Rotation/translation vector incorrect

I calibrated a camera according to:

ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpointslist, imgpointslist, imsize, None, None)

which resulted in:

rvecs = array([[ 0.01375037],
               [-3.03114683],
               [-0.01097119]])
tvecs = array([[ 0.16742439],
               [-0.33141961],
               [13.50338875]])

I calculated the Rotation matrix according to:

R = cv2.Rodrigues(rvecs)[0]
R = array([[-0.99387165, -0.00864604, -0.11020157],
           [-0.00944355,  0.99993285,  0.00671693],
           [ 0.1101361 ,  0.00771646, -0.99388656]])

and created an Rt matrix resulting in:

Rt = array([[-0.99387165, -0.00864604, -0.11020157,  0.16742439],
            [-0.00944355,  0.99993285,  0.00671693, -0.33141961],
            [ 0.1101361 ,  0.00771646, -0.99388656, 13.50338875],
            [ 0.        ,  0.        ,  0.        ,  1.        ]])

Now when I try to get the position of a realworld coordinate [0, 0.4495, 0] in the image according to:

realworldpoint = array([0.    , 0.4495, 0.    , 1.    ], dtype=float32)
imagepoint = np.dot(Rt, realworldpoint)

I get:

array([ 0.16353799,  0.1180502 , 13.5068573 ,  1.        ])

Instead of my expected [1308, 965] position in the image:

array([1308,  965,  0,  1])

I am doubting about the integrity of the rotation matrix and the translation vector outputs in the calibrate camera function, but maybe I am missing something? I double checked the inputs for the OpenCV's calibrate camera function (objpointslist: 3d coordinates of the center of the April tag Aruco markers, and the imgpointslist: 2d positions of the center of the markers in the image), but these were all correct...

Could one of you help me out?

I used this procedure according to OpenCV's calibration procedure:

EDIT (2022/02/03): I was able to solve it! Steps for solution:

1. Solve cv2.calibrateCamera() in order to get camera intrinsics (camera matrix) and extrinsics (rotation and translation vectors)
2. Calculate rotation matrix from rotation vector according to cv2.Rodrigues()
3. Create the Rt matrix
4. Use a known point (uv1, and its XwYwZw1 are known, 2D and 3D) to calculate the Scaling factor (s).

NOTE: In order to get the right scaling factor, you have to divide the final equation thus that you get [u v 1], so divide so that the 3rd element becomes a one, this results in your scaling factor:

Now that the scaling factor is known the same equation can be used to calculate an XYZ coordinate for a random point in the image (uv1) according to:

The crucial step in this solution was to calculate the scaling factor by dividing by the third element that you obtain in the [uv1] matrix. This makes that matrix actually [uv1].

The next step was to implement a solution to the lens distortion. This was easily done by using cv2.undistortPoints on the distorted uv/xy point in the image before feeding the u and v to the above equation to find the XYZ coordinate of that point.

The crucial step in the solution was to calculate the scaling factor by dividing by the third element that you obtain in the [uv1] matrix. I added an explanation to the original question. As well as explaining the next step involving correction for lens distortion.