mirror of
https://github.com/alicevision/Meshroom.git
synced 2025-04-29 10:17:27 +02:00
f4dcf6557f
## Description Fix various typos in the source code. This includes user facing code, documentation, and source comments. This PR has not been tested. Closes #1605
218 lines
8.6 KiB
Python
218 lines
8.6 KiB
Python
__version__ = "2.0"
|
|
|
|
from meshroom.core import desc
|
|
|
|
|
|
class FeatureMatching(desc.CommandLineNode):
|
|
commandLine = 'aliceVision_featureMatching {allParams}'
|
|
size = desc.DynamicNodeSize('input')
|
|
parallelization = desc.Parallelization(blockSize=20)
|
|
commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
|
|
|
|
category = 'Sparse Reconstruction'
|
|
documentation = '''
|
|
This node performs the matching of all features between the candidate image pairs.
|
|
|
|
It is performed in 2 steps:
|
|
|
|
1/ **Photometric Matches**
|
|
|
|
It performs the photometric matches between the set of features descriptors from the 2 input images.
|
|
For each feature descriptor on the first image, it looks for the 2 closest descriptors in the second image and uses a relative threshold between them.
|
|
This assumption kill features on repetitive structure but has proved to be a robust criterion.
|
|
|
|
2/ **Geometric Filtering**
|
|
|
|
It performs a geometric filtering of the photometric match candidates.
|
|
It uses the features positions in the images to make a geometric filtering by using epipolar geometry in an outlier detection framework
|
|
called RANSAC (RANdom SAmple Consensus). It randomly selects a small set of feature correspondences and compute the fundamental (or essential) matrix,
|
|
then it checks the number of features that validates this model and iterate through the RANSAC framework.
|
|
|
|
## Online
|
|
[https://alicevision.org/#photogrammetry/feature_matching](https://alicevision.org/#photogrammetry/feature_matching)
|
|
'''
|
|
|
|
inputs = [
|
|
desc.File(
|
|
name='input',
|
|
label='SfMData',
|
|
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.File(
|
|
name='imagePairsList',
|
|
label='Image Pairs',
|
|
description='Path to a file which contains the list of image pairs to match.',
|
|
value='',
|
|
uid=[0],
|
|
),
|
|
desc.ChoiceParam(
|
|
name='describerTypes',
|
|
label='Describer Types',
|
|
description='Describer types used to describe an image.',
|
|
value=['dspsift'],
|
|
values=['sift', 'sift_float', 'sift_upright', 'dspsift', 'akaze', 'akaze_liop', 'akaze_mldb', 'cctag3', 'cctag4', 'sift_ocv', 'akaze_ocv', 'tag16h5'],
|
|
exclusive=False,
|
|
uid=[0],
|
|
joinChar=',',
|
|
),
|
|
desc.ChoiceParam(
|
|
name='photometricMatchingMethod',
|
|
label='Photometric Matching Method',
|
|
description='For Scalar based regions descriptor\n'
|
|
' * BRUTE_FORCE_L2: L2 BruteForce matching\n'
|
|
' * ANN_L2: L2 Approximate Nearest Neighbor matching\n'
|
|
' * CASCADE_HASHING_L2: L2 Cascade Hashing matching\n'
|
|
' * FAST_CASCADE_HASHING_L2: L2 Cascade Hashing with precomputed hashed regions (faster than CASCADE_HASHING_L2 but use more memory) \n'
|
|
'For Binary based descriptor\n'
|
|
' * BRUTE_FORCE_HAMMING: BruteForce Hamming matching',
|
|
value='ANN_L2',
|
|
values=('BRUTE_FORCE_L2', 'ANN_L2', 'CASCADE_HASHING_L2', 'FAST_CASCADE_HASHING_L2', 'BRUTE_FORCE_HAMMING'),
|
|
exclusive=True,
|
|
uid=[0],
|
|
advanced=True,
|
|
),
|
|
desc.ChoiceParam(
|
|
name='geometricEstimator',
|
|
label='Geometric Estimator',
|
|
description='Geometric estimator: (acransac: A-Contrario Ransac, loransac: LO-Ransac (only available for "fundamental_matrix" model)',
|
|
value='acransac',
|
|
values=['acransac', 'loransac'],
|
|
exclusive=True,
|
|
uid=[0],
|
|
advanced=True,
|
|
),
|
|
desc.ChoiceParam(
|
|
name='geometricFilterType',
|
|
label='Geometric Filter Type',
|
|
description='Geometric validation method to filter features matches: \n'
|
|
' * fundamental_matrix\n'
|
|
' * fundamental_with_distortion\n'
|
|
' * essential_matrix\n'
|
|
' * homography_matrix\n'
|
|
' * homography_growing\n'
|
|
' * no_filtering',
|
|
value='fundamental_matrix',
|
|
values=['fundamental_matrix', 'fundamental_with_distortion', 'essential_matrix', 'homography_matrix', 'homography_growing', 'no_filtering'],
|
|
exclusive=True,
|
|
uid=[0],
|
|
advanced=True,
|
|
),
|
|
desc.FloatParam(
|
|
name='distanceRatio',
|
|
label='Distance Ratio',
|
|
description='Distance ratio to discard non meaningful matches.',
|
|
value=0.8,
|
|
range=(0.0, 1.0, 0.01),
|
|
uid=[0],
|
|
advanced=True,
|
|
),
|
|
desc.IntParam(
|
|
name='maxIteration',
|
|
label='Max Iteration',
|
|
description='Maximum number of iterations allowed in ransac step.',
|
|
value=2048,
|
|
range=(1, 20000, 1),
|
|
uid=[0],
|
|
advanced=True,
|
|
),
|
|
desc.FloatParam(
|
|
name='geometricError',
|
|
label='Geometric Validation Error',
|
|
description='Maximum error (in pixels) allowed for features matching during geometric verification.\n'
|
|
'If set to 0, it will select a threshold according to the localizer estimator used\n'
|
|
'(if ACRansac, it will analyze the input data to select the optimal value).',
|
|
value=0.0,
|
|
range=(0.0, 10.0, 0.1),
|
|
uid=[0],
|
|
advanced=True,
|
|
),
|
|
desc.FloatParam(
|
|
name='knownPosesGeometricErrorMax',
|
|
label='Known Poses Geometric Error Max',
|
|
description='Maximum error (in pixels) allowed for features matching guided by geometric information from known camera poses.\n'
|
|
'If set to 0 it lets the ACRansac select an optimal value.',
|
|
value=5.0,
|
|
range=(0.0, 100.0, 1.0),
|
|
uid=[0],
|
|
advanced=True,
|
|
),
|
|
desc.IntParam(
|
|
name='maxMatches',
|
|
label='Max Matches',
|
|
description='Maximum number of matches to keep.',
|
|
value=0,
|
|
range=(0, 10000, 1),
|
|
uid=[0],
|
|
advanced=True,
|
|
),
|
|
desc.BoolParam(
|
|
name='savePutativeMatches',
|
|
label='Save Putative Matches',
|
|
description='putative matches.',
|
|
value=False,
|
|
uid=[0],
|
|
advanced=True,
|
|
),
|
|
desc.BoolParam(
|
|
name='crossMatching',
|
|
label='Cross Matching',
|
|
description='Make sure that the matching process is symmetric (same matches for I->J than for J->I)',
|
|
value=False,
|
|
uid=[0],
|
|
),
|
|
desc.BoolParam(
|
|
name='guidedMatching',
|
|
label='Guided Matching',
|
|
description='the found model to improve the pairwise correspondences.',
|
|
value=False,
|
|
uid=[0],
|
|
),
|
|
desc.BoolParam(
|
|
name='matchFromKnownCameraPoses',
|
|
label='Match From Known Camera Poses',
|
|
description='Enable the usage of geometric information from known camera poses to guide the feature matching.\n'
|
|
'If some cameras have unknown poses (so there is no geometric prior), the standard feature matching will be performed.',
|
|
value=False,
|
|
uid=[0],
|
|
),
|
|
desc.BoolParam(
|
|
name='exportDebugFiles',
|
|
label='Export Debug Files',
|
|
description='debug files (svg, dot).',
|
|
value=False,
|
|
uid=[],
|
|
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='Matches Folder',
|
|
description='Path to a folder in which computed matches will be stored.',
|
|
value=desc.Node.internalFolder,
|
|
uid=[],
|
|
),
|
|
]
|