3pp-mirror/Meshroom
1
0
Fork 0
mirror of https://github.com/alicevision/Meshroom.git synced 2025-05-29 08:56:33 +02:00
Code Issues Projects Releases Packages Wiki Activity

[nodes] I-L: Harmonize and improve labels and descriptions

Browse source 
Use CamelCase for all labels, always end descriptions with periods, and
replace the mixed use of single and double quotes with double quotes
only.
This commit is contained in:
Candice Bentéjac 2023-06-16 10:31:18 +02:00
parent 5c2865968d
commit 3146dcface
11 changed files with 978 additions and 889 deletions
Download patch file Download diff file Expand all files Collapse all files

108
meshroom/nodes/aliceVision/ImageMatching.py
View file

@ -35,17 +35,17 @@ If images have known poses, use frustum intersection else use VocabularuTree.
inputs = [
desc.File(
name='input',
label='SfmData',
description='SfMData file .',
value='',
name="input",
label="SfMData",
description="Input SfMData file.",
value="",
uid=[0],
),
desc.ListAttribute(
elementDesc=desc.File(
name="featuresFolder",
label="Features Folder",
description="",
description="Folder containing some extracted features and descriptors.",
value="",
uid=[0],
),
@ -54,85 +54,87 @@ If images have known poses, use frustum intersection else use VocabularuTree.
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='SequentialAndVocabularyTree',
values=['VocabularyTree', 'Sequential', 'SequentialAndVocabularyTree', 'Exhaustive', 'Frustum', 'FrustumOrVocabularyTree'],
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="SequentialAndVocabularyTree",
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='${ALICEVISION_VOCTREE}',
name="tree",
label="Voc Tree: Tree",
description="Input name for the vocabulary tree file.",
value="${ALICEVISION_VOCTREE}",
uid=[],
enabled=lambda node: 'VocabularyTree' in node.method.value,
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='',
name="weights",
label="Voc Tree: Weights",
description="Input name for the weight file.\n"
"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,
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.',
name="minNbImages",
label="Voc Tree: Minimum Number Of Images",
description="Minimum number of images to use the vocabulary tree.\n"
"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,
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.',
name="maxDescriptors",
label="Voc Tree: Max Descriptors",
description="Limit the number of descriptors you load per image. 0 means no limit.",
value=500,
range=(0, 100000, 1),
uid=[0],
advanced=True,
enabled=lambda node: 'VocabularyTree' in node.method.value,
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).',
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=40,
range=(0, 1000, 1),
uid=[0],
advanced=True,
enabled=lambda node: 'VocabularyTree' in node.method.value,
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).',
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=5,
range=(0, 1000, 1),
uid=[0],
advanced=True,
enabled=lambda node: 'Sequential' in node.method.value,
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'],
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=[],
)
@ -140,10 +142,10 @@ If images have known poses, use frustum intersection else use VocabularuTree.
outputs = [
desc.File(
name='output',
label='Image Pairs',
description='Filepath to the output file with the list of selected image pairs.',
value=desc.Node.internalFolder + 'imageMatches.txt',
name="output",
label="Image Pairs",
description="Filepath to the output file with the list of selected image pairs.",
value=desc.Node.internalFolder + "imageMatches.txt",
uid=[],
),
]