Grillteller's profile - activity

Perhaps you can work with cv::isContourConvex() though the function output is undefined for intersected contours.

For these kind of problems I often used CLAHE (see also: https://answers.opencv.org/question/211398/detecting-very-light

For these kind of problems I often used CLAHE (see also: https://answers.opencv.org/question/211398/detecting-very-light

I updated my answer. I think it is possible to get even better results when refining the parameters of CLAHE and the thr

I updated my answer. I think it is possible to get even better results when refining the parameters of CLAHE and the thr

Hi, for me it works with a simple threshold + CLAHE: int main(int argc, char** argv) { cv::Mat img = cv::imread("

Oh sorry - my bad. I didn't read quite well :D. I will try something later. Seems to be more difficult.

Please show us what you have tried so far.

Hi, for me it works with a simple threshold: int main(int argc, char** argv) { cv::Mat img = cv::imread("img_test

Can you post an image for better understanding?

Ok, my bad. So the actual problem is finding the start and end point of the fitted line? If so, you could use findContou

Ok, you found your line as a ´cv::Vec4f´ where you have a starting point (x0, y0) and a vector collinear to the line (vx

That should work. What is your problem now?

Ok, I read the script again :D. I think you are right. The problem is that you have to be able to distinguish your proje

We have a structured light system with three different camera pairs in our lab and if we want to gain a higher accuracy

What happens if you convert your rgb image to grayscale values?

When you look at line 160-164 in: https://github.com/opencv/opencv_contrib/blob/master/modules/ximgproc/src/guided_filte

Could you be more specific? Are you referring to this: https://docs.opencv.org/3.4/de/d73/classcv_1_1ximgproc_1_1GuidedF

@Eduardo: Different question concerning that topic. Is it necessary to normalize your image point coordinates as stated

@Eduard: Different question concerning that topic. Is it necessary to normalize your image point coordinates as stated i

Since the calibration matrix or camera matrix can be defined using floating point coordinates I assume the principal poi

Is it possible that I only recompile the calib3d.hpp and the five-point.cpp using OpenCV 3.2.0 under OS Windows 10? How

Hi,

what is the easiest way to overload an OpenCV function on my computer?
I want to use the function cv::findEssentialMat with two different camera matrices. At the moment this is not implemented in the standard five-point.cpp .(https://github.com/opencv/opencv/blob/master/modules/calib3d/src/five-point.cpp). Nevertheless it would be very easy to overload the function using two different camera models. I do not come from a programming background so would it be possible if I just add an overloaded function with additional camera matrices to the data file five-point.cpp in my build directory on my pc?
Or can I send a request to someone to implement it? Only around 5 lines of code would have to be changed. For me it seems it would be a lot of work if I code the complete five-point algorithm in combination with RANSAC myself.

When calling SIFT_create() you can add an argument nfeatures to retain a certain number of best features e.g. SIFT_creat

When calling SIFT_create() you can add an argument nfeatures to retain a certain number of best features e.g. SIFT_creat

When calling SIFT_create() you can add an argument nfeatures to retain a certain number of best features e.g. `SIFT_crea

Easiest way to overload OpenCV function Hi, what is the easiest way to overload an OpenCV function on my computer? I w

Hi, I am usually working with historical images and thus having the same problems. For your problem I would try/consider

Hi, I am usually working with historical images and thus having the same problems. For your problem I would try/consider

Can you show one example of a Fundamentalmatrix and how do you determine the rank?

You really want an answer but I won't do the complete job for you ;). I think a lot of different strategies can lead to

I think the question is still too general. An image would be really nice here. Just some ideas here because it is not ve

Just FYI - Here is a crosspost: [SO] (https://stackoverflow.com/questions/53252242/opencv-pattern-recognition-with-locat

Since the octaves (=levels of the pyramid) are saved in the KeyPoint structure of OpenCV matches can be correctly drawn

I once had a similar problem and solved it with at first using morphological operations. Since you only have these singl

Ok, I am dumb. I assumed my matrix is 27 x 18 because I got it from src.size(). In fact the matrix is 18 x 27 which I co

Hi,

what are the parameters w, u, vt of cv::SVDecomp(src, w, u, vt) in comparison to Matlab's

[U,S,V] = svd(src);

I need the submatrices for further calculations that I already have given in Matlab. Can I directly translate w to U, u to S and vt to V. It seems like they are having varying Matrix sizes. I did not find further explanations on the SVDecomp function (e.g. here: documentation)

Edit:
I tried more stuff. I have the src matrix of size 27x18 (edited): In Matlab I get as result for the sizes U(27x27), S(27x18), V(18x18) which solves the SVD equation src=USV'

In OpenCV I open the same matrix src(27x18) and I get as result with cv::SVD::FULL_UV:
w(1x18), u(18x18), vt(27x27) which does not solve the equation. I am quite confused right now, what is going on in openCV SVD.

Ok, I am dumb. I assumed my matrix is 27 x 18 because I got it from src.size(). In fact the matrix is 18 x 27 which I co

Sry, had an error in the description. My source matrix is 27x18. Ok, you are even faster than me :D. Wait, I will try in

Sry, had an error in the description. My source matrix is 27x18.

Parameters of cv::SVDecomp Hi, what are the parameters w, u, vt of cv::SVDecomp(src, w, u, vt) in comparison to Matlab'

Parameters of cv::SVDecomp Hi, what are the parameters w, u, vt of cv::SVDecomp(src, w, u, vt) in comparison to Matlab'

Parameters of cv::SVDecomp Hi, what are the parameters w, u, vt of cv::SVDecomp(src, w, u, vt) in comparison to Matlab'

Parameters of cv::SVDecomp Hi, what are the parameters w, u, vt of cv::SVDecomp(src, w, u, vt) in comparison to Matlab'

I am doing some matrix calculations with the openCV Mat. Though, it is not the best to do it with. Can you try this for

See: http://answers.opencv.org/question/38194/understanding-findhomography-mask/ When I tried it with cv::findFundamenta