Function Label

python/mod_cvcuda/operators/OpLabel.cpp:90–138

}

TupleTensor3 Label(Tensor &input, NVCVConnectivityType connectivity, NVCVLabelType assignLabels,
                   NVCVLabelMaskType maskType, bool count, bool stats, int maxLabels, std::optional<Tensor> bgLabel,
                   std::optional<Tensor> minThresh, std::optional<Tensor> maxThresh, std::optional<Tensor> minSize,
                   std::optional<Tensor> mask, std::optional<Stream> pstream)
{
    constexpr nvcv::DataType outType = nvcv::TYPE_S32;

    auto inputData = input.exportData<nvcv::TensorDataStridedCuda>();
    if (!inputData)
    {
        throw nvcv::Exception(nvcv::Status::ERROR_INVALID_ARGUMENT, "Input must be a valid CUDA strided tensor");
    }
    auto inAccess = nvcv::TensorDataAccessStridedImagePlanar::Create(*inputData);
    if (!inAccess)
    {
        throw nvcv::Exception(nvcv::Status::ERROR_INVALID_ARGUMENT, "Input must be a valid image-based tensor");
    }
    int numSamples = inAccess->numSamples();

    Tensor                output = Tensor::Create(input.shape(), outType);
    std::optional<Tensor> countTensor, statsTensor;

    if (count)
    {
        countTensor = Tensor::Create({{numSamples}, "N"}, outType);
    }
    if (stats)
    {
        int numStats = 1;
        if (connectivity == NVCV_CONNECTIVITY_4_2D || connectivity == NVCV_CONNECTIVITY_8_2D)
        {
            numStats = 7;
        }
        if (connectivity == NVCV_CONNECTIVITY_6_3D || connectivity == NVCV_CONNECTIVITY_26_3D)
        {
            numStats = 9;
        }

        statsTensor = Tensor::Create(
            {
                {numSamples, maxLabels, numStats},
                "NMA"
        },
            outType);
    }

    return LabelInto(output, countTensor, statsTensor, input, connectivity, assignLabels, maskType, bgLabel, minThresh,
                     maxThresh, minSize, mask, pstream);
}

} // namespace