mirror of
https://github.com/alicevision/Meshroom.git
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3412a77b4d
The node outputs a response file that is named "response_INTRISICID.csv", as determined in AliceVision. Without it, the output attribute refers to a file that does not exist.
336 lines
14 KiB
Python
336 lines
14 KiB
Python
__version__ = "3.1"
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import json
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import math
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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 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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errorMessage="The set number of brackets is not a multiple of the number of input images.\n"
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"Errors will occur during the computation."
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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, 15, 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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" - Auto: If RAW images are detected, the 'Linear' calibration method will be used. Otherwise, the 'Debevec' calibration method will be used.\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), they will be automatically converted to linear.\n"
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" - Debevec: Standard method for HDR calibration.\n"
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" - Grossberg: Based on a learned database of cameras, allows to reduce the Camera Response Function to a 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=["auto", "linear", "debevec", "grossberg", "laguerre"],
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value="auto",
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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="Color space in which the data are processed.\n"
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"If 'auto' is selected, the working color space will be 'Linear' if RAW images are detected; otherwise, it will be set to 'sRGB'.",
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value="auto",
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values=["auto", "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_<INTRINSIC_ID>.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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# 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_LdrToHdrCalibration {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(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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node.userNbBrackets.validValue = True # Reset the status of "userNbBrackets"
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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 node.userNbBrackets.value != 0:
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# The number of brackets has been manually forced: check whether it is valid or not
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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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# The number of brackets should be a multiple of the number of input images
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if (len(viewpoints) % node.userNbBrackets.value != 0):
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node.userNbBrackets.validValue = False
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else:
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node.userNbBrackets.validValue = True
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node.nbBrackets.value = node.userNbBrackets.value
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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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# Find Fnumber
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fnumber = findMetadata(d, ["FNumber"], "")
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if fnumber == "":
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aperture = findMetadata(d, ["Exif:ApertureValue", "ApertureValue", "Aperture"], "")
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if aperture == "":
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fnumber = -1.0
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else:
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fnumber = pow(2.0, aperture / 2.0)
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# Get shutter speed and ISO
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shutterSpeed = findMetadata(d, ["ExposureTime", "Exif:ShutterSpeedValue", "ShutterSpeedValue", "ShutterSpeed"], -1.0)
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iso = findMetadata(d, ["Exif:PhotographicSensitivity", "PhotographicSensitivity", "Photographic Sensitivity", "ISO"], -1.0)
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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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prevExposure = None
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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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currentExposure = LdrToHdrCalibration.getExposure(exp)
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# Create a new group if the current image's exposure level is smaller than the previous image's, or
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# if a new dataset has been detected (with a change in the path of the images).
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if prevExposure and currentExposure < prevExposure 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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prevExposure = currentExposure
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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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node.nbBrackets.value = bestBracketSize
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@staticmethod
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def getExposure(exp, refIso = 100.0, refFnumber = 1.0):
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fnumber, shutterSpeed, iso = exp
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validShutterSpeed = shutterSpeed > 0.0 and math.isfinite(shutterSpeed)
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validFnumber = fnumber > 0.0 and math.isfinite(fnumber)
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if not validShutterSpeed and not validFnumber:
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return -1.0
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validRefFnumber = refFnumber > 0.0 and math.isfinite(refFnumber)
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if not validShutterSpeed:
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shutterSpeed = 1.0 / 200.0
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if not validFnumber:
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if validRefFnumber:
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fnumber = refFnumber
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else:
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fnumber = 2.0
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lRefFnumber = refFnumber
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if not validRefFnumber:
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lRefFnumber = fnumber
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isoToAperture = 1.0
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if iso > 1e-6 and refIso > 1e-6:
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isoToAperture = math.sqrt(iso / refIso)
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newFnumber = fnumber * isoToAperture
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expIncrease = (lRefFnumber / newFnumber) * (lRefFnumber / newFnumber)
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return shutterSpeed * expIncrease
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