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Merge pull request #2121 from alicevision/dev/bracketsSizeEquality
[nodes] HDR Fusion: Select group with largest bracket number in case of equality
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commit
24b60b3d42
3 changed files with 23 additions and 3 deletions
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@ -265,6 +265,13 @@ Calibrate LDR to HDR response curve from samples.
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if len(bracketSizes) == 0:
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if len(bracketSizes) == 0:
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node.nbBrackets.value = 0
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node.nbBrackets.value = 0
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else:
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else:
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bestTuple = bracketSizes.most_common(1)[0]
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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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bestBracketSize = bestTuple[0]
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bestCount = bestTuple[1]
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node.nbBrackets.value = bestBracketSize
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node.nbBrackets.value = bestBracketSize
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@ -351,8 +351,15 @@ Merge LDR images into HDR images.
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if len(bracketSizes) == 0:
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if len(bracketSizes) == 0:
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node.nbBrackets.value = 0
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node.nbBrackets.value = 0
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else:
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else:
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bestTuple = bracketSizes.most_common(1)[0]
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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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bestBracketSize = bestTuple[0]
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bestCount = bestTuple[1]
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node.nbBrackets.value = bestBracketSize
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node.nbBrackets.value = bestBracketSize
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def processChunk(self, chunk):
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def processChunk(self, chunk):
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@ -291,7 +291,13 @@ Sample pixels from Low range images for HDR creation.
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if len(bracketSizes) == 0:
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if len(bracketSizes) == 0:
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node.nbBrackets.value = 0
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node.nbBrackets.value = 0
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else:
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else:
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bestTuple = bracketSizes.most_common(1)[0]
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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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bestBracketSize = bestTuple[0]
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bestCount = bestTuple[1]
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bestCount = bestTuple[1]
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node.outliersNb = len(inputs) - (bestBracketSize * bestCount) # Compute number of outliers
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node.outliersNb = len(inputs) - (bestBracketSize * bestCount) # Compute number of outliers
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