mirror of
https://github.com/alicevision/Meshroom.git
synced 2025-07-24 03:57:28 +02:00
[multiview] New pipeline Photogrammetry and Camera Tracking
This commit is contained in:
parent
48ed0a82fb
commit
1999b7c5b2
4 changed files with 71 additions and 22 deletions
|
@ -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',
|
||||
|
|
Loading…
Add table
Add a link
Reference in a new issue