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https://github.com/alicevision/Meshroom.git
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[nodes] I-L: Harmonize and improve labels and descriptions
Use CamelCase for all labels, always end descriptions with periods, and replace the mixed use of single and double quotes with double quotes only.
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5c2865968d
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11 changed files with 978 additions and 889 deletions
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@ -35,17 +35,17 @@ If images have known poses, use frustum intersection else use VocabularuTree.
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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='SfMData file .',
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value='',
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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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uid=[0],
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),
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desc.ListAttribute(
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elementDesc=desc.File(
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name="featuresFolder",
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label="Features Folder",
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description="",
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description="Folder containing some extracted features and descriptors.",
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value="",
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uid=[0],
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),
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@ -54,85 +54,87 @@ If images have known poses, use frustum intersection else use VocabularuTree.
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description="Folder(s) containing the extracted features and descriptors."
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),
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desc.ChoiceParam(
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name='method',
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label='Method',
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description='Method used to select the image pairs to match:\n'
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' * VocabularyTree: It uses image retrieval techniques to find images that share some content without the cost of resolving all \n'
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'feature matches in details. Each image is represented in a compact image descriptor which allows to compute the distance between all \n'
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'images descriptors very efficiently. If your scene contains less than "Voc Tree: Minimal Number of Images", all image pairs will be selected.\n'
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' * Sequential: If your input is a video sequence, you can use this option to link images between them over time.\n'
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' * SequentialAndVocabularyTree: Combines sequential approach with VocTree to enable connections between keyframes at different times.\n'
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' * Exhaustive: Export all image pairs.\n'
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' * Frustum: If images have known poses, computes the intersection between cameras frustums to create the list of image pairs.\n'
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' * FrustumOrVocabularyTree: If images have known poses, use frustum intersection else use VocabularyTree.\n',
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value='SequentialAndVocabularyTree',
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values=['VocabularyTree', 'Sequential', 'SequentialAndVocabularyTree', 'Exhaustive', 'Frustum', 'FrustumOrVocabularyTree'],
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name="method",
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label="Method",
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description="Method used to select the image pairs to match:\n"
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" - VocabularyTree: It uses image retrieval techniques to find images that share some content without the cost of resolving all \n"
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"feature matches in details. Each image is represented in a compact image descriptor which allows to compute the distance between all \n"
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"images descriptors very efficiently. If your scene contains less than 'Voc Tree: Minimal Number of Images', all image pairs will be selected.\n"
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" - Sequential: If your input is a video sequence, you can use this option to link images between them over time.\n"
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" - SequentialAndVocabularyTree: Combines sequential approach with VocTree to enable connections between keyframes at different times.\n"
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" - Exhaustive: Export all image pairs.\n"
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" - Frustum: If images have known poses, computes the intersection between cameras frustums to create the list of image pairs.\n"
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" - FrustumOrVocabularyTree: If images have known poses, use frustum intersection else use VocabularyTree.\n",
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value="SequentialAndVocabularyTree",
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values=["VocabularyTree", "Sequential", "SequentialAndVocabularyTree", "Exhaustive", "Frustum", "FrustumOrVocabularyTree"],
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exclusive=True,
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uid=[0],
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),
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desc.File(
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name='tree',
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label='Voc Tree: Tree',
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description='Input name for the vocabulary tree file.',
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value='${ALICEVISION_VOCTREE}',
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name="tree",
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label="Voc Tree: Tree",
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description="Input name for the vocabulary tree file.",
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value="${ALICEVISION_VOCTREE}",
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uid=[],
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enabled=lambda node: 'VocabularyTree' in node.method.value,
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enabled=lambda node: "VocabularyTree" in node.method.value,
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),
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desc.File(
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name='weights',
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label='Voc Tree: Weights',
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description='Input name for the weight file, if not provided the weights will be computed on the database built with the provided set.',
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value='',
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name="weights",
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label="Voc Tree: Weights",
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description="Input name for the weight file.\n"
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"If not provided, the weights will be computed on the database built with the provided set.",
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value="",
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uid=[0],
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advanced=True,
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enabled=lambda node: 'VocabularyTree' in node.method.value,
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enabled=lambda node: "VocabularyTree" in node.method.value,
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),
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desc.IntParam(
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name='minNbImages',
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label='Voc Tree: Minimal Number of Images',
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description='Minimal number of images to use the vocabulary tree. If we have less features than this threshold, we will compute all matching combinations.',
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name="minNbImages",
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label="Voc Tree: Minimum Number Of Images",
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description="Minimum number of images to use the vocabulary tree.\n"
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"If we have less features than this threshold, we will compute all matching combinations.",
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value=200,
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range=(0, 500, 1),
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uid=[0],
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advanced=True,
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enabled=lambda node: 'VocabularyTree' in node.method.value,
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enabled=lambda node: "VocabularyTree" in node.method.value,
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),
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desc.IntParam(
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name='maxDescriptors',
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label='Voc Tree: Max Descriptors',
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description='Limit the number of descriptors you load per image. Zero means no limit.',
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name="maxDescriptors",
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label="Voc Tree: Max Descriptors",
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description="Limit the number of descriptors you load per image. 0 means no limit.",
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value=500,
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range=(0, 100000, 1),
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uid=[0],
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advanced=True,
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enabled=lambda node: 'VocabularyTree' in node.method.value,
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enabled=lambda node: "VocabularyTree" in node.method.value,
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),
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desc.IntParam(
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name='nbMatches',
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label='Voc Tree: Nb Matches',
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description='The number of matches to retrieve for each image (If 0 it will retrieve all the matches).',
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name="nbMatches",
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label="Voc Tree: Nb Matches",
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description="The number of matches to retrieve for each image. (If 0, it will retrieve all the matches).",
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value=40,
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range=(0, 1000, 1),
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uid=[0],
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advanced=True,
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enabled=lambda node: 'VocabularyTree' in node.method.value,
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enabled=lambda node: "VocabularyTree" in node.method.value,
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),
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desc.IntParam(
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name='nbNeighbors',
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label='Sequential: Nb Neighbors',
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description='The number of neighbors to retrieve for each image (If 0 it will retrieve all the neighbors).',
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name="nbNeighbors",
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label="Sequential: Nb Neighbors",
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description="The number of neighbors to retrieve for each image. (If 0, it will retrieve all the neighbors).",
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value=5,
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range=(0, 1000, 1),
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uid=[0],
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advanced=True,
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enabled=lambda node: 'Sequential' in node.method.value,
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enabled=lambda node: "Sequential" in node.method.value,
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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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value='info',
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values=['fatal', 'error', 'warning', 'info', 'debug', 'trace'],
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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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value="info",
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values=["fatal", "error", "warning", "info", "debug", "trace"],
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exclusive=True,
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uid=[],
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)
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@ -140,10 +142,10 @@ If images have known poses, use frustum intersection else use VocabularuTree.
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outputs = [
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desc.File(
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name='output',
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label='Image Pairs',
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description='Filepath to the output file with the list of selected image pairs.',
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value=desc.Node.internalFolder + 'imageMatches.txt',
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name="output",
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label="Image Pairs",
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description="Filepath to the output file with the list of selected image pairs.",
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value=desc.Node.internalFolder + "imageMatches.txt",
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uid=[],
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),
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]
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