I tried to understand the cornerSubPix API in OpenCV for its idea behind and usefulness. I read the explanation in the link and can't understand how it works and how it is useful.
Can somebody explain me how it works and how it is useful at corner refinement? I checked my corner detection application for without cornerSubPix and with cornerSubPix. Not much difference in output image, but it takes time to process.
Thanks
I can´t explain better how it works than the actual documentation but I can help you with the: "how it is useful at corner refinement" Part.
The corners you find are in Pixel Coordinates. This means a Pixel can be at Position (10,10) or (11,11) but not at Position (11.3,11.2)
Subpixel refinement allows edges at such positions and helps to find a better position.
Related
I apologize in advance for the lack of code, but I have been looking at this problem for some days now and couldn't proceed much.
I am trying to find a method that, given a template shape and a figure, finds the most fitting (edges-wise) placement of such shape in the (binarized) figure.
For example, in the picture above, I've highlighted 2 placement for the triangle on the right (I show 2, but there are probably 5). The blue one covers more "perimeter" and thus should be preferred.
The rotations are limited to steps of 45 degrees and no scaling is involved, so I don't think I need some particularly rotation/size invariant methods.
For now, I have obtained some results using opencv template matching, by first extracting the edges with canny and matching the resulting contour, but it is far from precise and I am afraid this would not work for more complex figures. Also by using canny I lose information about the binarized foreground and background, while the placement must be only inside the figure.
Any idea would be appreciated because I really could not find much in the literature (also I am quite inexpert in the field and probably lacking the proper terminology for effective searches), thank you in advance
I'm new to image processing and hope someone can help/guide me in the right direction.
So I have a picture in black/white and I want to find the corner coordinates of the inner black part of the preprocessed picture. My question is what kind of method/s will yield the most accurate result?
I want something like this (red dots shows the inner corners)
go with cv::goodFeaturesToTrack() and play with params until you get your result.
you can refer to this on why choose this and not cornerHarris: goodFeaturesToTrack vs cornerHarris
and also to this SO answer for an example: opencv-using-cvgoodfeaturestotrack-with-c-mat-variable
of course I assume you are using C++, if you are using python it won't change much...
have luck and try to do a search before asking next time
I don't know what language you're using which makes it harder to answer this question but the way I did it my last time was by applying openCV's canny edge detection algorithm. This allows you to see the edges on the image. Next find the contours. With those two functions and a little help from your friend Google, you will be able to figure this out. Good luck!
One of the aproaches would be to use just sobel independently in X and Y (in original image). Since you have binary already just find inner edge, which will be easy.
Those edges can be then sampled - take a few points of the edge. And with the help of OpenCV library function cv::fitLine find the line of the edge. Do the same with all the inner edges and compute the intersections. This approach should be fairly accurate since from the fitLine function on you basically compute the corners in sub-pixel accuracy.
First of all I'm a total newbie in image processing, so please don't be too harsh on me.
That being said, I'm developing an application to analyse changes in blood flow in extremities using thermal images obtained by a camera. The user is able to define a region of interest by placing a shape (circle,rectangle,etc.) on the current image. The user should then be able to see how the average temperature changes from frame to frame inside the specified ROI.
The problem is that some of the images are not steady, due to (small) movement by the test subject. My question is how can I determine the movement between the frames, so that I can relocate the ROI accordingly?
I'm using the Emgu OpenCV .Net wrapper for image processing.
What I've tried so far is calculating the center of gravity using GetMoments() on the biggest contour found and calculating the direction vector between this and the previous center of gravity. The ROI is then translated using this vector but the results are not that promising yet.
Is this the right way to do it or am I totally barking up the wrong tree?
------Edit------
Here are two sample images showing slight movement downwards to the right:
http://postimg.org/image/wznf2r27n/
Comparison between the contours:
http://postimg.org/image/4ldez2di1/
As you can see the shape of the contour is pretty much the same, although there are some small differences near the toes.
Seems like I was finally able to find a solution for my problem using optical flow based on the Lukas-Kanade method.
Just in case anyone else is wondering how to implement it in Emgu/C#, here's the link to a Emgu examples project, where they use Lukas-Kanade and Farneback's algorithms:
http://sourceforge.net/projects/emguexample/files/Image/BuildBackgroundImage.zip/download
You may need to adapt a few things, e.g. the parameters for the corner detection (the frame.GoodFeaturesToTrack(..) method) , but it's definetly something to start with.
Thanks for all the ideas!
did somebody tried to find a pizzamarker like this one with "only" OpenCV so far?
I was trying to detect this one but couldn't get good results so far. I do not know where this marker is in picture (no ROI is possible), the marker will be somewhere in the room (different ligthning effects) and not faceing orthoonal towards us. What I want - the corners and later the orientation of this marker extracted with the corners but first of all only the 5Corners. (up, down, left, right, center)
I was trying so far: threshold, noiseclearing, find contours but nothing realy helped for a good result. Chessboards or square markers are normaly found because of their (parallel) lines- i guess this can't help me here...
What is an easy way to find those markers?
How would you start?
Use other colorformat like HSV?
A step-by-step idea or tutorial would be realy helpfull. Cause i couldn't find tuts at the net. Maybe this marker isn't called pizzamarker -> does somebody knows the real name?
thx for help
First - thank you for all of your help.
It seems that several methods are usefull. Some more or less time expansive.
For me it was the easiest with a template matching but not with the same marker.
I used only a small part of it...
this can be found 5 times(4 times negative and one positive) in this new marker:
now I use only the 4 most negatives Points and the most positive and got my 5 points that I finaly wanted. To make this more sure, I check if they are close to each other and will do a cornerSubPix().
If you need something which can operate in real-time I'd go down the edge detection route and look for intersecting lines like these guys did. Seems fast and robust to lighting changes.
Read up on the Hough Line Transform in openCV to get started.
Addendum:
Black to White is the strongest edge you can have. If you create a gradient image and use the strongest edges found in the scene (via histogram or other) you will be able to limit the detection to only the black/white edges. Look for intersections. This should give you a small number of center points to apply Hough ellipse detection (or alternate) to. You could rotate in a template as a further check if you wish.
BTW.. OpenCV has Edge Detection, Hough transform and FitEllipse if you do go down this route.
actually this 'pizza' pattern is one of the building blocks of the haar featured used in the
Viola–Jones object detection framework.
So what I would do is compute the summed area table, or integral image using cv::integral(img) and then run exhaustive search for this pattern, on various scales (size dependant).
In each window you are using only 9 points (top-left, top-center, ..., bottom left).
You can train and use cvHaarDetectObjects to detect the marker using VJ.
Probably not the fastest method but it should work.
You can find more info on object detection methods using OpenCV here: http://opencv.willowgarage.com/documentation/object_detection.html
So I'm using openCV to do square recognition on this image. I compiled the squares.c file on an image that I took and here are the results:
http://www.learntobe.org/urs/index1.php
The image on the left is the original and on the right is the image that is a result of running the square detection.
The results aren't bad, but I really need this to detect ALL of the squares and I'm really new to this openCV and image processing stuff. Does anyone know of how I can edit the squares.c file to possibly get the detection to be more inclusive so that all of the squares are highlighted?
Thanks a lot ahead of time.
All the whitish colors are tough to detect. Nothing separates it from the page itself. Try doing some kind of edge detection (check cvCanny or cvSobel).
You should also "pre-process" the image. That is, increase the contrast, make the colors more saturated, etc.
Also check this article http://www.aishack.in/2010/01/an-introduction-to-contours/ It talks about how the squares.c sample works. Then you'll understand a bit about how to improves the detection in your case.
Hope this helps!