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Add getMatInblob function #24173

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Add getMatInblob function #24173

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16 changes: 16 additions & 0 deletions modules/dnn/include/opencv2/dnn/dnn.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1266,6 +1266,22 @@ CV__DNN_INLINE_NS_BEGIN
*/
CV_EXPORTS_W void imagesFromBlob(const cv::Mat& blob_, OutputArrayOfArrays images_);

/** @brief return a specific Mat in a blob.
* A blob is 3 or 4D matrix function return a specific plane.
* if dims blob is less or equal to 2 (N rows x M columns) blob is return
* if dims blob is 3: blob is an H x N x M array and mat at (coord(0),0, 0) is returned.
* if dims blob is 4: blob is T x H x N x C array
* mat at (coord(0), coord(1), 0, 0) is returned as Mat with C channel if channelOrder = DNN_LAYOUT_NHWC.
* mat at (coord(0), coord(1), 0, 0) is returned as Mat with one channel if channelOrder = DNN_LAYOUT_NCHW.
* @param[in] blob a blob with dim size less or equal than 4
* @param[in] coord blob coordinate
* @param[in] channelOrder blob coordinate
* @returns Mat with dim size less or equal than 2
*
* @note return Mat is original data in blob with read write access to blob data
*/
CV_EXPORTS Mat getMatInBlob(Mat blob, std::vector<int> coord, int channelOrder = DNN_LAYOUT_NCHW);

/** @brief Convert all weights of Caffe network to half precision floating point.
* @param src Path to origin model from Caffe framework contains single
* precision floating point weights (usually has `.caffemodel` extension).
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37 changes: 37 additions & 0 deletions modules/dnn/src/dnn_utils.cpp
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Expand Up @@ -259,6 +259,43 @@ void imagesFromBlob(const cv::Mat& blob_, OutputArrayOfArrays images_)
}
}

Mat getMatInBlob(Mat blob, std::vector<int> coord, int channelOrder)
{
CV_TRACE_FUNCTION();
CV_Assert(blob.dims <= 4);
CV_Assert(channelOrder == DNN_LAYOUT_NHWC || channelOrder == DNN_LAYOUT_NCHW);
Mat x, y, out;

switch (blob.dims)
{
case 0:
out = Mat();
break;
case 1:
case 2:
return blob;
break;
case 3:
CV_Assert(coord[0] < blob.size[0]);
x = blob.reshape(1, std::vector<int> {blob.size[0], blob.size[1] * blob.size[2]});
out = x.row(coord[0]).reshape(0, blob.size[1]);
break;
case 4:
CV_Assert(coord[0] < blob.size[0]);
x = blob.reshape(1, std::vector<int> {blob.size[0], blob.size[1] * blob.size[2] * blob.size[3]});
if (channelOrder == DNN_LAYOUT_NHWC)
out = x.row(coord[0]).reshape(blob.size[3], blob.size[1]);
else
{
CV_Assert(coord[1] < blob.size[1]);
out = x.row(coord[0]).reshape(1, std::vector<int> {blob.size[1], blob.size[2] * blob.size[3]});
out = out.row(coord[1]).reshape(1, blob.size[2]);
}
break;
}
return out;
}


CV__DNN_INLINE_NS_END
}} // namespace cv::dnn