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GROUP=PARSER.add_argument_group('Passthrough', description="Option to be passed to command lines (remove - in front of option names)\r\ne.g. --1 p ULTRA to use the ULTRA preset in openMVG_main_ComputeFeatures")
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GROUP=PARSER.add_argument_group('Passthrough', description="Option to be passed to command lines (remove - in front of option names)\r\ne.g. --1 p ULTRA to use the ULTRA preset in openMVG_main_ComputeFeatures\r\nFor example, running the script as follows,\r\nMvgMvsPipeline.py input_dir output_dir --1 p HIGH n 8 --3 n ANNL2\r\nwhere --1 refer to openMVG_main_ComputeFeatures, p refers to\r\ndescriberPreset option which HIGH was chosen, and n refers to\r\nnumThreads which 8 was used. --3 refer to second step (openMVG_main_ComputeMatches),\r\nn refers to nearest_matching_method option which ANNL2 was chosen")
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forninrange(STEPS.length()):
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GROUP.add_argument('--'+str(n), nargs='+')
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@@ -297,7 +316,7 @@ def mkdir_ine(dirname):
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os.mkdir(dirname)
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-
# Absolute path for input and ouput dirs
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# Absolute path for input and output dirs
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CONF.input_dir=os.path.abspath(CONF.input_dir)
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CONF.output_dir=os.path.abspath(CONF.output_dir)
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@@ -324,7 +343,7 @@ def mkdir_ine(dirname):
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try:
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CONF.steps=PRESET[CONF.preset]
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exceptKeyError:
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sys.exit("Unkown preset %s, choose %s"% (CONF.preset, ' or '.join([sforsinPRESET])))
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sys.exit("Unknown preset %s, choose %s"% (CONF.preset, ' or '.join([sforsinPRESET])))
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elifnotCONF.steps:
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CONF.steps=PRESET[PRESET_DEFAULT]
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@@ -336,9 +355,14 @@ def mkdir_ine(dirname):
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if4inCONF.steps: # GeometricFilter
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if6inCONF.steps: # GlobalReconstruction
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# Set the geometric_model of ComputeMatches to Essential
[OpenMVS (Multi-View Stereo)](http://cdcseacave.github.io/openMVS) is a library for computer-vision scientists and especially targeted to the Multi-View Stereo reconstruction community. While there are mature and complete open-source projects targeting Structure-from-Motion pipelines (like [OpenMVG](https://github.com/openMVG/openMVG)) which recover camera poses and a sparse 3D point-cloud from an input set of images, there are none addressing the last part of the photogrammetry chain-flow. *OpenMVS* aims at filling that gap by providing a complete set of algorithms to recover the full surface of the scene to be reconstructed. The input is a set of camera poses plus the sparse point-cloud and the output is a textured mesh. The main topics covered by this project are:
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