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It would probably be better to use the Lab color space, and then you only have to look at the a channel (which is the transformation of color between red and green).
I used this to great effect in edge detection because the Lab color space is based on eyes. Just changing images from RGB to the Lab color space increased the accuracy of edge detection (particularly for red/green items) by about 7%: http://austingwalters.com/edge-detection-in-computer-vision/
Further, I used this color space to design a super accurate fiducial marker (and QR code)[1], that can be used to store a lot more information, easier to track, etc.
I believe the grad student I was working with was going to publish a paper at some point. For what ever reason I wasn't involved with the paper and the paper doesn't appear to be published yet.
The HSV color space separates out hue and saturation, which helps remove the background noise, but it would have trouble with overlapping circles or circles where red and blue say mix to make purple. Converting to the Lab space would actually help distinguish overlapping colors as well.
Here's a link to my work: [1] http://austingwalters.com/chromatags/
Wish I had known this when I wrote a program that tracks a human wearing a shirt with a green square on it!
The biggest issue with HSV was that it required a ton of tuning to compensate for ambient lighting conditions. Would Lab help with that, say by taking some range in -a and considering all of L? Or is that a separate problem entirely?
The L in Lab represents "light" (saturation).
By removing or not looking at that channel, you are essentially looking at the normalized color channels. Look at the a channel (red to green) and b channel (yellow to blue) to get the color of the images.
This essentially removes lighting conditions, although obviously lighting conditions still impact the image to some degree, from the camera and original RGB image.
The L in HSL stands for lightness, and is similar to the L in Lab which also stands for lightness. Lab is meant to better represent how humans perceive color, but it's not inherently better for removing ambient lighting compared to HSL. The main difference between HSL and Lab is how good they are at distinguishing specific colors, and Lab tends to work better for things regarding human perception. I've always had better luck with HSL than Lab, but I tend to just try every color space when approaching a new problem.
Also, removing glare and other lighting abnormalities is one of the banes of computer vision. To the best of my knowledge, nobody has figured out a good way to do it yet (although I've seen some promising work on dealing with occlusion, which you could use to overcome moderate glare with proper segmentation).
Light intensity/amount, not saturation unless the word has a meaning I don't know.
Light is more correct, but in the terms of HSV or RGB when you decrease saturation it changes the colors to white/black respectively, essentially decreasing the separation between colors.
can you expand on why RGB wouldn't work? The most obvious way I can think of to include chroma information in apriltags would be to use 3 colors matched to the peak sensitivity of each channel. Does the RBG spectral sensitivity overlap too much for this to work?
The RGB color space works, it just has light encoded in each channel (making it unnormalized). Separating out the light, i.e. normalizing on the color is what gives LAB the advantage.
Further, because the way RGB is structured taking the difference between channels is more difficult (requires more calculations) than it does for the a & b channels. For instance, the a channel represents red to green, or positive a represents red and negative a represents green. This aids in finding edges between red and green with a simple addition/subtraction between pixels(a[i,i] - a[j,j]). If we were to try the same thing in the RGB space it would look something like (R[i,i] + G[i,i]) - (R[j,j] + G[j,j]), and you would still have the impact of light to deal with...
There has been previous work that was similar to what I think you mean (and predates AprilTags) [1]. The idea is to use markers that are grids, with each cell colored either red, green or blue; tag detection is then done by finding the rectangular region of the image over which the average of the RGB contrast function [2] is maximised. Thus tags are detected based on color without having to do edge detection across the whole image.
[1]: http://www.alexwg.org/publications/ProcSPIE_6623-662304.pdf
[2]: at a single pixel, this is sqrt((r-g)^2 + (g-b)^2 + (r-b)^2))
I once wrote a similar OpenCV application [1], but in my case I needed to detect a closed contour and evaluate how close that contour was to being a circle. To provide a "score" I took advantage of the fact that in a circle the ratio C^2/(4piA) = 1 (where C is the circumference and A is the area). This is a unique property of the circle (it is equivalent to saying that the ratio of perimeter to area is maximized in a circle) and it's always lower for any other closed shapes. And it also provides a nice 0..1 result which can be interpreted as a percentage score!
I followed pretty much the same image segmentation/binary threshold as in the article, but found that the gaussian blur step was unnecessary in my case, so I skipped it. Also, the closed contours API in OpenCV provides the area and perimeter results so it was very easy to get it all working. Most of the code was actually just writing a simple UI in Python.
This expression should work to get all the reds without having to do colourspace transform (standard unsigned 8-bit RGB expanded to signed ints of greater width):
r - g > 128 && r - b > 128
It doesn't properly handle the light, i.e. if the r or g channel is super dim it'll cause issues.
Getting an SSL privacy error (NET::ERR_CERT_AUTHORITY_INVALID) on this page. (I've removed the StartCom root CA from my OS after reading https://groups.google.com/forum/#!topic/mozilla.dev.security... )
Technically wrong. Those are red discs, not red circles.
</pedantic>
You think I'm joking, right? I'm not.
Think of the keywords that apply to the title. "OpenCV" and "circle" definitely are. I don't consider "detect" and "image" as keywords here because if you are using OpenCV, there's a very high probability that you want to detect something in an image. So "detect" and "image" are pretty much implied by "OpenCV". And I'm going to give you the benefit of doubt and include "red" as a keyword.
So we end up with "OpenCV", "circle", and "red". Three keywords. "circle" being technically wrong means that you got 1/3 of the keywords wrong. That's actually no small mistake.
Let's say you are working on a project and you are in urgent need to find out how to use OpenCV to effectively detect actual circles (not red discs). You type these keywords into your favourite search engine: ["opencv", "circle"]. And you get this article as the top result because it has the right keywords and it ranks high in popularity. Then congratulations, you just wasted your precious time. Of course, you can start from this article and get to your correct solution. But remember I said that you're in urgent need, you're in a hurry, you don't have time to waste.
The converse is also true: you want to detect actual red discs but you can't find this article because the article's keywords are wrong.
When I saw the title of the article, I really was expecting it to talk about detecting circles, not discs. I started at the beginning and I thought "ok, maybe the author's approach is to start from discs and eventually get to circles". I followed through to the end expecting to see actual circles, but then... nothing. I was really disappointed.
I could be even more pedantic and point out that in mathematics, a circle has no width, but if we take that extra pedantic definition, then we won't expect to detect anything at all in an image. The difference here is that when we talk about "circle", we know that we are really talking about an approximate representation of a circle. But a circle is not an approximate representation of a disc, or vice versa, period.
Or you could use the Circle Hough Transform: https://en.wikipedia.org/wiki/Circle_Hough_Transform
Which is what they did. It's inefficient, because it's a brute-force process, but it works.