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Hello everyone,
I've been trying to use localization feature:
- using recognition mode "PROPOSAL_ONLY".
- localization mechanism to be "LM_EDGE_BOXES"
and R200 is constantly giving me between
10 and 14 results for most of the frames.
None of the RecognizedObjectData is accompanied with
valid properties except 'roi'.
i.e. following properties are all filled with 0.
- boundingBox
- centerPos2D
- centerPos3D
- probability
(I guess 'label' is supposed to be always 0 in this mode)
Did I miss to set some property or to call some method
in order to make it work?
Because it is giving out so many candidates, it is very
important to have other information (especially depth
info contained in 'centerPos3D') to eliminate noise.
Could anybody help me how I can achieve such a goal?
# I tried to set ' SINGLE_RECOGNITION' mode and
# label recognition is working fine.
# (I'm not satisfied with the accuracy, though.)
Thank you very much for your help.
--
Tosh Satake
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Could you provide any screenshot and pic of real object? Thanks!
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Hi,
Here is the screen-shot of the test application.
- It uses PROPOSAL_ONLY mode with LM_EDGE_BOXES method.
- as I already described, if I change the code to use SINGLE_RECOGNITION mode, it can recognize mug / chair / keyboard and such.
- It hardly localize real 3D object, but it does pick up on-screen object such as windows and icons on my PC monitor.
- In either way, only ROI has values and other properties 2D/3D results are empty (filled with zeros).
- It is typical to have more than 10 results on every frame.
Thank you
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In PROPOSAL_ONLY mode the SDK is only find a regions of interest in the image without classifier- which means that there is no interpretation like “this is a chair” or “this is a sofa” and so on. Hence all irrelevant fields are with zeroes. Today there is no support in 3d objects in OR MW.
In SINGLE_RECOGNITION mode all the picture (or ROI) assumed to contain one main object which is catch the major area of the picture (or ROI) and classified as one object.
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