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Error when starting feature selection in object classification for 3D data #1432

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alexanderbrown opened this Issue Mar 20, 2017 · 4 comments

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@alexanderbrown

alexanderbrown commented Mar 20, 2017

Hi,

I'm running Ilastik 1.2.0. When I try to select features in object classification I get the following error message

Python argument types in
    vigra.analysis.extract2DConvexHullFeatures(VigraArray)
did not match C++ signature:
    extract2DConvexHullFeatures(vigra::NumpyArray<2u, vigra::Singleband<unsigned int>, vigra::StridedArrayTag> labels, boost::python::api::object ignoreLabel=None, bool list_features_only=False)
 (Advanced information about this error may be found in the log file: /home/abrown/ilastik_log.txt)

Is this related to an undocumented requirement or is it a bug?
I can send the log file if that's useful.
Running Ubuntu 16.04

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msschwartz21 Mar 26, 2017

I am also experiencing this problem on my computer running Windows 10.

I am also experiencing this problem on my computer running Windows 10.

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akreshuk Mar 27, 2017

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We are planning a new release later this week, this problem should be gone.

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akreshuk commented Mar 27, 2017

We are planning a new release later this week, this problem should be gone.

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imagejan Apr 12, 2017

Is there a new release available somewhere already? We just stumbled upon this very same issue as well 🙂

Is there a new release available somewhere already? We just stumbled upon this very same issue as well 🙂

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chaubold Apr 12, 2017

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We are currently testing our release candidate, it will be out very soon!

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chaubold commented Apr 12, 2017

We are currently testing our release candidate, it will be out very soon!

chaubold added a commit that referenced this issue Apr 12, 2017

TrackingFeatureExtraction: do no overwrite inputslot value depending …
…on data shape. Adresses #1432 in tracking workflows.

Overwriting the list of pre-selected features for tracking depending on the raw shape is too restrictive.
If the user first loads a 3D dataset but then, by adjusting the axestags, changes that to 2D+t, the 2D features should become available again.
If the feature slot value is overwritten but the original value is lost, this cannot be recovered. Hence here the initial value is
stored as member variable of the operator, and the input slot is overwritten with the correct value.

@chaubold chaubold closed this in 24f25f0 Apr 12, 2017

@chaubold chaubold changed the title from Error when starting feature selection in object classification to Error when starting feature selection in object classification for 3D data Apr 12, 2017

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