__version__ = "3.0" import json import os from collections import Counter 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], 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, 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 # 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_LdrToHdrCalibration {allParams}" if chunk.node.userNbBrackets.value == chunk.node.nbBrackets.value: self.commandLine += "{bracketsParams}" 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 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 inputs = [] 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) 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, (float(fnumber), float(shutterSpeed), float(iso)))) inputs.sort() exposureGroups = [] exposures = [] prevFnumber = 0.0 prevShutterSpeed = 0.0 prevIso = 0.0 prevPath = None # Stores the dirname of the previous parsed image newGroup = False # True if a new exposure group needs to be created (useful when there are several datasets) for path, exp in inputs: # If the dirname of the previous image and the dirname of the current image do not match, this means that the # dataset that is being parsed has changed. A new group needs to be created but will fail to be detected in the # next "if" statement if the new dataset's exposure levels are different. Setting "newGroup" to True prevents this # from happening. if prevPath is not None and prevPath != os.path.dirname(path): newGroup = True # A new group is created if the current image's exposure level is larger than the previous image's, if there # were any changes in the ISO or aperture value, or if a new dataset has been detected with the path. # Since the input images are ordered, the shutter speed should always be decreasing, so a shutter speed larger # than the previous one indicates the start of a new exposure group. fnumber, shutterSpeed, iso = exp if exposures: prevFnumber, prevShutterSpeed, prevIso = exposures[-1] if exposures and len(exposures) > 1 and (fnumber != prevFnumber or shutterSpeed > prevShutterSpeed or iso != prevIso) or newGroup: exposureGroups.append(exposures) exposures = [exp] else: exposures.append(exp) prevPath = os.path.dirname(path) newGroup = False exposureGroups.append(exposures) exposures = None bracketSizes = Counter() 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[len(expGroup)] += 1 if len(bracketSizes) == 0: node.nbBrackets.value = 0 else: bestTuple = None for tuple in bracketSizes.most_common(): if bestTuple is None or tuple[1] > bestTuple[1]: bestTuple = tuple elif tuple[1] == bestTuple[1]: bestTuple = tuple if tuple[0] > bestTuple[0] else bestTuple bestBracketSize = bestTuple[0] bestCount = bestTuple[1] node.nbBrackets.value = bestBracketSize