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 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 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 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 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 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 FeatureExtraction(desc.CommandLineNode):
commandLine = 'aliceVision_featureExtraction {allParams}'
size = desc.DynamicNodeSize('input')
parallelization = desc.Parallelization(blockSize=40)
commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
category = 'Sparse Reconstruction'
documentation = '''
This node extracts distinctive groups of pixels that are, to some extent, invariant to changing camera viewpoints during image acquisition.
Hence, a feature in the scene should have similar feature descriptions in all images.
This node implements multiple methods:
* **SIFT**
The most standard method. This is the default and recommended value for all use cases.
* **AKAZE**
AKAZE can be interesting solution to extract features in challenging condition. It could be able to match wider angle than SIFT but has drawbacks.
It may extract to many features, the repartition is not always good.
It is known to be good on challenging surfaces such as skin.
* **CCTAG**
CCTag is a marker type with 3 or 4 crowns. You can put markers in the scene during the shooting session to automatically re-orient and re-scale the scene to a known size.
It is robust to motion-blur, depth-of-field, occlusion. Be careful to have enough white margin around your CCTags.
## Online
[https://alicevision.org/#photogrammetry/natural_feature_extraction](https://alicevision.org/#photogrammetry/natural_feature_extraction)
'''
inputs = [
desc.File(
name='input',
label='SfMData',
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', 'dspsift', 'akaze', 'akaze_liop', 'akaze_mldb', 'cctag3', 'cctag4', 'sift_ocv', 'akaze_ocv'],
exclusive=False,
uid=[0],
joinChar=',',
),
desc.ChoiceParam(
name='describerPreset',
label='Describer Density',
description='Control the ImageDescriber density (low, medium, normal, high, ultra).\n'
'Warning: Use ULTRA only on small datasets.',
value='normal',
values=['low', 'medium', 'normal', 'high', 'ultra', 'custom'],
exclusive=True,
uid=[0],
group=lambda node: 'allParams' if node.describerPreset.value != 'custom' else None,
),
desc.IntParam(
name='maxNbFeatures',
label='Max Nb Features',
description='Max number of features extracted (0 means default value based on Describer Density).',
value=0,
range=(0, 100000, 1000),
uid=[0],
advanced=True,
enabled=lambda node: (node.describerPreset.value == 'custom'),
),
desc.ChoiceParam(
name='describerQuality',
label='Describer Quality',
description='Control the ImageDescriber quality (low, medium, normal, high, ultra).',
value='normal',
values=['low', 'medium', 'normal', 'high', 'ultra'],
exclusive=True,
uid=[0],
),
desc.ChoiceParam(
name='contrastFiltering',
label='Contrast Filtering',
description="Contrast filtering method to ignore features with too low contrast that can be considered as noise:\n"
"* Static: Fixed threshold.\n"
"* AdaptiveToMedianVariance: Based on image content analysis.\n"
"* NoFiltering: Disable contrast filtering.\n"
"* GridSortOctaves: Grid Sort but per octaves (and only per scale at the end).\n"
"* GridSort: Grid sort per octaves and at the end (scale * peakValue).\n"
"* GridSortScaleSteps: Grid sort per octaves and at the end (scale and then peakValue).\n"
"* NonExtremaFiltering: Filter non-extrema peakValues.\n",
value='GridSort',
values=['Static', 'AdaptiveToMedianVariance', 'NoFiltering', 'GridSortOctaves', 'GridSort', 'GridSortScaleSteps', 'GridSortOctaveSteps', 'NonExtremaFiltering'],
exclusive=True,
advanced=True,
uid=[0],
),
desc.FloatParam(
name='relativePeakThreshold',
label='Relative Peak Threshold',
description='Peak Threshold relative to median of gradiants.',
value=0.01,
range=(0.01, 1.0, 0.001),
advanced=True,
uid=[0],
enabled=lambda node: (node.contrastFiltering.value == 'AdaptiveToMedianVariance'),
),
desc.BoolParam(
name='gridFiltering',
label='Grid Filtering',
description='Enable grid filtering. Highly recommended to ensure usable number of features.',
value=True,
advanced=True,
uid=[0],
),
desc.BoolParam(
name='forceCpuExtraction',
label='Force CPU Extraction',
description='Use only CPU feature extraction.',
value=True,
uid=[],
advanced=True,
),
desc.IntParam(
name='maxThreads',
label='Max Nb Threads',
description='Specifies the maximum number of threads to run simultaneously (0 for automatic mode).',
value=0,
range=(0, 24, 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='Features Folder',
description='Output path for the features and descriptors files (*.feat, *.desc).',
value=desc.Node.internalFolder,
uid=[],
),
]
class LdrToHdrSampling(desc.CommandLineNode):
commandLine = 'aliceVision_LdrToHdrSampling {allParams}'
size = DividedInputNodeSize('input', 'nbBrackets')
parallelization = desc.Parallelization(blockSize=2)
commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
category = 'Panorama HDR'
documentation = '''
Sample pixels from Low range images for HDR creation
'''
inputs = [
desc.File(
name='input',
label='Input',
description='SfMData 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.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],
group='internal',
enabled= lambda node: node.nbBrackets.value != 1,
),
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.IntParam(
name='blockSize',
label='Block Size',
description='Size of the image tile to extract a sample.',
value=256,
range=(8, 1024, 1),
uid=[0],
advanced=True,
enabled= lambda node: node.byPass.enabled and not node.byPass.value,
),
desc.IntParam(
name='radius',
label='Patch Radius',
description='Radius of the patch used to analyze the sample statistics.',
value=5,
range=(0, 10, 1),
uid=[0],
advanced=True,
enabled= lambda node: node.byPass.enabled and not node.byPass.value,
),
desc.IntParam(
name='maxCountSample',
label='Max Number of Samples',
description='Max number of samples per image group.',
value=200,
range=(10, 1000, 10),
uid=[0],
advanced=True,
enabled= lambda node: node.byPass.enabled and not node.byPass.value,
),
desc.BoolParam(
name='debug',
label='Export Debug Files',
description="Export debug files to analyze the sampling strategy.",
value=False,
uid=[],
enabled= lambda node: node.byPass.enabled and not node.byPass.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 Folder',
description='Output path for the samples.',
value=desc.Node.internalFolder,
uid=[],
),
]
def processChunk(self, chunk):
if chunk.node.nbBrackets.value == 1 or chunk.node.byPass.value:
return
super(LdrToHdrSampling, self).processChunk(chunk)
@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 DepthMapFilter(desc.CommandLineNode):
commandLine = 'aliceVision_depthMapFiltering {allParams}'
gpu = desc.Level.NORMAL
size = desc.DynamicNodeSize('input')
parallelization = desc.Parallelization(blockSize=10)
commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
documentation = '''
Filter depth map values that are not coherent in multiple depth maps.
This allows to filter unstable points before starting the fusion of all depth maps in the Meshing node.
'''
inputs = [
desc.File(
name='input',
label='SfMData',
description='SfMData file.',
value='',
uid=[0],
),
desc.File(
name="depthMapsFolder",
label="DepthMaps Folder",
description="Input depth maps folder",
value="",
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="nNearestCams",
label="Number of Nearest Cameras",
description="Number of nearest cameras used for filtering.",
value=10,
range=(0, 20, 1),
uid=[0],
advanced=True,
),
desc.IntParam(
name="minNumOfConsistentCams",
label="Min Consistent Cameras",
description="Min Number of Consistent Cameras",
value=3,
range=(0, 10, 1),
uid=[0],
),
desc.IntParam(
name="minNumOfConsistentCamsWithLowSimilarity",
label="Min Consistent Cameras Bad Similarity",
description=
"Min Number of Consistent Cameras for pixels with weak similarity value",
value=4,
range=(0, 10, 1),
uid=[0],
),
desc.IntParam(
name="pixSizeBall",
label="Filtering Size in Pixels",
description="Filtering size in pixels",
value=0,
range=(0, 10, 1),
uid=[0],
advanced=True,
),
desc.IntParam(
name="pixSizeBallWithLowSimilarity",
label="Filtering Size in Pixels Bad Similarity",
description="Filtering size in pixels",
value=0,
range=(0, 10, 1),
uid=[0],
advanced=True,
),
desc.BoolParam(
name='computeNormalMaps',
label='Compute Normal Maps',
description='Compute normal maps per depth map.',
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='Filtered DepthMaps Folder',
description='Output folder for generated depth maps.',
value=desc.Node.internalFolder,
uid=[],
),
]
class FeatureMatching(desc.CommandLineNode):
commandLine = 'aliceVision_featureMatching {allParams}'
size = desc.DynamicNodeSize('input')
parallelization = desc.Parallelization(blockSize=20)
commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
documentation = '''
This node performs the matching of all features between the candidate image pairs.
It is performed in 2 steps:
1/ **Photometric Matches**
It performs the photometric matches between the set of features descriptors from the 2 input images.
For each feature descriptor on the first image, it looks for the 2 closest descriptors in the second image and uses a relative threshold between them.
This assumption kill features on repetitive structure but has proved to be a robust criterion.
2/ **Geometric Filtering**
It performs a geometric filtering of the photometric match candidates.
It uses the features positions in the images to make a geometric filtering by using epipolar geometry in an outlier detection framework
called RANSAC (RANdom SAmple Consensus). It randomly selects a small set of feature correspondences and compute the fundamental (or essential) matrix,
then it checks the number of features that validates this model and iterate through the RANSAC framework.
## Online
[https://alicevision.org/#photogrammetry/feature_matching](https://alicevision.org/#photogrammetry/feature_matching)
'''
inputs = [
desc.File(
name='input',
label='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],
advanced=True,
),
desc.ChoiceParam(
name='geometricEstimator',
label='Geometric Estimator',
description='Geometric estimator: (acransac: A-Contrario Ransac, loransac: LO-Ransac (only available for "fundamental_matrix" model)',
value='acransac',
values=['acransac', 'loransac'],
exclusive=True,
uid=[0],
advanced=True,
),
desc.ChoiceParam(
name='geometricFilterType',
label='Geometric Filter Type',
description='Geometric validation method to filter features matches: \n'
' * fundamental_matrix\n'
' * fundamental_with_distortion\n'
' * essential_matrix\n'
' * homography_matrix\n'
' * homography_growing\n'
' * no_filtering',
value='fundamental_matrix',
values=['fundamental_matrix', 'fundamental_with_distortion', 'essential_matrix', 'homography_matrix', 'homography_growing', 'no_filtering'],
exclusive=True,
uid=[0],
advanced=True,
),
desc.FloatParam(
name='distanceRatio',
label='Distance Ratio',
description='Distance ratio to discard non meaningful matches.',
value=0.8,
range=(0.0, 1.0, 0.01),
uid=[0],
advanced=True,
),
desc.IntParam(
name='maxIteration',
label='Max Iteration',
description='Maximum number of iterations allowed in ransac step.',
value=2048,
range=(1, 20000, 1),
uid=[0],
advanced=True,
),
desc.FloatParam(
name='geometricError',
label='Geometric Validation Error',
description='Maximum error (in pixels) allowed for features matching during geometric verification.\n'
'If set to 0, it will select a threshold according to the localizer estimator used\n'
'(if ACRansac, it will analyze the input data to select the optimal value).',
value=0.0,
range=(0.0, 10.0, 0.1),
uid=[0],
advanced=True,
),
desc.FloatParam(
name='knownPosesGeometricErrorMax',
label='Known Poses Geometric Error Max',
description='Maximum error (in pixels) allowed for features matching guided by geometric information from known camera poses.\n'
'If set to 0 it lets the ACRansac select an optimal value.',
value=5.0,
range=(0.0, 100.0, 1.0),
uid=[0],
advanced=True,
),
desc.IntParam(
name='maxMatches',
label='Max Matches',
description='Maximum number of matches to keep.',
value=0,
range=(0, 10000, 1),
uid=[0],
advanced=True,
),
desc.BoolParam(
name='savePutativeMatches',
label='Save Putative Matches',
description='putative matches.',
value=False,
uid=[0],
advanced=True,
),
desc.BoolParam(
name='guidedMatching',
label='Guided Matching',
description='the found model to improve the pairwise correspondences.',
value=False,
uid=[0],
),
desc.BoolParam(
name='matchFromKnownCameraPoses',
label='Match From Known Camera Poses',
description='Enable the usage of geometric information from known camera poses to guide the feature matching.\n'
'If some cameras have unknown poses (so there is no geometric prior), the standard feature matching will be performed.',
value=False,
uid=[0],
),
desc.BoolParam(
name='exportDebugFiles',
label='Export Debug Files',
description='debug files (svg, dot).',
value=False,
uid=[],
advanced=True
),
desc.ChoiceParam(
name='verboseLevel',
label='Verbose Level',
description='verbosity level (fatal, error, warning, info, debug, trace).',
value='info',
values=['fatal', 'error', 'warning', 'info', 'debug', 'trace'],
exclusive=True,
uid=[],
)
]
outputs = [
desc.File(
name='output',
label='Output Folder',
description='Path to a folder in which computed matches will be stored.',
value=desc.Node.internalFolder,
uid=[],
),
]
class DepthMapFilter(desc.CommandLineNode):
commandLine = 'aliceVision_depthMapFiltering {allParams}'
gpu = desc.Level.NORMAL
size = desc.DynamicNodeSize('ini')
parallelization = desc.Parallelization(blockSize=10)
commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
inputs = [
desc.File(
name="ini",
label="MVS Configuration file",
description="",
value="",
uid=[0],
),
desc.File(
name="depthMapFolder",
label="Depth Map Folder",
description="Input depth map folder",
value="",
uid=[0],
),
desc.IntParam(
name="nNearestCams",
label="Number of Nearest Cameras",
description="Number of nearest cameras used for filtering.",
value=10,
range=(0, 20, 1),
uid=[0],
),
desc.IntParam(
name="minNumOfConsistensCams",
label="Min Consistent Cameras",
description="Min Number of Consistent Cameras",
value=3,
range=(0, 10, 1),
uid=[0],
),
desc.IntParam(
name="minNumOfConsistensCamsWithLowSimilarity",
label="Min Consistent Cameras Bad Similarity",
description=
"Min Number of Consistent Cameras for pixels with weak similarity value",
value=4,
range=(0, 10, 1),
uid=[0],
),
desc.IntParam(
name="pixSizeBall",
label="Filtering Size in Pixels",
description="Filtering size in pixels",
value=0,
range=(0, 10, 1),
uid=[0],
),
desc.IntParam(
name="pixSizeBallWithLowSimilarity",
label="Filtering Size in Pixels Bad Similarity",
description="Filtering size in pixels",
value=0,
range=(0, 10, 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='Output folder for generated depth maps.',
value=desc.Node.internalFolder,
uid=[],
),
]
class FeatureExtraction(desc.CommandLineNode):
commandLine = 'aliceVision_featureExtraction {allParams}'
size = desc.DynamicNodeSize('input')
parallelization = desc.Parallelization(blockSize=40)
commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
documentation = '''
This node extracts distinctive groups of pixels that are, to some extent, invariant to changing camera viewpoints during image acquisition.
Hence, a feature in the scene should have similar feature descriptions in all images.
This node implements multiple methods:
* **SIFT**
The most standard method. This is the default and recommended value for all use cases.
* **AKAZE**
AKAZE can be interesting solution to extract features in challenging condition. It could be able to match wider angle than SIFT but has drawbacks.
It may extract to many features, the repartition is not always good.
It is known to be good on challenging surfaces such as skin.
* **CCTAG**
CCTag is a marker type with 3 or 4 crowns. You can put markers in the scene during the shooting session to automatically re-orient and re-scale the scene to a known size.
It is robust to motion-blur, depth-of-field, occlusion. Be careful to have enough white margin around your CCTags.
## Online
[https://alicevision.org/#photogrammetry/natural_feature_extraction](https://alicevision.org/#photogrammetry/natural_feature_extraction)
'''
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=[],
advanced=True,
),
desc.IntParam(
name='maxThreads',
label='Max Nb Threads',
description=
'Specifies the maximum number of threads to run simultaneously (0 for automatic mode).',
value=0,
range=(0, 24, 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 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.ListAttribute(
elementDesc=desc.File(
name="imagesFolder",
label="Images Folder",
description="",
value="",
uid=[0],
),
name="imagesFolders",
label="Images Folders",
description=
'Use images from specific folder(s). Filename should be the same or the image uid.',
),
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],
advanced=True),
desc.BoolParam(
name='saveMetadata',
label='Save Metadata',
description=
'Save projections and intrinsics information in images metadata (only for .exr images).',
value=True,
uid=[0],
advanced=True),
desc.BoolParam(
name='saveMatricesTxtFiles',
label='Save Matrices Text Files',
description=
'Save projections and intrinsics information in text files.',
value=False,
uid=[0],
advanced=True),
desc.BoolParam(
name='evCorrection',
label='Correct images exposure',
description='Apply a correction on images Exposure Value',
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',
description='''Output folder.''',
value=desc.Node.internalFolder,
uid=[],
),
desc.File(name='outputUndistorted',
label='Undistorted images',
description='List of undistorted images.',
value=desc.Node.internalFolder + '*.{outputFileTypeValue}',
uid=[],
group='',
advanced=True),
]
class PanoramaCompositing(desc.CommandLineNode):
commandLine = 'aliceVision_panoramaCompositing {allParams}'
size = desc.DynamicNodeSize('input')
parallelization = desc.Parallelization(blockSize=5)
commandLineRange = '--rangeIteration {rangeIteration} --rangeSize {rangeBlockSize}'
cpu = desc.Level.INTENSIVE
ram = desc.Level.INTENSIVE
category = 'Panorama HDR'
documentation = '''
Once the images have been transformed geometrically (in PanoramaWarping),
they have to be fused together in a single panorama image which looks like a single photography.
The Multi-band Blending method provides the best quality. It averages the pixel values using multiple bands in the frequency domain.
Multiple cameras are contributing to the low frequencies and only the best one contributes to the high frequencies.
'''
inputs = [
desc.File(
name='input',
label='Input SfMData',
description="Input SfMData.",
value='',
uid=[0],
),
desc.File(
name='warpingFolder',
label='Warping Folder',
description="Panorama Warping results",
value='',
uid=[0],
),
desc.File(
name='labels',
label='Labels image',
description="Panorama Seams results",
value='',
uid=[0],
),
desc.ChoiceParam(
name='compositerType',
label='Compositer Type',
description='Which compositer should be used to blend images:\n'
' * multiband: high quality transition by fusing images by frequency bands\n'
' * replace: debug option with straight transitions\n'
' * alpha: debug option with linear transitions\n',
value='multiband',
values=['replace', 'alpha', 'multiband'],
exclusive=True,
uid=[0]
),
desc.IntParam(
name='maxThreads',
label='Max Nb Threads',
description='Specifies the maximum number of threads to run simultaneously.',
value=4,
range=(0, 48, 1),
uid=[],
advanced=True,
),
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='overlayType',
label='Overlay Type',
description='Overlay on top of panorama to analyze transitions:\n'
' * none: no overlay\n'
' * borders: display image borders\n'
' * seams: display transitions between images\n'
' * all: display borders and seams\n',
value='none',
values=['none', 'borders', 'seams', 'all'],
exclusive=True,
advanced=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='',
value=desc.Node.internalFolder,
uid=[],
)
]
