2132403678d1b7985edecbad44d4d8575f8848af,ilastik/applets/thresholdTwoLevels/opThresholdTwoLevels.py,OpAnisotropicGaussianSmoothing,_getInputComputeRois,#OpAnisotropicGaussianSmoothing#Any#,82

Before Change

                       tuple(map(int, computeRoi[1])) )
        
        inputRoi = (list(inputSpatialRoi[0]), list(inputSpatialRoi[1]))
        inputRoi[0].insert( axiskeys.index("c"), 0 )
        inputRoi[1].insert( axiskeys.index("c"), 1 )

        return inputRoi, computeRoi

After Change

            zIndex = axiskeys.index("z")
            
        indices = [tIndex, cIndex, zIndex]
        indices = sorted(indices, reverse=True)
        for ind in indices:
            if ind:
                spatialRoi[0].pop(ind)
                spatialRoi[1].pop(ind)
        
        inputSpatialRoi = extendSlice(spatialRoi[0], spatialRoi[1], inputSpatialShape.values(), sigma, window=2.0)
        
        // Determine the roi within the input data we"re going to request
        inputRoiOffset = spatialRoi[0] - inputSpatialRoi[0]
        computeRoi = (inputRoiOffset, inputRoiOffset + spatialRoi[1] - spatialRoi[0])
        
        // For some reason, vigra.filters.gaussianSmoothing will raise an exception if this parameter doesn"t have the correct integer type.
        // (for example, if we give it as a numpy.ndarray with dtype=int64, we get an error)
        computeRoi = ( tuple(map(int, computeRoi[0])),
                       tuple(map(int, computeRoi[1])) )
        
        inputRoi = (list(inputSpatialRoi[0]), list(inputSpatialRoi[1]))
        for ind in reversed(indices):
            if ind:
                inputRoi[0].insert( ind, 0 )
                inputRoi[1].insert( ind, 1 )

        return inputRoi, computeRoi
        
    def propagateDirty(self, slot, subindex, roi):
        if slot == self.Input:

Instances