c++ - tutorial - Image Processing: Algorithm Improvement for 'Coca-Cola Can' Recognition
opencv tutorial (16)
Gather at least a few hundred images containing cola cans, annotate the bounding box around them as positive classes, include cola bottles and other cola products label them negative classes as well as random objects.
Unless you collect a very large dataset, perform the trick of using deep learning features for small dataset. Ideally using a combination of Support Vector Machines(SVM) with deep neural nets.
Once you feed the images to a previously trained deep learning model(e.g. GoogleNet), instead of using neural network's decision (final) layer to do classifications, use previous layer(s)' data as features to train your classifier.
OpenCV and Google Net: http://docs.opencv.org/trunk/d5/de7/tutorial_dnn_googlenet.html
One of the most interesting projects I've worked on in the past couple of years was a project about image processing. The goal was to develop a system to be able to recognize Coca-Cola 'cans' (note that I'm stressing the word 'cans', you'll see why in a minute). You can see a sample below, with the can recognized in the green rectangle with scale and rotation.
Some constraints on the project:
- The background could be very noisy.
- The can could have any scale or rotation or even orientation (within reasonable limits).
- The image could have some degree of fuzziness (contours might not be entirely straight).
- There could be Coca-Cola bottles in the image, and the algorithm should only detect the can!
- The brightness of the image could vary a lot (so you can't rely "too much" on color detection).
- The can could be partly hidden on the sides or the middle and possibly partly hidden behind a bottle.
- There could be no can at all in the image, in which case you had to find nothing and write a message saying so.
So you could end up with tricky things like this (which in this case had my algorithm totally fail):
I did this project a while ago, and had a lot of fun doing it, and I had a decent implementation. Here are some details about my implementation:
Language: Done in C++ using OpenCV library.
Pre-processing: For the image pre-processing, i.e. transforming the image into a more raw form to give to the algorithm, I used 2 methods:
- Changing color domain from RGB to HSV and filtering based on "red" hue, saturation above a certain threshold to avoid orange-like colors, and filtering of low value to avoid dark tones. The end result was a binary black and white image, where all white pixels would represent the pixels that match this threshold. Obviously there is still a lot of crap in the image, but this reduces the number of dimensions you have to work with.
- Noise filtering using median filtering (taking the median pixel value of all neighbors and replace the pixel by this value) to reduce noise.
- Using Canny Edge Detection Filter to get the contours of all items after 2 precedent steps.
Algorithm: The algorithm itself I chose for this task was taken from this awesome book on feature extraction and called Generalized Hough Transform (pretty different from the regular Hough Transform). It basically says a few things:
- You can describe an object in space without knowing its analytical equation (which is the case here).
- It is resistant to image deformations such as scaling and rotation, as it will basically test your image for every combination of scale factor and rotation factor.
- It uses a base model (a template) that the algorithm will "learn".
- Each pixel remaining in the contour image will vote for another pixel which will supposedly be the center (in terms of gravity) of your object, based on what it learned from the model.
In the end, you end up with a heat map of the votes, for example here all the pixels of the contour of the can will vote for its gravitational center, so you'll have a lot of votes in the same pixel corresponding to the center, and will see a peak in the heat map as below:
Once you have that, a simple threshold-based heuristic can give you the location of the center pixel, from which you can derive the scale and rotation and then plot your little rectangle around it (final scale and rotation factor will obviously be relative to your original template). In theory at least...
Results: Now, while this approach worked in the basic cases, it was severely lacking in some areas:
- It is extremely slow! I'm not stressing this enough. Almost a full day was needed to process the 30 test images, obviously because I had a very high scaling factor for rotation and translation, since some of the cans were very small.
- It was completely lost when bottles were in the image, and for some reason almost always found the bottle instead of the can (perhaps because bottles were bigger, thus had more pixels, thus more votes)
- Fuzzy images were also no good, since the votes ended up in pixel at random locations around the center, thus ending with a very noisy heat map.
- In-variance in translation and rotation was achieved, but not in orientation, meaning that a can that was not directly facing the camera objective wasn't recognized.
Can you help me improve my specific algorithm, using exclusively OpenCV features, to resolve the four specific issues mentioned?
I hope some people will also learn something out of it as well, after all I think not only people who ask questions should learn. :)
Looking at shape
Take a gander at the shape of the red portion of the can/bottle. Notice how the can tapers off slightly at the very top whereas the bottle label is straight. You can distinguish between these two by comparing the width of the red portion across the length of it.
Looking at highlights
One way to distinguish between bottles and cans is the material. A bottle is made of plastic whereas a can is made of aluminum metal. In sufficiently well-lit situations, looking at the specularity would be one way of telling a bottle label from a can label.
As far as I can tell, that is how a human would tell the difference between the two types of labels. If the lighting conditions are poor, there is bound to be some uncertainty in distinguishing the two anyways. In that case, you would have to be able to detect the presence of the transparent/translucent bottle itself.
It is implemented in OpenCV 2.3.1.
You can find a nice code example using features in Features2D + Homography to find a known object
Both algorithms are invariant to scaling and rotation. Since they work with features, you can also handle occlusion (as long as enough keypoints are visible).
Image source: tutorial example
The processing takes a few hundred ms for SIFT, SURF is bit faster, but it not suitable for real-time applications. ORB uses FAST which is weaker regarding rotation invariance.
The original papers
As alternative to all these nice solutions, you can train your own classifier and make your application robust to errors. As example, you can use Haar Training, providing a good number of positive and negative images of your target.
It can be useful to extract only cans and can be combined with the detection of transparent objects.
Hmm, I actually think I'm onto something (this is like the most interesting question ever - so it'd be a shame not to continue trying to find the "perfect" answer, even though an acceptable one has been found)...
Once you find the logo, your troubles are half done. Then you only have to figure out the differences between what's around the logo. Additionally, we want to do as little extra as possible. I think this is actually this easy part...
What is around the logo? For a can, we can see metal, which despite the effects of lighting, does not change whatsoever in its basic colour. As long as we know the angle of the label, we can tell what's directly above it, so we're looking at the difference between these:
Here, what's above and below the logo is completely dark, consistent in colour. Relatively easy in that respect.
Here, what's above and below is light, but still consistent in colour. It's all-silver, and all-silver metal actually seems pretty rare, as well as silver colours in general. Additionally, it's in a thin slither and close enough to the red that has already been identified so you could trace its shape for its entire length to calculate a percentage of what can be considered the metal ring of the can. Really, you only need a small fraction of that anywhere along the can to tell it is part of it, but you still need to find a balance that ensures it's not just an empty bottle with something metal behind it.
And finally, the tricky one. But not so tricky, once we're only going by what we can see directly above (and below) the red wrapper. Its transparent, which means it will show whatever is behind it. That's good, because things that are behind it aren't likely to be as consistent in colour as the silver circular metal of the can. There could be many different things behind it, which would tell us that it's an empty (or filled with clear liquid) bottle, or a consistent colour, which could either mean that it's filled with liquid or that the bottle is simply in front of a solid colour. We're working with what's closest to the top and bottom, and the chances of the right colours being in the right place are relatively slim. We know it's a bottle, because it hasn't got that key visual element of the can, which is relatively simplistic compared to what could be behind a bottle.
(that last one was the best I could find of an empty large coca cola bottle - interestingly the cap AND ring are yellow, indicating that the redness of the cap probably shouldn't be relied upon)
In the rare circumstance that a similar shade of silver is behind the bottle, even after the abstraction of the plastic, or the bottle is somehow filled with the same shade of silver liquid, we can fall back on what we can roughly estimate as being the shape of the silver - which as I mentioned, is circular and follows the shape of the can. But even though I lack any certain knowledge in image processing, that sounds slow. Better yet, why not deduce this by for once checking around the sides of the logo to ensure there is nothing of the same silver colour there? Ah, but what if there's the same shade of silver behind a can? Then, we do indeed have to pay more attention to shapes, looking at the top and bottom of the can again.
Depending on how flawless this all needs to be, it could be very slow, but I guess my basic concept is to check the easiest and closest things first. Go by colour differences around the already matched shape (which seems the most trivial part of this anyway) before going to the effort of working out the shape of the other elements. To list it, it goes:
- Find the main attraction (red logo background, and possibly the logo itself for orientation, though in case the can is turned away, you need to concentrate on the red alone)
- Verify the shape and orientation, yet again via the very distinctive redness
- Check colours around the shape (since it's quick and painless)
- Finally, if needed, verify the shape of those colours around the main attraction for the right roundness.
In the event you can't do this, it probably means the top and bottom of the can are covered, and the only possible things that a human could have used to reliably make a distinction between the can and the bottle is the occlusion and reflection of the can, which would be a much harder battle to process. However, to go even further, you could follow the angle of the can/bottle to check for more bottle-like traits, using the semi-transparent scanning techniques mentioned in the other answers.
Interesting additional nightmares might include a can conveniently sitting behind the bottle at such a distance that the metal of it just so happens to show above and below the label, which would still fail as long as you're scanning along the entire length of the red label - which is actually more of a problem because you're not detecting a can where you could have, as opposed to considering that you're actually detecting a bottle, including the can by accident. The glass is half empty, in that case!
As a disclaimer, I have no experience in nor have ever thought about image processing outside of this question, but it is so interesting that it got me thinking pretty deeply about it, and after reading all the other answers, I consider this to possibly be the easiest and most efficient way to get it done. Personally, I'm just glad I don't actually have to think about programming this!
Additionally, look at this drawing I did in MS Paint... It's absolutely awful and quite incomplete, but based on the shape and colours alone, you can guess what it's probably going to be. In essence, these are the only things that one needs to bother scanning for. When you look at that very distinctive shape and combination of colours so close, what else could it possibly be? The bit I didn't paint, the white background, should be considered "anything inconsistent". If it had a transparent background, it could go over almost any other image and you could still see it.
I like the challenge and wanted to give an answer, which solves the issue, I think.
- Extract features (keypoints, descriptors such as SIFT, SURF) of the logo
- Match the points with a model image of the logo (using Matcher such as Brute Force )
- Estimate the coordinates of the rigid body (PnP problem - SolvePnP)
- Estimate the cap position according to the rigid body
- Do back-projection and calculate the image pixel position (ROI) of the cap of the bottle (I assume you have the intrinsic parameters of the camera)
- Check with a method whether the cap is there or not. If there, then this is the bottle
Detection of the cap is another issue. It can be either complicated or simple. If I were you, I would simply check the color histogram in the ROI for a simple decision.
Please, give the feedback if I am wrong. Thanks.
I really like Darren Cook's and stacker's answers to this problem. I was in the midst of throwing my thoughts into a comment on those, but I believe my approach is too answer-shaped to not leave here.
In short summary, you've identified an algorithm to determine that a Coca-Cola logo is present at a particular location in space. You're now trying to determine, for arbitrary orientations and arbitrary scaling factors, a heuristic suitable for distinguishing Coca-Cola cans from other objects, inclusive of: bottles, billboards, advertisements, and Coca-Cola paraphernalia all associated with this iconic logo. You didn't call out many of these additional cases in your problem statement, but I feel they're vital to the success of your algorithm.
The secret here is determining what visual features a can contains or, through the negative space, what features are present for other Coke products that are not present for cans. To that end, the current top answer sketches out a basic approach for selecting "can" if and only if "bottle" is not identified, either by the presence of a bottle cap, liquid, or other similar visual heuristics.
The problem is this breaks down. A bottle could, for example, be empty and lack the presence of a cap, leading to a false positive. Or, it could be a partial bottle with additional features mangled, leading again to false detection. Needless to say, this isn't elegant, nor is it effective for our purposes.
To this end, the most correct selection criteria for cans appear to be the following:
- Is the shape of the object silhouette, as you sketched out in your question, correct? If so, +1.
- If we assume the presence of natural or artificial light, do we detect a chrome outline to the bottle that signifies whether this is made of aluminum? If so, +1.
- Do we determine that the specular properties of the object are correct, relative to our light sources (illustrative video link on light source detection)? If so, +1.
- Can we determine any other properties about the object that identify it as a can, including, but not limited to, the topological image skew of the logo, the orientation of the object, the juxtaposition of the object (for example, on a planar surface like a table or in the context of other cans), and the presence of a pull tab? If so, for each, +1.
Your classification might then look like the following:
- For each candidate match, if the presence of a Coca Cola logo was detected, draw a gray border.
- For each match over +2, draw a red border.
This visually highlights to the user what was detected, emphasizing weak positives that may, correctly, be detected as mangled cans.
The detection of each property carries a very different time and space complexity, and for each approach, a quick pass through http://dsp.stackexchange.com is more than reasonable for determining the most correct and most efficient algorithm for your purposes. My intent here is, purely and simply, to emphasize that detecting if something is a can by invalidating a small portion of the candidate detection space isn't the most robust or effective solution to this problem, and ideally, you should take the appropriate actions accordingly.
And hey, congrats on the Hacker News posting! On the whole, this is a pretty terrific question worthy of the publicity it received. :)
I would detect red rectangles: RGB -> HSV, filter red -> binary image, close (dilate then erode, known as
imclose in matlab)
Then look through rectangles from largest to smallest. Rectangles that have smaller rectangles in a known position/scale can both be removed (assuming bottle proportions are constant, the smaller rectangle would be a bottle cap).
This would leave you with red rectangles, then you'll need to somehow detect the logos to tell if they're a red rectangle or a coke can. Like OCR, but with a known logo?
If you are interested in it being realtime, then what you need is to add in a pre-processing filter to determine what gets scanned with the heavy-duty stuff. A good fast, very real time, pre-processing filter that will allow you to scan things that are more likely to be a coca-cola can than not before moving onto more iffy things is something like this: search the image for the biggest patches of color that are a certain tolerance away from the
sqrt(pow(red,2) + pow(blue,2) + pow(green,2)) of your coca-cola can. Start with a very strict color tolerance, and work your way down to more lenient color tolerances. Then, when your robot runs out of an allotted time to process the current frame, it uses the currently found bottles for your purposes. Please note that you will have to tweak the RGB colors in the
sqrt(pow(red,2) + pow(blue,2) + pow(green,2)) to get them just right.
Also, this is gona seem really dumb, but did you make sure to turn on
-oFast compiler optimizations when you compiled your C code?
If you are not limited to just a camera which wasn't in one of your constraints perhaps you can move to using a range sensor like the Xbox Kinect. With this you can perform depth and colour based matched segmentation of the image. This allows for faster separation of objects in the image. You can then use ICP matching or similar techniques to even match the shape of the can rather then just its outline or colour and given that it is cylindrical this may be a valid option for any orientation if you have a previous 3D scan of the target. These techniques are often quite quick especially when used for such a specific purpose which should solve your speed problem.
Also I could suggest, not necessarily for accuracy or speed but for fun you could use a trained neural network on your hue segmented image to identify the shape of the can. These are very fast and can often be up to 80/90% accurate. Training would be a little bit of a long process though as you would have to manually identify the can in each image.
Maybe too many years late, but nevertheless a theory to try.
The ratio of bounding rectangle of red logo region to the overall dimension of the bottle/can is different. In the case of Can, should be 1:1, whereas will be different in that of bottle (with or without cap). This should make it easy to distinguish between the two.
Update: The horizontal curvature of the logo region will be different between the Can and Bottle due their respective size difference. This could be specifically useful if your robot needs to pick up can/bottle, and you decide the grip accordingly.
Please take a look at Zdenek Kalal's Predator tracker. It requires some training, but it can actively learn how the tracked object looks at different orientations and scales and does it in realtime!
The source code is available on his site. It's in MATLAB, but perhaps there is a Java implementation already done by a community member. I have succesfully re-implemented the tracker part of TLD in C#. If I remember correctly, TLD is using Ferns as the keypoint detector. I use either SURF or SIFT instead (already suggested by @stacker) to reacquire the object if it was lost by the tracker. The tracker's feedback makes it easy to build with time a dynamic list of sift/surf templates that with time enable reacquiring the object with very high precision.
If you're interested in my C# implementation of the tracker, feel free to ask.
There are a bunch of color descriptors used to recognise objects, the paper below compares a lot of them. They are specially powerful when combined with SIFT or SURF. SURF or SIFT alone are not very useful in a coca cola can image because they don't recognise a lot of interest points, you need the color information to help. I use BIC (Border/Interior Pixel Classiﬁcation) with SURF in a project and it worked great to recognise objects.
There is a computer vision package called HALCON from MVTec whose demos could give you good algorithm ideas. There is plenty of examples similar to your problem that you could run in demo mode and then look at the operators in the code and see how to implement them from existing OpenCV operators.
I have used this package to quickly prototype complex algorithms for problems like this and then find how to implement them using existing OpenCV features. In particular for your case you could try to implement in OpenCV the functionality embedded in the operator find_scaled_shape_model. Some operators point to the scientific paper regarding algorithm implementation which can help to find out how to do something similar in OpenCV. Hope this helps...
To speed things up, I would take advantage of the fact that you are not asked to find an arbitrary image/object, but specifically one with the Coca-Cola logo. This is significant because this logo is very distinctive, and it should have a characteristic, scale-invariant signature in the frequency domain, particularly in the red channel of RGB. That is to say, the alternating pattern of red-to-white-to-red encountered by a horizontal scan line (trained on a horizontally aligned logo) will have a distinctive "rhythm" as it passes through the central axis of the logo. That rhythm will "speed up" or "slow down" at different scales and orientations, but will remain proportionally equivalent. You could identify/define a few dozen such scanlines, both horizontally and vertically through the logo and several more diagonally, in a starburst pattern. Call these the "signature scan lines."
Searching for this signature in the target image is a simple matter of scanning the image in horizontal strips. Look for a high-frequency in the red-channel (indicating moving from a red region to a white one), and once found, see if it is followed by one of the frequency rhythms identified in the training session. Once a match is found, you will instantly know the scan-line's orientation and location in the logo (if you keep track of those things during training), so identifying the boundaries of the logo from there is trivial.
I would be surprised if this weren't a linearly-efficient algorithm, or nearly so. It obviously doesn't address your can-bottle discrimination, but at least you'll have your logos.
(Update: for bottle recognition I would look for coke (the brown liquid) adjacent to the logo -- that is, inside the bottle. Or, in the case of an empty bottle, I would look for a cap which will always have the same basic shape, size, and distance from the logo and will typically be all white or red. Search for a solid color eliptical shape where a cap should be, relative to the logo. Not foolproof of course, but your goal here should be to find the easy ones fast.)
(It's been a few years since my image processing days, so I kept this suggestion high-level and conceptual. I think it might slightly approximate how a human eye might operate -- or at least how my brain does!)
You need a program that learns and improves classification accuracy organically from experience.
I'll suggest deep learning, with deep learning this becomes a trivial problem.
You can retrain the inception v3 model on Tensorflow:
In this case, you will be training a convolutional neural network to classify an object as either a coca-cola can or not.