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00e29b06a3
If there are several groups with different bracket numbers but identical counts (e.g. 3 groups with 7 brackets, and 3 groups with 3 brackets), select the groups with the largest bracket number (e.g. groups with 7 brackets instead of 3).
304 lines
12 KiB
Python
304 lines
12 KiB
Python
__version__ = "4.0"
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import json
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import os
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from collections import Counter
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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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# s is the total number of inputs and may include outliers, that will not be used
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# during computations and should thus be excluded from the size computation
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return (s - node.outliersNb) / 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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outliersNb = 0 # Number of detected outliers among the input images
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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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group="bracketsParams"
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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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# Trick to avoid sending --nbBrackets to the command line when the bracket detection is automatic.
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# Otherwise, the AliceVision executable has no way of determining whether the bracket detection was automatic
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# or if it was hard-set by the user.
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self.commandLine = "aliceVision_LdrToHdrSampling {allParams}"
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if chunk.node.userNbBrackets.value == chunk.node.nbBrackets.value:
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self.commandLine += "{bracketsParams}"
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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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node.outliersNb = 0 # Reset the number of detected outliers
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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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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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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 find 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, (float(fnumber), float(shutterSpeed), float(iso))))
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inputs.sort()
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exposureGroups = []
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exposures = []
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prevFnumber = 0.0
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prevShutterSpeed = 0.0
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prevIso = 0.0
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prevPath = None # Stores the dirname of the previous parsed image
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newGroup = False # True if a new exposure group needs to be created (useful when there are several datasets)
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for path, exp in inputs:
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# If the dirname of the previous image and the dirname of the current image do not match, this means that the
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# dataset that is being parsed has changed. A new group needs to be created but will fail to be detected in the
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# next "if" statement if the new dataset's exposure levels are different. Setting "newGroup" to True prevents this
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# from happening.
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if prevPath is not None and prevPath != os.path.dirname(path):
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newGroup = True
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# A new group is created if the current image's exposure level is larger than the previous image's, if there
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# were any changes in the ISO or aperture value, or if a new dataset has been detected with the path.
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# Since the input images are ordered, the shutter speed should always be decreasing, so a shutter speed larger
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# than the previous one indicates the start of a new exposure group.
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fnumber, shutterSpeed, iso = exp
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if exposures:
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prevFnumber, prevShutterSpeed, prevIso = exposures[-1]
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if exposures and len(exposures) > 1 and (fnumber != prevFnumber or shutterSpeed > prevShutterSpeed or iso != prevIso) or newGroup:
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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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prevPath = os.path.dirname(path)
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newGroup = False
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exposureGroups.append(exposures)
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exposures = None
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bracketSizes = Counter()
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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[len(expGroup)] += 1
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if len(bracketSizes) == 0:
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node.nbBrackets.value = 0
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else:
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bestTuple = None
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for tuple in bracketSizes.most_common():
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if bestTuple is None or tuple[1] > bestTuple[1]:
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bestTuple = tuple
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elif tuple[1] == bestTuple[1]:
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bestTuple = tuple if tuple[0] > bestTuple[0] else bestTuple
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bestBracketSize = bestTuple[0]
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bestCount = bestTuple[1]
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node.outliersNb = len(inputs) - (bestBracketSize * bestCount) # Compute number of outliers
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node.nbBrackets.value = bestBracketSize
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