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__version__ = "3.0"
import json
from meshroom.core import desc
def findMetadata(d, keys, defaultValue):
v = None
for key in keys:
v = d.get(key, None)
k = key.lower()
if v is not None:
return v
for dk, dv in d.items():
dkm = dk.lower().replace(" ", "")
if dkm == key.lower():
return dv
dkm = dkm.split(":")[-1]
dkm = dkm.split("/")[-1]
if dkm == k:
return dv
return defaultValue
class LdrToHdrCalibration(desc.CommandLineNode):
commandLine = 'aliceVision_LdrToHdrCalibration {allParams}'
size = desc.DynamicNodeSize('input')
cpu = desc.Level.INTENSIVE
ram = desc.Level.NORMAL
category = 'Panorama HDR'
documentation = '''
Calibrate LDR to HDR response curve from samples
'''
inputs = [
desc.File(
name='input',
label='Input',
description='SfMData file.',
value='',
uid=[0],
),
desc.File(
name='samples',
label='Samples folder',
description='Samples folder',
value=desc.Node.internalFolder,
uid=[0],
),
desc.IntParam(
name='userNbBrackets',
label='Number of Brackets',
description='Number of exposure brackets per HDR image (0 for automatic detection).',
value=0,
range=(0, 15, 1),
uid=[],
group='user', # not used directly on the command line
),
desc.IntParam(
name='nbBrackets',
label='Automatic Nb Brackets',
description='Number of exposure brackets used per HDR image. It is detected automatically from input Viewpoints metadata if "userNbBrackets" is 0, else it is equal to "userNbBrackets".',
value=0,
range=(0, 10, 1),
uid=[0],
),
desc.BoolParam(
name='byPass',
label='Bypass',
description="Bypass HDR creation and use the medium bracket as the source for the next steps",
value=False,
uid=[0],
group='internal',
enabled= lambda node: node.nbBrackets.value != 1,
),
desc.ChoiceParam(
name='calibrationMethod',
label='Calibration Method',
description="Method used for camera calibration \n"
" * 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"
" * Debevec: This is the standard method for HDR calibration. \n"
" * Grossberg: Based on learned database of cameras, it allows to reduce the CRF to few parameters while keeping all the precision. \n"
" * Laguerre: Simple but robust method estimating the minimal number of parameters. \n"
" * Robertson: First method for HDR calibration in the literature. \n",
values=['linear', 'debevec', 'grossberg', 'laguerre'],
value='debevec',
exclusive=True,
uid=[0],
enabled= lambda node: node.byPass.enabled and not node.byPass.value,
),
desc.ChoiceParam(
name='calibrationWeight',
label='Calibration Weight',
description="Weight function used to calibrate camera response \n"
" * default (automatically selected according to the calibrationMethod) \n"
" * gaussian \n"
" * triangle \n"
" * plateau",
value='default',
values=['default', 'gaussian', 'triangle', 'plateau'],
exclusive=True,
uid=[0],
enabled= lambda node: node.byPass.enabled and not node.byPass.value,
),
desc.IntParam(
name='channelQuantizationPower',
label='Channel Quantization Power',
description='Quantization level like 8 bits or 10 bits.',
value=10,
range=(8, 14, 1),
uid=[0],
advanced=True,
enabled= lambda node: node.byPass.enabled and not node.byPass.value,
),
desc.IntParam(
name='maxTotalPoints',
label='Max Number of Points',
description='Max number of points used from the sampling. This ensures that the number of pixels values extracted by the sampling\n'
'can be managed by the calibration step (in term of computation time and memory usage).',
value=1000000,
range=(8, 10000000, 1000),
uid=[0],
advanced=True,
enabled= lambda node: node.byPass.enabled and not node.byPass.value,
),
desc.ChoiceParam(
name='verboseLevel',
label='Verbose Level',
description='verbosity level (fatal, error, warning, info, debug, trace).',
value='info',
values=['fatal', 'error', 'warning', 'info', 'debug', 'trace'],
exclusive=True,
uid=[],
)
]
outputs = [
desc.File(
name='response',
label='Output response File',
description='Path to the output response file',
value=desc.Node.internalFolder + 'response.csv',
uid=[],
)
]
def processChunk(self, chunk):
if chunk.node.nbBrackets.value == 1 or chunk.node.byPass.value:
return
super(LdrToHdrCalibration, self).processChunk(chunk)
@classmethod
def update(cls, node):
if not isinstance(node.nodeDesc, cls):
raise ValueError("Node {} is not an instance of type {}".format(node, cls))
# TODO: use Node version for this test
if 'userNbBrackets' not in node.getAttributes().keys():
# Old version of the node
return
if node.userNbBrackets.value != 0:
node.nbBrackets.value = node.userNbBrackets.value
return
# logging.info("[LDRToHDR] Update start: version:" + str(node.packageVersion))
cameraInitOutput = node.input.getLinkParam(recursive=True)
if not cameraInitOutput:
node.nbBrackets.value = 0
return
if not cameraInitOutput.node.hasAttribute('viewpoints'):
if cameraInitOutput.node.hasAttribute('input'):
cameraInitOutput = cameraInitOutput.node.input.getLinkParam(recursive=True)
if cameraInitOutput and cameraInitOutput.node and cameraInitOutput.node.hasAttribute('viewpoints'):
viewpoints = cameraInitOutput.node.viewpoints.value
else:
# No connected CameraInit
node.nbBrackets.value = 0
return
# logging.info("[LDRToHDR] Update start: nb viewpoints:" + str(len(viewpoints)))
inputs = []
for viewpoint in viewpoints:
jsonMetadata = viewpoint.metadata.value
if not jsonMetadata:
# no metadata, we cannot found the number of brackets
node.nbBrackets.value = 0
return
d = json.loads(jsonMetadata)
fnumber = findMetadata(d, ["FNumber", "Exif:ApertureValue", "ApertureValue", "Aperture"], "")
shutterSpeed = findMetadata(d, ["Exif:ShutterSpeedValue", "ShutterSpeedValue", "ShutterSpeed"], "")
iso = findMetadata(d, ["Exif:ISOSpeedRatings", "ISOSpeedRatings", "ISO"], "")
if not fnumber and not shutterSpeed:
# If one image without shutter or fnumber, we cannot found the number of brackets.
# We assume that there is no multi-bracketing, so nothing to do.
node.nbBrackets.value = 1
return
inputs.append((viewpoint.path.value, (fnumber, shutterSpeed, iso)))
inputs.sort()
exposureGroups = []
exposures = []
for path, exp in inputs:
if exposures and exp != exposures[-1] and exp == exposures[0]:
exposureGroups.append(exposures)
exposures = [exp]
else:
exposures.append(exp)
exposureGroups.append(exposures)
exposures = None
bracketSizes = set()
if len(exposureGroups) == 1:
if len(set(exposureGroups[0])) == 1:
# Single exposure and multiple views
node.nbBrackets.value = 1
else:
# Single view and multiple exposures
node.nbBrackets.value = len(exposureGroups[0])
else:
for expGroup in exposureGroups:
bracketSizes.add(len(expGroup))
if len(bracketSizes) == 1:
node.nbBrackets.value = bracketSizes.pop()
# logging.info("[LDRToHDR] nb bracket size:" + str(node.nbBrackets.value))
else:
node.nbBrackets.value = 0
# logging.info("[LDRToHDR] Update end")
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