# Turning ArUco marker in parallel with camera plane

I need to warp the image to fix its perspective distortion based on detected marker. In other words - to get the plane where the marker lays become parallel to the camera plane.

In general it works for me, when I simply map points of perspective-distorted marker to its orthogonal position (Sketch) with getPerspectiveTranfrorm() and then warpPerspective(), which warps whole image:

The following are sample params for getPerspectiveTransform()

src1 (100, 100) => dst1 (100, 100)
src2 (110, 190) => dst2 (100, 200)
src3: (190, 190) => dst3 (200, 200)
src4: (200, 100) => dst4 (200, 100)

The result looks OK, but not always, so I think that this way is wrong.

My assumption that since for detected marker I can get its pose estimation (which shows its relation to camera) I can calculate required marker position (or camera position?) using marker points and rotation/translation vectors.

Now I'm stuck basically not understanding the math solution. Could you advise?

UPDATE

The following is a source image with detected markers. The white circles represent the desired position of marker that will be used in getPerspectiveTransform().

Source corners: [479, 335; 530, 333; 528, 363; 475, 365]
Result corners: [479, 335; 529, 335; 529, 385; 479, 385]

The following is the result image, which is still distorted:

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Maybe you can add a sample data: image + extracted corners points in text?

As the marker is planar, the transformation should be a homography. Knowing the two camera poses (current pose estimated and desired camera pose), you should be able to compute the homography matrix from the camera displacement. Once you have the homography, you will have to use warpPerspective(). You can also compare the two homography matrices.

( 2017-03-30 13:10:59 -0500 )edit

I added source images and corner coordinates. Will read more on homography. Thanks!

( 2017-03-31 07:20:58 -0500 )edit

I would use rather one or multiple corners but for all the markers to estimate the perspective transformation (you will have to change also the desired coordinates).

Looks like your extracted corner coordinates are integer numbers. Maybe you could check also if you can refine the coordinates of the corners (subpixel accuray, see here in Corner Refinement section) and use cv::Point2f or cv::Point2d.

( 2017-03-31 10:14:28 -0500 )edit

Yes, I plan to switch to subpixel accuracy too. Just wanted first to make sure that I'm not going in wrong direction by not calculating desired coordinates from vectors. I didn't check the homography topic yet though... I believe it will give me more understanding.

( 2017-03-31 10:28:43 -0500 )edit

I think the issue you have should come from some noise, incertitude in the corner coordinates that will affect the estimation of the perspective transformation. Using points more spread out should lead to better results in my opinion. The original image can also be distorted due to the camera lens and can have an impact somehow.

Note: I think that findHomography() or perspectiveTransform() should give you the same transformation matrix, you have to check.

( 2017-03-31 11:58:18 -0500 )edit

I switched to use ARUCO Board and it improved accuracy a lot. findHomography() and getPerspectiveTransform() provide the following result for me.

( 2017-04-04 03:33:05 -0500 )edit

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I have written in this answer some experimentations I did to understand more the concept of homography. Even if this is not really an answer of the original post, I hope it could also be useful to other people and it is a good way for me to summarize all the information I gathered. I have also added the necessary code to check and make the link between the theory and the practice.

What is the homography matrix?

For the theory, just refer to a computer vision course (e.g. Lecture 16: Planar Homographies, ...) or book (e.g. Multiple View Geometry in Computer Vision, Computer Vision: Algorithms and Applications, ...). Quickly, the planar homography relates the transformation between two planes (up to a scale):

This planar transformation can be between:

• a planar object and the image plane (image from here, p9):

• a planar surface viewed by two cameras (image from here, p56 and here, p10):

• a rotating camera around its axis of projection, equivalent to consider that the points are on a plane at infinity (image from here, p11):

How the homography can be useful?

• Camera pose estimation with coplanar points (see here or here, p30), the homography matrix can be estimated using the DLT (Direct Linear Transform) algorithm
• Perspective removal, correction:
• Panorama stitching:

Demo 1: perspective correction

The function findChessboardCorners() returns the chessboard corners location (the left image is the source, the right image is the desired perspective view):

The homography matrix can be estimated with findHomography() or getPerspectiveTransform():

H:
[0.3290339333220102, -1.244138808862929, 536.4769088231476;
0.6969763913334048, -0.08935909072571532, -80.34068504082408;
0.00040511729592961, -0.001079740100565012, 0.9999999999999999]

The first image can be warped to the desired perspective view using warpPerspective() (left: desired perspective view, right: left image warped):

Demo 2: compute the homography matrix from the camera displacement

With the function solvePnP(), we can estimate the camera poses (rvec1, tvec1 and rvec2, tvec2) for the two images and draw the corresponding object frames:

• Camera pose for the first camera:
• Camera pose for the second camera:
• Homogeneous transformation between the two cameras:

It is then possible to use the camera poses information to compute the homography transformation related to a specific object plane:

By Homography-transl.svg: Per Rosengren derivative work: Appoose (Homography-transl.svg) CC BY 3.0, via Wikimedia Commons

On this figure, n is the normal vector of the plane and d the distance between the camera frame and the plane along the plane normal. The equation to compute the homography from the camera displacement is:

Where is the homography matrix that maps the points in the first camera frame to the corresponding points in the second camera frame, is the rotation matrix that represents the rotation between the two camera frames and the translation vector between the two camera frames.

Here the normal vector n is the plane normal expressed in the camera frame 1 and can be computed as the cross product of 2 vectors (using 3 non collinear points that lie on the plane) or in ...

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@Eduardo, could you push this content into a tutorial on the whole topic. It would be a waste to see this information disappear :/

( 2017-04-19 05:07:33 -0500 )edit

what if my camera plane is parallel to image plane, can I assume then homography as identity matrix?

( 2019-06-05 05:27:32 -0500 )edit

If the camera plane is parallel to the planar object, you can describe the transformation using a different model.

If H is identity, it means that there is no transformation, the "images are the same".

( 2019-06-05 15:30:31 -0500 )edit

could you look at this question link text ? where I have taken R,t as identity matrix, because camera is simply downward to the table where I placed object on different locations on table. is this right method? it is working most of the time.

( 2019-06-07 03:31:22 -0500 )edit

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