I need to use a low-resolution (320 x 240) image in OpenCV and find a large exercise ball, either blue or red. The ball is 25 inches wide and is NOT guaranteed to be perfectly circular. I tried to use HoughCircles with a Canny-thresholded image. I had no success. What am I doing wrong and what is the best way to get the size of the ball in pixels and where it is? It'll let me calculate things like how far it is from the camera!
Let me collect all the other advice in one answer:
Use cv::inRange() to get the correct color (More Information on that). You might want to use something like RGB Normalization beforehand to make sure you retreive the complete ball.
Now you have all the pixel that relate to the ball (and maybe some noise that you have to get rid of). Retrieve the pixels that are farthest apart left/right and top/bottom in your ball (aka your connected Component that has a plausible size) to get the size of the ball. (If the ball doesn't have to be round you probably want to take the bigger value)
Compute the distance from the camera with the now known size of the ball in the picture. (You have to know the "real" size beforehand for this computation obviously ;))
There obviously are other ways (f.e. use edge detection), but this is imo the easiest.
It is easier to give you an answer if you post an example picture.
Related
I want to locate a service robot via infrared landmarks. The idea is to detect two landmarks, get the distance to the landmarks and calculate the robots position from these informations (the position of the landmarks are known).
For this I have built an artificial 2x3 matrix of IR LEDs, which are visible in the robots infrared camera image (shown in the image below).
As the first step, I want to detect a single landmark in a picture and get it's x-y coordinates. I can use these coordinates in the future to get the distance from the depth-image provided.
My first approach was to convert the image to a black and white image. Then I tried to filter out different cluster of points (which i dilated and contoured in the first place). I couldn't succeed with this method.
Now I wonder if there are any pattern recognition/computer vision methods which can help me to quite "easily" detect the pattern.
I've added a picture of the infrared image with the landmark in it and a converted black/white image.
a) Which method can help me to solve this problem?
b) Should I use a 3x3 Matrix or any other geometric form instead of the 2x3 Matrix ?
IR-Image
Black-White Image
A direct answer:
1) find all small circles in the image; 2) look among these small circles for ones that are the same size and close together, and, say, form parallel lines.
The reason for this approach is that you have coded the robot with a specific pattern of small objects. Therefore, look for the objects and then look for the pattern. (If the orientation and size wouldn't change, then you could just look for a sub-image within the larger image, but because it can, you need to look for elements of the pattern that remain consistent with motion in the 3D space, that is, the parallel lines.)
This will work in the example images, but to know whether this will work more generally, we need to know more than you told us: It depends on whether the variation in the images of the matrix and the variations in the background will let this be enough to distinguish between them. If not, maybe you need a more clever algorithm or maybe a different pattern of lights. In the extreme case, it's obvious that if you had another 2x3 matric around, it's not enough. It all depends on the variation of the object to be identified and the variations within the background scene, and because you don't tell us either of these things, it's hard to say the best way, what's good enough, what's a better way, etc.
If you have the choice, and here it sound like you do, good data is better than clever analysis. For this problem, I'd call good data to be anything that clearly distinguishes the object from the background. You need to think of it this way, and look at what the background is, and all the different perspectives on the lights that are possible, and make sure these can never be confused.
For example, if you have a lot of control over this, and enough time, temporal variations are often the easiest. Turning the lights (or a subset of the lights) on and off, etc, and then looking for the expected temporal variation is often the surest way to distinguish signal from noise — but really, this again is just making an assumption about the background and foreground (ie, that the background won't vary with some particular time pattern).
I am trying to detect a ball in an filtered image.
In this image I've already removed the stuff that can't be part of the object.
Of course I tried the HoughCircle function, but I did not get the expected output.
Either it didn't find the ball or there were too many circles detected.
The problem is that the ball isn't completly round.
Screenshots:
I had the idea that it could work, if I identify single objects, calculate their center and check whether the radius is about the same in different directions.
But it would be nice if it detect the ball also if he isn't completely visible.
And with that method I can't detect semi-circles or something like that.
EDIT: These images are from a video stream (real time).
What other method could I try?
Looks like you've used difference imaging or something similar to obtain the images you have..? Instead of looking for circles, look for a more generic loop. Suggestions:
Separate all connected components.
For every connected component -
Walk around the contour and collect all contour pixels in a list
Suggestion 1: Use least squares to fit an ellipse to the contour points
Suggestion 2: Study the curvature of every contour pixel and check if it fits a circle or ellipse. This check may be done by computing a histogram of edge orientations for the contour pixels, or by checking the gradients of orienations from contour pixel to contour pixel. In the second case, for a circle or ellipse, the gradients should be almost uniform (ask me if this isn't very clear).
Apply constraints on perimeter, area, lengths of major and minor axes, etc. of the ellipse or loop. Collect these properties as features.
You can either use hard-coded heuristics/thresholds to classify a set of features as ball/non-ball, or use a machine learning algorithm. I would first keep it simple and simply use thresholds obtained after studying some images.
Hope this helps.
I've been using matplotlib.pyplot.contour to plot the contour of images, and I wonder how to implement the contour plotting, but I found that the code of pyplot.contour hard for me to understand. I have this idea that, for a grayscale image, each pixel has an intensity value, to plot its contour, I might choose a set of intensity values, like partition the range [min-intensity-value, max-intensity-value] to 10 segments [min-intensity-value, val0, val1,..., val8, max-intensity-value], then for each segment's boundary intensity value (like val0, val1,...,val8), find out all those pixels which has the same intensity value, and I think those pixels will form a contour line.
Is my idea a right way to go? Hope anyone can give me a basic idea about how to implement it.
Thanks.
Your idea of connecting all values with the same colour won't work, as there is no guarantee that these are connected. Also, consider e.g. the case of two equal-coloured pixels touching at the corner: There is no way to say whether these are connected or e.g. the other diagonal.
I believe the question is basically what you want to achieve. If you just want to vectorize the image, there are existing tools for that, like e.g. POtrace. If you need something special or have special input data, then you will get better results when you tell people about this. In that case, I would also take an hour or two to look up the very good description for the POtrace algorithm from their website, maybe you can borrow a few good ideas from it?
I'm doing some image processing, and am trying to keep track of points similar to those circled below, a very dark spot of a couple of pixels diameter, with all neighbouring pixels being bright. I'm sure there are algorithms and methods which are designed for this, but I just don't know what they are. I don't think edge detection would work, as I only want the small spots. I've read a little about morphological operators, could these be a suitable approach?
Thanks
Loop over your each pixel in your image. When you are done considering a pixel, mark it as "used" (change it to some sentinel value, or keep this data in a separate array parallel to the image).
When you come across a dark pixel, perform a flood-fill on it, marking all those pixels as "used", and keep track of how many pixels were filled in. During the flood-fill, make sure that if the pixel you're considering isn't dark, that it's sufficiently bright.
After the flood-fill, you'll know the size of the dark area you filled in, and whether the border of the fill was exclusively bright pixels. Now, continue the original loop, skipping "used" pixels.
How about some kind of median filtering? Sample values from 3*3 grid (or some other suitable size) around the pixel and set the value of pixel to median of those 9 pixels.
Then if most of the neighbours are bright the pixel becomes bright etc.
Edit: After some thinking, I realized that this will not detect the outliers, it will remove them. So this is not the solution original poster was asking.
Are you sure that you don't want to do an edge detection-like approach? It seems like a comparing the current pixel to the average value of the neighborhood pixels would do the trick. (I would evaluate various neighborhood sizes to be sure.)
Personally I like this corner detection algorithms manual.
Also you can workout naive corner detection algorithm by exploiting idea that isolated pixel is such pixel through which intensity changes drastically in every direction. It is just a starting idea to begin from and move on further to better algorithms.
I can think of these methods that might work with some tweaking of parameters:
Adaptive thresholds
Morphological operations
Corner detection
I'm actually going to suggest simple template matching for this, if all your features are of roughly the same size.
Just copy paste the pixels of one (or a few features) to create few templates, and then use Normalized Cross Correlation or any other score that OpenCV provides in its template matching routines to find similar regions. In the result, detect all the maximal peaks of the response (OpenCV has a function for this too), and those are your feature coordinates.
Blur (3x3) a copy of your image then diff your original image. The pixels with the highest values are the ones that are most different from their neighbors. This could be used as an edge detection algorithm but points are like super-edges so set your threshold higher.
what a single off pixel looks like:
(assume surrounding pixels are all 1)
original blurred diff
1,1,1 8/9,8/9,8/9 1/9,1/9,1/9
1,0,1 8/9,8/9,8/9 1/9,8/9,1/9
1,1,1 8/9,8/9,8/9 1/9,1/9,1/9
what an edge looks like:
(assume surrounding pixels are the same as their closest neighbor)
original blurred diff
1,0,0 6/9,3/9,0/9 3/9,3/9,0/9
1,0,0 6/9,3/9,0/9 3/9,3/9,0/9
1,0,0 6/9,3/9,0/9 3/9,3/9,0/9
Its been a few years since i did any image processing. But I would probably start by converting to a binary representation. It doesn't seem like you're overly interested in the grey middle values, just the very dark/very light regions, so get rid of all the grey. At that point, various morphological operations can accentuate the points you're interested in. Opening and Closing are pretty easy to implement, and can yield pretty nice results, leaving you with a field of black everywhere except the points you're interested in.
Have you tried extracting connected components using cvContours? First thresholding the image (using Otsu's method say) and then extracting each contour. Since the spots you wish to track are (from what I see in your image) somewhat isolated from neighborhood they will some up as separate contours. Now if we compute the area of the Bounding Rectangle of each contour and filter out the larger ones we'd be left with only small dots separate from dark neighbors.
As suggested earlier a bit of Morphological tinkering before the contour separation should yield good results.
I'm trying to write an algorithm to generate the "ceiling panel" from a horiontally wrappable panoramic image like the one above. Images 1 to 4 are a straight cut out for the walls of the cube but the ceiling will be more complicated as I assume it needs to be composited from parts 5a to 5d. Does anyone know the solution in pseudocode?
my guess is that we need to iterate over the coordinates of the ceiling tile
i.e.
for y=0 to height
for x=0 to width
colorofsomecoordinateonoriginalimage = some function (poloar coords?)
set pixel(x,y) = colorofsomecoordinateonoriginalimage
next
next
Hum... I remember doing something like that for computer vision class one time back in grad school. It's not impossible but a LOT of work needs to be done. One way would be to degrade the entire product's quality. That's the easiest starting point. Once you degraded it enough (depending on how much you need to stretch the edges), you can start applying nonlinear transformations to the image. This is probably best done approximating by maybe cutting out sections of the cylinder by degrees and then applying one of the age old projections used in making flat maps (like Mercator or CADRG or something)... but you have to remember to interpolate the pixels, make sure you at least do an averaging of the pixels to approximate. That's the best I can think of.
You can't generate a panorama just by taking photos from a single location and stitch them. Well, you can for a single horizontal set, but it would look ugly (usually, you stitch many more than 4 photos to avoid distortions at the edges).
Here, you have even more data in the y-direction, which means even more pictures, and some sort fancy projection to generate the final image.
If you look at the panorama you have closely, you'll notice that the boundary of the region in sunlight is not straight. That is because your panorama was projected on a cylinder, not a cube. So I don't think 1/2/3/4 would look right directly mapped to a cube.
Bottom line, you really can't consider those 8 chunks as 8 pictures taken from a fixed point (If you need convincing, try yourself to take 8 pictures like that and try to stitch them together. You'll see how fun it is for the upper row, and even though it is easy for the bottom row, how ugly it looks on the stitched regions).
Now, why you need cube maps changes drastically what your options are. If you're only looking for a cube map to do cheap environment mapping effects, then the simplest is to find an arbitrary function that maps the edges where you want them to be, and simply linearly interpolate in between. It's completely the wrong projection, but ought to give a picture that looks good enough for the intended goal.
If you're looking for something more accurate, then you need to know how the projection was generated, so that you can unproject it before re-projecting it on the cube.
All that said, it's also a lot easier to just photograph cube maps rather than process a panorama to generate them, but that might not be possible for you.