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Accurately Converting a CV_32FC3 cv::mat to IE Blob

I've been working on a model that requires normalized image values i.e image/255. the images read by opencv are U8, so i first convert them to CV_32FC3 and than normalize but cant understand how to convert this mat to blob. There are 2 functions that most samples are using but these convert MatU8 to blob, so how to accurately convert a CV_32FC3 mat to blob? The functions in the samples are as follows:

 

template <typename T>

void matU8ToBlob(const cv::Mat& orig_image, InferenceEngine::Blob::Ptr& blob, int batchIndex = 0) {

InferenceEngine::SizeVector blobSize = blob->getTensorDesc().getDims();

const size_t width = blobSize[3];

const size_t height = blobSize[2];

const size_t channels = blobSize[1];

T* blob_data = blob->buffer().as<T*>();

 

cv::Mat resized_image(orig_image);

if (static_cast<int>(width) != orig_image.size().width ||

static_cast<int>(height) != orig_image.size().height) {

cv::resize(orig_image, resized_image, cv::Size(width, height));

}

 

int batchOffset = batchIndex * width * height * channels;

 

for (size_t c = 0; c < channels; c++) {

for (size_t h = 0; h < height; h++) {

for (size_t w = 0; w < width; w++) {

blob_data[batchOffset + c * width * height + h * width + w] =

resized_image.at<cv::Vec3b>(h, w);

}

}

}

}





 

InferenceEngine::Blob::Ptr wrapMat2Blob(const cv::Mat &mat) {

size_t channels = mat.channels();

size_t height = mat.size().height;

size_t width = mat.size().width;

 

size_t strideH = mat.step.buf[0];

size_t strideW = mat.step.buf[1];

 

bool is_dense =

strideW == channels &&

strideH == channels * width;

 

if (!is_dense) THROW_IE_EXCEPTION

<< "Doesn't support conversion from not dense cv::Mat";

 

InferenceEngine::TensorDesc tDesc(InferenceEngine::Precision::U8,

{1, channels, height, width},

InferenceEngine::Layout::NHWC);



 

return InferenceEngine::make_shared_blob<uint8_t>(tDesc, mat.data);

}

 

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