Comments (4)
Yes, maybe experiment with the MapImagePointToCameraUnitPlane function directly and see if that gives you better/different results? Beyond that, take a deeper look at the Research Mode APIs to see if there is anything there. We use Research Mode libraries to expose the IR sensor to \psi, but there might be more there we could leverage for computing intrinsics. Not sure though.
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@mrcfschr hi,
As far as I remember, I had performed camera calibration using a board. https://docs.opencv.org/4.x/dc/dbb/tutorial_py_calibration.html
I captured a couple of images from the IR sensor. They were in 16 bits so I had to convert them to 8 bits. OpenCV's histogram equalization function didn't support an input of 16 bits. Then I performed a histogram equalization so that the corner points are detectable in the camera calibration OpenCV functions.
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Thank you I'll give it a try!
this is what I've got so far https://drive.google.com/file/d/1rgc01WuRAIh_TM_ut0QdsQyjZ8_14m9z/view?usp=sharing
Parameter | PSI | Checkerboard | C++ RM Mapping |
---|---|---|---|
reprojection error | 1.374427541024572 | 0.04400390079043265 | ? |
Intrinsic parameters (camera matrix) | [[245.46107114, 0, 257.45927135], [0, 245.53169049, 257.08910023], [0, 0, 1]] |
[[245.31616967, 0, 255.09594219], [0, 246.12117205, 247.46196344], [0, 0, 1]] |
? |
Distortion coefficients | [[-0.22265327, 0.03156156, 0, 0, 0]] |
[[-0.300205711, 0.0969518716, 0.00391632399, -0.0000820810782, -0.0139891582]] |
? |
newcameramtx | [[148.94296265, 0, 256.84486832], [0, 148.97277832, 256.50581821], [0, 0, 1]] |
[[141.71080017, 0, 251.40767011], [0, 143.26325989, 250.54294455], [0, 0, 1]] |
? |
*rounded a bit in the table for visual purposes but not in the actual calculations
Distorted | PSI | Checkerboard |
---|---|---|
The tracked tool does look slightly more aligned but still not perfect..
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The tracked tool does look slightly more aligned but still not perfect.. closing this for now.
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