OpenCV Python Stitching: Define custom keypoints / matches skipping feature detector & matcher

Question

I'm trying to implement the panorama stitching via OpenCV into our process, but want to use control points / matches gathered from PTGui.

PTGui however just lists the keypoints and the matches between images, no angles or additional data.

When using the normal workflow, the feature set is a list of ImageFeatures containing some parameters and a list of Keypoints.

Since I'm using OpenCV in Python, I don't really know how to create the list of ImageFeatures from scratch.

This is the current implementation, which doesn't work:

f1 = cv.detail_ImageFeatures()
f1.img_idx = 0
f1.descriptors = None
f1.img_size = (1920, 1080)
f1.keypoints = (
    cv.KeyPoint(1412.0, 734.0, 5.0),
    cv.KeyPoint(1415.0, 856.0, 5.0),
    cv.KeyPoint(1398.0, 885.0, 5.0)
)

...

features = [f1, f2, ...]

I get the following error when using my custom list just on the next step, which would be the matching: cv2.error: Conversion error: features

In addition to the custom ImageFeatures, I would also need custom matches. According to the documentation, these should be a MatchesInfo struct containing matches of type DMatch.

These are currently implemented as follows, but can't be tested since the above error already occurs beforehand:

matches = (
  cv.detail_MatchesInfo(confidence=1.0, dst_img_idx=1, H=None, inliers_mask=None, matches=(
    cv.DMatch(0, 0, 0.0),
    cv.DMatch(1, 1, 0.0),
    cv.DMatch(2, 2, 0.0)), num_inliers=None, src_img_idx=0),
  ...
)

So I was wondering, if it is even possible to "import" custom control points and matches using OpenCV Python. If yes, what would have to be changed to make it work.