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Fabien Servant
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Add two new nodes for feature matching geometric filter
1 parent 76eb659 commit af15f4c

12 files changed

+848
-42
lines changed

‎meshroom/aliceVision/GeometricFilterApplying.py‎

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__version__ = "2.0"
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from meshroom.core import desc
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from meshroom.core.utils import DESCRIBER_TYPES, VERBOSE_LEVEL
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class GeometricFilterApplying(desc.AVCommandLineNode):
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commandLine = 'aliceVision_geometricFilterApplying {allParams}'
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size = desc.DynamicNodeSize('input')
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parallelization = desc.Parallelization(blockSize=20)
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commandLineRange = '--rangeIteration {rangeIteration} --rangeBlocksCount {rangeBlocksCount}'
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category = 'Sparse Reconstruction'
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documentation = '''
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Apply precomputed transforms to matches to filter geometric matches
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'''
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inputs = [
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desc.File(
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name="input",
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label="SfMData",
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description="Input SfMData file.",
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value="",
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),
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desc.ListAttribute(
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elementDesc=desc.File(
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name="featuresFolder",
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label="Features Folder",
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description="Folder containing some extracted features and descriptors.",
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value="",
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),
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name="featuresFolders",
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label="Features Folders",
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description="Folder(s) containing the extracted features and descriptors.",
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exposed=True,
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),
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desc.ListAttribute(
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elementDesc=desc.File(
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name="matchesFolder",
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label="Matches Folder",
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description="Folder containing some matches.",
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value="",
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),
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name="matchesFolders",
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label="Matches Folders",
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description="Folder(s) in which computed matches are stored.",
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exposed=True,
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),
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desc.File(
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name="filters",
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label="Filters Folder",
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description="Path to a folder in which the computed filters are stored.",
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value="",
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exposed=True
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),
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desc.ChoiceParam(
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name="describerTypes",
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label="Describer Types",
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description="Describer types used to describe an image.",
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values=DESCRIBER_TYPES,
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value=["dspsift"],
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exclusive=False,
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joinChar=",",
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exposed=True,
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),
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desc.IntParam(
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name="maxMatches",
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label="Max Matches",
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description="Maximum number of matches to keep.",
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value=0,
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range=(0, 10000, 1),
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advanced=True,
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),
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desc.ChoiceParam(
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name="verboseLevel",
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label="Verbose Level",
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description="Verbosity level (fatal, error, warning, info, debug, trace).",
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values=VERBOSE_LEVEL,
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value="info",
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),
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]
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outputs = [
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desc.File(
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name="output",
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label="Matches Folder",
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description="Path to a folder in which the computed matches are stored.",
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value="{nodeCacheFolder}",
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),
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]

‎meshroom/aliceVision/GeometricFilterEstimating.py‎

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__version__ = "2.0"
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from meshroom.core import desc
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from meshroom.core.utils import DESCRIBER_TYPES, VERBOSE_LEVEL
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class GeometricFilterEstimating(desc.AVCommandLineNode):
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commandLine = 'aliceVision_geometricFilterEstimating {allParams}'
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size = desc.DynamicNodeSize('input')
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parallelization = desc.Parallelization(blockSize=20)
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commandLineRange = '--rangeIteration {rangeIteration} --rangeBlocksCount {rangeBlocksCount}'
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category = 'Sparse Reconstruction'
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documentation = '''
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It performs a geometric filtering of the photometric match candidates.
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It uses the features positions in the images to make a geometric filtering by using epipolar geometry in an outlier detection framework
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called RANSAC (RANdom SAmple Consensus). It randomly selects a small set of feature correspondences and compute the fundamental (or essential) matrix,
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then it checks the number of features that validates this model and iterate through the RANSAC framework.
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## Online
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[https://alicevision.org/#photogrammetry/feature_matching](https://alicevision.org/#photogrammetry/feature_matching)
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'''
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inputs = [
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desc.File(
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name="input",
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label="SfMData",
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description="Input SfMData file.",
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value="",
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),
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desc.ListAttribute(
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elementDesc=desc.File(
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name="featuresFolder",
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label="Features Folder",
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description="Folder containing some extracted features and descriptors.",
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value="",
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),
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name="featuresFolders",
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label="Features Folders",
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description="Folder(s) containing the extracted features and descriptors.",
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exposed=True,
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),
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desc.ListAttribute(
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elementDesc=desc.File(
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name="matchesFolder",
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label="Matches Folder",
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description="Folder containing some matches.",
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value="",
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),
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name="matchesFolders",
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label="Matches Folders",
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description="Folder(s) in which computed matches are stored.",
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exposed=True,
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),
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desc.ChoiceParam(
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name="describerTypes",
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label="Describer Types",
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description="Describer types used to describe an image.",
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values=DESCRIBER_TYPES,
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value=["dspsift"],
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exclusive=False,
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joinChar=",",
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exposed=True,
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),
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desc.IntParam(
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name="maxIteration",
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label="Max Iterations",
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description="Maximum number of iterations allowed in the Ransac step.",
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value=50000,
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range=(1, 100000, 1),
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advanced=True,
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),
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desc.FloatParam(
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name="geometricError",
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label="Geometric Validation Error",
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description="Maximum error (in pixels) allowed for features matching during geometric verification",
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value=0.0,
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range=(0.0, 10.0, 0.1),
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advanced=True,
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),
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desc.IntParam(
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name="maxMatches",
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label="Max Matches",
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description="Maximum number of matches to keep.",
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value=0,
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range=(0, 10000, 1),
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advanced=True,
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),
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desc.ChoiceParam(
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name="verboseLevel",
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label="Verbose Level",
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description="Verbosity level (fatal, error, warning, info, debug, trace).",
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values=VERBOSE_LEVEL,
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value="info",
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),
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]
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outputs = [
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desc.File(
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name="output",
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label="Filters Folder",
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description="Path to a folder in which the computed filters are stored.",
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value="{nodeCacheFolder}",
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),
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]

‎src/aliceVision/matchingImageCollection/GeometricFilter.hpp‎

Lines changed: 61 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -13,6 +13,7 @@
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#include <aliceVision/matching/MatchesCollections.hpp>
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#include <aliceVision/matchingImageCollection/GeometricFilterMatrix.hpp>
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#include <aliceVision/system/ProgressDisplay.hpp>
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#include <aliceVision/matchingImageCollection/GeometricInfo.hpp>
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#include <map>
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#include <random>
@@ -28,7 +29,7 @@ using namespace aliceVision::matching;
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* or all the pairs and regions correspondences contained in the putativeMatches set.
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* Allow to keep only geometrically coherent matches.
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* It discards pairs that do not lead to a valid robust model estimation.
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* @param[out] geometricMatches
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* @param[out] out_geometricMatches
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* @param[in] sfmData
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* @param[in] regionsPerView
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* @param[in] functor
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*/
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template<typename GeometryFunctor>
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void robustModelEstimation(PairwiseMatches& out_geometricMatches,
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const sfmData::SfMData* sfmData,
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const sfmData::SfMData & sfmData,
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const feature::RegionsPerView& regionsPerView,
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const GeometryFunctor& functor,
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const PairwiseMatches& putativeMatches,
@@ -73,7 +74,6 @@ void robustModelEstimation(PairwiseMatches& out_geometricMatches,
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{
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MatchesPerDescType guidedGeometricInliers;
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geometricFilter.Geometry_guided_matching(sfmData, regionsPerView, imagePair, distanceRatio, guidedGeometricInliers);
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// ALICEVISION_LOG_DEBUG("#before/#after: " << putative_inliers.size() << "/" << guided_geometric_inliers.size());
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std::swap(inliers, guidedGeometricInliers);
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}
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@@ -87,6 +87,64 @@ void robustModelEstimation(PairwiseMatches& out_geometricMatches,
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}
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}
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/**
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* @brief Perform robust model estimation (with optional guided_matching)
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* or all the pairs and regions correspondences contained in the putativeMatches set.
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* Allow to keep only geometrically coherent matches.
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* It discards pairs that do not lead to a valid robust model estimation.
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* @param[out] out_geometricInfos
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* @param[in] sfmData
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* @param[in] regionsPerView
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* @param[in] functor
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* @param[in] putativeMatches
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* @param[in] randomNumberGenerator
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*/
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template<typename GeometryFunctor>
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void robustModelEstimation(PairwiseGeometricInfo& out_geometricInfos,
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const sfmData::SfMData & sfmData,
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const feature::RegionsPerView& regionsPerView,
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const GeometryFunctor& functor,
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const PairwiseMatches& putativeMatches,
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std::mt19937& randomNumberGenerator)
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{
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out_geometricInfos.clear();
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auto progressDisplay = system::createConsoleProgressDisplay(putativeMatches.size(), std::cout, "Robust Model Estimation\n");
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#pragma omp parallel for schedule(dynamic)
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for (int i = 0; i < (int)putativeMatches.size(); ++i)
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{
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PairwiseMatches::const_iterator iter = putativeMatches.begin();
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std::advance(iter, i);
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const Pair currentPair = iter->first;
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const MatchesPerDescType& putativeMatchesPerType = iter->second;
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const Pair& imagePair = iter->first;
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// apply the geometric filter (robust model estimation)
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{
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MatchesPerDescType inliers;
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GeometryFunctor geometricFilter = functor; // use a copy since we are in a multi-thread context
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const EstimationStatus state =
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geometricFilter.geometricEstimation(sfmData, regionsPerView, imagePair, putativeMatchesPerType, randomNumberGenerator, inliers);
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if (state.hasStrongSupport)
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{
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#pragma omp critical
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{
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PairGeometricInfo info;
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info.model = geometricFilter.getMatrix();
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info.inliers = inliers.getNbAllMatches();
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info.threshold = geometricFilter.m_dPrecision_robust;
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info.type = geometricFilter.getType();
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out_geometricInfos.emplace(currentPair, info);
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}
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}
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}
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++progressDisplay;
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}
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}
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/**
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* @brief removePoorlyOverlappingImagePairs Removes image pairs from the given list of geometric
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* matches that have poor overlap according to the supplied criteria.

‎src/aliceVision/matchingImageCollection/GeometricFilterMatrix.hpp‎

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@@ -7,6 +7,8 @@
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#pragma once
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#include <aliceVision/matchingImageCollection/GeometricFilterType.hpp>
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namespace aliceVision {
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namespace feature {
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m_stIteration(stIteration)
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{}
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virtual Eigen::Matrix3d getMatrix() = 0;
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virtual EGeometricFilterType getType() = 0;
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/**
3541
* @brief Geometry_guided_matching
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* @param sfm_data
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4046
* @param matches
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* @return
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*/
43-
virtual bool Geometry_guided_matching(const sfmData::SfMData* sfmData,
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virtual bool Geometry_guided_matching(const sfmData::SfMData& sfmData,
4450
const feature::RegionsPerView& regionsPerView,
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const Pair imageIdsPair,
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const double dDistanceRatio,

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