mirror of
https://github.com/alicevision/Meshroom.git
synced 2025-07-27 21:47:28 +02:00
286 lines
12 KiB
Python
286 lines
12 KiB
Python
__version__ = "4.0"
|
|
|
|
import json
|
|
import math
|
|
import os
|
|
from collections import Counter
|
|
|
|
from pyalicevision import sfmData as avsfmdata
|
|
from pyalicevision import hdr as avhdr
|
|
|
|
from meshroom.core import desc
|
|
from meshroom.core.utils import COLORSPACES, VERBOSE_LEVEL
|
|
|
|
|
|
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 DividedInputNodeSize(desc.DynamicNodeSize):
|
|
'''
|
|
The LDR2HDR will reduce the amount of views in the SfMData.
|
|
This class converts the number of LDR input views into the number of HDR output views.
|
|
'''
|
|
def __init__(self, param, divParam):
|
|
super(DividedInputNodeSize, self).__init__(param)
|
|
self._divParam = divParam
|
|
|
|
def computeSize(self, node):
|
|
s = super(DividedInputNodeSize, self).computeSize(node)
|
|
divParam = node.attribute(self._divParam)
|
|
if divParam.value == 0:
|
|
return s
|
|
# s is the total number of inputs and may include outliers, that will not be used
|
|
# during computations and should thus be excluded from the size computation
|
|
return (s - node.outliersNb) / divParam.value
|
|
|
|
|
|
class LdrToHdrSampling(desc.AVCommandLineNode):
|
|
commandLine = 'aliceVision_LdrToHdrSampling {allParams}'
|
|
size = DividedInputNodeSize('input', 'nbBrackets')
|
|
parallelization = desc.Parallelization(blockSize=2)
|
|
commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
|
|
|
|
category = 'Panorama HDR'
|
|
documentation = '''
|
|
Sample pixels from Low range images for HDR creation.
|
|
'''
|
|
|
|
outliersNb = 0 # Number of detected outliers among the input images
|
|
|
|
inputs = [
|
|
desc.File(
|
|
name="input",
|
|
label="SfMData",
|
|
description="Input SfMData file.",
|
|
value="",
|
|
),
|
|
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),
|
|
invalidate=False,
|
|
group="user", # not used directly on the command line
|
|
errorMessage="The set number of brackets is not a multiple of the number of input images.\n"
|
|
"Errors will occur during the computation.",
|
|
exposed=True,
|
|
),
|
|
desc.IntParam(
|
|
name="nbBrackets",
|
|
label="Automatic Nb Brackets",
|
|
description="Number of exposure brackets used per HDR image.\n"
|
|
"It is detected automatically from input Viewpoints metadata if 'userNbBrackets'\n"
|
|
"is 0, else it is equal to 'userNbBrackets'.",
|
|
value=0,
|
|
range=(0, 15, 1),
|
|
group="bracketsParams",
|
|
),
|
|
desc.BoolParam(
|
|
name="byPass",
|
|
label="Bypass",
|
|
description="Bypass HDR creation and use the medium bracket as the source for the next steps.",
|
|
value=False,
|
|
enabled=lambda node: node.nbBrackets.value != 1,
|
|
exposed=True,
|
|
),
|
|
desc.ChoiceParam(
|
|
name="calibrationMethod",
|
|
label="Calibration Method",
|
|
description="Method used for camera calibration:\n"
|
|
" - Auto: If RAW images are detected, the 'Linear' calibration method will be used. Otherwise, the 'Debevec' calibration method will be used.\n"
|
|
" - 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"
|
|
" - Debevec: Standard method for HDR calibration.\n"
|
|
" - 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"
|
|
" - Laguerre: Simple but robust method estimating the minimal number of parameters.",
|
|
values=["auto", "linear", "debevec", "grossberg", "laguerre"],
|
|
value="auto",
|
|
enabled=lambda node: node.byPass.enabled and not node.byPass.value,
|
|
exposed=True,
|
|
),
|
|
desc.IntParam(
|
|
name="channelQuantizationPower",
|
|
label="Channel Quantization Power",
|
|
description="Quantization level like 8 bits or 10 bits.",
|
|
value=10,
|
|
range=(8, 14, 1),
|
|
advanced=True,
|
|
enabled=lambda node: node.byPass.enabled and not node.byPass.value,
|
|
exposed=True,
|
|
),
|
|
desc.ChoiceParam(
|
|
name="workingColorSpace",
|
|
label="Working Color Space",
|
|
description="Color space in which the data are processed.\n"
|
|
"If 'auto' is selected, the working color space will be 'Linear' if RAW images are detected; otherwise, it will be set to 'sRGB'.",
|
|
values=COLORSPACES,
|
|
value="AUTO",
|
|
enabled=lambda node: node.byPass.enabled and not node.byPass.value,
|
|
exposed=True,
|
|
),
|
|
desc.IntParam(
|
|
name="blockSize",
|
|
label="Block Size",
|
|
description="Size of the image tile to extract a sample.",
|
|
value=256,
|
|
range=(8, 1024, 1),
|
|
advanced=True,
|
|
enabled=lambda node: node.byPass.enabled and not node.byPass.value,
|
|
),
|
|
desc.IntParam(
|
|
name="radius",
|
|
label="Patch Radius",
|
|
description="Radius of the patch used to analyze the sample statistics.",
|
|
value=5,
|
|
range=(0, 10, 1),
|
|
advanced=True,
|
|
enabled=lambda node: node.byPass.enabled and not node.byPass.value,
|
|
),
|
|
desc.IntParam(
|
|
name="maxCountSample",
|
|
label="Max Number Of Samples",
|
|
description="Maximum number of samples per image group.",
|
|
value=200,
|
|
range=(10, 1000, 10),
|
|
advanced=True,
|
|
enabled=lambda node: node.byPass.enabled and not node.byPass.value,
|
|
),
|
|
desc.BoolParam(
|
|
name="debug",
|
|
label="Export Debug Files",
|
|
description="Export debug files to analyze the sampling strategy.",
|
|
value=False,
|
|
invalidate=False,
|
|
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).",
|
|
values=VERBOSE_LEVEL,
|
|
value="info",
|
|
),
|
|
]
|
|
|
|
outputs = [
|
|
desc.File(
|
|
name="output",
|
|
label="Folder",
|
|
description="Output path for the samples.",
|
|
value=desc.Node.internalFolder,
|
|
),
|
|
]
|
|
|
|
def processChunk(self, chunk):
|
|
if chunk.node.nbBrackets.value == 1:
|
|
return
|
|
# Trick to avoid sending --nbBrackets to the command line when the bracket detection is automatic.
|
|
# Otherwise, the AliceVision executable has no way of determining whether the bracket detection was automatic
|
|
# or if it was hard-set by the user.
|
|
self.commandLine = "aliceVision_LdrToHdrSampling {allParams}"
|
|
if chunk.node.userNbBrackets.value == chunk.node.nbBrackets.value:
|
|
self.commandLine += "{bracketsParams}"
|
|
super(LdrToHdrSampling, 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
|
|
node.outliersNb = 0 # Reset the number of detected outliers
|
|
node.userNbBrackets.validValue = True # Reset the status of "userNbBrackets"
|
|
|
|
cameraInitOutput = node.input.getLinkParam(recursive=True)
|
|
if not cameraInitOutput:
|
|
node.nbBrackets.value = 0
|
|
return
|
|
if node.userNbBrackets.value != 0:
|
|
# The number of brackets has been manually forced: check whether it is valid or not
|
|
if cameraInitOutput and cameraInitOutput.node and cameraInitOutput.node.hasAttribute("viewpoints"):
|
|
viewpoints = cameraInitOutput.node.viewpoints.value
|
|
# The number of brackets should be a multiple of the number of input images
|
|
if (len(viewpoints) % node.userNbBrackets.value != 0):
|
|
node.userNbBrackets.validValue = False
|
|
else:
|
|
node.userNbBrackets.validValue = True
|
|
node.nbBrackets.value = node.userNbBrackets.value
|
|
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
|
|
|
|
inputs = avhdr.vectorli()
|
|
for viewpoint in viewpoints:
|
|
jsonMetadata = viewpoint.metadata.value
|
|
if not jsonMetadata:
|
|
# no metadata, we cannot find the number of brackets
|
|
node.nbBrackets.value = 0
|
|
return
|
|
d = json.loads(jsonMetadata)
|
|
|
|
# Find Fnumber
|
|
fnumber = findMetadata(d, ["FNumber"], "")
|
|
if fnumber == "":
|
|
aperture = findMetadata(d, ["Exif:ApertureValue", "ApertureValue", "Aperture"], "")
|
|
if aperture == "":
|
|
fnumber = -1.0
|
|
else:
|
|
fnumber = pow(2.0, aperture / 2.0)
|
|
|
|
# Get shutter speed and ISO
|
|
shutterSpeed = findMetadata(d, ["ExposureTime", "Exif:ShutterSpeedValue", "ShutterSpeedValue", "ShutterSpeed"], -1.0)
|
|
iso = findMetadata(d, ["Exif:PhotographicSensitivity", "PhotographicSensitivity", "Photographic Sensitivity", "ISO"], -1.0)
|
|
|
|
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
|
|
|
|
exposure = LdrToHdrSampling.getExposure((float(fnumber), float(shutterSpeed), float(iso)))
|
|
|
|
obj = avhdr.LuminanceInfo(viewpoint.viewId.value,viewpoint.path.value, exposure)
|
|
inputs.append(obj)
|
|
|
|
obj = avhdr.estimateGroups(inputs)
|
|
|
|
if len(obj) == 0:
|
|
node.nbBrackets.value = 0
|
|
return
|
|
|
|
bracketSize = len(obj[0])
|
|
bracketCount = len(obj)
|
|
|
|
node.nbBrackets.value = bracketSize
|
|
node.outliersNb = len(inputs) - (bracketSize * bracketCount)
|
|
|
|
@staticmethod
|
|
def getExposure(exp, refIso = 100.0, refFnumber = 1.0):
|
|
fnumber, shutterSpeed, iso = exp
|
|
obj = avsfmdata.ExposureSetting(shutterSpeed, fnumber, iso)
|
|
return obj.getExposure()
|