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3146dcface
Use CamelCase for all labels, always end descriptions with periods, and replace the mixed use of single and double quotes with double quotes only.
260 lines
10 KiB
Python
260 lines
10 KiB
Python
__version__ = "4.0"
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import json
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from meshroom.core import desc
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def findMetadata(d, keys, defaultValue):
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v = None
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for key in keys:
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v = d.get(key, None)
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k = key.lower()
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if v is not None:
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return v
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for dk, dv in d.items():
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dkm = dk.lower().replace(" ", "")
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if dkm == key.lower():
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return dv
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dkm = dkm.split(":")[-1]
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dkm = dkm.split("/")[-1]
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if dkm == k:
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return dv
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return defaultValue
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class DividedInputNodeSize(desc.DynamicNodeSize):
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'''
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The LDR2HDR will reduce the amount of views in the SfMData.
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This class converts the number of LDR input views into the number of HDR output views.
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'''
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def __init__(self, param, divParam):
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super(DividedInputNodeSize, self).__init__(param)
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self._divParam = divParam
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def computeSize(self, node):
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s = super(DividedInputNodeSize, self).computeSize(node)
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divParam = node.attribute(self._divParam)
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if divParam.value == 0:
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return s
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return s / divParam.value
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class LdrToHdrSampling(desc.AVCommandLineNode):
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commandLine = 'aliceVision_LdrToHdrSampling {allParams}'
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size = DividedInputNodeSize('input', 'nbBrackets')
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parallelization = desc.Parallelization(blockSize=2)
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commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
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category = 'Panorama HDR'
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documentation = '''
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Sample pixels from Low range images for HDR creation.
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'''
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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="Input SfMData file.",
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value="",
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uid=[0],
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),
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desc.IntParam(
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name="userNbBrackets",
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label="Number Of Brackets",
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description="Number of exposure brackets per HDR image (0 for automatic detection).",
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value=0,
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range=(0, 15, 1),
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uid=[],
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group="user", # not used directly on the command line
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),
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desc.IntParam(
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name="nbBrackets",
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label="Automatic Nb Brackets",
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description="Number of exposure brackets used per HDR image.\n"
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"It is detected automatically from input Viewpoints metadata if 'userNbBrackets'\n"
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"is 0, else it is equal to 'userNbBrackets'.",
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value=0,
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range=(0, 10, 1),
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uid=[0],
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),
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desc.BoolParam(
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name="byPass",
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label="Bypass",
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description="Bypass HDR creation and use the medium bracket as the source for the next steps.",
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value=False,
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uid=[0],
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enabled= lambda node: node.nbBrackets.value != 1,
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),
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desc.ChoiceParam(
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name="calibrationMethod",
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label="Calibration Method",
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description="Method used for camera calibration:\n"
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" - Linear: Disable the calibration and assumes a linear Camera Response Function. If images are encoded in a known colorspace (like sRGB for JPEG), the images will be automatically converted to linear.\n"
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" - Debevec: This is the standard method for HDR calibration.\n"
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" - Grossberg: Based on learned database of cameras, it allows to reduce the CRF to few parameters while keeping all the precision.\n"
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" - Laguerre: Simple but robust method estimating the minimal number of parameters.",
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values=["linear", "debevec", "grossberg", "laguerre"],
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value="debevec",
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exclusive=True,
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uid=[0],
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enabled= lambda node: node.byPass.enabled and not node.byPass.value,
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),
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desc.IntParam(
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name="channelQuantizationPower",
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label="Channel Quantization Power",
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description="Quantization level like 8 bits or 10 bits.",
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value=10,
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range=(8, 14, 1),
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uid=[0],
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advanced=True,
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enabled= lambda node: node.byPass.enabled and not node.byPass.value,
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),
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desc.ChoiceParam(
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name="workingColorSpace",
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label="Working Color Space",
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description="Allows you to choose the color space in which the data are processed.",
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value="sRGB",
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values=["sRGB", "Linear", "ACES2065-1", "ACEScg", "no_conversion"],
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exclusive=True,
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uid=[0],
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enabled= lambda node: node.byPass.enabled and not node.byPass.value,
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),
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desc.IntParam(
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name="blockSize",
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label="Block Size",
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description="Size of the image tile to extract a sample.",
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value=256,
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range=(8, 1024, 1),
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uid=[0],
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advanced=True,
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enabled= lambda node: node.byPass.enabled and not node.byPass.value,
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),
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desc.IntParam(
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name="radius",
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label="Patch Radius",
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description="Radius of the patch used to analyze the sample statistics.",
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value=5,
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range=(0, 10, 1),
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uid=[0],
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advanced=True,
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enabled= lambda node: node.byPass.enabled and not node.byPass.value,
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),
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desc.IntParam(
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name="maxCountSample",
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label="Max Number Of Samples",
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description="Maximum number of samples per image group.",
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value=200,
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range=(10, 1000, 10),
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uid=[0],
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advanced=True,
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enabled= lambda node: node.byPass.enabled and not node.byPass.value,
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),
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desc.BoolParam(
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name="debug",
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label="Export Debug Files",
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description="Export debug files to analyze the sampling strategy.",
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value=False,
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uid=[],
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enabled= lambda node: node.byPass.enabled and not node.byPass.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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exclusive=True,
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uid=[],
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)
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]
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outputs = [
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desc.File(
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name="output",
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label="Folder",
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description="Output path for the samples.",
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value=desc.Node.internalFolder,
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uid=[],
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),
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]
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def processChunk(self, chunk):
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if chunk.node.nbBrackets.value == 1:
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return
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super(LdrToHdrSampling, self).processChunk(chunk)
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@classmethod
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def update(cls, node):
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if not isinstance(node.nodeDesc, cls):
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raise ValueError("Node {} is not an instance of type {}".format(node, cls))
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# TODO: use Node version for this test
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if 'userNbBrackets' not in node.getAttributes().keys():
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# Old version of the node
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return
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if node.userNbBrackets.value != 0:
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node.nbBrackets.value = node.userNbBrackets.value
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return
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# logging.info("[LDRToHDR] Update start: version:" + str(node.packageVersion))
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cameraInitOutput = node.input.getLinkParam(recursive=True)
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if not cameraInitOutput:
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node.nbBrackets.value = 0
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return
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if not cameraInitOutput.node.hasAttribute('viewpoints'):
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if cameraInitOutput.node.hasAttribute('input'):
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cameraInitOutput = cameraInitOutput.node.input.getLinkParam(recursive=True)
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if cameraInitOutput and cameraInitOutput.node and cameraInitOutput.node.hasAttribute('viewpoints'):
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viewpoints = cameraInitOutput.node.viewpoints.value
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else:
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# No connected CameraInit
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node.nbBrackets.value = 0
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return
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# logging.info("[LDRToHDR] Update start: nb viewpoints:" + str(len(viewpoints)))
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inputs = []
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for viewpoint in viewpoints:
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jsonMetadata = viewpoint.metadata.value
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if not jsonMetadata:
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# no metadata, we cannot found the number of brackets
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node.nbBrackets.value = 0
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return
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d = json.loads(jsonMetadata)
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fnumber = findMetadata(d, ["FNumber", "Exif:ApertureValue", "ApertureValue", "Aperture"], "")
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shutterSpeed = findMetadata(d, ["Exif:ShutterSpeedValue", "ShutterSpeedValue", "ShutterSpeed"], "")
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iso = findMetadata(d, ["Exif:ISOSpeedRatings", "ISOSpeedRatings", "ISO"], "")
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if not fnumber and not shutterSpeed:
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# If one image without shutter or fnumber, we cannot found the number of brackets.
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# We assume that there is no multi-bracketing, so nothing to do.
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node.nbBrackets.value = 1
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return
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inputs.append((viewpoint.path.value, (fnumber, shutterSpeed, iso)))
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inputs.sort()
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exposureGroups = []
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exposures = []
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for path, exp in inputs:
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if exposures and exp != exposures[-1] and exp == exposures[0]:
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exposureGroups.append(exposures)
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exposures = [exp]
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else:
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exposures.append(exp)
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exposureGroups.append(exposures)
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exposures = None
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bracketSizes = set()
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if len(exposureGroups) == 1:
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if len(set(exposureGroups[0])) == 1:
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# Single exposure and multiple views
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node.nbBrackets.value = 1
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else:
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# Single view and multiple exposures
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node.nbBrackets.value = len(exposureGroups[0])
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else:
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for expGroup in exposureGroups:
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bracketSizes.add(len(expGroup))
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if len(bracketSizes) == 1:
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node.nbBrackets.value = bracketSizes.pop()
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# logging.info("[LDRToHDR] nb bracket size:" + str(node.nbBrackets.value))
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else:
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node.nbBrackets.value = 0
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# logging.info("[LDRToHDR] Update end")
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