Feature Detection Functioning Explanation

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But can't detect a sign

it wasn't made to detect things. if that's what you wanted, you're on the wrong boat with this.

Yeah, you told me so, but I have to see it by myself. I also started to learn in the meantime ImgProc module. I understand that 'template matching' is also not the way to go as it for "finding areas of an image that match (are similar) to a template image (patch).".

the 'What next' section -

If I will characterize the signs (if possible), by its vertical middle appearance (like here: https://imgur.com/a/D7YdEYr ).

And with the assumption that each sign has its own 'vertical middle signature'. Can I get good match results, if I detect the sign I want to find, and take only his 'vertical middle signature' using a mask operation.

In such case, img_hash can be my way to go ? Or histograms comparison ?

Thank you on your patience..

hmm, unless you're absolutely clear about what you are trying to ACHIEVE, it will always be an ill-fated XY-problem

Its going to be long, can skip on the unwanted sections : ( My Second comment will be on Point) Introduction Section: I'm a junior dev. One month ago I opened my first question. At the time I wasn't familiar with opencv, Android, Java, computer vision etc. Since I did some basic app's in Android. learned about openCV (Img_Proc and Feature Detection - the most.) The problem in DEFINE the problem you trying to solve, is not knowing what openCV can do.

Crying Section: The fact I don't know Python, or remember C++ , didn't help. Also, Java and android opencv examples is minority compared, to c++ and Python. Another problem, is the amount of tutorials of different versions. I first start with 2.4.* , understand what is going on, and then advanced to 3.4.5

Definition Section:

To my limited understanding:

Identify a Sign from a list of very - Similar signs, In various angles. Get the Best results you can achieve.

The assumption is that nothing will cover part of the sign,

How to Solve Section -

I think for now to :

1. Isolate the sign using HoughLine and warpPerspective(4 points)
2. Find the most similar Image(sign), from a set of isolated images of the signs, using PHash / Histogram Comparison, or both.

I getting my best results by now using P-Hash site. (or it seems to be).

I'm using HoughLine, because the image can be details crowded. findcontour gave me 10K contour's, and fail to detect the sign.