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I'm trying to convert an OpenCV 3-channel Mat to a 3D Eigen Tensor.
So far, I can convert 1-channel grayscale Mat by:
cv::Mat mat = cv::imread("/image/path.png", cv::IMREAD_GRAYSCALE);
Eigen::MatrixXd myMatrix;
cv::cv2eigen(mat, myMatrix);
My attempt to convert a BGR mat to a Tensor have been:
cv::Mat mat = cv::imread("/image/path.png", cv::IMREAD_COLOR);
Eigen::MatrixXd temp;
cv::cv2eigen(mat, temp);
Eigen::Tensor<double, 3> myTensor = Eigen::TensorMap<Eigen::Tensor<double, 3>>(temp.data(), 3, mat.rows, mat.cols);
However, I'm getting the following error :
libc++abi.dylib: terminating with uncaught exception of type cv::Exception: OpenCV(4.1.0) /tmp/opencv-20190505-12101-14vk1fh/opencv-4.1.0/modules/core/src/matrix_wrap.cpp:1195:
error: (-215:Assertion failed) !fixedType() || ((Mat*)obj)->type() == mtype in function 'create'
in the line: cv::cv2eigen(mat, temp);
Any help is appreciated!
The answer might be disappointing for you.
After going through 12 pages, My conclusion is you have to separate the RGB to individual channel MAT and then convert to eigenmatrix. Or create your own Eigen type and opencv convert function
In OpenCV it is tested like this. It only allows a single channel greyscale image
https://github.com/daviddoria/Examples/blob/master/c%2B%2B/OpenCV/ConvertToEigen/ConvertToEigen.cxx
And in OpenCV it is implemented like this. Which dont give you much option for custom type aka cv::scalar to eigen std::vector
https://github.com/stonier/opencv2/blob/master/modules/core/include/opencv2/core/eigen.hpp
And according to this post,
https://stackoverflow.com/questions/32277887/using-eigen-array-of-arrays-for-rgb-images
I think Eigen was not meant to be used in this way (with vectors as
"scalar" types).
they also have the difficulting in dealing with RGB image in eigen.
Take note that Opencv Scalar and eigen Scalar has a different meaning
It is possible to do so if and only if you use your own datatype aka matrix
So you either choose to store the 3 channel info in 3 eigen matrix and you can use default eigen and opencv routing.
Mat src = imread("img.png",CV_LOAD_IMAGE_COLOR); //load image
Mat bgr[3]; //destination array
split(src,bgr);//split source
//Note: OpenCV uses BGR color order
imshow("blue.png",bgr[0]); //blue channel
imshow("green.png",bgr[1]); //green channel
imshow("red.png",bgr[2]); //red channel
Eigen::MatrixXd bm,gm,rm;
cv::cv2eigen(bgr[0], bm);
cv::cv2eigen(bgr[1], gm);
cv::cv2eigen(bgr[2], rm);
Or you can define your own type and write you own version of the opencv cv2eigen function
custom eigen type follow this. and it wont be pretty
https://eigen.tuxfamily.org/dox/TopicCustomizing_CustomScalar.html
https://eigen.tuxfamily.org/dox/TopicNewExpressionType.html
Rewrite your own cv2eigen_custom function similar to this
https://github.com/stonier/opencv2/blob/master/modules/core/include/opencv2/core/eigen.hpp
So good luck.
Since you need tensor. forget about cv function
Mat image;
image = imread(argv[1], CV_LOAD_IMAGE_COLOR);
Tensor<float, 3> t_3d(image.rows, image.cols, 3);
// t_3d(i, j, k) where i is row j is column and k is channel.
for (int i = 0; i < image.rows; i++)
for (int j = 0; j < image.cols; j++)
t_3d(i, j, 0) = (float)image.at<cv::Vec3b>(i,j)[0];
t_3d(i, j, 1) = (float)image.at<cv::Vec3b>(i,j)[1];
t_3d(i, j, 2) = (float)image.at<cv::Vec3b>(i,j)[2];
//cv ref Mat.at<data_Type>(row_num, col_num)
watch out for i,j as em not sure about the order. I only write the code based on reference. didnt compile for it.
Also watch out for image type to tensor type cast problem. Some times you might not get what you wanted.
this code should in principle solve your problem
Edit number 2
following the example of this
int storage[128]; // 2 x 4 x 2 x 8 = 128
TensorMap<Tensor<int, 4>> t_4d(storage, 2, 4, 2, 8);
Applied to your case is
cv::Mat frame=imread('myimg.ppm');
TensorMap<Tensor<float, 3>> t_3d(frame.data, image.rows, image.cols, 3);
problem is I'm not sure this will work or not. Even it works, you still have to figure out how the inside data is being organized so that you can get the shape correctly. Good luck
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Updated answer - OpenCV now has conversion functions for Eigen::Tensor which will solve your problem. I needed this same functionality too so I made a contribution back to the project for everyone to use. See the documentation here:
https://docs.opencv.org/3.4/d0/daf/group__core__eigen.html
Note: if you want RGB order, you will still need to reorder the channels in OpenCV before converting to Eigen::Tensor
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