Wednesday, February 15, 2017

Rubiks Cube Tracker using OpenCV

I've been working on my own Rubiks Cube solver for the past few months, it is a mashup of the Lego 42009 Mobile-Crane-MK-II and an EV3 Mindstorms and can solve from 2x2x2 up to 5x5x5. The robot is called CraneCuber, it uses a USB webcam to see the cube and extract the colors for each square. I'll do a post on CraneCuber soon but I wanted to do a separate post about what is involved with finding a rubiks cube in an image and extracting the colors.

Here is a quick video showing all this in action.


Cube Location

The robot uses a webcam to take a picture of each side, we'll focus on this photo of a 4x4x4 cube. I use python opencv2 for all of the image processing.


Canny Edge Detection

The first steps are to load the image, gray it, blur it a little and then use Canny edge detection to locate all of the edges.

    self.image = cv2.imread(filename)
    gray = cv2.cvtColor(self.image, cv2.COLOR_BGR2GRAY)
    blurred = cv2.GaussianBlur(gray, (3, 3), 0)
    canny = cv2.Canny(blurred, 20, 40)

Canny Edges

Dilate

The next step is to dilate the lines a little, this makes the lines thicker which will make it easier to find the contours of the squares.

    kernel = np.ones((3,3), np.uint8)
    dilated = cv2.dilate(canny, kernel, iterations=2)

Dilated Canny Edges

Contours

At this point we have a black and white image with nice thick lines around each square.  opencv has the ability to find all of the contours in an image where a contour is defined as "a curve joining all the continuous points (along the boundary), having same color or intensity".

(contours, hierarchy) = cv2.findContours(dilated.copy(), 
                                         cv2.RETR_TREE,
                                         cv2.CHAIN_APPROX_SIMPLE)

Contours

Contour Approximations

It looks like someone spilled a plate of blue spaghetti noodles all over the cube doesn't it?  We need to reduce the complexity of the shapes of the contours.  We can do this via opencv's approxPolyDP which in a nutshell approximates the shape of a contour. In the image below the contours are again in blue but their approximations are in red.

Contours (blue) with approximations (red)

Square Contour Approximations

The approximations look much easier to work with in terms of figuring out which ones are squares. We look at each approximation and use the following three rules to determine if it is a square:
  • There must be four corners
  • All four lines must be roughly the same length
  • All four corners must be roughly 90 degrees
In the image below the contour is green instead of blue if the approximation for that contour satisfies our rules for being a square.


Contours with square approximations are green

At this point we can eliminate all of the contours that are not squares, that cleans things up a lot

Removed non-squarish contours

Squares within squares

Things are looking better but we have three squares in the middle where there is a square around the square.  This happens from finding the contours of the black sections between each square.  We eliminate these outside squares which leaves us with:

Removed squares around squares

Cube dimensions and boundries

We can now analyze the remaining contours and figure out two key pieces of information
  • We are dealing with a 4x4x4 cube
  • We can find the boundries of the cube
Since we know know the boundries of the cube we can throw away any contours outside the boundries which leaves us with

Removed contours outside cube boundry
Presto we have identified the 16 squares in this image!! We compute the mean RGB (Red, Green, Blue) values for each of the 16 contours and later on we hand this data off to the rubiks-color-resolver (more on that in a minute).

Handling Errors

Sometimes the squares of a cube are dented or the sticker is peeling off or there is some really bad glare or it is in the shadows or there is some other crazy problem that makes the contour approximation fail one of our three "is it a square" rules when in fact it is a square.  This happened in the image below, the square that is in the 3rd row, 5th column has a contour approximation that doesn't look like a square (the contour is blue, the approximation is red).  Here we see the approximation is a triangle so it clearly failed the "There must be four corners" check.

Non-square approximation for a square
There were enough good squares for us to determine that this is a 6x6x6 cube though and that we are missing one square
Missing One Square
We then look at all of the non-square contours inside the boundries of the cube and see if any of them are in a location that will give us six squares in the 5th column and 6 squares on the 3rd row.  The contour in yellow below (approximation in light blue) satisfies that condition.
All Squares Located

Color Extraction

Once we've processed the images for all six sides we have the RGB (Red, Green Blue) value for each square.  The 4x4x4 image from our example is side F in the cube below, the colors shown are the mean RGB values as extracted from the images.  Notice how much variation there is...40 is a good bit darker than 17 but these are both yellow squares.
Raw RGB values

The next step is to take those RGB values and identify which side (U, L, F, R, B, or D) the square belongs to.  We do this because all rubiks cube solvers need to know the exact state of the cube.

The actual mechanics of how this works isn't terribly exciting to write about in a blog but in a nutshell it does the following:

  • Identify the six colors used by the cube.  In this case the colors are red, orange, green, blue, yellow and white.
  • Create a list of the color combinations used by all edges (red/blue, yellow/red, etc)
  • Create a list of the color combinations used by all corners (white/orange/green, red/blue/white, etc)
  • Create a list of centers color squares
  • For each edge (say U5 L18 for example) compute the color distance of that edge vs. all of the edges that in the "list of edge color combinations that we need" and find the one that is the closest match.  The distance between two colors is calculated using the CIEDE2000 algorithm. For the U5 L18 example the needed color combination that matches with the lowest color distance is white/orange so we assigne white/orange to U5 L18 and remove white/orange from the needed list (since this is a 4x4x4 there is one more white/orange left on the needed list).
  • Repeat the process for the corners and centers
The end result is that we can nail down exactly which side each square belongs to.  We then redraw the cube with some brighter colors so we can sanity check against the "Raw RGB values" image above to make sure we got everything correct.


And finally we print out a big string that represents the state of the cube.  This string is what is passed to various rubiks cube solvers so they can compute a solution.  For the cube above the state string is 
RRBBURURBULLLBBUFFFRRRFLFBFDRDFDDRRLFBDDBBLRRLUBFDLUDLDRDRUUBBUFDLUBDDRLUBDLLFFUDDUFBLULBFFRLFUU

The color resolver code is available at https://github.com/dwalton76/rubiks-color-resolver

The Code

If you have a linux box, webcam and a Rubiks cube you can install the software that I used in the video.  It is available at https://github.com/dwalton76/rubiks-cube-tracker...just follow the install instructions in the README.

References


  • http://www.pyimagesearch.com/2015/04/06/zero-parameter-automatic-canny-edge-detection-with-python-and-opencv/
  • http://www.pyimagesearch.com/2016/02/08/opencv-shape-detection/
  • http://docs.opencv.org/trunk/d9/d8b/tutorial_py_contours_hierarchy.html
  • http://www.alanzucconi.com/2015/09/30/colour-sorting/
  • https://en.wikipedia.org/wiki/Color_difference

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