Meshroom/meshroom/nodes/aliceVision/ImageMatchingMultiSfM.py

__version__ = "1.0"

import os
from meshroom.core import desc


class ImageMatchingMultiSfM(desc.CommandLineNode):
    commandLine = 'aliceVision_imageMatching {allParams}'
    # use both SfM inputs to define Node's size
    size = desc.MultiDynamicNodeSize(['input', 'inputB'])

    category = 'Sparse Reconstruction'
    documentation = '''
The goal of this node is to select the image pairs to match in the context of an SfM augmentation.
The ambition is to find the images that are looking to the same areas of the scene.
Thanks to this node, the FeatureMatching node will only compute the matches between the selected image pairs.

## Online
[https://alicevision.org/#photogrammetry/image_matching](https://alicevision.org/#photogrammetry/image_matching)
'''
    inputs = [
        desc.File(
            name='input',
            label='Input A',
            description='SfMData file .',
            value='',
            uid=[0],
        ),
        desc.File(
            name='inputB',
            label='Input B',
            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.ChoiceParam(
            name='method',
            label='Method',
            description='Method used to select the image pairs to match.',
            value='VocabularyTree',
            values=['VocabularyTree', 'Sequential', 'SequentialAndVocabularyTree','Exhaustive','Frustum'],
            exclusive=True,
            uid=[0],
        ),
        desc.File(
            name='tree',
            label='Voc Tree: Tree',
            description='Input name for the vocabulary tree file.',
            value=os.environ.get('ALICEVISION_VOCTREE', ''),
            uid=[],
        ),
        desc.File(
            name='weights',
            label='Voc Tree: Weights',
            description='Input name for the weight file, if not provided the weights will be computed on the database built with the provided set.',
            value='',
            uid=[0],
            advanced=True,
        ),
        desc.ChoiceParam(
            name='matchingMode',
            label='Matching Mode',
            description='The mode to combine image matching between the input SfMData A and B:\n"a/a+a/b" for A with A + A with B.\n"a/ab" for A with A and B.\n"a/b" for A with B.',
            value='a/a+a/b',
            values=['a/a+a/b','a/ab', 'a/b'],
            exclusive=True,
            uid=[0],
        ),
        desc.IntParam(
            name='minNbImages',
            label='Voc Tree: Minimal Number of Images',
            description='Minimal number of images to use the vocabulary tree. If we have less features than this threshold, we will compute all matching combinations.',
            value=200,
            range=(0, 500, 1),
            uid=[0],
            advanced=True,
        ),
        desc.IntParam(
            name='maxDescriptors',
            label='Voc Tree: Max Descriptors',
            description='Limit the number of descriptors you load per image. Zero means no limit.',
            value=500,
            range=(0, 100000, 1),
            uid=[0],
            advanced=True,
        ),
        desc.IntParam(
            name='nbMatches',
            label='Voc Tree: Nb Matches',
            description='The number of matches to retrieve for each image (If 0 it will retrieve all the matches).',
            value=50,
            range=(0, 1000, 1),
            uid=[0],
            advanced=True,
        ),
        desc.IntParam(
            name='nbNeighbors',
            label='Sequential: Nb Neighbors',
            description='The number of neighbors to retrieve for each image (If 0 it will retrieve all the neighbors).',
            value=50,
            range=(0, 1000, 1),
            uid=[0],
            advanced=True,
        ),
        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 List File',
            description='Filepath to the output file with the list of selected image pairs.',
            value=desc.Node.internalFolder + 'imageMatches.txt',
            uid=[],
        ),
        desc.File(
            name='outputCombinedSfM',
            label='Output Combined SfM',
            description='Path for the combined SfMData file',
            value=desc.Node.internalFolder + 'combineSfM.sfm',
            uid=[],
        ),
    ]