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Sorry to bother you sir, you have posted twice your question, could you remove one of the two occurence.

Determining the proper vectorized from a fundamental one Hello In OpenCV 3.4.x the structure V_RegTraits128 was very he

Determining the proper vectorized from a fundamental one Hello In OpenCV 3.4.x the structure V_RegTraits128 was very he

Determining the proper vectorized from a fundamental one Hello In OpenCV 3.4.x the structure V_RegTraits128 was very he

Determining the proper vectorized from a fundamental one Hello In OpenCV 3.4.x the structure V_RegTraits128 was very he

The reason why I do not had the same output was related to the IQR code. Even when in the discussion with LBerger I stop

Thank you very much for your help. I finally got it.

I also edited the post in with the test codes.

difference in translation from python to C++ Hello I am facing hard time to undestand the difference between two algori

Hello I restart the evaluation without using the IQR and I still do not have the same output... But I have identically

I am not yet at my office, I'll check as soon as I'll be there. It would not very surprise me however I remember in one

I'll update later the code I use in both case. There is no version of C++ utils the utils::iqr it is my IQR code that I

During the test the main difference is after lines you refer in one case there is 32 elements in idx in python and only

During the test the main difference is after lines you refer in one case there is 32 elements in idx (in python) and onl

During the test the main difference is after lines you refer in one case there is 32 elements in idx (in python) and onl

During the test the main difference is that after lines you refer in one case there is 32 elements in idx (in python) an

LBerger during the test the main difference is that after lines you refer in one case there is 32 elements in idx (in py

I am not sure to understand your question. Idx is a list in python a cv::Mat1i in C++ it store the index of the boolean

difference in translation from python to C++ Hello I am facing hard time to undestand the difference between two algori

Hello

I know OpenCV allow to format matrix using different representation of the data thanks to the function: format e.g.:

std::cout<<cv::format(my_matrix, cv::Formatter::FMT_NUMPY)<<std::endl;

I would like to know if there is an already existing function that can read the formatted data from a text file.

Thanks in advance.

Hello Here is the code I made in order to read one or more matrices written in a text file. It is all but the most effi

@berak I made a mistake. Could you please reopen the question and mark it as solve. Sincerely Sorry.

Ok thank you very much to both of you :). I actually did a piece of code that can "interpret" the output of the example

read formated matrix from text file Hello I know OpenCV allow to format matrix using different representation of the da

read formated matrix from text file Hello I know OpenCV allow to format matrix using different representation of the da

read formated matrix from text file Hello I know OpenCV allow to format matrix using different representation of the da

read formated matrix from text file Hello I know OpenCV allow to format matrix using different representation of the da

read formated matrix from text file Hello I know OpenCV allow to format matrix using different representation of the da

read formated matrix from text file Hello I know OpenCV allow to format matrix using different representation of the da

read formated matrix from text file Hello I know OpenCV allow to format matrix using different representation of the da

In C++ it is possible but it seem that in python it is not.

Hello This solution is not optimal but ... it work in such case: import numpy as np import cv2 from matplotlib import

Hello This solution is not optimal but ... it work in such case: import numpy as np import cv2 from matplotlib import

cv::Mat1f k = (cv::Mat1f(9,9)<<0,0,0,0,0,0,1,0,0, 0,0,0,0,0,0,0,0,0, 0,0,0,0,0,1,0,0,0, 0,0,0,0,0,0,0,0,0, 0,0,0,0

Take a look here

After the thresholding apply a bitwise not then apply findContours but with RETR_TREE. findContours return three outputs

interface cusolver SVD with GpuMat Hello everyone I try to devellop a function that like SVDecomp but which work with c

Hello

I made a custom module in c++ in which I have a function that extract a sequence of images (aka a video with a uncommon format, not an image set) from a file. My function is declare as:

CV_EXPORTS_W void read_myJFile(const String& filename, OutputArrayOfArrays img);

And instantiate as:

void read_myJFile(const cv::String &filename, cv::OutputArrayOfArrays img, const int &fmt)
{

    CV_Assert(img.isMat() || img.isMatVector() || img.isUMatVector());

    std::vector<cv::Mat> data;
    std::vector<cv::Mat> dst;

// Here goes the code that  fill data with the content of the file specified.

    if(img.isMat()) // work as long data's size is lower than CV_MAX_CN.
        cv::merge(datas,img);

    img.create(data.size(),1,data.front().type());
    img.assign(data);


}

The compilation work like a charm, how when I try to call it from python I receive that message:

OpenCV(4.0.0-pre) /home/smile/lib_dir/opencv/opencv_400/opencv/modules/core/src/matrix_wrap.cpp:1280: error: (-215:Assertion failed) i < 0 in function 'create'

I investigate a little bit it is because the binder pass a Mat container as second argument of my function. Is there any way to tell the binder that the second argument of my function is std::vector<cv::mat> ? Somehow this case is a little bit like the builtin function cv::split in the sense it does not return a cv::Mat

Otherwise what should I do (write) for make the binder understand ?

Thanks in advance for any help

An example to adapt could be: // I supposed the type of A and B is CV_8UC3 cv::Mat dst = B; cv::Mat src = A; // adapt

An example to adapt could be: // I supposed the type of A and B is CV_8UC3 cv::Mat dst = B; cv::Mat src = A; // adapt

An example to adapt could be: // I supposed the type of A and B is CV_8UC3 cv::Mat dst = B; cv::Mat src = A; // adapt

An example to adapt could be: // I supposed the type of A and B is CV_8UC3 cv::Mat dst = B; cv::Mat src = A; // adapt