OpenCV get 3D coordinates from 2D pixel

Question

For my undergraduate paper I am working on a iPhone Application using openCV to detect domino tiles. The detection works well in close areas, but when the camera is angled the tiles far away are difficult to detect. My approach to solve this I would want to do some spacial calculations. For this I would need to convert a 2D Pixel value into world coordinates, calculate a new 3D position with a vector and convert these coordinates back to 2D and then check the colour/shape at that position.

Additionally I would need to know the 3D positions for Augmented Reality additions.

The Camera Matrix i got trough this link create opencv camera matrix for iPhone 5 solvepnp

The Rotationmatrix of the Camera I get from the Core Motion.

Using Aruco markers would be my last resort, as I woulnd't get the decided effect that I would need for the paper.

Now my question is, can i not make calculations when I know the locations and distances of the circles on a lets say Tile with a 5 on it? I wouldn't need to have a measurement in mm/inches, I can live with vectors without measurements.

The camera needs to be able to be rotated freely.

I tried to invert the calculation sm'=A[R|t]M' to be able to calculate the 2D coordinates in 3D. But I am stuck with inverting the [R|t] even on paper, and I don't know either how I'd do that in swift or c++.

I have read so many different posts on forums, in books etc. and I am completely stuck and appreciate any help/input you can give me. Otherwise I'm screwed.

Thank you so much for your help.

Update:

By using the solvePnP that was suggested by Micka I was able to get the Rotation and Translation Vectors for the angle of the camera. Meaning that if you are able to identify multiple 2D Points in your image and know their respective 3D World coordinates (in mm, cm, inch, ...), then you can get the mechanisms to project points from known 3D World coordinates onto the respective 2D coordinates in your image. (use the opencv projectPoints function).

What is up next for me to solve is the translation from 2D into 3D coordinates, where I need to follow ozlsn's approach with the inverse of the received matrices out of solvePnP.

Update 2: With a top down view I am getting along quite well to being able to detect the tiles and their position in the 3D world: tile from top Down

However if I am now angling the view, my calculations are not working anymore. For example I check the bottom Edge of a 9-dot group and the center of the black division bar for 90° angles. If Corner1 -> Middle Edge -> Bar Center and Corner2 -> Middle Edge -> Bar Center are both 90° angles, than the bar in the middle is found and the position of the tile can be found.

When the view is Angled, then these angles will be shifted due to the perspective to lets say 130° and 50°. (I'll provide an image later).

The Idea I had now is to make a solvePNP of 4 Points (Bottom Edge plus Middle), claculate solvePNP and then rotate the needed dots and the center bar from 2d position to 3d position (height should be irrelevant?). Then i could check with the translated points if the angles are 90° and do also other needed distance calculations.

Here is an image of what I am trying to accomplish: Markings for Problem

I first find the 9 dots and arrange them. For each Edge I try to find the black bar. As said above, seen from Top, the angle blue corner, green middle edge to yellow bar center is 90°. However, as the camera is angled, the angle is not 90° anymore. I also cannot check if both angles are 180° together, that would give me false positives. So I wanted to do the following steps:

1. Detect Center
2. Detect Edges (3 dots)
3. SolvePnP with those 4 points
4. rotate the edge and the center points (coordinates) to 3D positions
5. Measure the angles (check if both 90°)

Now I wonder how I can transform the 2D Coordinates of those points to 3D. I don't care about the distance, as I am just calculating those with reference to others (like 1.4 times distance Middle-Edge) etc., if I could measure the distance in mm, that would even be better though. Would give me better results.

With solvePnP I get the rvec which I could change into the rotation Matrix (with Rodrigues() I believe). To measure the angles, my understanding is that I don't need to apply the translation (tvec) from solvePnP.

This leads to my last question, when using the iPhone, can't I use the angles from the motion detection to build the rotation matrix beforehand and only use this to rotate the tile to show it from the top? I feel that this would save me a lot of CPU Time, when I don't have to solvePnP for each tile (there can be up to about 100 tile).

Find Homography

vector<Point2f> tileDots;
tileDots.push_back(corner1);
tileDots.push_back(edgeMiddle);
tileDots.push_back(corner2);
tileDots.push_back(middle.Dot->ellipse.center);

vector<Point2f> realLivePos;
realLivePos.push_back(Point2f(5.5,19.44));
realLivePos.push_back(Point2f(12.53,19.44));
realLivePos.push_back(Point2f(19.56,19.44));
realLivePos.push_back(Point2f(12.53,12.19));

Mat M = findHomography(tileDots, realLivePos, CV_RANSAC);

cout << "M = "<< endl << " "  << M << endl << endl;

vector<Point2f> barPerspective;
barPerspective.push_back(corner1);
barPerspective.push_back(edgeMiddle);
barPerspective.push_back(corner2);
barPerspective.push_back(middle.Dot->ellipse.center);
barPerspective.push_back(possibleBar.center);
vector<Point2f> barTransformed;

if (countNonZero(M) < 1)
{
    cout << "No Homography found" << endl;
} else {
    perspectiveTransform(barPerspective, barTransformed, M);
}

This however gives me wrong values, and I don't know anymore where to look (Sehe den Wald vor lauter Bäumen nicht mehr).

Image Coordinates https://i.stack.imgur.com/c67EH.png
World Coordinates https://i.stack.imgur.com/Im6M8.png
Points to Transform https://i.stack.imgur.com/hHjBM.png
Transformed Points https://i.stack.imgur.com/P6lLS.png

You see I am even too stupid to post 4 images here??!!?

The 4th index item should be at x 2007 y 717. I don't know what I am doing wrongly here.

Update 3: I found the following post Computing x,y coordinate (3D) from image point which is doing exactly what I need. I don't know maybe there is a faster way to do it, but I am not able to find it otherwise. At the moment I can do the checks, but still need to do tests if the algorithm is now robust enough.

Result with SolvePnP to find bar Center

Answer 1

The matrix [R|t] is not square, so by-definition, you cannot invert it. However, this matrix lives in the projective space, which is nothing but an extension of R^n (Euclidean space) with a '1' added as the (n+1)st element. For compatibility issues, the matrices that multiplies with vectors of the projective space are appended by a '1' at their lower-right corner. That is : R becomes

[R|0]
[0|1]

In your case [R|t] becomes

[R|t]
[0|1]

and you can take its inverse which reads as

[R'|-Rt]
[0 | 1 ]

where ' is a transpose. The portion that you need is the top row.

Since the phone translates in the 3D space, you need the distance of the pixel in consideration. This means that the answer to your question about whether you need distances in mm/inches is a yes. The answer changes only if you can assume that the ratio of camera translation to the depth is very small and this is called weak perspective camera . The question that you're trying to tackle is not an easy one. There is still people researching on this at PhD degree.