outputs = [
        desc.File(
            name='output',
            label='Output SfMData File',
            description='Path to the output sfmdata file',
            value='{cache}/{nodeType}/{uid0}/sfm.abc',
            uid=[],
        ),
        desc.File(
            name='outputViewsAndPoses',
            label='Output SfMData File',
            description='''Path to the output sfmdata file with cameras (views and poses).''',
            value='{cache}/{nodeType}/{uid0}/cameras.sfm',
            uid=[],
        ),
        desc.File(
            name='extraInfoFolder',
            label='Output Folder',
            description='Folder for intermediate reconstruction files and additional reconstruction information files.',
            value='{cache}/{nodeType}/{uid0}/',
            uid=[],
        ),
    ]

    @staticmethod
    def getViewsAndPoses(node):
        """
        Parse SfM result and return views and poses as two dict with viewId and poseId as keys.
        """
        reportFile = node.outputViewsAndPoses.value
        if not os.path.exists(reportFile):
            return {}, {}

        with open(reportFile) as jsonFile:
            report = json.load(jsonFile)

        views = dict()
        poses = dict()

        for view in report['views']:
            views[view['viewId']] = view

        for pose in report['poses']:
            poses[pose['poseId']] = pose['pose']

        return views, poses