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https://github.com/alicevision/Meshroom.git
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004b2a6f85
The detection of the number of brackets used to only work in a case where there was a single dataset / a single camera intrinsics. If two datasets with the same number of brackets were provided, the detection was failing because we expected the exposure levels to be uniform across all the images. If more than one dataset is provided, there is no guarantee that the exposure groups will be identical although the number of brackets is the same. The inputs are however sorted, and the shutter speeds are expected to be decreasing, meaning that a shutter speed N superior to a shutter speed N-1 indicates a new group. In the same manner, ISO or aperture values that change from one input to the next one indicate a new group. For the comparison between exposure levels to be valid, the aperture, shutter speed and ISO values need to be stored in tuples as floats instead of strings.
246 lines
9.8 KiB
Python
246 lines
9.8 KiB
Python
__version__ = "3.0"
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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 LdrToHdrCalibration(desc.AVCommandLineNode):
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commandLine = 'aliceVision_LdrToHdrCalibration {allParams}'
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size = desc.DynamicNodeSize('input')
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cpu = desc.Level.INTENSIVE
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ram = desc.Level.NORMAL
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category = 'Panorama HDR'
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documentation = '''
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Calibrate LDR to HDR response curve from samples.
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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.File(
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name="samples",
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label="Samples Folder",
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description="Samples folder.",
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value=desc.Node.internalFolder,
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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' is 0,\n"
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"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: Disables 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.ChoiceParam(
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name="calibrationWeight",
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label="Calibration Weight",
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description="Weight function used to calibrate camera response:\n"
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" - default (automatically selected according to the calibrationMethod)\n"
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" - gaussian\n"
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" - triangle\n"
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" - plateau",
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value="default",
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values=["default", "gaussian", "triangle", "plateau"],
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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"],
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exclusive=True,
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uid=[],
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group="user", # not used directly on the command line
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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="maxTotalPoints",
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label="Max Number Of Points",
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description="Maximum number of points used from the sampling.\n"
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"This ensures that the number of pixels values extracted by the sampling\n"
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"can be managed by the calibration step (in term of computation time and memory usage).",
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value=1000000,
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range=(8, 10000000, 1000),
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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="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="response",
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label="Response File",
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description="Path to the output response file.",
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value=desc.Node.internalFolder + "response.csv",
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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(LdrToHdrCalibration, 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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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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for path, exp in inputs:
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# A new group is created if the current image's exposure level is larger than the previous image's, or if there
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# were any changes in the ISO or aperture value.
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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):
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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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else:
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node.nbBrackets.value = 0
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