NewDeveloper's profile - activity

Thank you LBerger for taking time and give me helpfully answers! BR

oh sry I forget to say that I run it in release mode,.... in debug mode everything works like expected but in release it has some strange behavior

Dear great OpenCV community,

I apply the median image filter on an 8 bit unsigned char image with the resolution of 512x512. I have down sampled my image cause not all kernel sizes are supported by 16bit images. I'd checked the execution times of the mentioned filter for several kernel sizes. As a result I can observe that the execution time growth with the kernel size. Till there every thing is like I had expected. But if the kernel size is bigger than 15x15 the computational time decreases by a factor of two. Now I wonder that a kernel size of 99x99 takes about 9 ms and a kernel size of 15x15 about 16 ms. Can somebody give me an answer why this happens? My Thought is that the median filter is "just" a sorting algorithm and if the size becomes bigger the sorting should take more time.

I hope some one knows why this strange behaviour appears and can explain it a bit.

Best Regards NewDev

Thank you LBerger for the fast and helpful answer! I'll try it

you mean I should copy and paste the code?

Hello great openCV-community,

I'd like to find out if it is possible to perform an anisotropic diffusion operation on an image through openCV? I found out that the KAZE and AKAZE feature detectors use the anisotropic diffusion. But I think the functions behind are private and inaccessible. Is there a filter predefined for this filteringoperation or do I have to write my own filter? (or make usage of the filter which "occurs" for this purposes in this forum ? isotropic non-linear diffusion...

Thank you very much in advance, Best Regards

oke thank you really mutch for the fast answer! you gave me the hint i really needed! i have to multiply my h-value by 256. This make sense! now it also works for me in my application.

Dear great OpenCV community,

I have some issues by processing 16bit Images through the cv::fastNlMeansDenoising Image filter. I have read in the documentation that it is possible if you set the normType = NORM_L1 So i have tried it but nothing happens. The Images still look like the origin Images. So i thought maybe it is a issue with the depth of the Images. Therefore i have integrated in my program a down sampling of the Image before the filtering Operation (16bit -> 8bit) and after the filtering Operation is performed i integrated a up sampling of the Images (8bit -> 16bit). And now the supprising result was that it works with 8bit data perfectly and with 16bit data it seems like the filtering Operation isn't performed because the processed Images look exactly like the original Images

This is my code which i use for the filtering Operation. The Settings of the filter become inserted by the user of my program

//this works perfectly

cv::Mat inputIm;
cv::Mat outputIm;
filterInputCvImage.convertTo(inputIm, CV_8U, 1 / 256.);
cv::fastNlMeansDenoising(inputIm, outputIm, h, templateWindowSize, searchWindowSize, cv::NORM_L1);
outputIm.convertTo(filterOutputImage, CV_16U, 256.0);

//this does not work

cv::fastNlMeansDenoising(filterInputImage, filterOutputImage, h, templateWindowSize, searchWindowSize, cv::NORM_L1);

Yes i will post my results of the comparison here

okay, great! thank you for the fast reply!

@Vit i mean the performance of some filters which are available in both libraries like the median- , mean-, bilateral filter an so forth ... the task by it self is to compare the libraries , choose one of them, and work with it for Image processing purpose e.g. noise reduction in sonographic samples

@thdrksdfthmn the suggestion to compare the common functions of the libraries concerning the speed-, memorysize and the Quality difference is a nice broad hint. But how does usually a test as a speed test look like? Do i have to start a timer in the background... then perform a image processing step, and check which method was faster? Do i have do it in a statistical manner with different common functions and different Hardware components to get trustfull data?

Dear OpenCV-community, Like the topic of my question might suggest, i am interested in the evaluation of the performance of the open source library OpenCV. I am new at the developer front, and i am not really shure how a Performance test should look like. My Overall goal is to compare two open source libraries. The open source libraries i am looking for are the OpenCV- and the ITK library. I know that there are serveral ways to perform a comparison. One way i would do it is to check the performance differences between them.

I am looking Forward to here from you, high regard Andreas