openvino/docs/snippets/dldt_optimization_guide5.cpp

#include <opencv2/core/core.hpp>
#include <ie_core.hpp>

int main() {
InferenceEngine::InferRequest inferRequest;
//! [part5]
cv::Mat frame(cv::Size(100, 100), CV_8UC3);  // regular CV_8UC3 image, interleaved
// creating blob that wraps the OpenCV’s Mat
// (the data it points should persists until the blob is released):
InferenceEngine::SizeVector dims_src = {
    1         /* batch, N*/,
    (size_t)frame.rows  /* Height */,
    (size_t)frame.cols    /* Width */,
    (size_t)frame.channels() /*Channels,*/,
    };
InferenceEngine::TensorDesc desc(InferenceEngine::Precision::U8, dims_src, InferenceEngine::NHWC);
InferenceEngine::TBlob<uint8_t>::Ptr p = InferenceEngine::make_shared_blob<uint8_t>( desc, (uint8_t*)frame.data, frame.step[0] * frame.rows);
inferRequest.SetBlob("input", p);
inferRequest.Infer();
// …
// similarly, you can wrap the output tensor (let’s assume it is FP32)
// notice that the output should be also explicitly stated as NHWC with setLayout
auto output_blob = inferRequest.GetBlob("output");
const float* output_data = output_blob->buffer().as<float*>();
auto dims = output_blob->getTensorDesc().getDims();
cv::Mat res (dims[2], dims[3], CV_32FC3, (void *)output_data);
//! [part5]

return 0;
}