How to count tablets successfully?

Question

My last question on image recognition seemed to be too broad, so I would like to ask a more concrete question.

First the background. I have already developed a (round) pill counter. It uses something similar to this tutorial. After I made it I also found something similar with this other tutorial.

However my method fails for something like this image

Although the segmentation process is a bit complicated (because of the semi-transparency of the tablets) I have managed to get it

My problem is here. How can I count the elongated tablets, separating each one from the image, similar to the final results in the linked tutorials?

So far I have applied distance transform and then my own version of watershed and I got

As you can see it fails in the adjacent tablets (distance transform usually does).

Take into account that the solution does have to work for this image and also for other arrangements of the tablets, the most difficult being for example

I am open to use OpenCV or if necessary implement on my own algorithms. So far I have tried both (used OpenCV functions and also programmed my own libraries) I am also open to use C++, or python or other. (I programmed them in C++ and I have done it on C# too).

Answer 1

I am also working on this pill counting problem (I'm much earlier in this process than you are), and to solve the piece you are working on - of touching pills, my general idea how to solve this is to capture contours of the pills once you have a good mask of the pills, and then calculate the area of a single pill.

For this approach I'm assuming that I have enough pills in the image such that the amount of them that are untouching is greater than those which are touching, and no pills overlap one another. For my application, placing this restriction I think is reasonable (humans can do a quick look at the pills they've dumped out, and at least roughly make them not touching without too much work. It's also possible that I could design a tray with some sort of dimples in it such that it would coerce the pills to not be touching)

I do this by sorting the contour areas (which, with the right thresholding should lead to only pills and pill-groups being in the identified contours), and taking the median value.

Then, with a good value for the area of a pill, you can look for contours with areas that are a multiple of that median area (+/- some % error value). I also use that median value to filter out contours that are clearly not big enough to be pills, and ones that are far too large to be a pill (the latter though could be more troublesome, since it could still be a grouping of touching pills).