I was wondering how I would go about comparing contours, and moving it to show the best matched position.
So what I wish to achieve is for the purple contour to overlay where it matches with the pink one best.
I have used shape matching, it does its job, but does not show me where exactly its finding the best match.
What I am trying to do is to position an object/part of an object in an image so it is aligned with another picture with the same object in it, and from there I will be able to compare the images even more. For more differences.
I have tried looking at Hausdorff distance but couldn't get it working, something like what it does here where it detects and shows the best fit is ideally what I want.
I have looked around quite a bit, but I can't seem to find a working example of it, this method being implemented.
Related
I've been trying to extract hand-drawn circles from a document for a while now but every attempt I make doesn't have the level of consistency I need.
Process Album
The problem I keep coming up against is when 2 "circles" are too close they become a single contour, ruining my attempt to detect if a contour is curved. I'm sure there must be a better way to extract these circles, but their imperfection and inconsistency are really stumping me.
I've tried many other ways to single out the curves, the most accurate of which being:
Rather than use dilation to bridge the gap between the segmented contours, find the endpoints and attempt to continue the curve until it hits another contour.
Problem: I can't effectively find the turning points of the contour, otherwise this would be my preferable method
I apologize if this question is deemed "too specific", but I feel like Computer Vision stuff like this can always be applied elsewhere.
Thanks ahead of time for any and all help, I'm about at the end of my rope here.
EDIT: I've just realized the album wasn't working correctly, I think it should be fixed now though.
It looks like a very challenging problem so it is very likely that the things I am going to write wouldn't work very well in practice.
In order to ease the problem, I would probably try to remove as much of other stuff from the image as possible.
If the template of the document is always the same, it might be worth trying to remove horizontal and vertical lines along with grayed areas. For example, given the empty template, substract it from the document that you are processing. Probably, it might be possible to get rid of the text also. This would result in an image with only parts of hand drawn circles.
On such image, detecting circles or ellipses with hough transform might give some results (although shapes might be far from circles or ellipses).
I'm working with GPUImage to detect document edges, using GPUImageHoughTransformLineDetector without any previous filter.
I'm discarding returned lines that are similar between them, and then I'm calculating the points of intersection.
With those points I want to find possible rectangles, but I can't figure out how.
I found this post answer https://stackoverflow.com/a/26502570/3708095 that says:
The steps would be:
Edge detection using Sobel filter.
Hough transform to find all straight lines in the image.
Look at all parallel lines and then all lines 90 degrees to those parallel line pairs, to find possible rectangles.
Pick the rectangle you like best. This could be by area, or by being best aligned to the phone, or you require that all edges are
inside the visible camera image, or some other method.
Said that way seems quite easy to pick up the rectangles, but I wonder how I can find all the rectangles in an efficient way to be fast enough to do it real time, since using 4 nested loops to find all combinations of those corners is a really expensive calculation.
Besides, step 3 doesn't seem to be as easy as unapiedra describes. I think that you can find multiple parallel lines in an image... Am I misunderstanding something?
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
following up on my other question, do you guys know a good example in OpenCV, with a simple Black/White-Calibration Picture and appropriate detection-algorithms?
I just want to show some B&W-image on a screen, take a picture of that image from afar and calculate the size of the shown image, to calculate the distance to said screen.
Before I invent the wheel again, I recon this is so easy that it could be achieved through many different ways in OpenCV, yet I thought I'd ask if there's a preferred way around, possibly with some sample code.
(I got some face-detection code running using haarcascade-xml files already)
PS: I already have the resolution/dpi-part of my screen covered, so I know how big a picture would be in cm on my screen.
EDIT:
I'll make it real simple, I need:
A pattern, that is easily recognizable in an Image. Right now I'm experimenting with a checkerboard. The people who made ARDefender used this.
An appropriate algorithm to tell me the exact pixel coordinates of pattern 1) in a picture using OpenCV.
Well, it's hard to say which image is the best for recognition - in different illumination any color could be interpret as another color. Simple example:
As you can see both traffic signs have red color border but even on one image upper sign border is obviously not red.
So in my opinion you should use image with many different colors (like a rainbow). And also you said that it should be easy recognizable in different angles. That's why circle shape is the best for it.
That's why your image should look like this:
So idea of detection such object is the following:
Make different color segmentation (blue, red, green etc). For this use HSV color space.
Detect circles of specific color on image.
That area which has the biggest count of circles seems to be your object.
you just have to take pictures of your B&W object from several known distances (1m, 2m, 3m, ...) and then for each distance check the size of your object in the corresponding image.
From those datas, you will be able to create a linear function giving you the distance from the size in pixels (y = ax + b should do ;) ), translate it into your code and you're done.
Cheers
I am using some functions such as color contour tracking and image matching which are already available in OpenCV .. I am trying to identify a pink duck, more specifically the head of the duck, but these two functions don't give me the outcome I am expecting for some reasons such as :
the color thing don't always work perfect because the change in the lightning , which accordingly would change the color seen by the camera.
when I use the image matching thing, I use one image of the duck which I took from a specific position and it can identify the duck only when he is in that position, but I want to identify it even when I rotate the duck or play around with it.
Does anyone have an ideas about a better way to track a certain object ?
Thank you
Have you tried converting the image into the hsv colourspace? This colourspace tries to remove the effects of lighting so might be able to improve your colour-based segmentation.
To identify the head of the duck, once you have identified the duck as a whole you could perhaps identify the orientation (using template matching with a set of templates from different viewpoints, or haar cascades, or ...) and then use the known orientation and an empirical rule to determine where the head is located. For example, if you detect the duck in an upright position within a certain bounding box, the head is assumed to be located in the top third of that bounding box.
I think it might just take little more than what OpenCV provides straight forward way.
Given your specific question, you might just want to try shape descriptors of some sort.
Basically, try to take Duck's head's pictures shape from various angles and capture the shapes from it.
Now, you can find a likelihood model (forgive me for not a very accurate term) that can validate the hypothesis that a given captured shape indeed belongs to the class of Duck's head or not. Color can just be an additional feature that might help.
If you are a new person in this field - try catch hold of Duda and Hart: Pattern Classification. This doesn't have solution to find-the-duck-problem but will shape your thinking.