Hello Phogi,
I think the 2 methods are equivalent. Given a marker (<Marker 'PC01'>) with internal 3d coordinates (marker.position), its 2d coordinates on a given camera (<Camera 'IMG_5813.JPG'>) can be determined either by:
1. combining camera.sensor.calibration.project with camera.transform.inv() as in
camera.sensor.calibration.project(camera.transform.inv().mulp(marker.position))
Out[12]: 2021-06-10 07:53:29 Vector([1998.4666958189314, 2522.822857879895]);
2. just using camera.project as in
camera.project(marker.position)
Out[13]: 2021-06-10 07:54:11 Vector([1998.4666958189316, 2522.822857879895])As you can see result is the same. I would use camera.project as it is more elegant and recent. Especially as it treats rolling shutter type sensor projection correctly while old method (sensor.calibration.project + camera.transform.inv()) does not. See following and attached screen capture:
marker
Out[23]: 2021-06-10 08:10:29 <Marker '5'>
camera
Out[24]: 2021-06-10 08:10:35 <Camera 'DJI_0011_S'>
camera.sensor.rolling_shutter
Out[25]: 2021-06-10 08:10:44 True
camera.sensor.calibration.project(camera.transform.inv().mulp(marker.position))
Out[26]: 2021-06-10 08:10:59 Vector([2618.710760365361, 1049.2876409972341])
camera.project(marker.position)
Out[27]: 2021-06-10 08:11:15 Vector([2618.1837353917476, 1046.1846513344458])PS. the 2d pixel image coordinates given by these formula are relative to the top left corner....as specified in Appendix C of User Manual:
The image coordinate system has origin in the middle of the top-left pixel (with coordinates (0.5, 0.5)). The X axis in the image coordinate system points to the right, Y axis points down. Image coordinates are measured in pixels.
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