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165 lines
6.8 KiB
Python
165 lines
6.8 KiB
Python
__version__ = "1.3"
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from meshroom.core import desc
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from meshroom.core.utils import COLORSPACES, DESCRIBER_TYPES, VERBOSE_LEVEL
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class FeatureExtraction(desc.AVCommandLineNode):
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commandLine = 'aliceVision_featureExtraction {allParams}'
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size = desc.DynamicNodeSize('input')
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parallelization = desc.Parallelization(blockSize=40)
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commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
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category = 'Sparse Reconstruction'
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documentation = '''
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This node extracts distinctive groups of pixels that are, to some extent, invariant to changing camera viewpoints during image acquisition.
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Hence, a feature in the scene should have similar feature descriptions in all images.
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This node implements multiple methods:
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* **SIFT**
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The most standard method. This is the default and recommended value for all use cases.
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* **AKAZE**
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AKAZE can be interesting solution to extract features in challenging condition. It could be able to match wider angle than SIFT but has drawbacks.
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It may extract too many features, the repartition is not always good.
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It is known to be good on challenging surfaces such as skin.
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* **CCTAG**
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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.
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It is robust to motion-blur, depth-of-field, occlusion. Be careful to have enough white margin around your CCTags.
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## Online
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[https://alicevision.org/#photogrammetry/natural_feature_extraction](https://alicevision.org/#photogrammetry/natural_feature_extraction)
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'''
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inputs = [
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desc.File(
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name="input",
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label="SfMData",
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description="Input SfMData file.",
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value="",
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),
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desc.File(
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name="masksFolder",
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label="Masks Folder",
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description="Use masks to filter features. Filename should be the same or the image UID.",
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value="",
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),
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desc.ChoiceParam(
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name="maskExtension",
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label="Mask Extension",
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description="File extension for masks.",
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value="png",
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values=["png", "exr", "jpg"],
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),
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desc.BoolParam(
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name="maskInvert",
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label="Invert Masks",
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description="Invert mask values.",
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value=False,
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),
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desc.ChoiceParam(
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name="describerTypes",
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label="Describer Types",
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description="Describer types used to describe an image.",
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values=DESCRIBER_TYPES,
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value=["dspsift"],
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exclusive=False,
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joinChar=",",
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exposed=True,
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),
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desc.ChoiceParam(
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name="describerPreset",
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label="Describer Density",
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description="Control the ImageDescriber density (low, medium, normal, high, ultra).\n"
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"Warning: Use ULTRA only on small datasets.",
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value="normal",
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values=["low", "medium", "normal", "high", "ultra", "custom"],
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group=lambda node: 'allParams' if node.describerPreset.value != 'custom' else None,
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),
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desc.IntParam(
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name="maxNbFeatures",
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label="Max Nb Features",
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description="Maximum number of features extracted (0 means default value based on Describer Density).",
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value=0,
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range=(0, 100000, 1000),
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advanced=True,
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enabled=lambda node: (node.describerPreset.value == "custom"),
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),
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desc.ChoiceParam(
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name="describerQuality",
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label="Describer Quality",
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description="Control the ImageDescriber quality (low, medium, normal, high, ultra).",
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value="normal",
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values=["low", "medium", "normal", "high", "ultra"],
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),
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desc.ChoiceParam(
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name="contrastFiltering",
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label="Contrast Filtering",
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description="Contrast filtering method to ignore features with too low contrast that can be considered as noise:\n"
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" - Static: Fixed threshold.\n"
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" - AdaptiveToMedianVariance: Based on image content analysis.\n"
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" - NoFiltering: Disable contrast filtering.\n"
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" - GridSortOctaves: Grid Sort but per octaves (and only per scale at the end).\n"
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" - GridSort: Grid sort per octaves and at the end (scale * peakValue).\n"
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" - GridSortScaleSteps: Grid sort per octaves and at the end (scale and then peakValue).\n"
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" - NonExtremaFiltering: Filter non-extrema peakValues.\n",
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value="GridSort",
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values=["Static", "AdaptiveToMedianVariance", "NoFiltering", "GridSortOctaves", "GridSort", "GridSortScaleSteps", "GridSortOctaveSteps", "NonExtremaFiltering"],
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advanced=True,
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),
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desc.FloatParam(
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name="relativePeakThreshold",
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label="Relative Peak Threshold",
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description="Peak threshold relative to median of gradients.",
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value=0.01,
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range=(0.01, 1.0, 0.001),
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advanced=True,
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enabled=lambda node: (node.contrastFiltering.value == "AdaptiveToMedianVariance"),
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),
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desc.BoolParam(
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name="gridFiltering",
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label="Grid Filtering",
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description="Enable grid filtering. Highly recommended to ensure usable number of features.",
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value=True,
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advanced=True,
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),
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desc.ChoiceParam(
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name="workingColorSpace",
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label="Working Color Space",
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description="Allows you to choose the color space in which the data are processed.",
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values=COLORSPACES,
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value="sRGB",
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),
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desc.BoolParam(
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name="forceCpuExtraction",
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label="Force CPU Extraction",
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description="Use only CPU feature extraction.",
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value=True,
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advanced=True,
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),
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desc.IntParam(
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name="maxThreads",
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label="Max Nb Threads",
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description="Maximum number of threads to run simultaneously (0 for automatic mode).",
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value=0,
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range=(0, 24, 1),
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invalidate=False,
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advanced=True,
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),
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desc.ChoiceParam(
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name="verboseLevel",
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label="Verbose Level",
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description="Verbosity level (fatal, error, warning, info, debug, trace).",
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values=VERBOSE_LEVEL,
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value="info",
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),
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]
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outputs = [
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desc.File(
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name="output",
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label="Features Folder",
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description="Output path for the features and descriptors files (*.feat, *.desc).",
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value=desc.Node.internalFolder,
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),
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]
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