CVS-160560 nanobindings for keypoint detection and segmentation #258
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sovrasov
reviewed
Jan 23, 2025
sovrasov
reviewed
Jan 27, 2025
| nb::class_<InstanceSegmentationResult, ResultBase>(m, "InstanceSegmentationResult") | ||
| .def(nb::init<int64_t, std::shared_ptr<MetaData>>(), nb::arg("frameId") = -1, nb::arg("metaData") = nullptr) | ||
| .def_prop_ro( | ||
| "feature_vector", |
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Iseg result also contains a saliency map as a vector of cv::Mat. Perhaps, it's not that easy to expose but there is a chance a vector of nb::ndarray<uint8_t... would work here
sovrasov
reviewed
Jan 28, 2025
| .def_prop_ro( | ||
| "soft_prediction", | ||
| [](ResultBase& r) { | ||
| ImageResultWithSoftPrediction ir = r.asRef<ImageResultWithSoftPrediction>(); |
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Would that work if the result entity is actually an ImageResult?
In sseg wrapper now we have the following logic:
if (return_soft_prediction) {
return a ptr to ImageResultWithSoftPrediction
}
else
return a ptr to ImageResult
infer() method's return type is a ptr to ImageResult. I think that kind of design looks weird an overall inconvenient, because we always need to check if type conversion to ImageResultWithSoftPrediction is successful.
Related refactoring can be done in subsequent PR, here we just need to be sure that both the cases return_soft_prediction==true/false are handled correctly
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What does this PR do?
Ready for python replacement, this will have the same structure results as python version:
Still needs work: