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[multiview] New pipeline Photogrammetry and Camera Tracking

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This commit is contained in:
Fabien Castan 2021-05-27 17:02:39 +02:00
parent 48ed0a82fb
commit 1999b7c5b2
4 changed files with 71 additions and 22 deletions
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23
meshroom/nodes/aliceVision/ImageMatchingMultiSfM.py
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@ -48,9 +48,16 @@ Thanks to this node, the FeatureMatching node will only compute the matches betw
desc.ChoiceParam(
name='method',
label='Method',
description='Method used to select the image pairs to match.',
value='VocabularyTree',
values=['VocabularyTree', 'Sequential', 'SequentialAndVocabularyTree','Exhaustive','Frustum'],
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'
' * 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', 'SequentialAndVocabularyTree', 'Exhaustive', 'Frustum'],
exclusive=True,
uid=[0],
),
@ -60,6 +67,7 @@ Thanks to this node, the FeatureMatching node will only compute the matches betw
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',
@ -68,6 +76,7 @@ Thanks to this node, the FeatureMatching node will only compute the matches betw
value='',
uid=[0],
advanced=True,
enabled=lambda node: 'VocabularyTree' in node.method.value,
),
desc.ChoiceParam(
name='matchingMode',
@ -86,6 +95,7 @@ Thanks to this node, the FeatureMatching node will only compute the matches betw
range=(0, 500, 1),
uid=[0],
advanced=True,
enabled=lambda node: 'VocabularyTree' in node.method.value,
),
desc.IntParam(
name='maxDescriptors',
@ -95,24 +105,27 @@ Thanks to this node, the FeatureMatching node will only compute the matches betw
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,
value=40,
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,
value=5,
range=(0, 1000, 1),
uid=[0],
advanced=True,
enabled=lambda node: 'Sequential' in node.method.value,
),
desc.ChoiceParam(
name='verboseLevel',