class FeatureExtraction(desc.CommandLineNode):
commandLine = 'aliceVision_featureExtraction {allParams}'
size = desc.DynamicNodeSize('input')
parallelization = desc.Parallelization(blockSize=40)
commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
inputs = [
desc.File(
name='input',
label='Input',
description='SfMData file.',
value='',
uid=[0],
),
desc.ChoiceParam(
name='describerTypes',
label='Describer Types',
description='Describer types used to describe an image.',
value=['sift'],
values=['sift', 'sift_float', 'sift_upright', 'akaze', 'akaze_liop', 'akaze_mldb', 'cctag3', 'cctag4', 'sift_ocv', 'akaze_ocv'],
exclusive=False,
uid=[0],
joinChar=',',
),
desc.ChoiceParam(
name='describerPreset',
label='Describer Preset',
description='Control the ImageDescriber configuration (low, medium, normal, high, ultra). Configuration "ultra" can take long time !',
value='normal',
values=['low', 'medium', 'normal', 'high', 'ultra'],
exclusive=True,
uid=[0],
),
desc.BoolParam(
name='forceCpuExtraction',
label='Force CPU Extraction',
description='Use only CPU feature extraction.',
value=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 Folder',
description='Output path for the features and descriptors files (*.feat, *.desc).',
value=desc.Node.internalFolder,
uid=[],
),
]
class PrepareDenseScene(desc.CommandLineNode):
commandLine = 'aliceVision_prepareDenseScene {allParams}'
size = desc.DynamicNodeSize('input')
parallelization = desc.Parallelization(blockSize=40)
commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
inputs = [
desc.File(
name='input',
label='Input',
description='''SfMData file.''',
value='',
uid=[0],
),
desc.ChoiceParam(
name='outputFileType',
label='Output File Type',
description='Output file type for the undistorted images.',
value='exr',
values=['jpg', 'png', 'tif', 'exr'],
exclusive=True,
uid=[0],
),
desc.BoolParam(
name='saveMetadata',
label='Save Metadata',
description='Save projections and intrinsics informations in images metadata (only for .exr images).',
value=True,
uid=[0],
),
desc.BoolParam(
name='saveMatricesTxtFiles',
label='Save Matrices Text Files',
description='Save projections and intrinsics informations in text files.',
value=False,
uid=[0],
),
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',
description='''Output folder.''',
value=desc.Node.internalFolder,
uid=[],
)
]
class PanoramaCompositing(desc.CommandLineNode):
commandLine = 'aliceVision_panoramaCompositing {allParams}'
size = desc.DynamicNodeSize('input')
inputs = [
desc.File(
name='input',
label='Input',
description="Panorama Warping result",
value='',
uid=[0],
),
desc.ChoiceParam(
name='outputFileType',
label='Output File Type',
description='Output file type for the undistorted images.',
value='exr',
values=['jpg', 'png', 'tif', 'exr'],
exclusive=True,
uid=[0],
group=
'', # not part of allParams, as this is not a parameter for the command line
),
desc.ChoiceParam(
name='compositerType',
label='Compositer Type',
description='Which compositer should be used to blend images',
value='multiband',
values=['replace', 'alpha', 'multiband'],
exclusive=True,
uid=[0]),
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 Panorama',
description='',
value=desc.Node.internalFolder + 'panorama.{outputFileTypeValue}',
uid=[],
),
]
class ConvertMesh(desc.CommandLineNode):
commandLine = 'aliceVision_convertMesh {allParams}'
category = 'Utils'
documentation = '''This node allows to convert a mesh to another format.'''
inputs = [
desc.File(
name='inputMesh',
label='Input Mesh',
description=
'Input Mesh (*.obj, *.mesh, *.meshb, *.ply, *.off, *.stl).',
value='',
uid=[0],
),
desc.ChoiceParam(
name='outputMeshFormat',
label='Output Mesh Format',
description=
'''Output Mesh Format (*.obj, *.mesh, *.meshb, *.ply, *.off, *.stl).''',
value='obj',
values=['obj', 'mesh', 'meshb', 'ply', 'off', 'stl'],
exclusive=True,
uid=[0],
group='',
),
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 Mesh',
description=
'''Output mesh (*.obj, *.mesh, *.meshb, *.ply, *.off, *.stl).''',
value=desc.Node.internalFolder + 'mesh.' +
'{outputMeshFormatValue}',
uid=[],
),
]
class ExportColoredPointCloud(desc.CommandLineNode):
commandLine = 'aliceVision_exportColoredPointCloud {allParams}'
category = 'Export'
inputs = [
desc.File(
name='input',
label='Input SfMData',
description='SfMData file containing a complete SfM.',
value='',
uid=[0],
),
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 Point Cloud Filepath',
description='Output point cloud with visibilities as SfMData file.',
value="{cache}/{nodeType}/{uid0}/pointCloud.abc",
uid=[],
),
]
class PanoramaExternalInfo(desc.CommandLineNode):
commandLine = 'aliceVision_panoramaExternalInfo {allParams}'
size = desc.DynamicNodeSize('input')
inputs = [
desc.File(
name='input',
label='Input',
description="SfM Data File",
value='',
uid=[0],
),
desc.File(
name='config',
label='Xml Config',
description="XML Data File",
value='',
uid=[0],
),
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. (WORKAROUND for valid Tractor graph submission)",
group='forDependencyOnly',
),
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='outSfMDataFilename',
label='Output SfMData File',
description='Path to the output sfmdata file',
value=desc.Node.internalFolder + 'sfmData.abc',
uid=[],
)
]
class ExportUndistortedImages(desc.CommandLineNode):
commandLine = 'aliceVision_exportUndistortedImages {allParams}'
inputs = [
desc.File(
name='input',
label='Input SfMData',
description='SfMData file containing a complete SfM.',
value='',
uid=[0],
),
desc.ChoiceParam(
name='outputFileType',
label='Output File Type',
description='Output file type for the undistorted images.',
value='exr',
values=['jpg', 'png', 'tif', 'exr'],
exclusive=True,
uid=[0],
),
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 Folder',
description='Output folder for the undistorted images.',
value=desc.Node.internalFolder,
uid=[],
),
]
class CameraDownscale(desc.CommandLineNode):
commandLine = 'aliceVision_cameraDownscale {allParams}'
size = desc.DynamicNodeSize('input')
inputs = [
desc.File(
name='input',
label='Input',
description="SfM Data File",
value='',
uid=[0],
),
desc.FloatParam(
name='rescalefactor',
label='RescaleFactor',
description='Newsize = rescalefactor * oldsize',
value=0.5,
range=(0.0, 1.0, 0.1),
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='outSfMDataFilename',
label='Output SfMData File',
description='Path to the output sfmdata file',
value=desc.Node.internalFolder + 'sfmData.abc',
uid=[],
)
]
class PrepareDenseScene(desc.CommandLineNode):
commandLine = 'aliceVision_prepareDenseScene {allParams}'
size = desc.DynamicNodeSize('input')
inputs = [
desc.File(
name='input',
label='Input',
description='''SfMData file.''',
value='',
uid=[0],
),
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='ini',
label='MVS Configuration file',
description='',
value=desc.Node.internalFolder + 'mvs.ini',
uid=[],
group='', # not a command line arg
),
desc.File(
name='output',
label='Output',
description='''Output folder.''',
value=desc.Node.internalFolder,
uid=[],
)
]
class SfMAlignment(desc.CommandLineNode):
commandLine = 'aliceVision_utils_sfmAlignment {allParams}'
size = desc.DynamicNodeSize('input')
inputs = [
desc.File(
name='input',
label='Input',
description='''SfMData file .''',
value='',
uid=[0],
),
desc.File(
name='reference',
label='Reference',
description=
'''Path to the scene used as the reference coordinate system.''',
value='',
uid=[0],
),
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',
description='''Aligned SfMData file .''',
value=desc.Node.internalFolder + 'alignedSfM.abc',
uid=[],
),
]
class PanoramaWarping(desc.CommandLineNode):
commandLine = 'aliceVision_panoramaWarping {allParams}'
size = desc.DynamicNodeSize('input')
inputs = [
desc.File(
name='input',
label='Input',
description="SfM Data File",
value='',
uid=[0],
),
desc.IntParam(
name='panoramaWidth',
label='Panorama Width',
description='Panorama width (pixels). 0 For automatic size',
value=10000,
range=(0, 50000, 1000),
uid=[0]),
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 directory',
description='',
value=desc.Node.internalFolder,
uid=[],
),
]
class PanoramaPrepareImages(desc.CommandLineNode):
commandLine = 'aliceVision_panoramaPrepareImages {allParams}'
size = desc.DynamicNodeSize('input')
category = 'Panorama HDR'
documentation = '''
Prepare images for Panorama pipeline: ensures that images orientations are coherent.
'''
inputs = [
desc.File(
name='input',
label='Input',
description='SfMData file.',
value='',
uid=[0],
),
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',
description='Output sfmData.',
value=lambda attr: desc.Node.internalFolder + os.path.basename(
attr.node.input.value),
uid=[],
),
]
class CameraConnection(desc.CommandLineNode):
internalFolder = desc.Node.internalFolder
commandLine = 'aliceVision_cameraConnection {allParams}'
size = desc.DynamicNodeSize('ini')
inputs = [
desc.File(
name="ini",
label='MVS Configuration file',
description='',
value='',
uid=[0],
),
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=[],
),
]
class MeshResampling(desc.CommandLineNode):
commandLine = 'aliceVision_meshResampling {allParams}'
cpu = desc.Level.NORMAL
ram = desc.Level.NORMAL
category = 'Mesh Post-Processing'
documentation = '''
This node allows to recompute the mesh surface with a new topology and uniform density.
'''
inputs = [
desc.File(
name="input",
label='Input Mesh (OBJ file format).',
description='',
value='',
uid=[0],
),
desc.FloatParam(
name='simplificationFactor',
label='Simplification factor',
description='Simplification factor',
value=0.5,
range=(0.0, 1.0, 0.01),
uid=[0],
),
desc.IntParam(
name='nbVertices',
label='Fixed Number of Vertices',
description='Fixed number of output vertices.',
value=0,
range=(0, 1000000, 1),
uid=[0],
),
desc.IntParam(
name='minVertices',
label='Min Vertices',
description='Min number of output vertices.',
value=0,
range=(0, 1000000, 1),
uid=[0],
),
desc.IntParam(
name='maxVertices',
label='Max Vertices',
description='Max number of output vertices.',
value=0,
range=(0, 1000000, 1),
uid=[0],
),
desc.IntParam(
name='nbLloydIter',
label='Number of Pre-Smoothing Iteration',
description='Number of iterations for Lloyd pre-smoothing.',
value=40,
range=(0, 100, 1),
uid=[0],
),
desc.BoolParam(
name='flipNormals',
label='Flip Normals',
description=
'''Option to flip face normals. It can be needed as it depends on the vertices order in triangles and the convention change from one software to another.''',
value=False,
uid=[0],
),
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 mesh",
description="Output mesh (OBJ file format).",
value=desc.Node.internalFolder + 'mesh.obj',
uid=[],
),
]
class DepthMap(desc.CommandLineNode):
commandLine = 'aliceVision_depthMapEstimation {allParams}'
gpu = desc.Level.INTENSIVE
size = desc.DynamicNodeSize('input')
parallelization = desc.Parallelization(blockSize=3)
commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
category = 'Dense Reconstruction'
documentation = '''
For each camera that have been estimated by the Structure-From-Motion, it estimates the depth value per pixel.
Adjust the downscale factor to compute depth maps at a higher/lower resolution.
Use a downscale factor of one (full-resolution) only if the quality of the input images is really high (camera on a tripod with high-quality optics).
## Online
[https://alicevision.org/#photogrammetry/depth_maps_estimation](https://alicevision.org/#photogrammetry/depth_maps_estimation)
'''
inputs = [
desc.File(
name='input',
label='SfMData',
description='SfMData file.',
value='',
uid=[0],
),
desc.File(
name='imagesFolder',
label='Images Folder',
description=
'Use images from a specific folder instead of those specify in the SfMData file.\nFilename should be the image uid.',
value='',
uid=[0],
),
desc.ChoiceParam(
name='downscale',
label='Downscale',
description='Image downscale factor.',
value=2,
values=[1, 2, 4, 8, 16],
exclusive=True,
uid=[0],
),
desc.FloatParam(
name='minViewAngle',
label='Min View Angle',
description='Minimum angle between two views.',
value=2.0,
range=(0.0, 10.0, 0.1),
uid=[0],
advanced=True,
),
desc.FloatParam(
name='maxViewAngle',
label='Max View Angle',
description='Maximum angle between two views.',
value=70.0,
range=(10.0, 120.0, 1),
uid=[0],
advanced=True,
),
desc.IntParam(
name='sgmMaxTCams',
label='SGM: Nb Neighbour Cameras',
description='Semi Global Matching: Number of neighbour cameras.',
value=10,
range=(1, 100, 1),
uid=[0],
),
desc.IntParam(
name='sgmWSH',
label='SGM: WSH',
description=
'Semi Global Matching: Half-size of the patch used to compute the similarity.',
value=4,
range=(1, 20, 1),
uid=[0],
advanced=True,
),
desc.FloatParam(
name='sgmGammaC',
label='SGM: GammaC',
description='Semi Global Matching: GammaC Threshold.',
value=5.5,
range=(0.0, 30.0, 0.5),
uid=[0],
advanced=True,
),
desc.FloatParam(
name='sgmGammaP',
label='SGM: GammaP',
description='Semi Global Matching: GammaP Threshold.',
value=8.0,
range=(0.0, 30.0, 0.5),
uid=[0],
advanced=True,
),
desc.IntParam(
name='refineMaxTCams',
label='Refine: Nb Neighbour Cameras',
description='Refine: Number of neighbour cameras.',
value=6,
range=(1, 20, 1),
uid=[0],
),
desc.IntParam(
name='refineNSamplesHalf',
label='Refine: Number of Samples',
description='Refine: Number of samples.',
value=150,
range=(1, 500, 10),
uid=[0],
advanced=True,
),
desc.IntParam(
name='refineNDepthsToRefine',
label='Refine: Number of Depths',
description='Refine: Number of depths.',
value=31,
range=(1, 100, 1),
uid=[0],
advanced=True,
),
desc.IntParam(
name='refineNiters',
label='Refine: Number of Iterations',
description='Refine:: Number of iterations.',
value=100,
range=(1, 500, 10),
uid=[0],
advanced=True,
),
desc.IntParam(
name='refineWSH',
label='Refine: WSH',
description=
'Refine: Half-size of the patch used to compute the similarity.',
value=3,
range=(1, 20, 1),
uid=[0],
advanced=True,
),
desc.FloatParam(
name='refineSigma',
label='Refine: Sigma',
description='Refine: Sigma Threshold.',
value=15,
range=(0.0, 30.0, 0.5),
uid=[0],
advanced=True,
),
desc.FloatParam(
name='refineGammaC',
label='Refine: GammaC',
description='Refine: GammaC Threshold.',
value=15.5,
range=(0.0, 30.0, 0.5),
uid=[0],
advanced=True,
),
desc.FloatParam(
name='refineGammaP',
label='Refine: GammaP',
description='Refine: GammaP threshold.',
value=8.0,
range=(0.0, 30.0, 0.5),
uid=[0],
advanced=True,
),
desc.BoolParam(
name='refineUseTcOrRcPixSize',
label='Refine: Tc or Rc pixel size',
description=
'Refine: Use minimum pixel size of neighbour cameras (Tc) or current camera pixel size (Rc)',
value=False,
uid=[0],
advanced=True,
),
desc.BoolParam(
name='exportIntermediateResults',
label='Export Intermediate Results',
description=
'Export intermediate results from the SGM and Refine steps.',
value=False,
uid=[],
advanced=True,
),
desc.IntParam(
name='nbGPUs',
label='Number of GPUs',
description=
'Number of GPUs to use (0 means use all available GPUs).',
value=0,
range=(0, 5, 1),
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='Output',
description='Output folder for generated depth maps.',
value=desc.Node.internalFolder,
uid=[],
),
]
class CameraRigCalibration(desc.CommandLineNode):
commandLine = 'aliceVision_rigCalibration {allParams}'
inputs = [
desc.File(
name='sfmdata',
label='SfM Data',
description='''The sfmData file.''',
value='',
uid=[0],
),
desc.File(
name='mediapath',
label='Media Path',
description=
'''The path to the video file, the folder of the image sequence or a text file (one image path per line) for each camera of the rig (eg. --mediapath /path/to/cam1.mov /path/to/cam2.mov).''',
value='',
uid=[0],
),
desc.File(
name='cameraIntrinsics',
label='Camera Intrinsics',
description=
'''The intrinsics calibration file for each camera of the rig. (eg. --cameraIntrinsics /path/to/calib1.txt /path/to/calib2.txt).''',
value='',
uid=[0],
),
desc.File(
name='export',
label='Export',
description=
'''Filename for the alembic file containing the rig poses with the 3D points. It also saves a file for each camera named 'filename.cam##.abc'.''',
value='trackedcameras.abc',
uid=[0],
),
desc.File(
name='descriptorPath',
label='Descriptor Path',
description='''Folder containing the .desc.''',
value='',
uid=[0],
),
desc.ChoiceParam(
name='matchDescTypes',
label='Match Describer Types',
description='''The describer types to use for the matching''',
value=['sift'],
values=[
'sift', 'sift_float', 'sift_upright', 'dspsift', 'akaze',
'akaze_liop', 'akaze_mldb', 'cctag3', 'cctag4', 'sift_ocv',
'akaze_ocv'
],
exclusive=False,
uid=[0],
joinChar=',',
),
desc.ChoiceParam(
name='preset',
label='Preset',
description=
'''Preset for the feature extractor when localizing a new image (low, medium, normal, high, ultra)''',
value='normal',
values=['low', 'medium', 'normal', 'high', 'ultra'],
exclusive=True,
uid=[0],
),
desc.ChoiceParam(
name='resectionEstimator',
label='Resection Estimator',
description=
'''The type of *sac framework to use for resection (acransac,loransac)''',
value='acransac',
values=['acransac', 'loransac'],
exclusive=True,
uid=[0],
),
desc.ChoiceParam(
name='matchingEstimator',
label='Matching Estimator',
description=
'''The type of *sac framework to use for matching (acransac,loransac)''',
value='acransac',
values=['acransac', 'loransac'],
exclusive=True,
uid=[0],
),
desc.StringParam(
name='refineIntrinsics',
label='Refine Intrinsics',
description=
'''Enable/Disable camera intrinsics refinement for each localized image''',
value='',
uid=[0],
),
desc.FloatParam(
name='reprojectionError',
label='Reprojection Error',
description=
'''Maximum reprojection error (in pixels) allowed for resectioning. If set to 0 it lets the ACRansac select an optimal value.''',
value=4.0,
range=(0.0, 10.0, 0.1),
uid=[0],
),
desc.IntParam(
name='maxInputFrames',
label='Max Input Frames',
description=
'''Maximum number of frames to read in input. 0 means no limit.''',
value=0,
range=(0, 1000, 1),
uid=[0],
),
desc.File(
name='voctree',
label='Voctree',
description='''[voctree] Filename for the vocabulary tree''',
value=os.environ.get('ALICEVISION_VOCTREE', ''),
uid=[0],
),
desc.File(
name='voctreeWeights',
label='Voctree Weights',
description=
'''[voctree] Filename for the vocabulary tree weights''',
value='',
uid=[0],
),
desc.ChoiceParam(
name='algorithm',
label='Algorithm',
description='''[voctree] Algorithm type: {FirstBest,AllResults}''',
value='AllResults',
values=['FirstBest', 'AllResults'],
exclusive=True,
uid=[0],
),
desc.IntParam(
name='nbImageMatch',
label='Nb Image Match',
description=
'''[voctree] Number of images to retrieve in the database''',
value=4,
range=(0, 50, 1),
uid=[0],
),
desc.IntParam(
name='maxResults',
label='Max Results',
description=
'''[voctree] For algorithm AllResults, it stops the image matching when this number of matched images is reached. If 0 it is ignored.''',
value=10,
range=(0, 100, 1),
uid=[0],
),
desc.FloatParam(
name='matchingError',
label='Matching Error',
description=
'''[voctree] Maximum matching error (in pixels) allowed for image matching with geometric verification. If set to 0 it lets the ACRansac select an optimal value.''',
value=4.0,
range=(0.0, 10.0, 0.1),
uid=[0],
),
desc.IntParam(
name='nNearestKeyFrames',
label='N Nearest Key Frames',
description='''[cctag] Number of images to retrieve in database''',
value=5,
range=(0, 50, 1),
uid=[0],
),
]
outputs = [
desc.File(
name='outfile',
label='Output File',
description=
'''The name of the file where to store the calibration data''',
value=desc.Node.internalFolder + 'cameraRigCalibration.rigCal',
uid=[],
),
]
class ImageProcessing(desc.CommandLineNode):
commandLine = 'aliceVision_utils_imageProcessing {allParams}'
size = desc.DynamicNodeSize('input')
# parallelization = desc.Parallelization(blockSize=40)
# commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
documentation = '''
Convert or apply filtering to the input images.
'''
inputs = [
desc.File(
name='input',
label='Input',
description=
'SfMData file input, image filenames or regex(es) on the image file path.\nsupported regex: \'#\' matches a single digit, \'@\' one or more digits, \'?\' one character and \'*\' zero or more.',
value='',
uid=[0],
),
desc.ListAttribute(
elementDesc=desc.File(
name="inputFolder",
label="input Folder",
description="",
value="",
uid=[0],
),
name="inputFolders",
label="Images input Folders",
description='Use images from specific folder(s).',
),
desc.ListAttribute(
elementDesc=desc.StringParam(
name="metadataFolder",
label="Metadata Folder",
description="",
value="",
uid=[0],
),
name="metadataFolders",
label="Metadata input Folders",
description='Use images metadata from specific folder(s).',
),
desc.ChoiceParam(
name='extension',
label='Output File Extension',
description='Output Image File Extension.',
value='',
values=['', 'exr', 'jpg', 'tiff', 'png'],
exclusive=True,
uid=[0],
),
desc.BoolParam(
name='reconstructedViewsOnly',
label='Only Reconstructed Views',
description='Process Only Reconstructed Views',
value=False,
uid=[0],
),
desc.BoolParam(
name='fixNonFinite',
label='Fix Non-Finite',
description=
'Fix non-finite pixels based on neighboring pixels average.',
value=False,
uid=[0],
),
desc.BoolParam(
name='exposureCompensation',
label='Exposure Compensation',
description='Exposure Compensation',
value=False,
uid=[0],
),
desc.FloatParam(
name='scaleFactor',
label='ScaleFactor',
description='Scale Factor.',
value=1.0,
range=(0.0, 1.0, 0.01),
uid=[0],
),
desc.FloatParam(
name='contrast',
label='Contrast',
description='Contrast.',
value=1.0,
range=(0.0, 100.0, 0.1),
uid=[0],
),
desc.IntParam(
name='medianFilter',
label='Median Filter',
description='Median Filter.',
value=0,
range=(0, 10, 1),
uid=[0],
),
desc.BoolParam(
name='fillHoles',
label='Fill Holes',
description='Fill holes based on the alpha channel.\n'
'Note: It will enable fixNonFinite, as it is required for the image pyramid construction used to fill holes.',
value=False,
uid=[0],
),
desc.GroupAttribute(name="sharpenFilter",
label="Sharpen Filter",
description="Sharpen Filtering Parameters.",
joinChar=":",
groupDesc=[
desc.BoolParam(
name='sharpenFilterEnabled',
label='Enable',
description='Use sharpen.',
value=False,
uid=[0],
),
desc.IntParam(
name='width',
label='Width',
description='Sharpen Width.',
value=3,
range=(1, 9, 2),
uid=[0],
enabled=lambda node: node.sharpenFilter.
sharpenFilterEnabled.value,
),
desc.FloatParam(
name='contrast',
label='Contrast',
description='Sharpen Contrast.',
value=1.0,
range=(0.0, 100.0, 0.1),
uid=[0],
enabled=lambda node: node.sharpenFilter.
sharpenFilterEnabled.value,
),
desc.FloatParam(
name='threshold',
label='Threshold',
description='Sharpen Threshold.',
value=0.0,
range=(0.0, 1.0, 0.01),
uid=[0],
enabled=lambda node: node.sharpenFilter.
sharpenFilterEnabled.value,
),
]),
desc.GroupAttribute(
name="bilateralFilter",
label="Bilateral Filter",
description="Bilateral Filtering Parameters.",
joinChar=":",
groupDesc=[
desc.BoolParam(
name='bilateralFilterEnabled',
label='Enable',
description='Bilateral Filter.',
value=False,
uid=[0],
),
desc.IntParam(
name='bilateralFilterDistance',
label='Distance',
description=
'Diameter of each pixel neighborhood that is used during bilateral filtering.\nCould be very slow for large filters, so it is recommended to use 5.',
value=0,
range=(0, 9, 1),
uid=[0],
enabled=lambda node: node.bilateralFilter.
bilateralFilterEnabled.value,
),
desc.FloatParam(
name='bilateralFilterSigmaSpace',
label='Sigma Coordinate Space',
description=
'Bilateral Filter sigma in the coordinate space.',
value=0.0,
range=(0.0, 150.0, 0.01),
uid=[0],
enabled=lambda node: node.bilateralFilter.
bilateralFilterEnabled.value,
),
desc.FloatParam(
name='bilateralFilterSigmaColor',
label='Sigma Color Space',
description='Bilateral Filter sigma in the color space.',
value=0.0,
range=(0.0, 150.0, 0.01),
uid=[0],
enabled=lambda node: node.bilateralFilter.
bilateralFilterEnabled.value,
),
]),
desc.GroupAttribute(
name="claheFilter",
label="Clahe Filter",
description="Clahe Filtering Parameters.",
joinChar=":",
groupDesc=[
desc.BoolParam(
name='claheEnabled',
label='Enable',
description=
'Use Contrast Limited Adaptive Histogram Equalization (CLAHE) Filter.',
value=False,
uid=[0],
),
desc.FloatParam(
name='claheClipLimit',
label='Clip Limit',
description='Sets Threshold For Contrast Limiting.',
value=4.0,
range=(0.0, 8.0, 1.0),
uid=[0],
enabled=lambda node: node.claheFilter.claheEnabled.value,
),
desc.IntParam(
name='claheTileGridSize',
label='Tile Grid Size',
description=
'Sets Size Of Grid For Histogram Equalization. Input Image Will Be Divided Into Equally Sized Rectangular Tiles.',
value=8,
range=(4, 64, 4),
uid=[0],
enabled=lambda node: node.claheFilter.claheEnabled.value,
),
]),
desc.GroupAttribute(
name="noiseFilter",
label="Noise Filter",
description="Noise Filtering Parameters.",
joinChar=":",
groupDesc=[
desc.BoolParam(
name='noiseEnabled',
label='Enable',
description='Add Noise.',
value=False,
uid=[0],
),
desc.ChoiceParam(
name='noiseMethod',
label='Method',
description=
" * method: There are several noise types to choose from:\n"
" * uniform: adds noise values uninformly distributed on range [A,B).\n"
" * gaussian: adds Gaussian (normal distribution) noise values with mean value A and standard deviation B.\n"
" * salt: changes to value A a portion of pixels given by B.\n",
value='uniform',
values=['uniform', 'gaussian', 'salt'],
exclusive=True,
uid=[0],
enabled=lambda node: node.noiseFilter.noiseEnabled.value,
),
desc.FloatParam(
name='noiseA',
label='A',
description=
'Parameter that have a different interpretation depending on the method chosen.',
value=0.0,
range=(0.0, 1.0, 0.0001),
uid=[0],
enabled=lambda node: node.noiseFilter.noiseEnabled.value,
),
desc.FloatParam(
name='noiseB',
label='B',
description=
'Parameter that have a different interpretation depending on the method chosen.',
value=1.0,
range=(0.0, 1.0, 0.0001),
uid=[0],
enabled=lambda node: node.noiseFilter.noiseEnabled.value,
),
desc.BoolParam(
name='noiseMono',
label='Mono',
description=
'If is Checked, a single noise value will be applied to all channels otherwise a separate noise value will be computed for each channel.',
value=True,
uid=[0],
enabled=lambda node: node.noiseFilter.noiseEnabled.value,
),
]),
desc.ChoiceParam(
name='outputFormat',
label='Output Image Format',
description='Allows you to choose the format of the output image.',
value='rgba',
values=['rgba', 'rgb', 'grayscale'],
exclusive=True,
uid=[0],
),
desc.ChoiceParam(
name='storageDataType',
label='Storage Data Type for EXR output',
description='Storage image data type:\n'
' * float: Use full floating point (32 bits per channel)\n'
' * half: Use half float (16 bits per channel)\n'
' * halfFinite: Use half float, but clamp values to avoid non-finite values\n'
' * auto: Use half float if all values can fit, else use full float\n',
value='float',
values=['float', 'half', 'halfFinite', 'auto'],
exclusive=True,
uid=[0],
),
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='outSfMData',
label='Output sfmData',
description='Output sfmData.',
value=lambda attr: (desc.Node.internalFolder + os.path.basename(
attr.node.input.value)) if (os.path.splitext(
attr.node.input.value)[1] in ['.abc', '.sfm']) else '',
uid=[],
group='', # do not export on the command line
),
desc.File(
name='output',
label='Output Folder',
description='Output Images Folder.',
value=desc.Node.internalFolder,
uid=[],
),
desc.File(
name='outputImages',
label='Output Images',
description='Output Image Files.',
value=outputImagesValueFunct,
group='', # do not export on the command line
uid=[],
),
]
class SfMAlignment(desc.CommandLineNode):
commandLine = 'aliceVision_utils_sfmAlignment {allParams}'
size = desc.DynamicNodeSize('input')
inputs = [
desc.File(
name='input',
label='Input',
description='''SfMData file .''',
value='',
uid=[0],
),
desc.File(
name='reference',
label='Reference',
description=
'''Path to the scene used as the reference coordinate system.''',
value='',
uid=[0],
),
desc.ChoiceParam(
name='method',
label='Alignment Method',
description="Alignment Method:\n"
" * from_cameras_viewid: Align cameras with same view Id\n"
" * from_cameras_poseid: Align cameras with same pose Id\n"
" * from_cameras_filepath: Align cameras with a filepath matching, using 'fileMatchingPattern'\n"
" * from_cameras_metadata: Align cameras with matching metadata, using 'metadataMatchingList'\n"
" * from_markers: Align from markers with the same Id\n",
value='from_cameras_viewid',
values=[
'from_cameras_viewid', 'from_cameras_poseid',
'from_cameras_filepath', 'from_cameras_metadata',
'from_markers'
],
exclusive=True,
uid=[0],
),
desc.StringParam(
name='fileMatchingPattern',
label='File Matching Pattern',
description=
'Matching regular expression for the "from_cameras_filepath" method. '
'You should capture specific parts of the filepath with parenthesis to define matching elements.\n'
'Some examples of patterns:\n'
' - Match the filename without extension (default value): ".*\/(.*?)\.\w{3}"\n'
' - Match the filename suffix after "_": ".*\/.*(_.*?\.\w{3})"\n'
' - Match the filename prefix before "_": ".*\/(.*?)_.*\.\w{3}"\n',
value='.*\/(.*?)\.\w{3}',
uid=[0],
),
desc.ListAttribute(
elementDesc=desc.File(
name="metadataMatching",
label="Metadata",
description="",
value="",
uid=[0],
),
name="metadataMatchingList",
label="Metadata Matching List",
description=
'List of metadata that should match to create the correspondences. If the list is empty, the default value will be used: ["Make", "Model", "Exif:BodySerialNumber", "Exif:LensSerialNumber"].',
),
desc.BoolParam(name='applyScale',
label='Scale',
description='Apply scale transformation.',
value=True,
uid=[0]),
desc.BoolParam(name='applyRotation',
label='Rotation',
description='Apply rotation transformation.',
value=True,
uid=[0]),
desc.BoolParam(name='applyTranslation',
label='Translation',
description='Apply translation transformation.',
value=True,
uid=[0]),
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',
description='''Aligned SfMData file .''',
value=desc.Node.internalFolder + 'alignedSfM.abc',
uid=[],
),
]
class StructureFromMotion(desc.CommandLineNode):
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', 'sift_upright', '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='lockScenePreviouslyReconstructed',
label='Lock Scene Previously Reconstructed',
description='This option is useful for SfM augmentation. Lock previously reconstructed poses and intrinsics.',
value=False,
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],
),
desc.FloatParam(
name='minAngleForTriangulation',
label='Min Angle For Triangulation',
description='Minimum angle for triangulation.',
value=3.0,
range=(0.1, 10, 0.1),
uid=[0],
),
desc.FloatParam(
name='minAngleForLandmark',
label='Min Angle For Landmark',
description='Minimum angle for landmark.',
value=2.0,
range=(0.1, 10, 0.1),
uid=[0],
),
desc.FloatParam(
name='maxReprojectionError',
label='Max Reprojection Error',
description='Maximum reprojection error.',
value=4.0,
range=(0.1, 10, 0.1),
uid=[0],
),
desc.FloatParam(
name='minAngleInitialPair',
label='Min Angle Initial Pair',
description='Minimum angle for the initial pair.',
value=5.0,
range=(0.1, 10, 0.1),
uid=[0],
),
desc.FloatParam(
name='maxAngleInitialPair',
label='Max Angle Initial Pair',
description='Maximum angle for the initial pair.',
value=40.0,
range=(0.1, 60, 0.1),
uid=[0],
),
desc.BoolParam(
name='useOnlyMatchesFromInputFolder',
label='Use Only Matches From Input Folder',
description='Use only matches from the input matchesFolder parameter.\n'
'Matches folders previously added to the SfMData file will be ignored.',
value=False,
uid=[],
),
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=desc.Node.internalFolder + 'sfm.abc',
uid=[],
),
desc.File(
name='outputViewsAndPoses',
label='Output SfMData File',
description='''Path to the output sfmdata file with cameras (views and poses).''',
value=desc.Node.internalFolder + 'cameras.sfm',
uid=[],
),
desc.File(
name='extraInfoFolder',
label='Output Folder',
description='Folder for intermediate reconstruction files and additional reconstruction information files.',
value=desc.Node.internalFolder,
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
class LightingEstimation(desc.CommandLineNode):
commandLine = 'aliceVision_utils_lightingEstimation {allParams}'
category = 'Utils'
inputs = [
desc.File(
name='input',
label='Input',
description='SfMData file.',
value='',
uid=[0],
),
desc.File(
name="depthMapsFilterFolder",
label='Filtered Depth Maps Folder',
description='Input filtered depth maps folder',
value='',
uid=[0],
),
desc.File(
name='imagesFolder',
label='Images Folder',
description=
'Use images from a specific folder instead of those specify in the SfMData file.\nFilename should be the image uid.',
value='',
uid=[0],
),
desc.ChoiceParam(
name='lightingEstimationMode',
label='Lighting Estimation Mode',
description='Lighting Estimation Mode.',
value='global',
values=['global', 'per_image'],
exclusive=True,
uid=[0],
advanced=True,
),
desc.ChoiceParam(
name='lightingColor',
label='Lighting Color Mode',
description='Lighting Color Mode.',
value='RGB',
values=['RGB', 'Luminance'],
exclusive=True,
uid=[0],
advanced=True,
),
desc.ChoiceParam(
name='albedoEstimationName',
label='Albedo Estimation Name',
description='Albedo estimation method used for light estimation.',
value='constant',
values=['constant', 'picture', 'median_filter', 'blur_filter'],
exclusive=True,
uid=[0],
advanced=True,
),
desc.IntParam(
name='albedoEstimationFilterSize',
label='Albedo Estimation Filter Size',
description=
'Albedo filter size for estimation method using filter.',
value=3,
range=(0, 100, 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 Folder',
description='Folder for output lighting vector files.',
value=desc.Node.internalFolder,
uid=[],
),
]
class PanoramaInit(desc.CommandLineNode):
commandLine = 'aliceVision_panoramaInit {allParams}'
size = desc.DynamicNodeSize('input')
category = 'Panorama HDR'
documentation = '''
This node allows to setup the Panorama:
1/ Enables the initialization the cameras from known position in an XML file (provided by
["Roundshot VR Drive"](https://www.roundshot.com/xml_1/internet/fr/application/d394/d395/f396.cfm) ).
2/ Enables to setup Full Fisheye Optics (to use an Equirectangular camera model).
3/ To automatically detects the Fisheye Circle (radius + center) in input images or manually adjust it.
'''
inputs = [
desc.File(
name='input',
label='Input',
description="SfM Data File",
value='',
uid=[0],
),
desc.ChoiceParam(
name='initializeCameras',
label='Initialize Cameras',
description='Initialize cameras.',
value='No',
values=['No', 'File', 'Horizontal', 'Horizontal+Zenith', 'Zenith+Horizontal', 'Spherical'],
exclusive=True,
uid=[0],
),
desc.File(
name='config',
label='Xml Config',
description="XML Data File",
value='',
uid=[0],
enabled=lambda node: node.initializeCameras.value == 'File',
),
desc.BoolParam(
name='yawCW',
label='Yaw CW',
description="Yaw ClockWise or CounterClockWise",
value=1,
uid=[0],
enabled=lambda node: ('Horizontal' in node.initializeCameras.value) or (node.initializeCameras.value == "Spherical"),
),
desc.ListAttribute(
elementDesc=desc.IntParam(
name='nbViews',
label='',
description='',
value=-1,
range=[-1, 20],
uid=[0],
),
name='nbViewsPerLine',
label='Spherical: Nb Views Per Line',
description='Number of views per line in Spherical acquisition. Assumes angles from [-90,+90deg] for pitch and [-180,+180deg] for yaw. Use -1 to estimate the number of images automatically.',
joinChar=',',
enabled=lambda node: node.initializeCameras.value == 'Spherical',
),
desc.ListAttribute(
elementDesc=desc.File(
name='dependency',
label='',
description="",
value='',
uid=[],
),
name='dependency',
label='Dependency',
description="Folder(s) in which computed features are stored. (WORKAROUND for valid Tractor graph submission)",
group='forDependencyOnly', # not a command line argument
),
desc.BoolParam(
name='useFisheye',
label='Full Fisheye',
description='To declare a full fisheye panorama setup',
value=False,
uid=[0],
),
desc.BoolParam(
name='estimateFisheyeCircle',
label='Estimate Fisheye Circle',
description='Automatically estimate the Fisheye Circle center and radius instead of using user values.',
value=True,
uid=[0],
enabled=lambda node: node.useFisheye.value,
),
desc.GroupAttribute(
name="fisheyeCenterOffset",
label="Fisheye Center",
description="Center of the Fisheye circle (XY offset to the center in pixels).",
groupDesc=[
desc.FloatParam(
name="fisheyeCenterOffset_x", label="x", description="X Offset in pixels",
value=0.0,
uid=[0],
range=(-1000.0, 10000.0, 1.0)),
desc.FloatParam(
name="fisheyeCenterOffset_y", label="y", description="Y Offset in pixels",
value=0.0,
uid=[0],
range=(-1000.0, 10000.0, 1.0)),
],
group=None, # skip group from command line
enabled=lambda node: node.useFisheye.value and not node.estimateFisheyeCircle.value,
),
desc.FloatParam(
name='fisheyeRadius',
label='Radius',
description='Fisheye visibillity circle radius (% of image shortest side).',
value=96.0,
range=(0.0, 150.0, 0.01),
uid=[0],
enabled=lambda node: node.useFisheye.value and not node.estimateFisheyeCircle.value,
),
desc.ChoiceParam(
name='inputAngle',
label='input Angle offset',
description='Add a rotation to the input XML given poses (CCW).',
value='None',
values=['None', 'rotate90', 'rotate180', 'rotate270'],
exclusive=True,
uid=[0]
),
desc.BoolParam(
name='debugFisheyeCircleEstimation',
label='Debug Fisheye Circle Detection',
description='Debug fisheye circle detection.',
value=False,
uid=[0],
enabled=lambda node: node.useFisheye.value,
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='outSfMData',
label='Output SfMData File',
description='Path to the output sfmdata file',
value=desc.Node.internalFolder + 'sfmData.sfm',
uid=[],
)
]
class DepthMap(desc.CommandLineNode):
commandLine = 'aliceVision_depthMapEstimation {allParams}'
gpu = desc.Level.INTENSIVE
size = desc.DynamicNodeSize('ini')
parallelization = desc.Parallelization(blockSize=3)
commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
inputs = [
desc.File(
name="ini",
label='MVS Configuration File',
description='',
value='',
uid=[0],
),
desc.ChoiceParam(
name='downscale',
label='Downscale',
description='Image downscale factor.',
value=2,
values=[1, 2, 4, 8, 16],
exclusive=True,
uid=[0],
),
desc.IntParam(
name='sgmMaxTCams',
label='SGM: Nb Neighbour Cameras',
description='Semi Global Matching: Number of neighbour cameras.',
value=10,
range=(1, 100, 1),
uid=[0],
),
desc.IntParam(
name='sgmWSH',
label='SGM: WSH',
description=
'Semi Global Matching: Half-size of the patch used to compute the similarity.',
value=4,
range=(1, 20, 1),
uid=[0],
),
desc.FloatParam(
name='sgmGammaC',
label='SGM: GammaC',
description='Semi Global Matching: GammaC Threshold.',
value=5.5,
range=(0.0, 30.0, 0.5),
uid=[0],
),
desc.FloatParam(
name='sgmGammaP',
label='SGM: GammaP',
description='Semi Global Matching: GammaP Threshold.',
value=8.0,
range=(0.0, 30.0, 0.5),
uid=[0],
),
desc.IntParam(
name='refineNSamplesHalf',
label='Refine: Number of Samples',
description='Refine: Number of samples.',
value=150,
range=(1, 500, 10),
uid=[0],
),
desc.IntParam(
name='refineNDepthsToRefine',
label='Refine: Number of Depths',
description='Refine: Number of depths.',
value=31,
range=(1, 100, 1),
uid=[0],
),
desc.IntParam(
name='refineNiters',
label='Refine: Number of Iterations',
description='Refine:: Number of iterations.',
value=100,
range=(1, 500, 10),
uid=[0],
),
desc.IntParam(
name='refineWSH',
label='Refine: WSH',
description=
'Refine: Half-size of the patch used to compute the similarity.',
value=3,
range=(1, 20, 1),
uid=[0],
),
desc.IntParam(
name='refineMaxTCams',
label='Refine: Nb Neighbour Cameras',
description='Refine: Number of neighbour cameras.',
value=6,
range=(1, 20, 1),
uid=[0],
),
desc.FloatParam(
name='refineSigma',
label='Refine: Sigma',
description='Refine: Sigma Threshold.',
value=15,
range=(0.0, 30.0, 0.5),
uid=[0],
),
desc.FloatParam(
name='refineGammaC',
label='Refine: GammaC',
description='Refine: GammaC Threshold.',
value=15.5,
range=(0.0, 30.0, 0.5),
uid=[0],
),
desc.FloatParam(
name='refineGammaP',
label='Refine: GammaP',
description='Refine: GammaP threshold.',
value=8.0,
range=(0.0, 30.0, 0.5),
uid=[0],
),
desc.BoolParam(
name='refineUseTcOrRcPixSize',
label='Refine: Tc or Rc pixel size',
description=
'Refine: Use minimum pixel size of neighbour cameras (Tc) or current camera pixel size (Rc)',
value=False,
uid=[0],
),
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',
description='Output folder for generated depth maps.',
value=desc.Node.internalFolder,
uid=[],
),
]
class SfMTransform(desc.CommandLineNode):
commandLine = 'aliceVision_utils_sfmTransform {allParams}'
size = desc.DynamicNodeSize('input')
inputs = [
desc.File(
name='input',
label='Input',
description='''SfMData file .''',
value='',
uid=[0],
),
desc.ChoiceParam(
name='method',
label='Transformation Method',
description=
'''Transformation method (transformation, auto_from_cameras, auto_from_landmarks).''',
value='auto_from_landmarks',
values=[
'transformation', 'auto_from_cameras', 'auto_from_landmarks'
],
exclusive=True,
uid=[0],
),
desc.StringParam(
name='transformation',
label='Transformation',
description=
'''Align [X,Y,Z] to +Y-axis, rotate around Y by R deg, scale by S; syntax: X,Y,Z;R;S. (required only for 'transformation' method)''',
value='',
uid=[0],
),
desc.ChoiceParam(
name='landmarksDescriberTypes',
label='Landmarks Describer Types',
description=
'Image describer types used to compute the mean of the point cloud. (only for "landmarks" method).',
value=['sift', 'akaze'],
values=[
'sift', 'sift_float', 'sift_upright', 'akaze', 'akaze_liop',
'akaze_mldb', 'cctag3', 'cctag4', 'sift_ocv', 'akaze_ocv'
],
exclusive=False,
uid=[0],
joinChar=',',
),
desc.FloatParam(
name='scale',
label='Additional Scale',
description='Additional scale to apply.',
value=10.0,
range=(1, 100.0, 1),
uid=[0],
),
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',
description='''Aligned SfMData file .''',
value=desc.Node.internalFolder + 'transformedSfM.abc',
uid=[],
),
]
class LdrToHdrMerge(desc.CommandLineNode):
commandLine = 'aliceVision_LdrToHdrMerge {allParams}'
size = desc.DynamicNodeSize('input')
parallelization = desc.Parallelization(blockSize=2)
commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
documentation = '''
Calibrate LDR to HDR response curve from samples
'''
inputs = [
desc.File(
name='input',
label='Input',
description='SfMData file.',
value='',
uid=[0],
),
desc.File(
name='response',
label='Response file',
description='Response file',
value='',
uid=[0],
),
desc.IntParam(
name='userNbBrackets',
label='Number of Brackets',
description=
'Number of exposure brackets per HDR image (0 for automatic detection).',
value=0,
range=(0, 15, 1),
uid=[],
group='user', # not used directly on the command line
),
desc.IntParam(
name='nbBrackets',
label='Automatic Nb Brackets',
description=
'Number of exposure brackets used per HDR image. It is detected automatically from input Viewpoints metadata if "userNbBrackets" is 0, else it is equal to "userNbBrackets".',
value=0,
range=(0, 10, 1),
uid=[0],
),
desc.IntParam(
name='offsetRefBracketIndex',
label='Offset Ref Bracket Index',
description=
'Zero to use the center bracket. +N to use a more exposed bracket or -N to use a less exposed backet.',
value=1,
range=(-4, 4, 1),
uid=[0],
enabled=lambda node: node.nbBrackets.value != 1,
),
desc.BoolParam(
name='byPass',
label='Bypass',
description=
"Bypass HDR creation and use the medium bracket as the source for the next steps.",
value=False,
uid=[0],
enabled=lambda node: node.nbBrackets.value != 1,
),
desc.ChoiceParam(
name='fusionWeight',
label='Fusion Weight',
description="Weight function used to fuse all LDR images together:\n"
" * gaussian \n"
" * triangle \n"
" * plateau",
value='gaussian',
values=['gaussian', 'triangle', 'plateau'],
exclusive=True,
uid=[0],
enabled=lambda node: node.byPass.enabled and not node.byPass.value,
),
desc.IntParam(
name='channelQuantizationPower',
label='Channel Quantization Power',
description='Quantization level like 8 bits or 10 bits.',
value=10,
range=(8, 14, 1),
uid=[0],
advanced=True,
enabled=lambda node: node.byPass.enabled and not node.byPass.value,
),
desc.FloatParam(
name='highlightCorrectionFactor',
label='Highlights Correction',
description=
'Pixels saturated in all input images have a partial information about their real luminance.\n'
'We only know that the value should be >= to the standard hdr fusion.\n'
'This parameter allows to perform a post-processing step to put saturated pixels to a constant\n'
'value defined by the `highlightsMaxLuminance` parameter.\n'
'This parameter is float to enable to weight this correction.',
value=1.0,
range=(0.0, 1.0, 0.01),
uid=[0],
enabled=lambda node: node.byPass.enabled and not node.byPass.value,
),
desc.FloatParam(
name='highlightTargetLux',
label='Highlight Target Luminance (Lux)',
description=
'This is an arbitrary target value (in Lux) used to replace the unknown luminance value of the saturated pixels.\n'
'\n'
'Some Outdoor Reference Light Levels:\n'
' * 120,000 lux: Brightest sunlight\n'
' * 110,000 lux: Bright sunlight\n'
' * 20,000 lux: Shade illuminated by entire clear blue sky, midday\n'
' * 1,000 lux: Typical overcast day, midday\n'
' * 400 lux: Sunrise or sunset on a clear day\n'
' * 40 lux: Fully overcast, sunset/sunrise\n'
'\n'
'Some Indoor Reference Light Levels:\n'
' * 20000 lux: Max Usually Used Indoor\n'
' * 750 lux: Supermarkets\n'
' * 500 lux: Office Work\n'
' * 150 lux: Home\n',
value=120000.0,
range=(1000.0, 150000.0, 1.0),
uid=[0],
enabled=lambda node: node.byPass.enabled and not node.byPass.value
and node.highlightCorrectionFactor.value != 0,
),
desc.ChoiceParam(
name='storageDataType',
label='Storage Data Type',
description='Storage image data type:\n'
' * float: Use full floating point (32 bits per channel)\n'
' * half: Use half float (16 bits per channel)\n'
' * halfFinite: Use half float, but clamp values to avoid non-finite values\n'
' * auto: Use half float if all values can fit, else use full float\n',
value='float',
values=['float', 'half', 'halfFinite', 'auto'],
exclusive=True,
uid=[0],
),
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='outSfMData',
label='Output SfMData File',
description='Path to the output sfmdata file',
value=desc.Node.internalFolder + 'sfmData.sfm',
uid=[],
)
]
@classmethod
def update(cls, node):
if not isinstance(node.nodeDesc, cls):
raise ValueError("Node {} is not an instance of type {}".format(
node, cls))
# TODO: use Node version for this test
if 'userNbBrackets' not in node.getAttributes().keys():
# Old version of the node
return
if node.userNbBrackets.value != 0:
node.nbBrackets.value = node.userNbBrackets.value
return
# logging.info("[LDRToHDR] Update start: version:" + str(node.packageVersion))
cameraInitOutput = node.input.getLinkParam(recursive=True)
if not cameraInitOutput:
node.nbBrackets.value = 0
return
if not cameraInitOutput.node.hasAttribute('viewpoints'):
if cameraInitOutput.node.hasAttribute('input'):
cameraInitOutput = cameraInitOutput.node.input.getLinkParam(
recursive=True)
if cameraInitOutput and cameraInitOutput.node and cameraInitOutput.node.hasAttribute(
'viewpoints'):
viewpoints = cameraInitOutput.node.viewpoints.value
else:
# No connected CameraInit
node.nbBrackets.value = 0
return
# logging.info("[LDRToHDR] Update start: nb viewpoints:" + str(len(viewpoints)))
inputs = []
for viewpoint in viewpoints:
jsonMetadata = viewpoint.metadata.value
if not jsonMetadata:
# no metadata, we cannot found the number of brackets
node.nbBrackets.value = 0
return
d = json.loads(jsonMetadata)
fnumber = findMetadata(
d,
["FNumber", "Exif:ApertureValue", "ApertureValue", "Aperture"],
"")
shutterSpeed = findMetadata(d, [
"Exif:ShutterSpeedValue", "ShutterSpeedValue", "ShutterSpeed"
], "")
iso = findMetadata(
d, ["Exif:ISOSpeedRatings", "ISOSpeedRatings", "ISO"], "")
if not fnumber and not shutterSpeed:
# If one image without shutter or fnumber, we cannot found the number of brackets.
# We assume that there is no multi-bracketing, so nothing to do.
node.nbBrackets.value = 1
return
inputs.append((viewpoint.path.value, (fnumber, shutterSpeed, iso)))
inputs.sort()
exposureGroups = []
exposures = []
for path, exp in inputs:
if exposures and exp != exposures[-1] and exp == exposures[0]:
exposureGroups.append(exposures)
exposures = [exp]
else:
exposures.append(exp)
exposureGroups.append(exposures)
exposures = None
bracketSizes = set()
if len(exposureGroups) == 1:
if len(set(exposureGroups[0])) == 1:
# Single exposure and multiple views
node.nbBrackets.value = 1
else:
# Single view and multiple exposures
node.nbBrackets.value = len(exposureGroups[0])
else:
for expGroup in exposureGroups:
bracketSizes.add(len(expGroup))
if len(bracketSizes) == 1:
node.nbBrackets.value = bracketSizes.pop()
# logging.info("[LDRToHDR] nb bracket size:" + str(node.nbBrackets.value))
else:
node.nbBrackets.value = 0
class PanoramaWarping(desc.CommandLineNode):
commandLine = 'aliceVision_panoramaWarping {allParams}'
size = desc.DynamicNodeSize('input')
parallelization = desc.Parallelization(blockSize=5)
commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
documentation = '''
Compute the image warping for each input image in the panorama coordinate system.
'''
inputs = [
desc.File(
name='input',
label='Input',
description="SfM Data File",
value='',
uid=[0],
),
desc.BoolParam(
name='estimateResolution',
label='Estimate Resolution',
description='Estimate output panorama resolution automatically based on the input images resolution.',
value=True,
uid=[0],
group=None, # skip group from command line
),
desc.IntParam(
name='panoramaWidth',
label='Panorama Width',
description='Choose the output panorama width (in pixels).',
value=10000,
range=(0, 50000, 1000),
uid=[0],
enabled=lambda node: (not node.estimateResolution.value),
),
desc.IntParam(
name='percentUpscale',
label='Upscale Ratio',
description='Percentage of upscaled pixels.\n'
'\n'
'How many percent of the pixels will be upscaled (compared to its original resolution):\n'
' * 0: all pixels will be downscaled\n'
' * 50: on average the input resolution is kept (optimal to reduce over/under-sampling)\n'
' * 100: all pixels will be upscaled\n',
value=50,
range=(0, 100, 1),
enabled=lambda node: (node.estimateResolution.value),
uid=[0]
),
desc.IntParam(
name='maxPanoramaWidth',
label='Max Panorama Width',
description='Choose the maximal output panorama width (in pixels). Zero means no limit.',
value=35000,
range=(0, 100000, 1000),
uid=[0],
enabled=lambda node: (node.estimateResolution.value),
),
desc.ChoiceParam(
name='storageDataType',
label='Storage Data Type',
description='Storage image data type:\n'
' * float: Use full floating point (32 bits per channel)\n'
' * half: Use half float (16 bits per channel)\n'
' * halfFinite: Use half float, but clamp values to avoid non-finite values\n'
' * auto: Use half float if all values can fit, else use full float\n',
value='float',
values=['float', 'half', 'halfFinite', 'auto'],
exclusive=True,
uid=[0],
),
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 directory',
description='',
value=desc.Node.internalFolder,
uid=[],
),
]
class MeshDecimate(desc.CommandLineNode):
commandLine = 'aliceVision_meshDecimate {allParams}'
cpu = desc.Level.NORMAL
ram = desc.Level.NORMAL
inputs = [
desc.File(
name="input",
label='Input Mesh (OBJ file format).',
description='',
value='',
uid=[0],
),
desc.FloatParam(
name='simplificationFactor',
label='Simplification factor',
description='Simplification factor',
value=0.5,
range=(0.0, 1.0, 0.01),
uid=[0],
),
desc.IntParam(
name='nbVertices',
label='Fixed Number of Vertices',
description='Fixed number of output vertices.',
value=0,
range=(0, 1000000, 1),
uid=[0],
),
desc.IntParam(
name='minVertices',
label='Min Vertices',
description='Min number of output vertices.',
value=0,
range=(0, 1000000, 1),
uid=[0],
),
desc.IntParam(
name='maxVertices',
label='Max Vertices',
description='Max number of output vertices.',
value=0,
range=(0, 1000000, 1),
uid=[0],
),
desc.BoolParam(
name='flipNormals',
label='Flip Normals',
description='Option to flip face normals.\n'
'It can be needed as it depends on the vertices order in triangles\n'
'and the convention change from one software to another.',
value=False,
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 mesh",
description="Output mesh (OBJ file format).",
value=desc.Node.internalFolder + 'mesh.obj',
uid=[],
),
]
class MeshFiltering(desc.CommandLineNode):
commandLine = 'aliceVision_meshFiltering {allParams}'
inputs = [
desc.File(
name='inputMesh',
label='Input Mesh',
description='''Input Mesh (OBJ file format).''',
value='',
uid=[0],
),
desc.FloatParam(
name='removeLargeTrianglesFactor',
label='Filter Large Triangles Factor',
description='Remove all large triangles. We consider a triangle as large if one edge is bigger than N times the average edge length. Put zero to disable it.',
value=60.0,
range=(1.0, 100.0, 0.1),
uid=[0],
),
desc.BoolParam(
name='keepLargestMeshOnly',
label='Keep Only the Largest Mesh',
description='Keep only the largest connected triangles group.',
value=False,
uid=[0],
),
desc.IntParam(
name='iterations',
label='Smoothing Iterations',
description='Number of smoothing iterations',
value=5,
range=(0, 50, 1),
uid=[0],
),
desc.FloatParam(
name='lambda',
label='Lambda',
description='',
value=1.0,
range=(0.0, 10.0, 0.1),
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='outputMesh',
label='Output Mesh',
description='''Output mesh (OBJ file format).''',
value=desc.Node.internalFolder + 'mesh.obj',
uid=[],
),
]
class ImageMatching(desc.CommandLineNode):
commandLine = 'aliceVision_imageMatching {allParams}'
size = desc.DynamicNodeSize('input')
documentation = '''
The goal of this node is to select the image pairs to match. 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.
It provides multiple methods:
* **VocabularyTree**
It uses image retrieval techniques to find images that share some content without the cost of resolving all feature matches in details.
Each image is represented in a compact image descriptor which allows to compute the distance between all images descriptors very efficiently.
If your scene contains less than "Voc Tree: Minimal Number of Images", all image pairs will be selected.
* **Sequential**
If your input is a video sequence, you can use this option to link images between them over time.
* **SequentialAndVocabularyTree**
Combines sequential approach with Voc Tree to enable connections between keyframes at different times.
* **Exhaustive**
Export all image pairs.
* **Frustum**
If images have known poses, computes the intersection between cameras frustums to create the list of image pairs.
* **FrustumOrVocabularyTree**
If images have known poses, use frustum intersection else use VocabularuTree.
## Online
[https://alicevision.org/#photogrammetry/image_matching](https://alicevision.org/#photogrammetry/image_matching)
'''
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.ChoiceParam(
name='method',
label='Method',
description='Method used to select the image pairs to match:\n'
' * VocabularyTree: It uses image retrieval techniques to find images that share some content without the cost of resolving all \n'
'feature matches in details. Each image is represented in a compact image descriptor which allows to compute the distance between all \n'
'images descriptors very efficiently. If your scene contains less than "Voc Tree: Minimal Number of Images", all image pairs will be selected.\n'
' * Sequential: If your input is a video sequence, you can use this option to link images between them over time.\n'
' * SequentialAndVocabularyTree: Combines sequential approach with VocTree to enable connections between keyframes at different times.\n'
' * Exhaustive: Export all image pairs.\n'
' * Frustum: If images have known poses, computes the intersection between cameras frustums to create the list of image pairs.\n'
' * FrustumOrVocabularyTree: If images have known poses, use frustum intersection else use VocabularyTree.\n',
value='VocabularyTree',
values=[
'VocabularyTree', 'Sequential', 'SequentialAndVocabularyTree',
'Exhaustive', 'Frustum', 'FrustumOrVocabularyTree'
],
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=[],
enabled=lambda node: 'VocabularyTree' in node.method.value,
),
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,
enabled=lambda node: 'VocabularyTree' in node.method.value,
),
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,
enabled=lambda node: 'VocabularyTree' in node.method.value,
),
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,
enabled=lambda node: 'VocabularyTree' in node.method.value,
),
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,
enabled=lambda node: 'VocabularyTree' in node.method.value,
),
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,
enabled=lambda node: 'Sequential' in node.method.value,
),
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=[],
),
]
class FeatureMatching(desc.CommandLineNode):
commandLine = 'aliceVision_featureMatching {allParams}'
size = desc.DynamicNodeSize('input')
parallelization = desc.Parallelization(blockSize=20)
commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
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.File(
name='imagePairsList',
label='Image Pairs List',
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=['sift'],
values=[
'sift', 'sift_float', 'sift_upright', 'akaze', 'akaze_liop',
'akaze_mldb', 'cctag3', 'cctag4', 'sift_ocv', 'akaze_ocv'
],
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],
),
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],
),
desc.ChoiceParam(
name='geometricFilterType',
label='Geometric Filter Type',
description=
'Geometric validation method to filter features matches: \n'
' * fundamental_matrix\n'
' * essential_matrix\n'
' * homography_matrix\n'
' * homography_growing\n'
' * no_filtering',
value='fundamental_matrix',
values=[
'fundamental_matrix', 'essential_matrix', 'homography_matrix',
'homography_growing', 'no_filtering'
],
exclusive=True,
uid=[0],
),
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],
),
desc.IntParam(
name='maxIteration',
label='Max Iteration',
description='Maximum number of iterations allowed in ransac step.',
value=2048,
range=(1, 20000, 1),
uid=[0],
),
desc.IntParam(
name='maxMatches',
label='Max Matches',
description='Maximum number of matches to keep.',
value=0,
range=(0, 10000, 1),
uid=[0],
),
desc.BoolParam(
name='savePutativeMatches',
label='Save Putative Matches',
description='putative matches.',
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='exportDebugFiles',
label='Export Debug Files',
description='debug files (svg, dot).',
value=False,
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 Folder',
description=
'Path to a folder in which computed matches will be stored.',
value=desc.Node.internalFolder,
uid=[],
),
]
class SfMTransform(desc.CommandLineNode):
commandLine = 'aliceVision_utils_sfmTransform {allParams}'
size = desc.DynamicNodeSize('input')
documentation = '''
This node allows to change the coordinate system of one SfM scene.
The transformation can be based on:
* transformation: Apply a given transformation
* auto_from_cameras: Fit all cameras into a box [-1,1]
* auto_from_landmarks: Fit all landmarks into a box [-1,1]
* from_single_camera: Use a specific camera as the origin of the coordinate system
* from_markers: Align specific markers to custom coordinates
'''
inputs = [
desc.File(
name='input',
label='Input',
description='''SfMData file .''',
value='',
uid=[0],
),
desc.ChoiceParam(
name='method',
label='Transformation Method',
description="Transformation method:\n"
" * transformation: Apply a given transformation\n"
" * manual: Apply the gizmo transformation (show the transformed input)\n"
" * auto_from_cameras: Use cameras\n"
" * auto_from_landmarks: Use landmarks\n"
" * from_single_camera: Use a specific camera as the origin of the coordinate system\n"
" * from_markers: Align specific markers to custom coordinates",
value='auto_from_landmarks',
values=['transformation', 'manual', 'auto_from_cameras', 'auto_from_landmarks', 'from_single_camera', 'from_markers'],
exclusive=True,
uid=[0],
),
desc.StringParam(
name='transformation',
label='Transformation',
description="Required only for 'transformation' and 'from_single_camera' methods:\n"
" * transformation: Align [X,Y,Z] to +Y-axis, rotate around Y by R deg, scale by S; syntax: X,Y,Z;R;S\n"
" * from_single_camera: Camera UID or image filename",
value='',
uid=[0],
enabled=lambda node: node.method.value == "transformation" or node.method.value == "from_single_camera",
),
desc.GroupAttribute(
name="manualTransform",
label="Manual Transform (Gizmo)",
description="Translation, rotation (Euler ZXY) and uniform scale.",
groupDesc=[
desc.GroupAttribute(
name="manualTranslation",
label="Translation",
description="Translation in space.",
groupDesc=[
desc.FloatParam(
name="x", label="x", description="X Offset",
value=0.0,
uid=[0],
range=(-20.0, 20.0, 0.01)
),
desc.FloatParam(
name="y", label="y", description="Y Offset",
value=0.0,
uid=[0],
range=(-20.0, 20.0, 0.01)
),
desc.FloatParam(
name="z", label="z", description="Z Offset",
value=0.0,
uid=[0],
range=(-20.0, 20.0, 0.01)
)
],
joinChar=","
),
desc.GroupAttribute(
name="manualRotation",
label="Euler Rotation",
description="Rotation in Euler degrees.",
groupDesc=[
desc.FloatParam(
name="x", label="x", description="Euler X Rotation",
value=0.0,
uid=[0],
range=(-90.0, 90.0, 1)
),
desc.FloatParam(
name="y", label="y", description="Euler Y Rotation",
value=0.0,
uid=[0],
range=(-180.0, 180.0, 1)
),
desc.FloatParam(
name="z", label="z", description="Euler Z Rotation",
value=0.0,
uid=[0],
range=(-180.0, 180.0, 1)
)
],
joinChar=","
),
desc.FloatParam(
name="manualScale",
label="Scale",
description="Uniform Scale.",
value=1.0,
uid=[0],
range=(0.0, 20.0, 0.01)
)
],
joinChar=",",
enabled=lambda node: node.method.value == "manual",
),
desc.ChoiceParam(
name='landmarksDescriberTypes',
label='Landmarks Describer Types',
description='Image describer types used to compute the mean of the point cloud. (only for "landmarks" method).',
value=['sift', 'dspsift', 'akaze'],
values=['sift', 'sift_float', 'sift_upright', 'dspsift', 'akaze', 'akaze_liop', 'akaze_mldb', 'cctag3', 'cctag4', 'sift_ocv', 'akaze_ocv', 'unknown'],
exclusive=False,
uid=[0],
joinChar=',',
),
desc.FloatParam(
name='scale',
label='Additional Scale',
description='Additional scale to apply.',
value=1.0,
range=(0.0, 100.0, 0.1),
uid=[0],
),
desc.ListAttribute(
name="markers",
elementDesc=desc.GroupAttribute(name="markerAlign", label="Marker Align", description="", joinChar=":", groupDesc=[
desc.IntParam(name="markerId", label="Marker", description="Marker Id", value=0, uid=[0], range=(0, 32, 1)),
desc.GroupAttribute(name="markerCoord", label="Coord", description="", joinChar=",", groupDesc=[
desc.FloatParam(name="x", label="x", description="", value=0.0, uid=[0], range=(-2.0, 2.0, 1.0)),
desc.FloatParam(name="y", label="y", description="", value=0.0, uid=[0], range=(-2.0, 2.0, 1.0)),
desc.FloatParam(name="z", label="z", description="", value=0.0, uid=[0], range=(-2.0, 2.0, 1.0)),
])
]),
label="Markers",
description="Markers alignment points",
),
desc.BoolParam(
name='applyScale',
label='Scale',
description='Apply scale transformation.',
value=True,
uid=[0],
enabled=lambda node: node.method.value != "manual",
),
desc.BoolParam(
name='applyRotation',
label='Rotation',
description='Apply rotation transformation.',
value=True,
uid=[0],
enabled=lambda node: node.method.value != "manual",
),
desc.BoolParam(
name='applyTranslation',
label='Translation',
description='Apply translation transformation.',
value=True,
uid=[0],
enabled=lambda node: node.method.value != "manual",
),
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='''Aligned SfMData file .''',
value=lambda attr: desc.Node.internalFolder + (os.path.splitext(os.path.basename(attr.node.input.value))[0] or 'sfmData') + '.abc',
uid=[],
),
desc.File(
name='outputViewsAndPoses',
label='Output Poses',
description='''Path to the output sfmdata file with cameras (views and poses).''',
value=desc.Node.internalFolder + 'cameras.sfm',
uid=[],
),
]