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What is the difference between Vec<Mat> and Mat

asked 2013-01-14 14:24:50 -0500

southpark gravatar image

Hi Guys,

I got confused with the usage of vector<mat> and Mat.

Example: vector<mat> images; images.push_back(imread("example.jpg")); //loop

bool CvSVM::train(const Mat& trainData, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), CvSVMParams params=CvSVMParams() )

For SVM, it requires the input to be Mat structure. How do you make the conversion?

Another question is with the usage of push_back.

"The methods add one or more elements to the bottom of the matrix. They emulate the corresponding method of the STL vector class. When elem is Mat , its type and the number of columns must be the same as in the container matrix."-from opencv.org

However, is push_back a concatenation operator or just grouping them into a cell structure? You can access data by images[0], images[1],...

If you define "Mat images" and then use images.push_back, the result is a concatenation of matrix.

Thanks for your clarification.

Sorry if the question seems naive.

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answered 2013-01-14 15:10:22 -0500

matt.hammer gravatar image
vector<mat> images; images.push_back(imread("example.jpg"))

This creates an array (vector is a C++ STL wrapper around arrays) of matrices and puts the image "example.jpg" into a matrix and pushes that matrix onto the first position of the array.

Is your confusion attributable to the fact that both a vector and an OpenCV matrix have a push_back() member function? They pretty much do the same thing, but to different data types.

To "convert" from a vector of matrices to a single matrix with concatenation, i think you could do something like this to use OpenCV push_back to build a "compound" matrix (by popping matrices off the front of the vector and concatenating):

Mat imageConcat;   while(images.size() != 0)  {imageConcat.push_back(images.front());  images.erase(0);}

However, in my (admittedly limited) experience with SVMs I don't think you want to dump a whole image in there. All you'd be doing is training on each horizontal row of pixel data - I don't think that's what you want (unless you've got some cool algorithm going that does exactly that). Training on raw image data is probably too many SVM dimensions anyways.

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Asked: 2013-01-14 14:24:50 -0500

Seen: 9,242 times

Last updated: Jan 14 '13