Meshroom/meshroom/nodes/aliceVision/LdrToHdrCalibration.py

__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.AVCommandLineNode):
    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="SfMData",
            description="Input 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.\n"
                        "It is detected automatically from input Viewpoints metadata if 'userNbBrackets' is 0,\n"
                        "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],
            enabled= lambda node: node.nbBrackets.value != 1,
        ),
        desc.ChoiceParam(
            name="calibrationMethod",
            label="Calibration Method",
            description="Method used for camera calibration:\n"
                        " - 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"
                        " - 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.",
            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.ChoiceParam(
            name="workingColorSpace",
            label="Working Color Space",
            description="Allows you to choose the color space in which the data are processed.",
            value="sRGB",
            values=["sRGB", "Linear", "ACES2065-1", "ACEScg"],
            exclusive=True,
            uid=[],
            group="user",  # not used directly on the command line
            enabled= lambda node: node.byPass.enabled and not node.byPass.value,
        ),
        desc.IntParam(
            name="maxTotalPoints",
            label="Max Number Of Points",
            description="Maximum number of points used from the sampling.\n"
                        "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="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:
            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")